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title: High sucrose consumption decouples intrinsic and synaptic excitability of AgRP
neurons without altering body weight
authors:
- Austin C. Korgan
- Klausen Oliveira-Abreu
- Wei Wei
- Sophie L. A. Martin
- Zoey J. D. Bridges
- José Henrique Leal-Cardoso
- Catherine C. Kaczorowski
- Kristen M. S. O’Connell
journal: International Journal of Obesity (2005)
year: 2023
pmcid: PMC10023568
doi: 10.1038/s41366-023-01265-w
license: CC BY 4.0
---
# High sucrose consumption decouples intrinsic and synaptic excitability of AgRP neurons without altering body weight
## Abstract
### Background/Objective
As the obesity epidemic continues, the understanding of macronutrient influence on central nervous system function is critical for understanding diet-induced obesity and potential therapeutics, particularly in light of the increased sugar content in processed foods. Previous research showed mixed effects of sucrose feeding on body weight gain but has yet to reveal insight into the impact of sucrose on hypothalamic functioning. Here, we explore the impact of liquid sucrose feeding for 12 weeks on body weight, body composition, caloric intake, and hypothalamic AgRP neuronal function and synaptic plasticity.
### Methods
Patch-clamp electrophysiology of hypothalamic AgRP neurons, metabolic phenotyping and food intake were performed on C57BL/6J mice.
### Results
While mice given sugar-sweetened water do not gain significant weight, they do show subtle differences in body composition and caloric intake. When given sugar-sweetened water, mice show similar alterations to AgRP neuronal excitability as in high-fat diet obese models. Increased sugar consumption also primes mice for increased caloric intake and weight gain when given access to a HFD.
### Conclusions
Our results show that elevated sucrose consumption increased activity of AgRP neurons and altered synaptic excitability. This may contribute to obesity in mice and humans with access to more palatable (HFD) diets.
## Introduction
While significant public health efforts have been made to combat the growing prevalence of obesity, recent evidence shows that these efforts have failed to slow this progression [1]. Recent studies have identified central nervous system (CNS) pathways as a key driver contributing to increased caloric intake and body weight gain [2–6]. The American Medical Association has recognized obesity as a disease since 2013 [7], but few therapeutic treatments have succeeded in reducing body weight or diet-induced obesity (DIO) in human patients [8]. The most efficacious treatments target the hypothalamus and melanocortin system, specifically glucagon-like protein 1 receptor (GLP-1R) and melanocortin 4 receptor (MC4R) agonists [9–11]. While these new pharmacological treatments are encouraging, diet and exercise remain the safest and most common lifestyle interventions; however, results are often temporary and many individuals regain weight in less than 5 years [12–18]. Understanding obesogenic factors that alter CNS function may allow for development of interventions that ‘reset’ these mechanisms and promote sustained weight loss through reduced appetite.
The role of AgRP/NPY neurons as signal integrators of peripheral and central cues to drive feeding and behavior has been studied extensively in the context of normal chow fed (NCD) [19–23], caloric restriction (fasting) [24–26], and high-fat diet (HFD) [24, 27–31]. Further, this hypothalamic neuron population interacts with midbrain dopamine neurons to modulate response to rewarding stimuli (e.g. palatable food and drugs) [32]. Recent studies have identified AgRP neuronal activation and synaptic plasticity responsible for driving feeding in response to fasting [26, 31]. Similarly, we and others have recently described acute and long-term synaptic mechanisms [31] that mediate AgRP neuronal hyperactivity ex vivo [24, 28] and postprandial desensitization identified by in vivo calcium imaging studies [29, 30]. While recent studies have identified distinct gut-brain pathways for fat and sugar signaling to AgRP/NPY neurons [33–35] the long-term influence of high dietary sucrose consumption on AgRP neuronal function, plasticity, and body weight has not been explored.
Recent research has identified functional behavioral and metabolic differences between HFD, high sucrose diet, and liquid sucrose (SucrW) consumption [36, 37]. Generally, long-term SucrW consumption does not result in significant changes in body weight, caloric intake, or glucose and insulin processing [37–39]. Separate circuitries regulate the homeostatic and hedonistic rewards associated with sugar consumption [40]. Regulation of Sucrose consumption by peripheral hormones, including ghrelin, leptin, and insulin, suggest that CNS and AgRP neuronal signal integration mechanisms coordinate homeostatic consumption of Sucrose diet [38, 41–43]. Further, ‘motivated’ (non-homeostatic or hedonistic) SucrW consumption demonstrates ‘top-down’ processing within the CNS and is associated with disrupted reward processing and diminished valence of the stimulus reward [44] likely linked to plasticity within the hypothalamus [45, 46] but could also be regulated by taste receptors [47–49] or leptin and insulin signaling [43, 50]. Further, control of Sucrose intake by AgRP (and POMC) neuronal output has been shown within the melanocortin system, where α-MSH and AgRP have opposite effects on SucrW consumption [51–53]. However, specific changes in AgRP neuronal intrinsic excitability and synaptic plasticity following long-term SucrW consumption have not been described.
In the current study, we investigated the impact of long-term SucrW consumption on AgRP neuronal function and adaptation in the absence of body weight gain. We identified a SucrW-dependent increase in intrinsic excitability, though not as robust as that seen in HFD fed mice, along with an increase in inhibitory post synaptic currents (mIPSC), replicating a decoupling between AgRP neuronal activity and GABAergic synaptic inputs previously identified in DIO mice [31]. Additionally, we found that leptin-mediated inhibition of AgRP neurons was attenuated independent of weight gain, along with a SucrW priming effect for DIO that further amplified AgRP neuronal activity. Together, these findings highlight a mechanism through which high Sucrose consumption primes an individual for increased caloric intake by remodeling AgRP neurons comparable to DIO. While dietary macronutrients (i.e. fat and sugar) engage divergent pathways to communicate with the CNS, we show that alterations to hypothalamic circuitry, specifically AgRP neurons, follow similar mechanistic responses and may present attractive treatment options, especially in an obesogenic food environment that promotes over consumption of many macronutrients [5, 54–56].
## Animals
The transgenic strain hrGFP-NPY (JAX Stock #006417) and C57Bl/6 J (JAX Stock #000664) were used in this study. Founder mice were obtained from the JAX Repository and maintained by backcrossing with C57Bl/6 J. hrGFP-NPY mice were SNPtyped to confirm that the strain is on a congenic C57Bl/6 J background except for the transgene insertion site on Chr7. All animal care and experimental procedures were approved by The Animal Care and Use Committee at The University of Tennessee Health Science Center and The Jackson Laboratory. Mice were maintained at 22–24 °C on a 12 h:12 h light/dark cycle (lights on at 0600–1800). All mice used for breeding were fed standard lab chow (UTHSC–Teklad 7912: 3.1 kcal/g metabolizable energy, 17 kcal% fat or JAX–LabDiets 5K0Q: 3.15 kcal/g metabolizable energy, 16.8 kcal% from fat). There was no significant effect of control diet manufacturer (Teklad v LabDiets) or performance site (UTHSC v JAX), so data from both sites were combined as described previously [31]. Mice were weaned at 21 days and group housed with same-sex litter mates ($$n = 2$$–5 mice/pen). At 8 weeks of age, experimental mice were randomly assigned to either normal drinking water (NDW) or to a high-Sucrose group (SucrW; Sucrose (Fisher Scientific; S250)) in which the standard acidified water was replaced with water sweetened with $10\%$ (w/v) Sucrose (0.52 kcal/g metabolizable energy) [39, 43] for 10–12 weeks; no other water was available to the SucrW group and all mice were fed a NCD (Fig. 1A, inset). The same Sucrose concentration was used at both UTHSC and JAX and there was no significant effect of site on response to Sucrose water, so data from both sites were combined. Fig. 1Male mice are resistant to changes in bodyweight gain, body composition, and locomotor activity despite increased caloric intake on high Sucrose diet. A Cumulative body weight curves for male mice during 12 weeks of NDW ($$n = 22$$) or SucrW ($$n = 57$$) feeding and feeding timeline (inset) B Body fat (%) was not significantly different between NDW ($$n = 10$$) and SucrW ($$n = 17$$) but lean mass (%) was slightly reduced in SucrW ($$n = 17$$) mice compared to NDW ($$n = 10$$) mice. C Blood glucose (mg/mL) from fed and fasted NDW (fed: $$n = 20$$, fast: 42) and SucrW mice (fed: $$n = 12$$, fast: 21). D Baseline chow (NDW: $$n = 8$$, SucrW: 13) and total intake (NDW: $$n = 8$$ SucrW: 9) (kcal) per pen/mouse during a 24 h period following 10 weeks of NDW or SucrW feeding. E Chow (NDW: $$n = 4$$, SucrW: 6) and total intake (NDW: $$n = 4$$, SucrW: 6) (kcal) per pen/mouse during a 6 h period, following a 12 h fast. For all violin plots, dashed line indicates median, dotted lines indicate quartiles. For all analysis, diet conditions compared to NDW using standard t-test or parametric ANOVA with a post hoc Tukey’s multiple comparisons test and a mixed-effects RM ANOVA for repeated measures (*$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$).
Separate cohorts of mice were given access to SucrW for only 2 days and were then fed a high-fat diet (HFD; Research Diets D12451: 4.73 kcal/g metabolizable energy, 45 kcal% from fat); HFD available ad libitum for mice randomly assigned to the 2-day (HFD2d; Fig. 6A) condition as previously described [28]. All water was available ad libitum. Mice were weighed weekly and used for experiments at 16–20 weeks of age.
## Open field test
Locomotor activity was measured in the open-field test (OFT). The OFT consists of solid white polyurethane foam panels (30.5 cm high) and a floor (40 cm × 40 cm square) illuminated at 150 lux. Locomotor activity was tracked by an overhead camera and analyzed with ANY-maze (Stoelting Co., version 7.14). Following >1 h habituation to the test room, mice were placed into the center square (10 cm × 10 cm) of the arena for a 10 min trial. Behaviors scored were distance travelled (m), thigmotaxis entries, thigmotaxis exits, total thigmotaxis time, center entries, center exits, and center time. Following each trial, the arena was cleaned with $70\%$ ethanol.
## Feeding behavior
Feeding behavior was assessed by measuring food intake over four days. During the first three days, baseline NCD and NDW or SucrW consumption were collected by daily weighing. On the third day, NCD and SucrW were removed at 17:00 h. The next day, NCD and SucrW were returned at 0900 and intake was measured for 6 h. Body weights were measured pre- and post-fast and post-refeeding. Food intake is represented as total kcal consumed via NCD, HFD, and SucrW per pen divided by the total number of mice per pen. All mice were group housed to avoid isolation-induced stress and potential changes in feeding behavior [57–59].
## Body composition, blood glucose, and liver weight
Body composition of mice was measured at baseline (8 weeks old) and following 10–12 weeks of NDW or SucrW feeding (18–20 weeks old). All mice were ad lib fed at the time of body composition measurements. Mice were placed in a plastic cylinder (50 mm diameter) and inserted into the nuclear magnetic resonance (NMR) instrument (Bruker Minispec LF50, Billerica, MA USA) for the duration of the scan (<2 min).
Blood glucose was measured following 9–11 weeks of NDW or SucrW feeding (17–19 weeks old). Mice were tested in both fed and fasted groups with random order of testing for both timepoints. For all groups, blood was collected between 8–10 AM. For blood glucose measurements, a small nick in the tail was made with scissors and a drop of blood was collected on a clean blood glucose test strip and inserted into the blood glucose meter (TRUEtest, Trividia Health, FL USA).
Whole livers were collected and weighed during the harvest for brain slice electrophysiology experiments as described below.
## Slice preparation
For all experiments, brain slices were prepared between 0900 and 1030. Mice (16–17 weeks old) used for electrophysiology experiments were deeply anesthetized using isoflurane before decapitation and rapid removal of the brain. The brain was then submerged in ice-cold, oxygenated ($95\%$ O2/$5\%$ CO2) cutting solution (in mM: 119 NaCl, 90 Sucrose, 2.5 KCl, 1 MgSO4, 2 CaCl2, 1.25 NaH2PO4, 23 NaHCO3, and 10 glucose). Coronal slices (250 μm) were cut using a vibratome (VT1000S, Leica) and incubated in oxygenated aCSF (in mM: 119 NaCl, 2.5 KCl, 1 MgSO4, 2 CaCl2, 1.25 NaH2PO4, 23 NaHCO3, and 10 glucose) for at least 1 h prior to recording.
## Slice recording
Slices were transferred to a recording chamber constantly perfused (~2 ml/min) with oxygenated aCSF. GFP-positive AgRP/NPY neurons were identified using epifluorescence and standard GFP filters on a fixed-stage Scientifica (Uckfield, UK) SliceScope 1000 microscope equipped with a digital camera (Q-Imaging, Surry, BC, Canada). All recordings were performed using a Multiclamp 700B amplifier and Digidata 1550 A, controlled using Clampex 10.7 (Molecular Devices, San Jose, CA, USA). Data were digitized at 20 kHz and filtered at 5 kHz using the built-in four-pole Bessel filter of the Multiclamp 700B.
Recording pipettes were pulled from filamented thin-wall borosilicate glass (TW150F-4, World Precision Instruments) and had a resistance of 4-7 MΩ when filled with internal solution (for intrinsic excitability (AP) recordings, in mM: 130 K-gluc, 10 KCl, 0.3 CaCl2, 1 MgCl2, 1 EGTA, 3 MgATP, 0.3 NaGTP, and 10 HEPES, pH 7.35 with KOH; for synaptic recordings, in mM: 140 KCl, 0.3 CaCl2, 1 MgCl2, 1 EGTA, 3 MgATP, 0.3 NaGTP, and 10 HEPES, pH 7.35 with KOH). The liquid junction potential (LJP) between normal aCSF and the K-gluconate solution used for intrinsic recordings was +14.7 mV and was corrected. The LJP between aCSF and the KCl intracellular solution was +4.75 mV and was not corrected.
Whole-cell current clamp recordings of resting membrane potential and spontaneous firing were recorded in the presence of DNQX (10 μM; Tocris) and picrotoxin (100 μM; Tocris). For experiments testing inhibition of AgRP neurons by leptin, mice were fasted overnight to promote increased intrinsic excitability and 100 nM leptin (Tocris; 116–130) was bath applied. Whole-cell voltage-clamp recordings of mEPSC and mIPSC were conducted in the presence of TTX (1 µM; Tocris) and Picrotoxin (100 uM; Tocris) for mEPSCs and DNQX (10 µM; Tocris) for mIPSCs.
## Data analysis and statistics
Post-synaptic current frequencies, amplitudes, inter-event intervals and τ decay were measured using Axograph (AxoGraph, Inc). Statistical outliers were identified using the ROUT method ($Q = 1$% cutoff threshold) as implemented in GraphPad Prism 9. Group differences were analyzed with two-way ANOVA followed by Tukey’s multiple comparisons post hoc test or unpaired t-tests using Prism 9 (GraphPad). Variance between groups was measured with either an F (t-tests) or Bartlett’s (ANOVA) test. Contingency tables were analyzed for group differences with Fisher’s exact test. For cumulative distribution of mEPSC and mIPSC amplitudes, group differences were compared with the nonparametric Kolgoromov–Smirnoff test. For repeated measures analysis, group differences were analyzed by two-way RM-ANOVA using Prism 9 (GraphPad). Data visualization was performed using Prism or r/ggplot2 (version 4.2.1). For all statistical tests, a value of $p \leq 0.05$ was considered significant. All analyses were conducted by an experimenter blinded to treatment group. Data are presented as the mean ± SEM; violin plots are presented as median ± quartile. For all experiments, a priori estimation of sample sizes was performed using statistical tools in G*Power3.1. The partial η2 from previous experiments was used to estimate effect sizes for α = 0.05 and power (1-β) = 0.95. For detailed ANOVA and t-test results, see Table 1.Table 1Full statistical details for ANOVA and t-test results. Figurenn unitF(DFn, DFd) or t(df)p1ANDW = 22, SucrW = 57mice0.7845[12,744]0.6669S1ANDW = 9, SucrW = 39mice0.541[46]0.591S1BNDW = 6, SucrW = 33mice1.082[37]0.2861BNDW = 10, SucrW = 17miceFat = 1.974[25]; Lean = 2.580[25]0.0596 0.01621CNDWfed = 14, SucrWfed = 12, NDWfast = 31, SucrWfast = 31miceDiet = 0.4081[1,91]Fast = 48.74[1,91]0.5245<0.0001S1CNDWfed = 9, SucrWfed = 6, NDWfast = 9, SucrWfast = 4mice9.86[1,24]0.0044S1DNDWfed = 3; SucrW12Wk = 3; HFD8Wk = 44.555[2,9]<0.0001S1ENDW = 7, SucrW = 9mice0.0296[14]0.9768S1FNDW = 7, SucrW = 9mice1.71 [14]0.10941D ChowNDW = 8, SucrW = 13pens4.662[19]0.00071D TotalNDW = 8, SucrW = 9pens7.523[13]<0.00011E ChowNDW: $$n = 4$$; SucrW: $$n = 6$$pens3.471[8]0.00461E TotalNDW: $$n = 4$$; SucrW: $$n = 6$$pens0.3965[8]0.9156S2ANDW: $$n = 10$$; SucrW: $$n = 10$$mice1.207[12,216]0.2798S2BNDW: $$n = 6$$; SucrW: $$n = 6$$pens4.439[5]0.0013S2CNDW: $$n = 6$$; SucrW: $$n = 6$$pens8.194[5]<0.0001S2DNDW: $$n = 13$$; SucrW: $$n = 14$$cells0.5412[12]0.5932S2ENDW: $$n = 13$$; SucrW: $$n = 14$$cells1.202[12]0.24072BNDWfed = 44, NDWfast = 21, SucrW12Wk = 62, SucrW12Wk+Fast = 11cellsDiet = 3.272[1,134]Fast = 6.973[1,134]0.07270.00932CNDWfed = 44, NDWfast = 21, SucrW12Wk = 62, SucrW12Wk+Fast = 11cellsDiet = 0.2979 [1,134]Fast = 0.6521[1,134]0.58610.4208S3BNDWfed = 21, NDWfast = 19, SucrW12Wk = 53, SucrW12Wk+Fast = 11cellsDiet = 1.138[1,101]Fast = 1.674[1,101]0.28860.1986S3DNDWfed = 38, NDWfast = 15, SucrW12Wk = 44, SucrW12Wk+Fast = 7cellsDiet = 3.272[1,134]Fast = 6.973[1,134]0.07270.00933BNDWfast = 9, SucrW12Wk = 6cellsNDW = 4.6677[8]SucrW = 1.942[5]0.00160.10983CNDWfast = 9, SucrW12Wk = 6cellsNDW = 3.724[8]SucrW = 1.367[5]0.00290.11493DNDWfed = 5; SucrW12Wk = 3; HFD8Wk = 4mice37.87[2,9]<0.00014BNDW = 9, SucrW12Wk = 9cells0.169[16]0.0868S4ASucrW12Wk Low fmEPSC(s-1) = 5, SucrW12Wk High fmEPSC(s-1) = 4cells10.84[7]<0.00014CNDW = 9, SucrW12Wk = 9cells2.156[16]0.0484DNDW = 9, SucrW12Wk = 9cells1.21[16]0.2444ENDW = 9, SucrW12Wk = 9cells2.561[16]0.0214FNDW = 9, SucrW12Wk = 9cells4GNDW = 9, SucrW12Wk = 9cells5BNDW = 6, SucrW12Wk = 8cells2.854[12]0.0155CNDW = 6, SucrW12Wk = 8cells2.857[12]0.0145DNDW = 6, SucrW12Wk = 8cells0.024[12]0.9815ENDW = 6, SucrW12Wk = 8cells1.531[12]0.1525FNDW = 6, SucrW12Wk = 8cells5GNDW = 6, SucrW12Wk = 8cells6BNDW = 6, SucrW = 6mice6.092[10]0.00016CNDW = 6, SucrW = 6mice6.909[10]<0.00016DNDW = 10, SucrW2d+2d HFD = 8, SucrW12Wk+2d HFD = 4, HFD2d = 7mice20.65[3,25]<0.00016ENDW = 9, SucrW2d = 10, SucrW2d+2d HFD = 12, SucrW12Wk+2d HFD = 11mice8.287[3,40]0.00026FNDW = 9, SucrW2d = 5, SucrW2d+2d HFD = 7, SucrW12Wk+2d HFD = 4mice8.510[3,21]0.0007S5ANDW = 4, SucrW2d = 4, SucrW2d+2d HFD = 7, SucrW12Wk+2d HFD = 4mice2.466[3,15]0.3323S5BNDW = 9, SucrW2d+2d HFD = 11, SucrW12Wk+2d HFD = 10mice3.663[2,27]0.03916GNDWfed = 17, SucrW2d = 24, SucrW2d+2d HFD = 16, SucrW12Wk+2d HFD = 13cells3.111[3,66]0.03226HNDWfed = 17, SucrW2d = 24, SucrW2d+2d HFD = 16, SucrW12Wk+2d HFD = 13cells0.1740[3, 66]0.91366KSucrW2d+2d HFD = 8cells3.656[7]0.00816MSucrW12Wk+2d HFD = 8cells1.027[7]0.3388n number of mice or cells (designated by n unit), F(DFn, DFd) = ANOVA F value and Degrees of Freedom for the numerator or denominator, respectively, and p = p value for a given statistic.
## Male mice are resistant to changes in bodyweight gain, body composition, and locomotor activity despite increased caloric intake on high sucrose diet
To quantify the impact of high dietary sucrose on weight gain and obesity, mice were weighed weekly throughout the Sucrose diet feeding. Consistent with previous reports [37–39], there was no difference in body weight between NDW and SucrW mice (Fig. 1A) at any point following sucrose administration (Fig. S1A, B). Female mice were also resistant to weight gain following SucrW consumption (Fig. S2A) and based on previous studies, we predict dissimilar metabolic and synaptic mechanisms that will require future investigation [31, 60–63]. NMR for body composition revealed no difference in fat mass in SucrW mice compared to NDW controls but did have a decrease in lean mass (Fig. 1B). There was a main effect of fasting on blood glucose, which was decreased in both SucrW and NDW fasted mice (Fig. 1C). Liver weight was elevated in SucrWfed mice compared to NDWfed, NDWfast, and SucrWfast mice (Fig. S1C). The OFT did not reveal differences in locomotor or anxiety-like behavior between NDW and SucrW mice (Fig. S1D, E). Baseline caloric intake from chow was decreased in SucrWfed mice compared to NDWfed, however total caloric intake (chow + SucrW water) was increased in SucrWfed mice (Fig. 1D). Upon refeeding following a 16 h fast (with access to NDW during the fast), SucrWfast mice consumed less chow than NDWfast mice but equivalent total calories (Fig. 1E).
## Long-term high sucrose intake increases intrinsic activity in AgRP neurons and decreases leptin sensitivity
Commonly utilized rodent HFD (Research Diets, D12451) contains both added sucrose (17 kcal%) and increased fat content, even when compared to other commonly used diets such as ResearchDiets D12492 (7 kcal% sucrose). We previously demonstrated that a high-fat/high-sugar diet rapidly and persistently induced an increase in intrinsic excitability of AgRP neurons [24, 28–31], so we were interested in determining the extent to which elevated dietary sucrose alone impacts AgRP neuronal function. As previously reported [24, 26, 28, 31, 64], in ad libitum fed control mice (NCD + NDW), the average baseline firing rate of AgRP neurons is low (<1 Hz, Fig. 2B) and is significantly increased following an overnight fast (Fig. 2B). There were significant main effects of diet and fasting; both 16 h fast and SucrW12Wk consumption resulted in increased firing rates in AgRP neurons (Fig. 2A, B). There was no interaction between fasting and sucrose, as the mean firing rate was not significantly different between the NDW and SucrW12Wk groups after an overnight fast. There was no difference in resting membrane potential (RMP) (Fig. 2C).Fig. 2Long-term high Sucrose diet increases intrinsic activity in AgRP neurons and decreases leptin sensitivity in male mice. A Representative traces of fed and fasted NDW and SucrW mice B AgRP neuronal firing rate was increased in SucrW12Wk ($$n = 62$$) and NDWFast ($$n = 21$$) and SucrW12Wk+Fast ($$n = 11$$) mice compared to NDWFed ($$n = 44$$). C There was no effect of diet or fast on RMP. D Contingency tables from fed and fasted NDW and SucrW mice. NDWFed mice had an altered proportion of silent (FAP < 25 percentile; 0.08659 Hz), medium firing (FAP > 25 and < 75 percentile), and high firing (FAP > 75 percentile; 1.75 Hz) cells compared to NDWFast and SucrW12Wk and there was no difference between SucrW12Wk and SucrW12Wk+Fast. NDWfed: FAP < 25 percentile = $52.27\%$ ($$n = 23$$), FAP > 25 percentile and <75 percentile = $34.09\%$ ($$n = 15$$), and FAP > 75 percentile = $13.64\%$ ($$n = 6$$); NDWfast: FAP < 25 percentile = $9.52\%$ ($$n = 2$$), FAP > 25 percentile and <75 percentile = $61.9\%$ ($$n = 13$$), and FAP > 75 percentile = $28.57\%$ ($$n = 6$$), $$p \leq 0.0004$$; SucrW12Wk: FAP < 25 percentile = $14.52\%$ ($$n = 9$$), FAP > 25 percentile and <75 percentile = $56.45\%$ ($$n = 35$$), and FAP > 75 percentile = $29.03\%$ ($$n = 18$$), $p \leq 0.0001$; SucrW12Wk+Fast: FAP < 25 percentile = $0\%$ ($$n = 0$$), FAP > 25 percentile and <75 percentile = $63.64\%$ ($$n = 7$$), and FAP > 75 percentile = $36.36\%$ ($$n = 4$$), ($$p \leq 0.3864$$). For all violin plots, dashed line indicates median, dotted lines indicate quartiles. For all analysis, diet conditions compared to NDW using standard parametric ANOVA with a post hoc Tukey’s multiple comparisons test or Fisher’s Exact test (*$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$).
A significant difference in the firing rate of AgRP neurons following long-term sucrose consumption may arise due to either [1] an overall increase in neuronal output across the population due an increase in the maximal rate or [2] a shift in the distribution of rates to higher frequencies within the existing bounds of the underlying distribution. To determine which potential mechanism underlies the significant difference in mean firing rates with SucrW12Wk, we evaluated the distribution of firing rates within each group. Fisher’s exact test identified a significant decrease in the proportion of silent (FAP < 25 percentile; 0.08659 Hz), medium firing (FAP > 25 and <75 percentile), and high firing (FAP > 75 percentile; 1.75 Hz) AgRP neurons from NDWfast (Fig. 2D; $$p \leq 0.0004$$) and SucrW12Wk (Fig. 2D; $p \leq 0.0001$) mice compared to NDWfed mice and no difference between SucrW12Wk and SucrW12Wk+Fast mice (Fig. 2D; $$p \leq 0.3864$$). Frequency distribution plots of the firing rates of all cells (Fig. S3A) suggest that the maximal firing rate of AgRP neurons is not altered by sucrose, rather that sucrose induces a shift in the center of the distribution to higher values without changes the maximal firing rate of AgRP neurons. Consistent with this, when we stratify the entire dataset into quartiles and remove the bottom $25\%$ of firing rates (FAP < 0.08659 Hz), there is no longer a significant main effect of either Sucrose or fasting. However, if we repeat this same analysis but instead remove the top quartile of values (FAP > 1.75 Hz), the significant main effects of both Sucrose and Fasting remain, suggesting that neither chronic sucrose consumption nor fasting impact the maximal firing rate of AgRP neurons, rather each of these interventions causes a general shift of AgRP neuronal firing to higher values within the bounds of the original distribution as established from NDW mice. While the activity of AgRP neurons from DIO mice is largely uniform, with the entire population shifting to a higher rate of activity [24], our results indicate that the response to SucrW is more variable, with only a subset of neurons exhibiting hyperexcitability, suggesting that the response of AgRP neurons to diet is sensitive to macronutrient content.
Leptin inhibition of AgRP neuronal firing has been well defined [65], and leptin resistance likely contributes to the DIO-associated hyperexcitability of AgRP neurons in obese mice [24]. As expected, 100 nm leptin inhibited AgRP neurons from NDWfast mice (Fig. 3A, B), due in part to a significant hyperpolarization of the resting membrane potential (Fig. 3A, C). However, we did not observe a significant effect of leptin on either neuronal firing or membrane potential in SucrW12Wk mice, similar to effects identified in DIO mice [24] (Fig. 3A, C). However, the unidirectionality of the response of AgRP neurons to leptin suggests that SucrW feeding results in an attenuated leptin response rather than overt leptin resistance. Further, unlike HFD mice, SucrW12Wk mice did not have elevated plasma leptin (Fig. 3D) or insulin (S1F) compared to NDWfed mice, consistent with SucrW12Wk mice having fat mass comparable to lean controls (Fig. 1B).Fig. 3Long-term Sucrose consumption attenuates leptin inhibition of AgRP neuronal activity in male mice. A Representative traces of NDWFast (top) or SucrW12Wk (bottom) with bath Leptin (100 nm) application. B Firing rate of NDWFast ($$n = 9$$; left) and SucrW12Wk ($$n = 6$$; right) before and after bath leptin application. C Leptin application resulted in a hyperpolarization of the RMP in NDWfast mice, no change was seen in SucrW12Wk mice after bath leptin application. D Plasma leptin levels (pg/mL) were not changed in SucrW12Wk mice ($$n = 3$$) compared to NDW mice ($$n = 5$$) and unlike HFD8Wk mice ($$n = 4$$). For all violin plots, dashed line indicates median, dotted lines indicate quartiles. Statistical comparisons using t-tests (*$p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$, ****$p \leq 0.0001$).
## Long-term sucrose consumption does not alter excitatory synaptic input to AgRP neurons
Given the persistent hyperexcitability of AgRP neurons following SucrW12Wk consumption, we next examined whether these diet-induced changes in firing rate were associated with increased excitatory neurotransmission, as seen in NDWfast mice [31, 66, 67]. When we investigated the impact of long-term SucrW consumption on the frequency of excitatory inputs to AgRP neurons (fmEPSC), we found no difference in the fmEPSC (Fig. 4A, B), similar to what we previously reported in DIO mice [31]. However, the fmEPSC from SucrW mice was not normally distributed (Shapiro–Wilk test; $W = 0.806$, $$p \leq 0.024$$), with a significant difference between the low and high fmEPSC SucrW groups (Fig. S4A). Notably, there was representation of each mouse ($$n = 3$$) in both the low and high fmEPSC groups (Fig. S4B), with some cells exhibiting a significant decrease in the fmEPSC, while excitatory input to others is unchanged, suggesting that a heterogeneous population of presynaptic inputs [68] is differentially responding to dietary sucrose. Fig. 4Long-term Sucrose consumption does not alter excitatory synaptic input to AgRP neurons in male mice. A Representative traces of mEPSCs onto AgRP neurons in fed or fasted male mice on either NDW or SucrW12Wk. B Mean mEPSC frequency onto AgRP neurons from NDW ($$n = 9$$) or SucrW12Wk ($$n = 9$$). C Mean mEPSC amplitude in AgRP neurons. D Mean tDecay in AgRP neurons from NDW or SucrW12wk mice. E Mean charge transfer in AgRP neurons from NDW or SucrW12Wk mice. F Cumulative frequency of inter-stimulus interval intervals in AgRP neurons from NDW or SucrW12Wk mice. G Cumulative frequency of amplitudes in AgRP neurons from NDW or SucrW12Wk mice. For all violin plots, dashed line indicates median, dotted lines indicate quartiles. Statistical comparisons using t-tests and cumulative frequencies compared using Kolgoromov-Smirnoff test (*$p \leq 0.05$).
When we investigated the impact of SucrW consumption on the amplitude of mEPSCs, we identified a small decrease in amplitude of mEPSC (Fig. 4C) with no change in decay constant (Tdecay) (Fig. 4D), resulting in a small increase in charge transfer (Fig. 4E). The left-shifted distribution of cumulative mEPSC amplitudes reflects the decrease in amplitude between groups (Fig. 4G). Together, these results suggest that AgRP neurons receive excitatory inputs from presynaptic neurons with differing responses to increased SucrW consumption and that SucrW consumption may influence postsynaptic response to excitatory input onto AgRP neurons.
## Long-term sucrose consumption alters inhibitory synaptic input to AgRP neurons
We identified significant differences in the fmIPSC (Fig. 5A, B) and amplitude of mIPSC (Fig. 5C) but no significant difference in decay constant (Tdecay) (Fig. 5D) or charge transfer (Fig. 5E). Left-shifted distribution of cumulative inter-event intervals (IEI) reflect the increased fmIPSC of SucrW12Wk mice (Fig. 5F), despite an increase in the overall excitability of AgRP neurons from these mice (Fig. 2). The left-shifted distribution of mIPSC amplitudes reflects the difference between groups (Fig. 5G). Overall, these results suggest that SucrW12Wk fed mice have altered synaptic inputs and decoupling from intrinsic firing rates, at least partially driven by leptin and GABA resistance. However, differences in bodyweight did not occur in SucrW12Wk mice, suggesting that Sucrose induced decoupling of inhibition (via leptin and GABA) may prime an organism for DIO if presented with more palatable (HFD) options. Fig. 5Long-term Sucrose consumption alters inhibitory synaptic input to AgRP neurons in male mice. A Representative traces of mIPSCs onto AgRP neurons in fed or fasted male mice on either NDW or SucrW12Wk. B Mean mIPSC frequency onto AgRP neurons from NDW ($$n = 6$$) or SucrW12Wk ($$n = 8$$) males: $$n = 6$$–8 (neurons/group). C Mean mIPSC amplitude in AgRP neurons. D Mean tDecay in AgRP neurons from NDW or SucrW12wk mice. E Mean charge transfer in AgRP neurons from NDW or SucrW12Wk mice. F Cumulative frequency of inter-stimulus interval intervals in AgRP neurons from NDW or SucrW12Wk mice. G Cumulative frequency of amplitudes in AgRP neurons from NDW or SucrW12Wk mice. For all violin plots, dashed line indicates median, dotted lines indicate quartiles. Statistical comparisons using t-tests and cumulative frequencies compared using Kolgoromov–Smirnoff test (*$p \leq 0.05$).
## Acute sucrose consumption does not alter AgRP neuronal activity but does drive increased food intake, bodyweight gain, and AgRP neuronal activity with acute HFD feeding
Previous studies have identified acute HFD feeding as drivers of altered function of arcuate AgRP neurons [28]. To explore this effect in SucrW mice, we provided access to sucrose water for only 2 days (Fig. 6A), which we previously demonstrated to be sufficient to induce hyperexcitability in mice fed a HFD [28]. Similar to SucrW12Wk mice, acute Sucrose fed mice (SucrW2d) consumed fewer kcal from chow (Fig. 6B) but more total kcal (chow + water) compared to NDW mice (Fig. 6C). Unlike what we previously observed in 2d HFD feeding, acute SucrW consumption did not significantly increase intrinsic AgRP neuronal activity ($$p \leq 0.3587$$; Fig. 6G, H).Fig. 6Acute Sucrose consumption does not alter AgRP neuronal activity but does drive increased food intake and AgRP neuronal activity with acute HFD feeding in male mice. A Timeline of feeding schedules for combined SucrW and acute HFD feeding experiments. B Baseline food (NCD) intake per mouse (kcal) from NDW ($$n = 6$$) and SucrW2d ($$n = 6$$) mice. C Baseline total (chow + water) intake per mouse (kcal) from NDW and SucrW2d mice. D HFD intake per mouse (kcal) compared to NCD/NDW ($$n = 10$$) baseline intake following 2d of HFD feeding in SucrW2d ($$n = 8$$), SucrW12Wk ($$n = 4$$), and NDW/HFD2d ($$n = 7$$). E. Bodyweight gain during the first two days of SucrW or HFD feeding in NDW ($$n = 9$$), SucrW2d ($$n = 10$$), SucrW2d+HFD ($$n = 12$$), and SucrW12Wk+HFD ($$n = 11$$). F. Plasma leptin during the two days of SucrW or HFD feeding in NDW ($$n = 9$$), SucrW2d ($$n = 5$$), SucrW2d+HFD ($$n = 7$$), and SucrW12Wk+HFD ($$n = 4$$). G. AgRP neuronal firing rate in NDW ($$n = 17$$), SucrW2d ($$n = 24$$), SucrW2d+HFD ($$n = 16$$), and SucrW12Wk+HFD ($$n = 13$$) following HFD2d feeding. H Resting membrane potential of AgRP neurons from SucrW2d and SucrW12Wk with HFD2d. I Representative traces of NDW, SucrW2d, SucrW2d+2d HFD, SucrW12Wk+2d HFD mice. J Representative trace and change in firing rate (K) from bath application of leptin in SucrW2d+2dHFD mice ($$n = 8$$). L Representative trace and change in firing rate (M) from bath application of leptin in SucrW12Wk+2dHFD mice ($$n = 8$$). For all violin plots, dashed line indicates median, dotted lines indicate quartiles. Statistical comparisons using t-tests or ordinary one-way ANOVA with a post hoc Tukey’s multiple comparisons test. (* $p \leq 0.05$; **$p \leq 0.01$; ***$p \leq 0.001$, ****$p \leq 0.0001$).
To determine whether there is an additive effect of liquid sucrose consumption and HFD, mice given either short-term (2d) or long-term (12wk) access to sucrose water were also given access to HFD for 2 days in place of normal chow (Fig. 6A). Following either short- (SucrW2d+2d HFD; $p \leq 0.0001$) or long-term (SucrW12Wk+2d HFD; $p \leq 0.0001$) sucrose consumption, HFD intake was increased similar to HFD2d feeding alone (HFD2d; $$p \leq 0.0016$$) compared to NDW mice (Fig. 6D). This corresponded with a trend for increased bodyweight in SucrW2d+2d HFD ($$p \leq 0.0504$$) and an increase in bodyweight in SucrW12Wk+2d HFD mice ($$p \leq 0.0074$$) not seen in SucrW2d mice ($$p \leq 0.9179$$) compared to NDWfed mice and both SucrW2d ($$p \leq 0.0066$$) and SucrW12Wk ($$p \leq 0.0008$$) weighed more after 2 days of HFD feeding than SucrW2d mice (Fig. 6E). Plasma leptin levels were also increased in SucrW12Wk+2d HFD ($$p \leq 0.0007$$) compared to NDWfed mice (Fig. 6F). Acute HFD feeding did not lead to significant differences in plasma insulin or liver weights between NDWfed, SucrW2d, SucrW2d+2d HFD, and SucrW12Wk+2d HFD mice (Fig. S5A, B). Unlike acute sucrose alone, acute HFD feeding following Sucrose consumption (SucrW2d+2d HFD, $$p \leq 0.0155$$; SucrW12Wk+2d HFD, $$p \leq 0.0307$$) did significantly increase AgRP neuronal activity compared to NDWfed mice (Fig. 6G–I), with no difference in RMP (Fig. 6H, I). The increased baseline firing in SucrW2d+2d HFD mice did not correspond to a deficiency in leptin signaling (Fig. 6J, K) like acute HFD feeding alone [28]. However, SucrW12Wk+2d HFD mice had an attenuated leptin response (Fig. 6L, M), similar to SucrW12Wk and DIO mice [24].
## Discussion
As the obesity crisis remains one of the largest public health concerns in developed countries, safe, effective, and lasting therapeutics remain elusive [5]. While considerable research has highlighted the importance of neuronal regulation of food intake and obesity [3, 6], the influence of dietary components on the CNS has received less attention. Most research has focused on the effects of high-fat diets [24, 29–31] on driving DIO and we have identified that diet composition (not necessarily caloric intake) may promote DIO [28]. Here, we show that high sugar diet drives increased caloric intake and AgRP neuronal activity, though not to the extent of HFD [24]. When given HFD for 2 days, caloric intake, bodyweight gain, and AgRP neuronal activity match that seen in HFD fed mice, suggesting that more AgRP neurons respond to fat + sugar than to sugar alone. While other components typical of the western diet (carbohydrates, fats, and proteins) [69] remain to be investigated, we identify a link between sugar consumption and AgRP neuronal activity in the absence of weight gain and hyperphagic behavior.
Consistent with previous studies of sugar sweetened water diets [37–39], we did not identify significant changes in bodyweight or body composition in SucrW12Wk mice. This is likely due to [1] decreases in NCD intake as mice attempt to regulate total caloric intake in response to increased SucrW intake, [2] robust sucrose preference, [3] gastric distention from liquid intake that prevents significant solid diet intake, or a combination of these factors [37–40, 42]. While fat mass was unchanged in SucrW mice, lean mass was decreased; this decoupling between fat and lean mass has been described previously in studies examining the function of AgRP neurons and the ghrelin receptor (GHSR1) [70–74]. Blood glucose is slightly elevated in SucrW12Wk mice while plasma leptin and insulin levels remain comparable to lean mice. Caloric intake following a fast further suggests that peripheral maintenance of bodyweight and food intake remain intact [75, 76]. However, liver weight was elevated in SucrW12Wk mice, suggesting an increase in hepatic fat content and impaired liver function [37], though this weight difference was reversed following a fast. Overall, these differences suggest that CNS control of food and caloric intake may be altered in SucrW12Wk mice. Based on these differences in caloric preference, we investigated the role of putative nutrient sensing neurons in the ARH of the hypothalamus.
AgRP neurons play a key role in the integration of peripheral and central signals and their activity is tied to food intake and body weight [19–23, 25]. Previous studies from our lab and others have identified DIO-related changes in AgRP neuronal activity and function linked to synaptic and intrinsic remodeling. Wei et al., 2015, provide evidence that diet composition may be sufficient to alter baseline AgRP neuronal activity that precedes any changes in bodyweight or peripheral hormone disruption. In accordance, we found that SucrW12Wk feeding increased AgRP neuronal activity and caloric intake without significantly altering bodyweight or body composition. These changes in neuronal firing mirror HFD-induced changes to AgRP neuronal firing rate in DIO mice [24, 31], though to a lesser extent, likely driven by functional differences in fat- and sugar-sensitive afferents to the hypothalamus and AgRP neurons [33–35, 77] or even disparate circuitries for hedonic and nutritional sugar preference [40]. Interestingly, fasting failed to further increase the FAP of AgRP neurons suggesting that [1] AgRP neurons are refractory to additional relevant stimuli, or [2] this rate represents a ceiling beyond which AgRP neurons are not able to sustain action potential firing. Our analysis of intrinsic AgRP neuronal firing distribution suggests that diet manipulation (SucrW, fast, HFD) does not change the maximum FAP of AgRP neurons, instead shifting the lower and medium firing populations to a higher ‘set-point’ [24, 76].
Additionally, leptin signaling was functionally altered in SucrW12Wk fed mice, an effect previously described in HFD fed mice utilizing both ex-vivo electrophysiological recordings [24] and p-STAT3 activation [78] and in rats fed sugar sweetened water [38]. Future research should explore the potential mechanisms of diet induced leptin resistance in the absence of obesity (but in the presence of AgRP neuronal hyperexcitability). Our results suggest that rather than inducing complete insensitivity to the inhibitory effects of leptin, long-term consumption of SucrW attenuates the response of AgRP neurons to leptin. We found that most AgRP neurons from SucrW mice exhibited some degree of inhibition; however, compared to the NDW controls, this response was more variable, with some AgRP neurons exhibiting a high level of activity after administration of leptin, suggesting an incomplete or attenuated leptin sensitivity. As phosphorylation of STAT3 in response to leptin has also been shown to be altered in HFD-induced obese animals, evaluation of p-STAT3 activation may offer additional insight into the broader response of these signaling pathways to leptin in SucrW treated mice. Further, synaptic plasticity of excitatory and inhibitory inputs to AgRP neurons in lean SucrW12Wk mice were akin to DIO HFD8Wk fed mice [31]. Briefly, decreased mEPSC and mIPSC amplitudes in SucrW12Wk mice suggest that AgRP response to synaptic input is altered compared to NDWfed mice. Increased mIPSC frequency in lean SucrW12Wk mice reflects previously described plasticity of these inputs [66, 67]. Specifically, inhibitory inputs from the ventral compartment of the dorsomedial nucleus of the hypothalamus (vDMH) [25] and the anterior bed nuclei of the stria terminalis (aBNST) [79] to AgRP neurons have been identified. While AgRP afferent neurons in the vDMH appear to be diet sensitive [80–82], the response of presynaptic aBNST neurons to diet manipulation remains unclear. These data support previous evidence that Sucrose consumption alters synaptic connectivity [36] and the ‘top-down’ mechanisms to inhibit persistently activated AgRP neurons function without obesity and are similar to DIO mice both ex vivo [31] and in vitro [29, 30].
Predictably, SucrW2d mice preferred Sucrose water to NCD and consumed more calories than NDW mice. Similar to previous studies [83–85], we show that short- and long-term Sucrose feeding increased HFD intake, slightly exceeding the hyperphagic behavior seen during the first 2 days of HFD feeding. This corresponded with trending and significant increases in bodyweight of SucrW2d+2d HFD and SucrW12Wk+2d HFD mice, respectively which precedes bodyweight changes from acute Sucrose or HFD [28]. AgRP neuronal activity was increased in both SucrW2d+2d HFD and SucrW12Wk+2d HFD groups, though this effect was predicted as both SucrW12Wk and HFD2d are sufficient to increase AgRP neuronal activity alone [28]. Finally, we found that leptin signaling was intact in lean SucrW2d+2d HFD, which aligns with our prior slice electrophysiology data [28] but not biochemical assays of leptin signaling [86, 87]. SucrW12Wk+2d HFD mice had elevated plasma leptin and an attenuated leptin response, while SucrW12Wk mice that had not consumed HFD had normal plasma leptin and an attenuated leptin response.
## Limitations
The current study was limited to observing the effects of Sucrose consumption on ex vivo AgRP neurons. Future studies will consider the impact of diet and specific macronutrient sensing in the gut using in vivo calcium imaging techniques following both short- and long-term feeding schedules as has been previously established in studies using HFD [29, 30]. Further, these experiments should be conducted longitudinally with more precise automated systems to measure diet and water consumption [88], allowing for quantification of individual caloric intake. While we utilized a group housing model to reduce isolation stress [57–59], future studies should incorporate individual housing for more precise quantification of individual intake. Further, it is understood that Sucrose consumption can alter the magnitude of astrocyte and microglia inflammation [89] and this may influence intrinsic and synaptic plasticity of AgRP neurons [90]. While C57Bl/6 J female mice are more resistant to DIO than males [31, 91] and did not display metabolic changes on SucrW12Wk diet, future studies should explore sex-specific resistance factors in females that might be associated with functional or synaptic changes in AgRP neuron populations. Finally, the influence of hypothalamic function on throughout the lifespan should be considered; as consumption of diets high in Sucrose or other obesogenic macronutrients likely have profound effects on early- [92–94] and later-life [95–99] hypothalamic and AgRP neuronal function.
## Conclusions
We have identified effects of short- and long-term Sucrose consumption on bodyweight and food intake, along with intrinsic and synaptic plasticity changes of AgRP neurons. Combined, these data suggest changes in AgRP neuronal function coincide with increased calorie intake and precede differences in bodyweight. Long-term sucrose consumption does not alter AgRP neuronal function to the same extent as HFD, suggesting differential innervation of specific gut-brain afferents. Further, Sucrose feeding for 2-days or 12-weeks augmented HFD intake and bodyweight gain. This effect corresponded with increased AgRP neuronal activity. Overall, this highlights the important role for top-down regulation of neuronal circuits that regulate food intake and bodyweight, supporting hypotheses that therapeutics for obesity will require ‘resetting’ of homeostatic circuitries within the CNS.
## Supplementary information
Supplemental Figure Legends Supplemental Figure S1 Supplemental Figure S2 Supplemental Figure S3 Supplemental Figure S4 Supplemental Figure S5 The online version contains supplementary material available at 10.1038/s41366-023-01265-w.
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|
---
title: 'Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models
With Minimal Bias Mitigation'
journal: Cureus
year: 2023
pmcid: PMC10023594
doi: 10.7759/cureus.35037
license: CC BY 3.0
---
# Racial Equity in Healthcare Machine Learning: Illustrating Bias in Models With Minimal Bias Mitigation
## Abstract
Background and objective While the potential of machine learning (ML) in healthcare to positively impact human health continues to grow, the potential for inequity in these methods must be assessed. In this study, we aimed to evaluate the presence of racial bias when five of the most common ML algorithms are used to create models with minimal processing to reduce racial bias.
Methods By utilizing a CDC public database, we constructed models for the prediction of healthcare access (binary variable). Using area under the curve (AUC) as our performance metric, we calculated race-specific performance comparisons for each ML algorithm. We bootstrapped our entire analysis 20 times to produce confidence intervals for our AUC performance metrics.
Results With the exception of only a few cases, we found that the performance for the White group was, in general, significantly higher than that of the other racial groups across all ML algorithms. Additionally, we found that the most accurate algorithm in our modeling was Extreme Gradient Boosting (XGBoost) followed by random forest, naive Bayes, support vector machine (SVM), and k-nearest neighbors (KNN).
Conclusion Our study illustrates the predictive perils of incorporating minimal racial bias mitigation in ML models, resulting in predictive disparities by race. This is particularly concerning in the setting of evidence for limited bias mitigation in healthcare-related ML. There needs to be more conversation, research, and guidelines surrounding methods for racial bias assessment and mitigation in healthcare-related ML models, both those currently used and those in development.
## Introduction
Health equity, the ability for everyone to “attain his or her full health potential regardless of socially-determined circumstances,” is one of the most fundamental aims of healthcare and public health [1]. All aspects of a health system landscape - including culture and socioeconomic status, healthcare access and coverage, quality of care, and provider implicit bias - impact the level of health equity in society.
One of the biggest changes in the healthcare landscape has been the rise of machine learning (ML) [2,3]. ML is being progressively incorporated into all parts of healthcare, including the development of diagnostic, clinical prediction, and patient recruitment tools. For example, ML methods have been applied in the prediction of heart failure and various types of cancer [4,5]. These tools have also been involved in the diagnosis of diabetic retinopathy, breast tumors, skin cancer, certain hematological diseases, and even coronary artery disease [6-10]. The power of ML in healthcare continues to grow, and its potential in this setting is vast [2].
While healthcare-related ML is growing more and more powerful in its ability to positively impact human health, the potential for inequity in these methods is concerning. For instance, differing performance and predictive accuracy of ML methods for different social groups can have dramatic implications and further exacerbate health inequities along gender or racial lines. In fact, there have been several studies that have found differing predictive accuracy of ML algorithms by race [11,12]. However, the root causes of the predictive disparities that can occur in ML have not been as well studied. It has been theorized that bias can be introduced at any stage [13]. More specifically, there can be bias involving data collection (e.g., historical bias and measurement bias), data selection (e.g., representation bias), model training (e.g., algorithmic bias), and model deployment (e.g., translational bias) [14].
Opportunities to mitigate potential biases exist at each step of the ML model development pipeline. During the pre-processing stage (before model training), one can reweight training data to increase representation, combine data sets to increase heterogeneity, or even remove race information from the data altogether [15]. During the in-processing stage (during model training), one can use techniques such as regularization or adversarial debiasing [15,16]. During the post-processing stage (after model training), one can calibrate their results or use varying cut-point selections to boost equity in performance [14]. Building ML models with no/minimal bias mitigation techniques can increase the risk of racial model performance disparities.
While ML has seen a steep rise, evidence suggests that the adoption of bias mitigation has not kept pace. A recent meta-analysis showed that while many healthcare-related ML studies assess for racial bias, some of these studies do not correct for this bias [15]. Also, those that do attempt to correct racial bias may use a limited array of bias mitigation techniques. Additionally, only a small number of studies published their code for bias assessment or debiasing [15].
Given this underwhelming attempt at bias mitigation in healthcare-related ML, in our study, we aim to evaluate the presence of racial bias when five of the most common ML algorithms are used to create models with minimal processing to reduce racial bias. We assessed the following five different ML methods: Extreme Gradient Boosting (XGBoost), random forest, naive Bayes, support vector machine (SVM), and k-nearest neighbors (KNN). Further, we a priori chose healthcare access - one of the most important drivers of health equity - as the outcome of prediction for model creation in our analyses.
## Materials and methods
Dataset We utilized the Behavioral Risk Factor Surveillance System (BRFSS) 2020 sample for our study [17]. We chose this database for its large sample size (useful for training ML models) and its wide array of variables - including medical, psychological, and social variables. The BRFSS is the largest health survey in the world, collecting data on over 400000 individuals every year in all 50 states, as well as the District of Columbia and three US territories.
Outcome *We a* priori chose healthcare access as the outcome for our ML models. We used a single survey question for our measure of healthcare access: “Do you have any kind of healthcare coverage, including health insurance, prepaid plans such as HMOs, or government plans such as Medicare, or Indian Health Service?” Outcome choices were “Yes” and “No”; all other answer choices were grouped into missing values. We coded “No” as “1” and “Yes” as “0,” and hence our analysis would be focused on identifying those without healthcare access.
Predictors All other survey questions (besides our outcome) were considered as possible predictors for our ML models. We excluded variables from our dataset that were [1] related closely to our outcome, [2] survey components (e.g., time of interview), [3] age or sex-specific (e.g., mammography, prostate-specific antigen, or colonoscopy results), [4] redundant, or [5] having over $50\%$ missing data. Race consisted of the following six categories: White, Black, American Indian or Alaskan Native, Asian, Native Hawaiian or other Pacific Islander, or Hispanic. Individuals whose race was classified as "Other" or "Multiracial" were not included in our analysis.
Missing data We removed any observations that were missing racial or health access data. For all other variables, we assumed that our data were missing at random and used multiple imputation techniques from the MICE (Multiple Imputation by Chained Equations) package in R to prevent bias from list-wise deletion [18]. Our MICE algorithm used predictive mean matching, logistic regression, and polynomial regression to impute values for our predictors. Additionally, we specified the model to use proportional odds logistic regression as the imputation technique for our ordinal variables.
Assessment of racial bias for each machine learning algorithm After missing data imputation, we performed a variance analysis and confirmed that no variables had zero variance, which would have interfered with the model-building process. Next, we performed a test-train split stratified by our outcome variable (healthcare access). Given the significant size of our total data, we only used $3\%$ of our total 399896 observations for the training set. We chose a variety of the most common ML algorithms for classification for our study: [1] XGBoost, [2] random forest, [3] naive Bayes, [4] SVM, and [5] KNN. To validate each model, we used k-fold cross-validation with $k = 10.$ We used the CARET (Classification and Regression Training) package in R to build all of our models [19]. We predicted healthcare access (binary classification problem) with our specified list of predictors for our test set ($97\%$ of data). We used the area under the curve (AUC) as our measure of performance throughout our analysis - an ideal metric for binary classification problems [20]. We split up our test set into each racial category and then compared AUC values for each race for each ML method predicting healthcare access. Additionally, we bootstrapped this analysis 20 times for each of the five ML algorithms to produce confidence intervals for the AUC performance metric for each race for each algorithm.
## Results
Descriptive statistics of the study population *After data* cleaning, our final dataset cumulatively consisted of 399896 observations; 51 predictors remained after variable selection. Variables were excluded if those were [1] related to our outcome (3 variables), [2] survey components (42 variables), [3] age or sex-specific (96 variables), [4] redundant (45 variables), or [5] having over $50\%$ missing data (44 variables). Of our study population, $8.5\%$ reported lacking healthcare access. The most common age groups were 65-69 years ($10.4\%$), followed by 60-64 years ($10.3\%$), and 70-74 years ($9.5\%$). Of note, $54.3\%$ of our study population were female, and $51.7\%$ were married. The racial distribution was as follows: $73.7\%$ White, $7.5\%$ Black, $1.7\%$ American Indian or Alaskan Native, $2.5\%$ Asian, $0.5\%$ Pacific Islander, and $9.0\%$ Hispanic. Table 1 presents the full race-stratified descriptive statistics of our study population (before missing data imputation).
**Table 1**
| Variables | White (n=294702) (73.7%), n (%) | Black (n=29943) (7.5%), n (%) | American Indian (n=6760) (1.7%), n (%) | Asian (n=10081) (2.5%), n (%) | Pacific Islander (n=1994) (0.5%), n (%) | Other (n=3317) (0.8%), n (%) | Multiracial (n=8267) (2.1%), n (%) | Hispanic (n=36078) (9.0%), n (%) | Overall (N=399896) (100%)*, n (%) |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Age (years) | | | | | | | | | |
| 18-24 | 14981 (5.1%) | 1922 (6.4%) | 481 (7.1%) | 1421 (14.1%) | 241 (12.1%) | 155 (4.7%) | 960 (11.6%) | 4527 (12.5%) | 25037 (6.3%) |
| 25-29 | 12638 (4.3%) | 1714 (5.7%) | 414 (6.1%) | 1083 (10.7%) | 204 (10.2%) | 177 (5.3%) | 690 (8.3%) | 3547 (9.8%) | 20795 (5.2%) |
| 30-34 | 14688 (5.0%) | 1954 (6.5%) | 482 (7.1%) | 978 (9.7%) | 183 (9.2%) | 216 (6.5%) | 739 (8.9%) | 3635 (10.1%) | 23304 (5.8%) |
| 35-39 | 16623 (5.6%) | 2090 (7.0%) | 541 (8.0%) | 985 (9.8%) | 172 (8.6%) | 228 (6.9%) | 704 (8.5%) | 3579 (9.9%) | 25407 (6.4%) |
| 40-44 | 16869 (5.7%) | 2238 (7.5%) | 506 (7.5%) | 864 (8.6%) | 160 (8.0%) | 226 (6.8%) | 665 (8.0%) | 3624 (10.0%) | 25656 (6.4%) |
| 45-49 | 18099 (6.1%) | 2296 (7.7%) | 530 (7.8%) | 763 (7.6%) | 172 (8.6%) | 233 (7.0%) | 602 (7.3%) | 3238 (9.0%) | 26367 (6.6%) |
| 50-54 | 22231 (7.5%) | 2663 (8.9%) | 640 (9.5%) | 770 (7.6%) | 170 (8.5%) | 241 (7.3%) | 644 (7.8%) | 3078 (8.5%) | 31014 (7.8%) |
| 55-59 | 27416 (9.3%) | 2878 (9.6%) | 694 (10.3%) | 639 (6.3%) | 164 (8.2%) | 304 (9.2%) | 672 (8.1%) | 2741 (7.6%) | 36124 (9.0%) |
| 60-64 | 32142 (10.9%) | 3154 (10.5%) | 717 (10.6%) | 675 (6.7%) | 165 (8.3%) | 339 (10.2%) | 726 (8.8%) | 2376 (6.6%) | 41024 (10.3%) |
| 65-69 | 33484 (11.4%) | 3023 (10.1%) | 600 (8.9%) | 565 (5.6%) | 134 (6.7%) | 324 (9.8%) | 595 (7.2%) | 2026 (5.6%) | 41480 (10.4%) |
| 70-74 | 31442 (10.7%) | 2386 (8.0%) | 481 (7.1%) | 493 (4.9%) | 105 (5.3%) | 257 (7.7%) | 515 (6.2%) | 1518 (4.2%) | 37885 (9.5%) |
| 75-79 | 22506 (7.6%) | 1423 (4.8%) | 305 (4.5%) | 277 (2.7%) | 48 (2.4%) | 190 (5.7%) | 299 (3.6%) | 975 (2.7%) | 26535 (6.6%) |
| ≥80 | 26848 (9.1%) | 1619 (5.4%) | 233 (3.4%) | 329 (3.3%) | 47 (2.4%) | 296 (8.9%) | 341 (4.1%) | 890 (2.5%) | 31297 (7.8%) |
| Missing | 4735 (1.6%) | 583 (1.9%) | 136 (2.0%) | 239 (2.4%) | 29 (1.5%) | 131 (3.9%) | 115 (1.4%) | 324 (0.9%) | 7971 (2.0%) |
| Sex | | | | | | | | | |
| Male | 134947 (45.8%) | 11951 (39.9%) | 3055 (45.2%) | 5139 (51.0%) | 907 (45.5%) | 1731 (52.2%) | 3872 (46.8%) | 16505 (45.7%) | 182766 (45.7%) |
| Female | 159755 (54.2%) | 17992 (60.1%) | 3705 (54.8%) | 4942 (49.0%) | 1087 (54.5%) | 1586 (47.8%) | 4395 (53.2%) | 19573 (54.3%) | 217130 (54.3%) |
| Marital status | | | | | | | | | |
| Married | 163949 (55.6%) | 9510 (31.8%) | 2444 (36.2%) | 5502 (54.6%) | 901 (45.2%) | 1588 (47.9%) | 3248 (39.3%) | 15604 (43.3%) | 206781 (51.7%) |
| Divorced | 38124 (12.9%) | 4808 (16.1%) | 1134 (16.8%) | 666 (6.6%) | 184 (9.2%) | 482 (14.5%) | 1256 (15.2%) | 4148 (11.5%) | 51751 (12.9%) |
| Widowed | 35160 (11.9%) | 3286 (11.0%) | 691 (10.2%) | 450 (4.5%) | 138 (6.9%) | 356 (10.7%) | 643 (7.8%) | 1840 (5.1%) | 43454 (10.9%) |
| Separated | 3915 (1.3%) | 1394 (4.7%) | 220 (3.3%) | 127 (1.3%) | 56 (2.8%) | 92 (2.8%) | 215 (2.6%) | 1741 (4.8%) | 7930 (2.0%) |
| Never married | 42304 (14.4%) | 9846 (32.9%) | 1837 (27.2%) | 3041 (30.2%) | 590 (29.6%) | 616 (18.6%) | 2378 (28.8%) | 9234 (25.6%) | 71198 (17.8%) |
| A member of an unmarried couple | 9579 (3.3%) | 841 (2.8%) | 356 (5.3%) | 216 (2.1%) | 114 (5.7%) | 109 (3.3%) | 469 (5.7%) | 3231 (9.0%) | 15169 (3.8%) |
| Missing | 1671 (0.6%) | 258 (0.9%) | 78 (1.2%) | 79 (0.8%) | 11 (0.6%) | 74 (2.2%) | 58 (0.7%) | 280 (0.8%) | 3613 (0.9%) |
| Healthcare access | | | | | | | | | |
| Yes | 276949 (94.0%) | 26654 (89.0%) | 6095 (90.2%) | 9237 (91.6%) | 1716 (86.1%) | 2939 (88.6%) | 7468 (90.3%) | 26940 (74.7%) | 365862 (91.5%) |
| No | 17753 (6.0%) | 3289 (11.0%) | 665 (9.8%) | 844 (8.4%) | 278 (13.9%) | 378 (11.4%) | 799 (9.7%) | 9138 (25.3%) | 34034 (8.5%) |
Racial bias assessment With only a few exceptions, we found that the performance for the White group was, in general, significantly higher than that of any other racial group across all ML algorithms. For the XGBoost algorithm, the most accurate ML algorithm in our analysis, the performance for the White group was statistically significantly higher than any other racial group. For the random forest algorithm, the next most accurate algorithm, the performance for Whites was significantly greater than all other groups except for the Hispanic group (although the point estimate for the White group was still greater than for the Hispanic group). Using the naive Bayes algorithm, the point estimate of the performance for the White group was higher than all other groups and this comparison was statistically significant for all groups except the Pacific Islander group. For SVM, the performance for the White group had the highest point estimate, although it was not statistically significant in terms of comparison with all the other groups. In KNN, the worst-performing algorithm in our analysis, the performance for the Hispanic group was significantly better than for the White group; however, the performance for the White group was still higher than for any other racial group - and the difference was significant when compared to every group except for the Black group. Figure 1 and Table 2 present the full results of our racial bias assessment for each ML algorithm.
**Figure 1:** *Race-specific performance for each machine learning algorithmIntervals represent 95% confidence intervals created from 20 iterations of bootstrapping the analysisAI or AN: American Indian or Alaskan Native*
Comparative performance of machine learning algorithms XGBoost had the highest AUC of any ML algorithm for the prediction of healthcare access with race-specific average AUCs ranging from 0.74 to 0.83 (averaged across 20 iterations). The next highest in performance was the random forest algorithm, which had race-specific average AUCs of 0.71-0.81. The rest of the algorithms had lower performances with race-specific average AUC ranges of 0.65-0.77 (naive Bayes), 0.67-0.75 (SVM), and 0.58-0.70 (KNN). Table 2 shows the full list of race-specific performance metrics for each ML algorithm.
**Table 2**
| Unnamed: 0 | XGBoost | Random forest | Naive Bayes | Support vector machine | K-nearest neighbors |
| --- | --- | --- | --- | --- | --- |
| American Indian or Alaskan Native | 0.742 [0.733, 0.751]* | 0.705 [0.691, 0.719]* | 0.667 [0.675, 0.677]* | 0.671 [0.582, 0.760] | 0.588 [0.552, 0.624]* |
| Asian | 0.780 [0.775, 0.785]* | 0.751 [0.734, 0.768]* | 0.711 [0.698, 0.725]* | 0.711 [0.635, 0.786] | 0.601 [0.566, 0.635]* |
| Black | 0.784 [0.777, 0.791]* | 0.758 [0.749, 0.767]* | 0.739 [0.732, 0.746]* | 0.713 [0.613, 0.813] | 0.651 [0.633, 0.668] |
| Hispanic | 0.801 [0.793, 0.808]* | 0.791 [0.776, 0.807] | 0.730 [0.721, 0.738]* | 0.748 [0.659, 0.837] | 0.696 [0.686, 0.705]* |
| Pacific Islander | 0.767 [0.753, 0.781]* | 0.741 [0.726, 0.756]* | 0.647 [0.529, 0.766] | 0.697 [0.604, 0.789] | 0.579 [0.507, 0.650]* |
| White | 0.831 [0.827, 0.835] (Ref) | 0.805 [0.800, 0.811] (Ref) | 0.772 [0.763, 0.781] (Ref) | 0.759 [0.659, 0.858] (Ref) | 0.668 [0.658, 0.678] (Ref) |
## Discussion
In our study with an a priori-specified ML plan with minimal racial bias mitigation, we found overall higher model performance for the White group compared to all other racial groups across all five ML algorithms. Bootstrapping our analysis, we can visualize that this difference in performance between the White group and all other racial groups was, for most algorithms, statistically significant. Even using public data and traditional ML methods and packages in this project, our study illustrates the predictive perils of incorporating minimal racial bias mitigation, resulting in predictive disparities. While we did not directly study the underlying reason for this predictive discrepancy, the explanation is most likely multifactorial with the most dominant reason potentially being the fact that the majority of the training data consists of individuals from the White group. Perhaps a more representative training set would yield more equitable models. Other possible contributing factors include historical and measurement bias in the pre-processing phase stemming from historical racial inequities affecting health, healthcare access, and participation in research.
Secondarily, we found that XGBoost was the overall best prediction algorithm for our application with random forest following and the other algorithms following still. We a priori expected XGBoost to outperform the other models. XGBoost is a relatively newer, more powerful algorithm that has been widely successful and shown to outperform many other models in a variety of settings [21]. Next, the lack of significant predictive differences for SVM seems less to do with closer point estimates but rather wider variances of the models. The wider variances of these models suggest the tendency of SVM to produce more variability in its models; however, the underlying reason is not entirely clear. Perhaps, the size of the training data set is also a factor in performance variability; for the naive Bayes analysis, the Pacific Islander group was the smallest group in our data set and produced models with the largest variances compared to the other racial groups. Additionally, the one outlier in our results is the fact that the KNN algorithm predicted best for the Hispanic group (given that all the other algorithms predicted best for the White group). The reason for this is unclear and could reflect random chance or the nature of the KNN algorithm. The KNN algorithm works by classifying observations based on the status of those with best matching covariates (“neighbors”). There may be more homogeneity in the covariates of those in the Hispanic group without healthcare access; further, the Hispanic group also had the highest rates of our outcome - lack of healthcare access - compared to other racial groups ($25.3\%$).
This study fits in with existing literature suggesting the prevalence of racial bias and predictive disparities in the performance of healthcare-related ML algorithms [11,12]. This is particularly concerning given recent literature showing that even when racial bias assessments are done, no or minimal resulting bias mitigation is performed [15].
The implications of this research are manifold. While ML in healthcare has seen a dramatic rise, guidelines and conversations regarding the assurance of equity of these models have lagged behind. Given the rise of ML and the importance of bias-resistance models across social lines, there needs to be more conversation, research, and guidelines surrounding methods for racial bias assessment and mitigation in models currently used and those in development.
Limitations Several factors limited the predictive accuracy of the models created in this analysis. Significant levels of non-viable variables and missing data were both limitations for the models created in this analysis; however, we were able to limit this concern with our large sample size and use of multiple imputations. Another limitation was the relatively low prevalence of our outcome (lack of healthcare access), which can lead to models with increased specificity at the expense of sensitivity; however, using AUC as our performance metric affords a more comprehensive metric taking into account varying levels of sensitivity and specificity. Also, we could have used a wider range of tuning parameters for the ML models; however, we did try many different ML methods.
## Conclusions
Our study illustrates the racial bias that can result when creating ML models without proper bias mitigation. Healthcare-related ML models, both those currently being used and those in development, must incorporate robust racial bias assessment and mitigation methods. Only through crafting fair models can ML, a powerful tool, be a powerful force for promoting equitable healthcare for all.
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|
---
title: Weight stigma speaks Italian, too
authors:
- G. Muscogiuri
- L. Barrea
- L. Verde
- A. Docimo
- S. Savastano
- D. Di Pauli
- A. Colao
journal: Journal of Endocrinological Investigation
year: 2022
pmcid: PMC10023597
doi: 10.1007/s40618-022-01971-8
license: CC BY 4.0
---
# Weight stigma speaks Italian, too
## Abstract
### Purpose
Weight stigma is the negative weight related attitudes and beliefs towards individuals because of their overweight or obesity. Subjects with obesity are often victim of weight-related stigma resulting in a significant negative social consequence. As obesity epidemic is growing so fast, there is urgency to act on weight-stigma related social consequences being potentially serious and pervasive. This study investigated experiences, interpersonal sources, and context of weight stigma in Italy in a sample of adult subjects with obesity.
### Methods
An online questionnaire was distributed to respondents via a snowball sampling method among subjects with obesity belonging to Italian Associations for people living with obesity aged 18 years and above.
### Results
Four hundred and three respondents (47.18 ± 9.44 years; body mass index (BMI) 33.2 ± 8.48 kg/m2) participated to the study. Most respondents were females ($94.8\%$). The age first dieted was 15.82 ± 7.12 years.
The mean period of obesity was 27.49 ± 11.41 years. Frequency analyses reported that stigmatizing situations were experienced by $98\%$ of participants: $94.82\%$ during adulthood, $89.88\%$ during adolescence and $75.39\%$ during childhood. Verbal mistreatments ($92.43\%$) was the most reported stigmatizing situation, strangers ($92.43\%$) were the most common interpersonal sources of stigma and public settings ($88.08\%$) were the most common location of stigma.
### Conclusions
Identifying strategies acting on the identified weight stigma targets could contribute to reduce weight stigma and thus to result in important implications for obesity treatment in Italy.
## Introduction
Weight stigma is the negative weight related attitudes and beliefs including stereotypes, rejection, and prejudice towards individuals because of their overweight or obesity [1]. Subjects with obesity are often victim of weight-related stigma resulting in a significant negative social consequence [2]. Weight stigma experiences are very common in subjects with obesity [3] that in turn internalize them blaming and criticizing themselves as the only cause of their weight condition [4].
Previous studies showed that weight stigma is worldwide and over $50\%$ of adults surveyed across six different countries (Australia, Canada, France, Germany, the UK, and the US) report experiencing weight stigma [5] but there are missing information regarding “the weight” of weight stigma in Italy.
As obesity epidemic is growing so fast [6], there is urgency to act on weight-stigma related social consequences since these could potentially be serious and pervasive leading to unfair treatment, prejudice and even discrimination. In addition, weight stigma could create a fertile ground for the onset of depression, body images distress, psychiatric symptoms, and decreased self-acceptance [7–9]. The negative consequences for physical health include unhealthy eating patterns (binge eating and increased food consumption), avoidance of physical activity, and less use of health care [5, 10–12]. Weight-related stigma includes a wide variety of stages, going from repeated mocking and mobbing to harassment and hostility [13, 14]. In addition, people affected by obesity report experiencing undesired attention, social rejection, and discrimination [15].
People from any age can suffer from this stigma. Indeed, the anti-fat attitudes has been reported to begin early in the childhood as young as preschool age [16]. In a study carried out in 2016 adolescents, weight-based teasing has been associated to binge eating at a 5 years of follow up in both males and females and even after adjustment for confounding factors such as age, race/ethnicity and socioeconomic status [17]. Weight stigma has been reported in educational settings towards students with obesity by peers, classmate, teachers and school administrators [18]. It has also been detected in healthcare environments, where patients with obesity are subjected by bias by healthcare professionals including those specialized in obesity [19–24] and in workplace settings where overweight employees are victim of negative judgements by co-workers and supervisors [25, 26].
A gender difference in weight-stigma related effects also emerged: indeed, weight teasing results in unhealthy weight control behaviors among men and frequent dieting in women [12].
Although increasing attention on weight stigma because of its important repercussions on the management of obesity and overall quality of life, there is a need of epidemiologic studies in order to quantify this phenomenon and to set up a strategy to act.
Thus, the objective of the present study was to carry out a survey in a large sample of Italian adults with obesity by documenting and examining sources of weight bias, life domains and locations where it occurs.
## Materials and methods
A snowball sampling method was used to spread an online questionnaire, on the Google Form platform, between subjects affected by obesity who belonged to Italian Associations for people living with obesity. The minimum age required was 18 years old and the maximum age was 71 years old. The BMI range was between 18.7 and 59.9 kg/m2. It was also asked to the subjects to involve the people they know, inviting them to answer the questionnaire too. In this cross-sectional study all search procedures were carried out in accordance with the pertinent guidelines and regulations of the Declaration of Helsinki. All respondents signed a valid informed consent.
## Questionnaire on weight stigma
The questionnaire was developed through a literature review [1, 12]. Questions from 1 to 7 covered sociodemographic and anthropometric characteristics. Participants were asked to report their age (years), gender, height (cm), weight (kg), childhood weight status (using “underweight”, “normal weight” or “overweight” as response choices), age of first dieting attempt and how many years they have been suffering from obesity. Questions from 8 to 11 dealt with the current therapeutic management of obesity (using “nutritional”, “pharmacological” and “psychological” as response choices) and any previous or planned bariatric surgery. Questions from 12 to 45 covered ‘type of stigma’ (verbal, physical barriers, being avoided, excluded, ignored, job discrimination, being attacked), ‘context of stigma’ (home, public place, school, work, medical facility, mode of transportation, sports facility) and ‘source of stigma’ (men, women, children, adolescent, adult, peer/friends, parent, sibling, boyfriend/girlfriend, spouse, stranger, other family member, health professional, nurse, boss/supervisor, sales clerk/server, teacher/professor, administrative staff). The available answers were never, rarely, occasionally, often, always. Question 46 was an open-ended question: “Can you describe where, when and by whom you suffered what you consider to be the worst experience of stigma experienced because of your weight?”. A pilot assessment was conducted among the first 30 respondents recruited through snowball sampling to ensure the questions were clearly written, easily understood and unambiguous.
## Statistical analysis
Results have been described as mean ± (standard deviation) SD or number (percentage). Differences in multiple groups were analyzed by ANOVA test followed by the Bonferroni post-hoc test. SPSS software (PASW version 21.0, SPSS Inc., Chicago, IL, USA) and the MedCalc® package (version 12.3.0 1993–2012 MedCalc Software bvba-MedCalc Software, Mariakerke, Belgium) were used to analyze the collected data.
## Results
A total of 403 respondents (47.18 ± 9.44 years; BMI: 33.2 ± 8.48 kg/m2) participated to the study. Most respondents were females ($94.8\%$). The age first dieted was 15.82 ± 7.12 years. With respects to childhood weight, $32.75\%$ had normal weight, $61.46\%$ was overweight and $5.79\%$ was underweight. The mean period of obesity was 27.49 ± 11.41 years. Sixty % of subjects reported to be on treatment for weight excess: $71.18\%$ underwent to bariatric surgery, $8.47\%$ were taking anti-obesity drugs, $5.51\%$ was candidate for bariatric surgery, $14.4\%$ were following psychological and $77.12\%$ nutritional treatments.
Regarding subjects that underwent to bariatric surgery, $7.77\%$ underwent to gastric banding, $45.95\%$ to sleeve gastrectomy, $11.82\%$ to gastric by-pass, $15.54\%$ to mini-gastric by-pass, $6.76\%$ to Roux-en-Y gastric bypass, $1.69\%$ to intragastric balloon and $1.35\%$ to other types of surgery, whilst $9.12\%$ of subjects underwent to more than one bariatric surgery procedure.
As expected, subjects that underwent to bariatric procedures had significantly lower BMI (30.52 ± 6.36 kg/m2) than subjects that did not (BMI 38.81 ± 9.73 kg/m2) and then subjects candidate for bariatric surgery (BMI 43.25 ± 7.29 kg/m2) ($p \leq 0.001$). Subjects that underwent to intragastric balloon had significantly higher BMI (45.3 ± 4.3 kg/m2) than other groups of subjects underwent to other bariatric surgery procedures (BMI 34.4 ± 6.4 kg/m2 gastric banding, 31.2 ± 7.5 kg/m2 sleeve gastrectomy, 29.8 ± 6.1 kg/m2 gastric bypass, 30.2 ± 7.1 kg/m2 mini-gastric bypass, 30.5 ± 5.7 kg/m2 Roux-en-Y gastric bypass) ($$p \leq 0.003$$). Subjects that underwent to more than one bariatric surgery procedure were still in a weight-excess state (BMI 31.32 ± 6.41 kg/m2).
The answers to the qualitative question (“Can you describe where, when and by whom you suffered what you consider to be the worst experience of stigma experienced because of your weight?”) showed that the worst stigma experiences were very variable in terms of settings and individuals. The majority of the subjects reported their worst stigma experience occurred in adulthood and were enacted by another adult. Selected examples of response are reported below:“When I was trying to get my gynecologist to understand that I was having strong contractions in the seventh month of pregnancy, she told me that it was just my body being tired because of too much weight, whereas it was pre-eclampsia. She didn't even examine me, just a phone interview because the only problem with my pregnancy for her was the weight”“At the bank I was stuck between the security doors. The automated voice said: 'Enter one person at a time'. The vigilante watched me go in and out, snickering. A terrible humiliation!”“At school, a teacher explaining how the scales worked made a joke about my weight in front of everyone… they all laughed out loud, especially him.”
## Experiences of stigma
Frequency analyses reported that stigmatizing situations were experienced by $98\%$ of participants: $94.82\%$ during adulthood, $89.88\%$ during adolescence and $75.39\%$ during childhood.
## Stigmatizing situations
Regarding stigmatizing situations, we found that verbal mistreatments was the most common: indeed, $92.43\%$ of respondents experienced nasty comments from children, family members and strangers. The $81.36\%$ of the participants reported to have experienced physical barriers and obstacles, whilst the $77.45\%$ of them felt to be avoided, excluded, or ignored. Job discrimination was referred from the $67.42\%$ of the subjects and $37\%$ of them were attacked (Fig. 1).Fig. 1Type of stigma in the study population Table 1 presents descriptive statistics on the stigma subscales. Table 1Type of stigma in the study populationType of stigmaNeverRarelyOccasionallyOftenAlwaysVerbal29 (7.6)65 (17.0)85 (22.2)146 (38.1)58 (15.1)Physical barriers71 (18.6)55 (14.4)64 (16.8)126 (33.07)65 (17.1)Being avoided, excluded, or ignored83 (22.6)80 (21.7)84 (22.8)95 (25.8)26 (7.1)Job discrimination115 (32.6)65 (18.4)73 (20.7)73 (20.7)27 (7.7)Being attacked218 (63.0)60 (17.3)39 (9.8)21 (6.1)8 (2.3)Data are expressed as n (%).
## Interpersonal sources of stigma
Ninety-four % of respondents were stigmatized by men and $95\%$ by women. The most common and frequently reported sources of stigma were adults ($95.6\%$) followed by adolescents ($88.28\%$) and children ($81.44\%$). The most reported interpersonal source of stigma were strangers ($92.43\%$). The respondents also reported to be stigmatized by peer/friends ($88.28\%$), health professionals ($80.9\%$), family members ($77.84\%$), sales clerks/servers ($77.75\%$), nurses ($66.75\%$), parents ($65.27\%$), boss/supervisors ($59.31\%$), teachers/professors ($56.43\%$), boyfriend/girlfriend ($46.06\%$), administrative staff ($45.4\%$), siblings ($44.19\%$), and spouse ($42.86\%$) (Fig. 2).Fig. 2Interpersonal sources of stigma in the study population Table 2 report descriptive statistics of interpersonal sources of stigma. Table 2Interpersonal sources of stigma in the study populationInterpersonal sources of stigmaNeverRarelyOccasionallyOftenAlwaysGender of perpetrator Men23 (6.1)74 (19.6)120 (31.8)129 (34.1)32 (8.5) Women19 (4.9)58 (15.3)140 (36.3)141 (36.5)28 (7.3)Age of perpetrator Adult17 (4.4)79 (20.5)121 (31.4)140 (36.3)29 (7.5) Adolescent43 (11.7)59 (16.1)99 (27.0)136 (37.1)30 (8.2) Children67 (18.6)88 (24.4)98 (27.1)93 (25.8)15 (4.2)Source of stigma Strangers28 (7.6)66 (17.8)104 (28.1)131 (35.4)41 (11.1) Peer/friends43 (11.7)93 (25.3)90 (24.6)122 (33.2)19 (5.2) Health professional72 (19.1)78, (20.7)87, (23.1)110, (29.2)30, (8.0) Other family member80 (22.2)99 (27.4)93 (25.8)69 (19.1)20 (5.5) Sales clerk/server79 (22.3)63 (17.8)96 (27.0)86 (24.2)31 (8.7) Nurse118 (33.2)75, (21.1)75, (21.1)69, (19.4)18 (5.1) Parent124 (34.7)76 (21.3)64 (17.9)69 (19.3)24 (6.7) Boss/supervisor142 (40.7)81 (23.2)74 (21.2)37 (10.6)15 (4.3) Teacher/professor149 (43.6)72 (21.1)72 (21.1)41 (12.0)8 (2.3) Boyfriend/girlfriend185 (54.0)77 (22.5)47 (13.7)28 (8.2)6 (1.8) Administrative staff190 (54.6)81 (23.3)46 (13.2)25 (7.2)6 (1.7) Sibling192 (55.8)58 (16.9)50 (14.5)31 (9.0)13 (3.8) Spouse192 (57.1)74 (22.0)36 (10.7)24 (7.1)10 (83.0)Data are expressed as n (%)
## Context of stigma
Seventy-five % of the respondents reported public places ($88.08\%$) as the most common location of stigma followed by school ($86.94\%$), medical facility ($80.93\%$), work ($77.97\%$), home ($75.35\%$), mode of transportation ($75\%$) and sports facilities ($74.72\%$) (Fig. 3).Fig. 3Context of stigma in the study population Table 3 report descriptive statistics of context of stigma. Table 3Context of stigma in the study populationContext of stigmaNeverRarelyOccasionallyOftenAlwaysPublic place44 (11.9)70 (19.0)140 (37.9)104 (28.2)11 (3.0)School47 (13.1)53 (14.7)93 (25.8)126 (35.0)41 (11.4)Medical facility70 (19.1)78 (21.3)113 (30.8)87 (23.7)19 (5.2)Work78 (22.0)86 (24.3)121 (31.2)57 (16.1)12 (3.4)Home88 (24.7)116 (32.5)75 (21.0)58 (16.3)20 (5.6)Mode of transportation88 (25.0)76 (21.6)105 (29.8)74 (21.0)9 (2.6)Sports facility90 (25.3)74 (20.8)96 (27.0)77 (21.6)19 (5.3)Data are expressed as n (%)
## Discussion
Being a target of weight stigma in a variety of forms and occasions has been reported by participants in this study. Most respondents reported to be victim of stigmatizing situations during adulthood. In particular, the most common types of weight stigma reported was verbal mistreatments experiencing nasty comments from children, family members and strangers. This finding has been previously reported by Puhl et al. that investigated experiences of weight stigma, sources of stigma, coping strategies and psychological functioning and eating behaviors in a sample of 2671 subjects with overweight or obesity [12]. In agreement with our finding, they found that the most common stigmatizing situation reported verbal mistreatments, i.e., others making negative assumptions, receiving nasty comments from children, encountering inappropriate comments from doctors and receiving negative comments from family members [12].
Participants reported being stigmatized by a variety of interpersonal sources, the most frequent being strangers followed by friends, health professionals, sales clerks/servers at stores, family members, nurses, parents, boss/supervisors, teachers/professors, administrative staff, sibling, boyfriend/girlfriend and spouse, thus suggesting that stigma reduction strategies need to target a range of individuals in multiple settings. Our results are consistent with previous research that identify strangers as the most common interpersonal source of weight stigma [27–29].
Indeed, Falkner et al. found that the most reported sources of mistreatment among 800 women enrolled in a weight gain prevention were strangers followed by spouse or loved one [27]. Similarly, Himmelstein et al. reported that the most common sources of weight stigma in 1513 men were strangers followed by peers and family members [28]. This is broadly consistent with a study in 46 community man and women who took part in an ecological momentary assessment study of their experiences with weight stigma that found that stigma was perpetrated by a variety of sources and in several different settings but mostly occurred by strangers [29].
An interesting finding of our study was that doctors, that should be immune to weight bias, were not and conversely, they were referred as ones of the most frequent source of stigma [30, 31].
This is in accord with a previous research carried out by Schwartz et al., who studied 389 health professionals attending the opening session of an international obesity conference [30]. The Implicit Associations Test (IAT) was used to assess the overall implicit attitude to weight bias (through the automatic memory-based associations of “people with obesity” and “thin people” with “good” vs. “bad”). Then, 3 ranges of stereotypes were identified: lazy-motivated, smart-stupid, and valuable-worthless.
Interestingly, in health professional, a significant pro-thin and anti-fat implicit bias was highlighted on the IAT, and the subjects endorsed significantly the implicit stereotypes of lazy, stupid, and worthless [30]. Similarly, Teachman and Brownell found evidence for implicit anti-fat bias for both the attitude and stereotype measures (evaluated also in this case with the IAT) in 84 health professionals who treated obesity [31].
Some strategies have been demonstrated to help reducing the weight stigma between the health professionals, such as developing dedicated educational programs and giving information about obesity determinants, which often are not dependent on the patient’s will [32]. In addition, it has to be considered that not always the weight loss is achievable and safe for all the subjects with an increased weight [32].
Finally, as previously reported [29, 33], we found that weight stigma occurred most in public places. Vartanian et al. carried out an ecological momentary assessment study of their experiences with weight stigma in 46 community adults finding that weight stigma occurred frequently in public places as well as at home [29]. In agreement with this evidence, Hatzenbuehler et al. carried out a study in 22 231 individuals with overweight or obesity from Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) (a cross-sectional nationally representative study of noninstitutionalized US adults) study, to investigate the associations between perceived weight discrimination and the prevalence of psychiatric disorders [33]. Results showed that perceived weight discrimination is most likely to be experienced in public, followed by insurance and health care settings [33].
In conclusion our study is the first to investigate the weight stigma in Italian adults with obesity by documenting and examining sources of weight bias, life domains and locations where it occurs. In the studied population verbal mistreatments was the most reported stigmatizing situation, strangers were the most common interpersonal sources of stigma and public settings were the most common location of stigma. Even if the population sample is relatively small and the data may be impacted by small biases, as they are self-reported, this study has demonstrated the impact of the wight stigma on the patients. Identifying strategies acting on these targets could contribute to reduce weight stigma and thus to improve the management of obesity in Italy.
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|
---
title: 'Prevalence of Overweight and Obese Prepregnancy BMI and Excessive Gestational
Weight Gain Using Asian-Specific Cutoffs Among Asian and Mixed-Asian Women Living
in Hawaii: A Retrospective Cohort Study'
authors:
- Y. Daida
- K. Pedula
journal: Maternal and Child Health Journal
year: 2022
pmcid: PMC10023603
doi: 10.1007/s10995-022-03560-w
license: CC BY 4.0
---
# Prevalence of Overweight and Obese Prepregnancy BMI and Excessive Gestational Weight Gain Using Asian-Specific Cutoffs Among Asian and Mixed-Asian Women Living in Hawaii: A Retrospective Cohort Study
## Abstract
### Introduction
The use of Asian-specific Body Mass Index (aBMI) cutoffs may be more appropriate than general World Health Organization BMI (gBMI) cutoffs in determining recommended gestational weight gain (GWG) for Asian women. Since aBMI cutoffs are lower than gBMI, more Asian women will be reclassified into higher aBMI categories from gBMI. The prevalence of reclassification and its impact on GWG are not known.
### Methods
We utilized the electronic health records of 8886 Kaiser Permanente Hawaii members aged ≥ 18 with a singleton live birth. Prepregnancy BMI was first classified using gBMI criteria, then aBMI criteria. BMI categories were “underweight”, “normal”, “overweight” and “obese”; GWG was classified into lower (“lGWG”), met (“mGWG”), and exceed (“eGWG”) GWG per WHO recommendations. Self-reported race/ethnicity include Asian, Asian + Pacific Islander, and Asian + white. Multiple logistic regression was used to estimate adjusted odds of reclassification. The Cochran-Mantel–Haenszel test was used to evaluate associations between race/ethnicity and GWG.
### Results
> $40\%$ of women in each racial/ethnic group were reclassified. Asian + Pacific Islander women had significantly higher odds of being reclassified ($p \leq .0001$). In the normal gBMI and aBMI category, Asian + Pacific Islander women had the largest eGWG group. In the overweight gBMI category, Asian + Pacific Islander women had the largest eGWG group; in the overweight aBMI category, Asian + white women had the largest eGWG group.
### Discussion
A sizable percent of women were reclassified into higher BMI categories when aBMI was applied. Mixed-race Asian women were more likely to exceed GWG recommendations than Asian women.
## Significance Statement
What is already known on this subject? Asian women are at higher risk for obesity-related disease than white women with the same BMI. Studies suggest that aBMI should be used to estimate optimal GWG goals for Asian women.
What does this study add? In changing from gBMI to aBMI, at least $40\%$ of Asian and mixed-race Asian women were reclassified into higher BMI categories. Asian + Pacific Islander women were more likely to be reclassified to a higher BMI category when aBMI was applied. More mixed-race Asian women exceeded recommended GWG than Asian women.
## Introduction
Excessive gestational weight gain (GWG) increases the burden of poor maternal and infant outcomes such as gestational diabetes (GDM), preeclampsia, and macrosomia. Each year, $6\%$ to $9\%$ of women develop GDM and the percentage of women with GDM increased $56\%$ between 2000 and 2010 (Centers for Disease Control & Prevention, 2018). Preeclampsia occurs in about $4\%$ of pregnant women in the United States (American College of Obstetricians and Gynecologists Task Force on Hypertension in Pregnancy, 2013). The Institute of Medicine (IOM) has published GWG recommendations to optimize health outcomes for both the woman giving birth and her infant (Institute of Medicine & and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines, 2009). GWG is based on physiologic and metabolic changes that take place during pregnancy. Beyond weight gain due to placenta, fetus, amniotic fluid and hypertrophy of maternal tissues, additional gains in weight are due to increase in fat mass (Rasmussen et al. 2009). Clinicians in the U.S. have adopted the IOM’s recommendations and use them to provide weight gain goals to their patients. However, $48\%$ of women in the U.S. exceed these recommendations (Branum, 2016). While poor maternal and child outcomes affect all racial/ethnic groups, Asian women are at higher risk for developing GDM (Shah et al., 2021) and suffer complications from preeclampsia (Minhas et al., 2021).
IOM recommends that during pregnancy, underweight women gain 28–40 pounds, normal weight women gain 25–35 pounds, overweight women gain 15–25 pounds, and obese women gain 11–20 pounds. IOM’s GWG recommendations (Institute of Medicine, 2009) are based on a woman’s prepregnancy body mass index (BMI, calculated as kg/m2) as defined by World Health Organization (WHO) for the general population. General BMI (gBMI) cutoffs are < 18.5 kg/m2, underweight; 18.5–24.9 kg/m2, normal weight; 25–29.9 kg/m2, overweight; and > 30 kg/m2, obese. However, WHO also has Asian-specific BMI (aBMI) cutoffs, which are lower than gBMI: 18.5–22.9 kg/m2, normal weight; 23–24.9 kg/m2, overweight; ≥ 25 kg/m2, obese (World Health Organization and Regional Office for the Western Pacific, 2000).
Previous research shows that Asian individuals with the same BMI as white individuals have higher obesity-related disease risks (Deurenberg-Yap et al., 2002; Lin et al., 2002; World Health Organization, 2004). Underlying this is the fact that body fat percent and distribution differ: Asian individuals have a higher percentage of body fat (Deurenberg et al., 2002) and upper-body fat (Lim et al., 2011), both of which are pro-inflammatory. BMI is widely used as an estimate of body fat (Freedman et al., 2013; Garrow & Webster, 1985; Wohlfahrt-Veje et al., 2014), and GWG guidelines reflect the importance of maternal prepregnancy body fat (as estimated by BMI) for outcome of pregnancy (Institute of Medicine, 2009; Institute of Medicine & and National Research Council Committee to Reexamine IOM Pregnancy Weight Guidelines, 2009). Since aBMI is a better reflection of body fat in Asian women, using gBMI to estimate GWG in Asian populations may unintentionally lead to excessive weight gain. The appropriateness of using using gBMI to estimate GWG goals in Asian populations is debatable, and studies have been published in Japan (Morisaki et al., 2017), Korea (Choi et al., 2017), China (Wu et al., 2018), Vietnam (Ota et al., 2011), Singapore (Ee et al., 2014) using aBMI instead of gBMI to assess optimal weight gain.
Recent evidence suggests that Asian women experience higher rates of GDM at lower gBMI levels (Bryant et al., 2014; Oteng-Ntim et al., 2013; Wong, 2012). Shah et al. [ 2011] found that using gBMI as a screening tool for GDM in Asian women identified only $25\%$ of the study population, compared with > $76\%$ of Black, $58\%$ of Latina, and $46\%$ of Caucasian women. One study found that among individuals with GDM, South Asian women had higher skinfold thickness and serum leptin (associated with insulin resistance and diabetes) even though their gBMI was lower than that of white women (Sommer et al., 2015). Furthermore, Yang et al. [ 2021] found that risks for preeclampsia increased more rapidly with gBMI for Chinese women than Swedish women.
The Asian population in the U.S. grew $81\%$ from 2000 to 2019, making it the fastest growing growing racial or ethnic group. Furthermore, this population is increasingly diversifying, as reflected by the number of births of multiracial infants. For example, in 2013, $24\%$ of infants were Asian + white and $2\%$ were Asian + Pacific Islander (Pew Research Center, 2015). To date, only one study has compared prepregnancy BMI classifications using both gBMI and aBMI (Gao et al., 2020), and it reported on single-race Asian groups only. Moreover, no studies have reported changes in prevalence of excessive GWG using both gBMI and aBMI.
The goal of this study was to determine the prevalence of prepregnancy BMI reclassification in Asian and mixed-race Asian women when using aBMI. We also investigated which groups of women were most likely to be reclassified, and describe the prevalence of excessive weight gain in this paper.
## Methods
This retrospective population-based cohort study utilized the electronic health records (EHRs) of 8886 Kaiser Permanente Hawaii members who were at least 18 years old and gave birth to live singletons at ≥ 37 weeks (calculated from date of last menstrual period) between January 1, 2006, and December 31, 2019. Women with missing data on age, prepregnancy BMI, GWG, and race/ethnicity were excluded. Hispanic and Black women were also excluded. Each eligible patient was included once in the sample; if a woman had multiple pregnancies during the study period, only the most recent pregnancy was included for analyses. The following measures were available in the EHR: maternal race/ethnicity, age at the birth of child, parity, prepregnancy weight, smoking status, and weight gain during pregnancy. Single and multiple race groups were captured in the EHR and classified as Asian, Asian + Pacific Islander, Asian + white, or white. We calculated prepregnancy BMI using any weight measurement up to 1 year prior to date of pregnancy diagnosis; if more than one weight was available, we used the weight closest to the pregnancy index date. We divided BMI into four categories (underweight, normal weight, overweight, and obese) for both gBMI and aBMI indexes.
We calculated GWG using the difference between prepregnancy BMI and weight at the start of pregnancy. We allowed a window of 60 days after pregnancy diagnosis and 1 month prior to delivery to capture pregnancy weight at the start and end of pregnancy, respectively. GWG was classified into lower (“lGWG”), met (“mGWG”), and exceed (“eGWG”) GWG per WHO recommendations. Maternal age (years), smoking at any point during pregnancy (smoker vs. nonsmoker) and parity (no previous live births, one or more live births) and race/ethnicity were also derived from patients’ EHRs.
This study followed the “Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement: Guidelines for Reporting Observational Studies” and SAMPL Guidelines in reporting statistics. This study was exempt from Kaiser Permanente Hawaii Institutional Review Board approval because data were de-identified.
## Statistical Analysis
We calculated and summarized descriptive statistics, including means, standard deviations, and proportions (Table 1). Maternal age and BMI were treated as continuous variables; all other variables were treated as categorical. BMI was also treated as categorical when classified into the gBMI and aBMI groups as described above. The association between racial and ethnic group and each of the categorical and continuous variables was tested with chi-square and ANOVA respectively. People with data missing for a specific variable were excluded from the analyses that included that variable. Statistical analyses were conducted using SAS 9.4 (SAS Institute, Cary, NC). All reported statistical tests are two-sided; p-values < 0.05 were considered statistically significant. To allow the reader to interpret p-values more easily, we did not adjust for multiple comparisons. Table 1Characteristics of study population ($$n = 8886$$)Asian($$n = 3623$$)Asian + Pacific Islander($$n = 2195$$)Asian + white $$n = 732$$)White($$n = 2336$$)p valueaAge (years), mean ± SD31.9 ± 5.528.8 ± 5.730.7 ± 5.732.1 ± 5.3 <.0001BMI (kg/m2), mean ± SD24.8 ± 5.028.7 ± 6.826.0 ± 5.925.2 ± 5.7 <.0001 Gestational weight gain (lbs) per gBMIb classification, mean ± SD Underweight27.9 ± 9.134.6 ± 12.627.6 ± 11.531.43 ± 12.40.0017 Normal weight28.1 ± 11.432.4 ± 11.831.1 ± 12.732.6 ± 12.3 <.0001 Overweight25.1 ± 14.129.2 ± 14.628.2 ± 14.731.6 ± 14.0 <.0001 Obese21.6 ± 20.824.3 ± 18.020.0 ± 15.822.9 ± 18.00.0141Parity, n (%) 01247 ($34.4\%$)535 ($24.4\%$)221 ($30.2\%$)930 ($39.8\%$) ≥ 12355 ($65.0\%$)1647 ($75.0\%$)509 ($69.5\%$)1393 ($59.6\%$) <.0001 Missingc21 ($0.6\%$)13 ($0.6\%$)2 ($0.3\%$)13 ($0.6\%$)Smoking, n (%) <.0001 Yes217 ($6.0\%$)325 ($14.8\%$)81 ($11.1\%$)152 ($6.5\%$) No3394 ($93.7\%$)1864 ($84.9\%$)647 ($88.4\%$)2178 ($93.2\%$) Missingc12 ($0.3\%$)6 ($0.3\%$)4 ($0.5\%$)6 ($0.3\%$)ap-values based on ANOVA for age, BMI and gestational weight gain, and on chi-square for parity and smokingbgBMI: WHO BMI cutoffs for the general population (underweight: < 18.5 kg/m2, normal weight: 18.5–24.9 kg/m2, overweight: 25–29.9 kg/m2, obese: ≥ 30 kg/m2)cExcluded from statistical comparison The cutoff for the underweight category remained the same for both gBMI and aBMI, and all women who were classified as overweight according to gBMI were reclassified as obese. Women assigned to the obese category under aBMI are therefore women in both the overweight and obese gBMI categories. Hence, racial and ethnic differences were analyzed only among normal weight and overweight women. Logistic regression, adjusting for a priori confounders (maternal age, parity, and smoking status) was used to estimate the odds of being reclassified.
Pregnancy weight gain for each was divided into three groups: lower than recommended GWG (lGWG), met recommended GWG (mGWG) and exceeded than recommended GWG (eGWG). We used the Cochran–Mantel–Haenszel test to check for linear associations between race/ethnicity and GWG across prepregnancy gBMI and aBMI categories.
## Population Characteristics
Among 11,684 women who had singleton live birth, 2798 ($24\%$) were excluded due to missing prepregnancy and/or weight at 1 month prior to deliver. Women included in our analyses were comparable to women who were excluded in terms of mean age and smoking status. However, the women included were more likely to be multiparous because we selected the most recent pregnancy for women with multiple pregnancies.
A total of 8886 women (3623 Asian, 2195 Asian + Pacific Islander, 732 Asian + white, and 2336 white) met study criteria and were included in analyses (Table 1). Age was significantly different between race/ethnic groups ($p \leq .0001$), where Asian + Pacific Islander and white women had the lowest and highest mean ages, respectively. Prepregnancy BMI was also significantly different between race/ethnic groups ($p \leq .0001$): Asian women had the lowest mean prepregnancy BMI and were the only group with mean BMI within the normal gBMI category. The mean BMI of all other groups was in the overweight category, and Asian + Pacific Islander women had the highest mean BMI. GWG within gBMI categories varied across racial/ethnic groups. In the underweight category, Asian + Pacific Islander women gained the most weight, and Asian + white women gained the least. White women in the normal gBMI category gained the most weight, while Asian women gained the least. The same was seen in women in the overweight gBMI category. Among women classified by gBMI as obese, Asian + Pacific Islander women had the highest GWG, and Asian + white women had the lowest. Across all racial/ethnic groups, most women had given birth to at least one infant prior, although parity was significantly different across all racial/ethnic groups ($p \leq .0001$). The Asian + Pacific Islander group had the largest percentage of women who had previously given birth; white women had the smallest percentage. The majority of women did not smoke, although racial/ethnic differences were observed ($p \leq .0001$). The largest group of smokers were Asian + Pacific Islander women, while Asian women formed the smallest group.
## BMI Category Misclassification and Redistribution
A sizable number of women were reclassified into higher aBMI categories. Asian, Asian + Pacific Islander, and Asian + white women ($17\%$, $11.8\%$, and $14.6\%$, respectively) were reclassified from normal gBMI to overweight aBMI (Table 2). Under aBMI, all women in the overweight gBMI category were reclassified into the obese category. The total percent of women who were reclassified from a gBMI category to a higher aBMI category was $42.1\%$, $40.2\%$, and $41.4\%$ of Asian women, Asian + Pacific Islander women, and Asian + white women, respectively. After controlling for maternal age, parity, and smoking status, Asian + Pacific Islander women had the highest odds (vs. Asian) of being reclassified (OR 1.80, $95\%$ CI 1.56, 2.07), as shown in Table 3.Table 2Prevalence of Reclassification from gBMI to aBMI by racial groupRemained in normal BMIReclassified from gBMI normal category to overweight aBMI categoryReclassified from gBMI overweight category to obese aBMI categoryTotal reclassified from gBMI to higher aBMI categoryAsian ($$n = 3623$$)1412 ($39.0\%$)615 ($17.0\%$)909 ($25.1\%$)1524 ($42.1\%$)Asian + Pacific Islander ($$n = 2195$$)440 ($20.0\%$)259 ($11.8\%$)623 ($28.4\%$)882 ($40.2\%$)Asian + white ($$n = 732$$)254 ($34.7\%$)107 ($14.6\%$)196 ($26.8\%$)303 ($41.4\%$)gBMI WHO BMI categories based on the general population, aBMI WHO Asian-specific BMI categoriesTable 3Adjusted odds of being reclassified from gBMI category to a higher aBMI category among Asian and mixed-Asian ($$n = 4$$,788)EffectAdjusted odds ratio($95\%$ confidence limits)Race/ethnicityAsian–Asian + Pacific Islander1.80*(1.56, 2.07)Asian + white1.09(0.91,1.31)Odds ratios are based on multiple logistic regression and adjusted for maternal age, parity and smoking status*$p \leq .0001$gBMI WHO BMI categories based on the general population, aBMI WHO Asian-specific BMI categories Figure 1 shows BMI category distributions of by race/ethnicity according to gBMI and aBMI. When aBMI was applied, normal weight women were no longer the largest BMI group in Asian and Asian + white women. Instead, women in the obese category became the largest BMI group. Among Asian + Pacific Islander, women categorized as obese remained the largest group. Within the aBMI classification, across all ethnic groups, more women were in the overweight and obese categories combined than normal weight women. Fig. 1gBMI and aBMI distributions by race/ethnicity. gBMI: WHO BMI (underweight: < 18.5 kg/m2; normal weight: 18.5–24.9 kg/m2; overweight: 25–29.9 kg/m2; obese: > 30 kg/m2). aBMI: WHO Asian-specific cutoffs (underweight: < 18.5 kg/m2; normal weight: 18.5–22.9 kg/m2; overweight: 23–24.9 kg/m2; obese: ≥ 25 kg/m2)
## Racial/Ethnic Differences in Meeting Weight Gain Guidelines
We found racial/ethnic differences in the eGWG group among normal weight and overweight women. Within the normal BMI category, the Asian + Pacific Islander had the largest eGWG group, followed by Asian + white women; this finding was observed when using gBMI ($p \leq .0001$) and aBMI ($$p \leq .0029$$). Racial/ethnic differences were also observed among the overweight women (gBMI $$p \leq .0005$$; aBMI $$p \leq .006$$). Using gBMI cutoffs, Asian + Pacific Islander had the largest eGWG group. However, when aBMI was applied, Asian + white women had the largest eGWG group (Fig. 2).Fig. 2Racial/ethnic differences in meeting recommended gestational weight gain. gBMI: WHO BMI (underweight: < 18.5 kg/m2; normal weight: 18.5–24.9 kg/m2; overweight: 25–29.9 kg/m2; obese: > 30 kg/m2). aBMI: WHO Asian-specific cutoffs (underweight: < 18.5 kg/m2; normal weight: 18.5–22.9 kg/m2; overweight: 23–24.9 kg/m2; obese: ≥ 25 kg/m2). lGWG indicates those whose gestational weight gain was less than WHO recommendation for BMI category: normal BMI category < 25 lbs; overweight BMI category < 15 lbs; obese BMI category < 11 lbs. mGWG indicates those whose gestational weight gain was within WHO recommendation for BMI category: normal BMI between 25 and 35 lbs; overweight BMI between 15 and 25 lbs; overweight BMI between 11 and 20 lbs. eGWG indicates those whose gestational weight gain was greater than WHO recommendation for BMI category: normal BMI ≥ 35 lbs; obese BMI > 25 lbs; obese BMI > 20 lbs Among overweight women, eGWG group was the largest weight-gain group under both gBMI and aBMI. In changing from gBMI to aBMI, the eGWG group increased—the largest increase was seen in overweight Asian + white women.
## Discussion
To the best of our knowledge, this study is the first to report on the prevalence of overweight and obese prepregnancy BMI and eGWG in mixed-race Asian women. We showed that the application of aBMI cutoffs increased an already high number of women who were considered overweight or obese at the start of their pregnancy, especially among Asian + Pacific Islander women. While the prevalence of reclassification was similar among the three groups, Asian + Pacific Islander women were nearly twice as likely to be reclassified, after adjusting for age, parity, and smoking. Finally, we saw ethnic variability in GWG, where mixed-race Asian women exceeded recommended GWG more than Asian women.
Our findings were similar to results published by Gao et al. [ 2020], the only existing study that compared the prevalence of overweight and obese prepregnancy BMI among Asian women using both gBMI and aBMI cutoffs. Utilizing data on more than 1 million Asian women from the National Vital Statistics System, the study reported that $17.4\%$ of women were reclassified from normal BMI to overweight BMI when aBMI was applied, which is similar to results from our study (11.8–$17\%$). As Gao et al. excluded all mixed-race and Pacific Islander women, the closest comparison group in our study would be the Asian group, in which $17\%$ of the women were reclassified from normal gBMI.
While we were unable to compare reclassification from overweight to obese between the studies due to different cutoffs for obesity, the combined percentage of women in the overweight and obese categories was comparable (40.2–$42.1\%$ of our study population vs. $46.8\%$ in Gao et al.). Of note, reclassification using aBMI resulted in the obese category becoming the largest group among all race/ethnic group. The high prevalence of overweight and obesity among Asian women in our study is only slightly lower than the prevalence of prepregnancy overweight and obesity found among a national sample of U.S. women (Deputy et al., 2018).
Mixed-race Asian women had a higher prevalence of exceeding recommended GWG than Asian women. This was especially true among the overweight gBMI and aBMI groups, where most women in each group exceeded GWG guidelines, with mixed-race Asian women leading in prevalence. Although we do not know what the optimal GWG for Asian women should be, various studies outside of the U.S. have demonstrated that Asian women who gained less weight than recommended by IOM had better pregnancy outcomes. Studies from Asian countries using aBMI cutoffs have found that GWG that is less than IOM recommendations is associated with reduced risks of gestational hypertension (Choi et al., 2017; Morisaki et al., 2017; Park et al., 2011), C-section (Choi et al., 2017; Ee et al., 2014; Park et al., 2011) and large for gestational age (Chen et al., 2015; Choi et al., 2017; Ee et al., 2014; Morisaki et al., 2017; Ota et al., 2011). The disparities between Asian and mixed-race Asian groups in the overweight and obese BMI classifications warrant further research. Acculturation is associated with GWG Understanding modifiable factors that contribute to these differences in prepregnancy BMI and GWG will inform interventions for better maternal and child outcomes.
## Strengths and Limitations
Interpretation of our study results should be done in light of its limitations. We were not able to break down specific Asian and mixed-race Asian groups such as Chinese, Japanese, Hawaiian and Samoan because our sample size was not large enough. Our study population was derived from Asian women in Hawaii only and thus not generalizable to the rest of the country. Ours was a secondary analysis of existing data, and we did not have the opportunity to collect additional information such as psychosocial and other social determinants of health. Immigrant women in the United States have lower prepregnancy obesity than U.S.-born women (Singh & DiBari, 2019), and we were not able to adjust for nativity in our analyses. In addition, women with lower income and education have higher BMIs (Singh & DiBari, 2019), which might explain differences in the number of women who were reclassified. Best practice alerts notify clinicians of the need to recommend a range of GWG to patients; however, we cannot confirm that all women in our study were informed about their recommended GWG. Women included in our analyses were more likely to be multiparous. Nulliparous women tend to have fewer and less frequent health care utilization than multiparous women (Lau et al., 2014) (Kurata et al., 2020; Lau et al., 2014) and were therefore less likely to have had a pre-pregnancy BMI measured in the study time period. Studies indicate that multiparous women start their pregnancy at a higher weight (Ziauddeen et al., 2019) and gain more weight during pregnancy (Lan-Pidhainy et al., 2013) suggesting that the pre-pregnancy BMI and GWG in our study may be on the higher end and not generalizable to all pregnant women.
Strengths of our study include detailed racial/ethnic data that allowed us to delineate Asian, Asian + Pacific Islander, and Asian + white groups. Another strength of our study was the use of EHR records for GWG data, which were measured by clinicians rather than self-reported.
## Conclusions
In light of the many studies demonstrating the association of high prepregnancy BMI with adverse outcomes (Choi et al., 2011; Soltani et al., 2017; Sun et al., 2020), we find it worrisome that application of an aBMI cutoff resulted in more women being classified as overweight/obese than normal weight across all Asian groups in our study. The possibility exists that a large proportion of these women were given weight gain goals that might have been higher than optimal in a population where a significant number of women already exceed GWG recommendations. Including aBMI to estimate GWG goals, along with individualized prenatal care for Asian women, may be the first step toward better pregnancy outcomes.
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|
---
title: 'Twenty-four-hour movement guidelines during adolescence and its association
with obesity at adulthood: results from a nationally representative study'
authors:
- Antonio García-Hermoso
- Yasmin Ezzatvar
- Alicia M. Alonso-Martinez
- Robinson Ramírez-Vélez
- Mikel Izquierdo
- José Francisco López-Gil
journal: European Journal of Pediatrics
year: 2022
pmcid: PMC10023604
doi: 10.1007/s00431-022-04760-w
license: CC BY 4.0
---
# Twenty-four-hour movement guidelines during adolescence and its association with obesity at adulthood: results from a nationally representative study
## Abstract
To determine the association between adherence to the 24-h movement guidelines during adolescence with obesity at adulthood 14 years later in a nationally representative cohort. We analyzed data from 6984 individuals who participated in Waves I (1994–1995) and IV (2008–2009) of the National Longitudinal Study of Adolescent Health (Add Health) in the USA. Obesity was defined by the International Obesity Task Force cut-off points at Wave I and adult cut-points at Wave IV (body mass index [BMI]≥30 kg/m2 and waist circumference [WC]≥102 cm in male and 88 cm in female). Physical activity, screen time and sleep duration were self-reported. Adolescents who met screen time recommendation alone (β = −1.62 cm, $95\%$CI −2.68 cm to −0.56), jointly with physical activity (β = −2.25 cm, $95\%$CI −3.75 cm to −0.75 cm), and those who met all three recommendations (β = −1.92 cm, $95\%$CI −3.81 cm to −0.02 cm) obtained lower WC at Wave IV than those who did not meet any of these recommendations. Our results also show that meeting with screen time recommendations (IRR [incidence rate ratio] = 0.84, $95\%$CI 0.76 to 0.92) separately and jointly with physical activity recommendations (IRR = 0.86, $95\%$CI 0.67 to 0.97) during adolescence is associated with lower risk of abdominal obesity at adulthood. In addition, adolescents who met all 24-h movement recommendations had lower risk of abdominal obesity later in life (IRR = 0.76, $95\%$CI 0.60 to 0.97).
Conclusion: Promoting the adherence to the 24-h movement guidelines from adolescence, especially physical activity and screen time, seems to be related with lower risk of abdominal obesity later in life, but not for BMI. What is Known:• Some studies have shown a relationship between adherence to 24-h movement guidelines and adiposity or obesity markers in youth. However, most of these studies have a cross-sectional design or a short follow-up. What is New:• *This is* the first study which determined the association between adherence to the 24-h movement guidelines during adolescence with obesity at adulthood 14 years later in a nationally representative US cohort.• Meeting the 24-h movement guidelines from adolescence seems to be related with lower risk of abdominal obesity later in life, but not for body mass index.
## Introduction
The prevalence of obesity is rapidly increasing worldwide, which may portend undesirable adverse health effects [1], such as an increase in the incidence of cardiovascular disease risk factors and a greater risk for cardiovascular morbidity and mortality in adulthood [2, 3]. As the burden of obesity and related noncommunicable diseases continues to rise in USA [4], there is a need for research on modifiable risk factors in adolescence that could be integrated into intervention strategies.
Existing evidence indicates that the composition of movement behaviors (e.g., physical activity, sedentary behavior, and sleep duration) are time-dependent, eliciting distinct biological processes that interact throughout a 24-h period [5]. Meeting current 24-h movement guidelines is linked to several health outcomes in young population [6] and later during adulthood [7]. However, from adolescence to adulthood, the time spent in some of these behaviors has shown to be reduced (e.g., physical activity [8], sleep duration [9]), and increased in others (e.g., sedentary behavior [10]). Adolescence is characterized by fast physical growth and modifications in body composition generated by the hormonal fluctuations related to puberty [11]. Further, the health-related behaviors that influence on excess weight in adulthood frequently begin or are strengthened during adolescence [12]. Thus, the adherence to healthy behaviors during adolescence can offer both short and long-term benefits on health [13].
Current 24-h movement guidelines recommend that a healthy 24-h day in adolescents should include at least 60 min of moderate-to-vigorous physical activity (MVPA), no more than 2 h of screen time, and 8–10 h of sleep [5]. However, a recent meta-analysis reported that only $2.68\%$ of adolescents from 23 countries met all these recommendations for MVPA, screen time, and sleep [14]. The healthy behaviors that compound the 24-h movement guidelines are modifiable and have been independently linked to body composition (e. g., physical activity [15], sedentary behavior [15], and sleep duration [16]). More favorable movement patterns have been associated with reduced adiposity or obesity in cross-sectional studies [17–21]. The few existing prospective studies also reported that not meeting the guidelines in childhood is associated with higher adiposity at follow-up [22–24]. For example, Leppänen et al. [ 24] showed that meeting most of the guidelines was longitudinally associated with lower abdominal obesity two years later. Chemtob et al. [ 22] also suggested that not meeting the guidelines in childhood is associated with higher adiposity 2 and 7 years later. This finding is consistent with that of Micklesfield et al. [ 23] who suggested that patterns of adolescent physical activity, sedentary behavior and sleep are related to young-adult body composition in urban South Africa. Nevertheless, none of the previous studies has reported this association between 24-h movement behaviors during adolescence and obesity at mid-adulthood. Therefore, the aim of our study was to determine the association between adherence to the 24-h movement guidelines during adolescence with obesity at adulthood in a nationally representative US cohort.
## Population sample and study design
This is a longitudinal study with data from the Add Health study, a nationally representative sample of adolescents in grades 7–12 in the USA followed from adolescence through adulthood. During 1994 and 1995, over 90,000 students from a sample of US high schools were selected with unequal probability of selection completed in-school questionnaires (i.e., the units in the population do not have the same probability of being included in a sample), and 20,745 of them were selected to participate in the Wave I in-home interview in 1994 [25]. Wave IV in-home sample was followed in 2008–2009 ($$n = 15$$,701; age range 24–32 years). For this study, we used data from Waves I and IV. Wave I provided data from 24-h movement behaviors. Wave IV provided obesity outcomes (i.e., waist circumference [WC] and body mass index [BMI]). The current analysis included those who completed Wave IV assessment (defined by if they had a Wave IV sample weight; $$n = 15$$,701). Participants with missing data on 24-h movement behaviors ($$n = 3524$$), and/or some covariates ($$n = 142$$) at Wave I, and/or BMI and WC data at Wave IV ($$n = 1494$$) were excluded. Also, participants with obesity at Wave I (body mass index z-score [zBMI] ≥ 2 standard deviation determined using the International Obesity Task Force growth references chart [26]) ($$n = 3841$$) were removed. The final sample included 6984 participants ($59.4\%$ female) (Fig. 1).Fig. 1Flow chart of the participants Add Health study was approved by the Institutional Review Board (IRB) at the University of North Carolina at Chapel Hill. The permission to conduct secondary analyses was obtained by the Ethics Committee of the University Hospital of Navarra (PI_$\frac{2020}{143}$).
Independent samples t-test and chi-square analyses were conducted to compare adolescents included in the final sample compared with the remaining participants who were not included in the analysis. There were no differences in any demographic variables (race/ethnicity, $$p \leq 0.112$$; region, $$p \leq 0.151$$) and parental income ($$p \leq 0.504$$) between adolescents who were included or not in the final sample. Therefore, it could be assumed that missing data did not meaningfully influenced results within the analytic sample.
## Anthropometric measures
At Wave IV, height and weight to the nearest $\frac{1}{2}$ pound and height to the nearest $\frac{1}{8}$ inch were measured by trained study staff, and BMI was calculated by dividing body weight (in kg) by height (in m [2]). Height and weight at Wave I were self-reported, whereas those at Waves IV were measured by the field examiners following standardized protocols (details can be found in the Add Health website, https://addhealth.cpc.unc.edu/documentation/codebooks/). As above mentioned, adolescents were considered obese when presented a zBMI ≥ to 2 standard deviation using the International Obesity Task Force growth references chart [26]. Adults were classified as participants with obesity if his or her BMI was 30 kg/m [2] or greater [27]. WC was also measured to the nearest 0.5 cm at the superior border of the iliac crest as recommended by the National Cholesterol Education Program Third Adult Treatment Panel during the Wave IV in-home assessment, using a cut-off point of ≥ 102 cm in male and 88 cm in female [28].
## Twenty-four-hour movement guidelines
In the Wave I in-home interview, adolescents reported their engagement in moderate-to-vigorous physical activity (MVPA) during the past 7 days by a previously described scale and three different questions [29]: “During the past week, how many times did you go rollerblading, roller-skating, skateboarding, or bicycling?”; “ During the past week, how many times did you play an active sport, such as baseball, softball, basketball, soccer, swimming, or football?”; “ During the past week, how many times did you exercise, such as jogging, walking, karate, jumping rope, gymnastics or dancing?”. Responses ranged from not at all to five or more times and were scored as: 0 times = not at all, 1.5 times = 1 or 2 times, 3.5 times = 3 or 4 times, and 6 times = 5 or more times. Responses to the three questions were summed to create a measure of total times of MVPA each week, classified as no (0 times), some (1–4 times), and high (5 or more times) MVPA per week. Meeting physical activity recommendations was considered when adolescents reported 5 or more times MVPA per week following the Gordon-Larsen et al. [ 29] criterion.
Screen time was measured by a previously described scale [29], using the following the questions: “How many hours a week do you watch television?” “ How many hours a week do you watch videos?” and “How many hours a week do you play video or computer games?” Hours given in the three responses were summed to create a measure of recreational screen time per week. Meeting screen time recommendations was considered when adolescents reported no more than 2 h per day [30].
Adolescents reported their sleep duration (in hours) in response to one question in the Wave I in-home interview: “How many hours of sleep do you usually get per day/night?”. The prevalence of meeting sleep duration guidelines was estimated by the National Sleep Foundation’s sleep duration guidelines from 9 to 11 h and 8 to 10 h per day of sleep [31] (i.e., those adolescents aged 14–17 years who slept less than 8 h and more than 10 h were categorized as “not meeting the guidelines”) in those aged 12–13 and 14–17 years old, respectively.
## Covariates
Information on sociodemographic factors, such as age, sex, race/ethnicity (operationalized as a four-level: White, Black, native American, and Asian), region (coded as West, Midwest, South, and Northeast), and parental income (range $0 to $999 thousand), was collected through in-home questionnaires.
## Statistical analysis
Descriptive information is shown as numbers and percentages for categorical variables and the mean and standard deviation for continuous variables. Preliminary analyses showed no significant interactions between sex and all three movement behaviors in relation to BMI ($$p \leq 0.273$$) and WC ($$p \leq 0.126$$) at Wave IV; therefore, all analyses were performed with male and female together. All model assumptions were checked (i.e., normality and homoscedasticity) and both dependent variables were log-transformed (base 10) to normalize the distribution.
We conducted a series of linear regression models to examine the associations between meeting specific and general combinations (i.e., physical activity and screen time, physical activity and sleep duration, screen duration and sleep time, and all three) of 24-h movement guidelines at Wave I with WC and BMI at Wave IV. For these analyses, not meeting the guideline(s) was used as the reference group.
Poisson regression with robust error variance analyses [32] were used to estimate the incidence rate ratio (IRR) of obesity (dependent variable), according to meeting specific and combinations (i.e., physical activity and screen time, physical activity and sleep duration, screen duration and sleep time, and all the three) of 24-h movement guidelines at Wave I (independent variables). In all cases, not meeting the guideline(s) was the reference group. All analyses were adjusted by sex, age, zBMI, race/ethnicity, region, and parental income at baseline. Further, each individual recommendation (i.e., physical activity, screen time, and sleep duration) was additionally adjusted by the rest of behaviors (e.g., physical activity was also adjusted by screen time and sleep duration). We used STATA version 17.0 (StataCorp LLC, College Station, TX, USA) with SVY command and set significance at $p \leq 0.05.$ For the analyses, we used population weights so that it is representative of the actual composition of each school based on grade level and gender and corrects for the unequal probability of selection of schools across regions. Finally, we considered the nesting structure of the data in our analyses, by adjusting standard errors proportional to the degree of nesting.
## Results
Descriptive statistics for the full sample at Wave I and at Wave IV are presented in Table 1. At Wave I (age = 15.18 years), only $7.4\%$ of the adolescents met all three recommendations, and $18.7\%$ met none of them. Specifically, $52.3\%$ of the sample reported recommended sleep duration, $40.8\%$ reported recommended screen time use, and $33.2\%$ were physically active. At Wave IV (age = 28.31 years), $21.6\%$ and $40.6\%$ of the sample were classified as participant with obesity according to their BMI and WC, respectively. Table 1Descriptive characteristics of the analyzed study sample at Wave IWaves I [1994]$$n = 6984$$ Sex (women), n (%)4147 (59.4) Age, years15.18 (1.29)Anthropometric measurements Body mass, kg55.13 (9.36) Height, m1.55 (0.10) Body mass index, kg/m222.85 (2.71) Body mass index, z-score0.97 (0.76) Race category White, n (%)4889 (70.0) Black, n (%)1467 (21.0) Native American, n (%)112 (1.6) Asian, n (%)519 (7.4) Region West, n (%)1677 (24.0) Midwest, n (%)1767 (25.3) South, n (%)2507 (35.9) Northeast, n (%)1041 (14.9)Parental income, $ thousand1112.74 (3074.79) Guideline component met Physical activity, n (%)2316 (33.2) Screen time, n (%)2850 (40.8) Sleep duration, n (%)3656 (52.3) Physical activity + screen time930 (13.3) Physical activity + sleep duration1262 (18.1) Screen time + sleep duration1468 (21.0) None1303 (18.7) All three recommendations519 (7.4) Table 2 shows the differences in BMI and WC at follow-up between adolescents that met vs. did not meet the physical activity, screen time, and sleep duration recommendations (individually and combined). Adolescents who met screen time in isolation (β = − 1.62 cm, $95\%$ CI − 2.68 to − 0.56 cm), jointly with physical activity (β = − 2.25 cm, $95\%$ CI − 3.75 to − 0.75 cm), and all three recommendations (β = − 1.92 cm, $95\%$ CI − 3.81 to − 0.02 cm) obtained lower WC at Wave IV than those who did not meet any of these recommendations. Table 2Differences in BMI and WC at follow-up between adolescents that met vs. not met physical activity, screen time, and sleep duration recommendations and combinations of these recommendationsBody mass index (kg/m2)Waist circumference (cm)β ($95\%$ CI)pβ ($95\%$ CI)pMeeting (vs. not meeting) individual guidelinePhysical activitya − 0.30 (− 0.73 to 1.28)0.169 − 0.90 (− 1.95 to 0.14)0.091Screen timea − 0.27 (− 0.71 to 1.65)0.223 − 1.62 (− 2.68 to -0.56)0.003Sleep durationa0.32 (− 0.11 to 0.78)0.1450.84 (− 0.20 to 1.89)0.112Meeting (vs. not meeting) specific combinationsPhysical activity & Screen time − 0.48 (− 1.12 to 0.15)0.133 − 2.25 (− 3.75 to − 0.75)0.003Physical activity & Sleep duration − 0.16 (− 0.64 to 0.31)0.499 − 0.03 (− 1.19 to 1.12)0.951Screen time & Sleep duration − 0.09 (− 0.58 to 0.40)0.724 − 1.08 (− 2.27 to 0.12)0.077All three recommendations − 0.31 (− 1.09 to 0.47)0.436 − 1.92 (− 3.81 to − 0.02)0.048Analysis adjusted by sex, age, zBMI, race/ethnicity, region, and parental income at baselineto aid interpretation, data were back-transformed from the log scale for presentation in the resultsCI confidence intervalaEach independent individual guideline was additionally adjusted by the rest of behaviors (e.g., physical activity was also adjusted by screen time and sleep duration) Finally, Table 3 shows the IRR for obesity associated with meeting vs. not meeting 24-h movement guidelines. Adolescents who only met screen time recommendations (IRR = 0.84, $95\%$ CI 0.76 to 0.92), adolescents who met screen time jointly with physical activity recommendations (IRR = 0.86, $95\%$ CI 0.67 to 0.97), and adolescents who met all the three recommendations (IRR = 0.76, $95\%$ CI 0.60 to 0.97) had lower odds of abdominal obesity at adulthood than those who did not meet any of these recommendations. Table 3Incidence rate ratio for obesity at Wave IV associated with meeting vs. not meeting physical activity, screen time, and sleep duration and combinations of these recommendations during adolescenceObesityAbdominal obesityMeeting the following recommendationIRR ($95\%$ CI)pIRR ($95\%$ CI)pMeeting (vs. not meeting) individual guidelinePhysical activitya0.93 (0.78 to 1.10)0.4060.97 (0.87 to 1.08)0.588Screen timea0.94 (0.80 to 1.09)0.4180.84 (0.76 to 0.92) < 0.001Sleep durationa1.18 (0.94 to 1.37)0.061.02 (0.93 to 1.12)0.683Meeting (vs. not meeting) specific combinationsPhysical activity and Screen time0.86 (0.67 to 1.10)0.2420.86 (0.67 to 0.97)0.042Physical activity and Sleep duration0.97 (0.79 to 1.18)0.7550.97 (0.79 to 1.18)0.755Screen time and Sleep duration0.96 (0.80 to 1.15)0.6610.96 (0.80 to 1.15)0.661All three recommendations0.96 (0.67 to 1.38)0.850.76 (0.60 to 0.97)0.027Analysis adjusted by sex, age, zBMI, race/ethnicity, region, and parental income at baselineIRR incidence rate ratioaEach independent variable was additionally adjusted by the rest of behaviors (e.g., physical activity was also adjusted by screen time and sleep duration)
## Discussion
To our knowledge, this is the first study that determined the relationship between meeting the 24-h movement guidelines from adolescence with obesity at adulthood 14 years later. Overall, our results show that meeting screen time alone or jointly with physical activity recommendations during adolescence was linked to lower WC and abdominal obesity at adulthood. In addition, adolescents who met all movement behavior recommendations had lower risk of presenting abdominal obesity later in life.
A growing body of evidence highlights the health advantages of increasing physical activity and reducing sedentary behaviors for all populations [33]. Despite this, the prevalence of insufficient physical activity remains a worrying public health concern in both adolescents [34] and adults [35]. Scientific literature also shows that young population in most of the countries present low prevalence of overall physical activity levels [36], a high prevalence of sedentary behavior levels [36], and an increasing prevalence of obesity [37]. The long-term results of our study confirm and support previously reported findings of an earlier study that analyzed the same cohort (wave III), which found that adolescent screen time, but not physical activity was related with incidence of general body obesity at 21 years old [38]. Our results continue to show this association, but with a longer follow-up; seven years later. Also, our results show that meeting both physical activity and screen time recommendations jointly during adolescence was associated with lower abdominal obesity at adulthood 14 years later. Supporting our results, one systematic review suggested that increased physical activity and decreased sedentary behavior are protective against relative weight and fatness gains over childhood and adolescence [39]. Similarly, some studies using isotemporal substitution models reported that reallocating time from sedentary behavior to moderate-to-vigorous physical activity showed significant reductions in several obesity indicators, such as WC [40, 41], body fat percentage [42] or BMI [41]. Further, a longitudinal study by Barbour-Tuck et al. [ 43] showed the importance of engaging in high levels of physical activity to mitigate the accumulation of fat mass in the trunk and prevent the transition from having healthy weight to excess weight during emerging adulthood.
In relation to sleep duration, a longitudinal study by Sokol et al. [ 44] suggested that greater BMI could lead shorter sleep during adolescence to young adulthood. Similarly, one meta-analysis by Fatima et al. [ 45] provided evidence that short sleep duration in young subjects is significantly associated with future overweight/obesity. Despite this fact, we were not able to confirm this association in our study when we assessed sleep duration alone; not being so when it was analyzed in combination with the rest of movement behaviors. This could be because movement behaviors could have accumulative effects on health when considered together rather than individually [46].
In line with the above, it is noteworthy that, until recently, studies determining the influence of physical activity, sedentary behavior, and sleep duration on different health outcomes have mainly been conducted individually or separately from the other behaviors [6]. Notwithstanding, since time is limited (i.e., 24-h), adolescents have to make choices between different activities during the day, so it is conceptually wrong to consider that the impact of a particular behavior is independent of other [47]. In this sense, when we analyzed all behaviors together, we observed that meeting all behaviors recommendations during adolescence was linked to lower odds of presenting abdominal obesity during adulthood, but not with general body obesity. For young population, scientific literature shows strong evidence about the associations between adherence to all three 24-h movement guidelines and reduced body fat mass and lower risk of having obesity [6]. Furthermore, a longitudinal study by Chemtob et al. [ 22] that evaluated the relationship between adiposity (from childhood to adolescence) and 24-h movement guidelines showed greater body fat mass 2 and 7 years later in those who did not meet the guidelines [22]. Another longitudinal study carried out by Micklesfield et al. [ 23] found less optimal body composition (in girls) in those who were constantly physically active, had a higher sleep duration, and were more sedentary through adolescence, in their study conducted in South Africa [23]. There are some possible explanations that could justify our findings. Firstly, movement behaviors are codependent since one movement behavior can replace an equal amount of time of one (or more) of the others [46] (e.g., sedentary behaviors that displace physical activity). Supporting this fact, a study by Kim et al. [ 48] with a composited data analysis showed reductions in BMI when replacing sedentary behaviors by physical activity or sleep time. Moreover, it has been pointed out that changes from a physically active to a more sedentary lifestyle in later life entails a reduction of energy consumption [49]. Secondly, physical activity has a dose–response relationship, is time-consistent and has biological plausibility on obesity development and, also, the reduction in physical activity levels is considered a key factor in the increasing worldwide prevalence of obesity [50]. Also, there are a number of studies which have specifically investigated the effect of physical activity on abdominal obesity, irrespective of total body weight [51]. For example, Lee et al. [ 52] found that, even in the absence of weight loss, moderate-intensity exercise (30 – 60 min of brisk walking) was associated with significant reductions in total, abdominal fat, and WC. This could be one of the possible reasons which could explain the lack of association with general body obesity in our study. Finally, short sleep duration has shown to significantly increase the risk of obesity [16, 45], due to some suggested factors, such as endocrine and metabolic changes, increased appetite which could provoke a greater caloric intake, higher systemic inflammation and reduced physical activity linked to daytime sleepiness [53].
There are several limitations that should be declared. Firstly, the longitudinal study design does not allow us to establish causal-effect relationships. Secondly, information about movement behaviors were self-reported by adolescents, which is subject to bias. A stronger methodology would be more appropriate to obtain objective measures of physical activity, sedentary behavior and sleep duration (e.g., accelerometers). Also, we classified respondents as meeting physical activity recommendations when they performed MVPA five or more times per week. The physical activity instrument used is limited in scope since does not include a school component and give no indication of time, which makes it really difficult to pinpoint if youth met the 60 min of MVPA per day recommendation. Therefore, results need to be interpreted with caution. Thirdly, we did not include information about dietary patterns, which could influence on obesity markers as previously suggested [54]. Moreover, dataset from 1994 to 1996 is being used to gauge meeting a recommendation made later. Given that the prevalence and conditions in which sedentary behaviors occur have changed in the last two decades, findings of this study should be interpreted with caution. Also, screen time questions did not specifically mention “leisure screen-based activities” and therefore we cannot be certain that this includes only “recreational screen time”. Lastly, height and weight at Wave I were self-reported. However, self-reported height and weight in the Add Health cohort seems to be highly correlated with measured height and weight at later waves [55].
In conclusion, our results show that meeting screen time separately, jointly with physical activity recommendations, and meeting all movement behavior recommendations during adolescence was associated with lower risk of abdominal obesity 14 years later in a nationally representative US cohort, therefore, our findings may not be generalizable outside the USA. Our study highlights the importance of promoting the adherence to the 24-h movement guidelines during adolescence to prevent the risk of suffering abdominal obesity later in life. Nevertheless, further studies with robust measurements are required to confirm our findings.
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|
---
title: circFLNA promotes intestinal injury during abdominal sepsis through Fas-mediated
apoptosis pathway by sponging miR-766-3p
authors:
- Ling Ye
- Yuan Shi
- Huifeng Zhang
- Chao Chen
- Jingjing Niu
- Jianxu Yang
- Zhifeng Li
- Huanzhang Shao
- Bingyu Qin
journal: Inflammation Research
year: 2023
pmcid: PMC10023616
doi: 10.1007/s00011-023-01688-1
license: CC BY 4.0
---
# circFLNA promotes intestinal injury during abdominal sepsis through Fas-mediated apoptosis pathway by sponging miR-766-3p
## Abstract
### Background
Intra-abdominal infections are the second most common cause of sepsis in the intensive care unit. Intestinal epithelial injury due to abdominal sepsis results in a variety of pathological changes, such as intestinal bacteria and toxins entering the blood, leading to persistent systemic inflammation and multiple organ dysfunction. The increased apoptosis of intestinal epithelial cells induced by sepsis further exacerbates the progression of sepsis. Although several studies have revealed that circRNAs are involved in intestinal epithelial injury in sepsis, few studies have identified the roles of circRNAs in intestinal epithelial apoptosis.
### Methods
We used laser capture microdissection to obtain purified epithelial cells located in intestinal crypts from four patients with abdominal sepsis induced by intestinal perforation and four samples from age and sex-matched non-septic patients. Microarray analysis of circRNAs was conducted to assess differentially expressed circRNAs between patients with and without sepsis. Lastly, in vitro and in vivo assays were performed to study the mechanism of circFLNA in intestinal epithelial apoptosis during sepsis.
### Results
circFLNA was upregulated in the intestinal epithelium after abdominal sepsis induced by intestinal perforation. Inhibition of miR-766-3p impaired si-circFLNA-mediated inhibition of apoptosis and inflammation factor levels in lipopolysaccharide (LPS)-treated HIEC-6 cells. circFLNA aggravated apoptosis and inflammation through the Fas-mediated apoptosis pathway in both LPS-treated HIEC-6 cells and a mouse cecal ligation and puncture model.
### Conclusion
Our findings showed that circFLNA promotes intestinal injury in abdominal sepsis through the Fas-mediated apoptosis pathway by sponging miR-766-3p. The circFLNA/miR-766-3p/*Fas axis* has potential as a novel therapeutic target for treating intestinal injury in sepsis.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00011-023-01688-1.
## Introduction
Intra-abdominal infections are second only to pneumonia as the most common cause of sepsis in the intensive care unit (ICU) patients, with a high incidence of mortality [1, 2]. Once severe abdominal sepsis or septic shock is identified, the mortality rate can be as high as $42.3\%$ [3, 4]. Despite the implementation of optimal management principles, such as early diagnosis, personalized fluid resuscitation, damage control, empiric antibiotic therapy, and organ support in the ICU, the mortality due to abdominal sepsis has not significantly improved over the past decades [5, 6]. Based on a previous study, the increase in intestinal permeability caused by mucosal injury in sepsis results in a variety of pathological changes such as intestinal bacteria and toxins entering the blood, leading to persistent systemic inflammation and multiple organ dysfunction [7, 8]. It has been reported that apoptosis of intestinal epithelial cells (IECs) is an important biological process which leads to intestinal injury [9]. Nevertheless, the detailed mechanisms that regulate IEC apoptosis in abdominal sepsis are still not fully elucidated.
Circular RNAs (circRNAs), a novel class of non-coding RNAs with covalently closed loop structures, are widely found in eukaryotic cells and exhibit tissue-specific expression [10]. The sequence of circRNAs is highly conserved among different species and is resistant to ribonuclease R (RNase R) because of their circular structure [11]. Instead of coproducts of RNA splicing, circRNAs mainly function as sponges of microRNAs (miRNAs) and regulate the expression of target genes [12]. miRNAs repress gene expression by binding to complementary sequences in the 3ʹ untranslated region (3ʹ-UTR) of target mRNAs [13]. Recent studies have shown that circRNAs are aberrantly expressed in sepsis and have the potential to serve as diagnostic biomarkers and therapeutic targets [14, 15]. For instance, circHIPK3 serves as a sponge for miR-29b to enhance the repair of intestinal epithelial cells in patients with sepsis [16]. Furthermore, circTLK1 promotes inflammation and oxidative stress via the miR-106a-5p/HMGB1 axis in sepsis-associated acute kidney injury, and hsa_circ_0003091 mediates sepsis-induced lung injury by reducing miR-149 [17, 18]. Although several studies have revealed that circRNAs are involved in intestinal epithelial injury in sepsis [16, 19, 20], few studies have investigated the roles of circRNAs in intestinal epithelial apoptosis.
Laser capture microdissection (LCM) is a microscopic-guided system that can isolate specific cell types or regions of interest from tissue sections with a laser beam [21]. A variety of studies have indicated that LCM is powerful for isolating distinct cells and is helpful for the subsequent analysis of DNA, RNA, or protein profiles by sequencing or microarray [22–24]. With respect to the intestine, LCM has been used to obtain high-integrity RNA samples from freshly resected human intestinal tissue and to evaluate the gene expression profiles between crypts and villi of ileal epithelial cells [25, 26]. Furthermore, compared to villi, intestinal crypts have been shown to contribute to antimicrobial activity by expressing certain proteins [27]. In addition, intestinal stem cells (ISCs), which renovate the intestinal epithelium, are located at the bottom of crypts [28]. Therefore, we used LCM to obtain particular cells from the intestinal crypts of patients with abdominal sepsis induced by intestinal perforation and non-sepsis patients separately and then analyzed the circRNA expression profiles using circRNA microarray.
In this study, we found that circFLNA was upregulated in the intestinal crypts of patients with intestinal perforation. Moreover, we discovered that circFLNA originated from exons 9–15 of the FLNA gene, and its expression was significantly increased in HIEC-6 cells after being treated with lipopolysaccharide (LPS). Further mechanistic studies revealed that circFLNA promotes intestinal epithelial apoptosis and inflammation via the miR-766-3p/*Fas axis* in vitro and in vivo.
## Patients intestinal tissues
The resected intestinal tissues were obtained from 20 patients with intestinal perforation diagnosed with sepsis according to Sepsis 3.0. Discarded tissues resected from 23 patients with gastrointestinal stromal tumors were used as controls. All patients provided informed consent, and this study was approved by the Ethics Committee of Henan Provincial People’s Hospital.
## Laser capture microdissection and CircRNA microarray analysis
Highly purified epithelial cells located in intestinal crypts from four patients with abdominal sepsis induced by intestinal perforation and four samples from age- and sex-matched non-sepsis patients were obtained with LCM, according to a previously described protocol [29]. The enriched circRNAs were sent to BoHao Bio-tech (Shanghai, China) for circRNA microarray analysis after the total RNA was digested with ribonuclease R (RNase R).
## Cell culture and chemicals
Normal HIEC-6 cells, human undifferentiated crypt enterocytes, were acquired from the Type Culture Collection of the Chinese Academy of Sciences (Shanghai, China). The cells were incubated in Dulbecco’s modified Eagle’s medium (DMEM, Gibco; Grand Island, NY, USA) supplemented with $10\%$ fetal bovine serum (FBS, Gibco) and cultured in a humid atmosphere at 37 °C with $5\%$ CO2 in air. LPS from *Escherichia coli* O111:B4 was purchased from Sigma-Aldrich (St Louis, MO, USA).
## Total RNA extraction, actinomycin D assay, RNase treatment, and quantitative real-time PCR (qRT-PCR)
Total RNA, including circRNA, was extracted from intestinal tissues and HIEC-6 cells using RNAiso Plus (TaKaRa, Shiga, Japan) according to the manufacturer’s instructions. The transcription of RNAs extracted from HIEC-6 cells was blocked with 2 μg/mL actinomycin D (Sigma-Aldrich) for 8, 16, and 24 h. The RNAs extracted from HIEC-6 cells were treated with RNase R (Abcam, Cambridge, UK) for 30 min at 37 ℃ according to the protocol previously described [30]. Nuclear and cytoplasmic RNAs were isolated using the Cytoplasmic & Nuclear RNA Purification Kit (Norgen Biotek, Ontario, Canada), according to the manufacturer’s instructions. qRT-PCR was performed using a SYBR-Green PCR Master Mix Kit (Takara) on a QuantStudio Dx system (Applied Biosystems, CA, USA). The abundance of circRNAs and mRNAs was normalized to that of GAPDH, and small nuclear U6 was used as an endogenous control for miRNAs. The relative expression of circRNAs, miRNAs, and mRNA was measured using the 2−ΔΔCt (Ct; cycle threshold) method. The primers used for qRT-PCR are listed in Supplementary Table S1.
## Fluorescence in situ hybridization and immunofluorescent staining
The signals of Cy5-labeled probes specific to circFLNA were detected using a fluorescence in situ hybridization (FISH) kit (GenePharma, Shanghai, China) according to the manufacturer’s instructions. Nuclei were counterstained with 4’, 6-diamidino-2-phenylindole (DAPI). Immunofluorescence (IF) staining analysis to detect Fas localization in HIEC-6 cells was performed according to a previously described protocol [31]. Images were acquired using a TCS SP2 AOBS confocal microscope (Leica Microsystems, Mannheim, Germany).
## Plasmid construction and transfection
Lentiviral vectors encoding circFLNA, small interfering RNAs (siRNA) targeting circFLNA, miRNA-766-3p mimics, and miRNA-766-3p inhibitor sequences were designed and synthesized by BioLink (Shanghai, China). The Fas (CD95) gene was synthesized according to the mRNA sequence of the human *Fas* gene and cloned into a lentiviral vector. HIEC-6 cells were transfected with 50 nM overexpressing circFLNA (circFLNA OE), si-circFLNA, Fas, miR-766-3p mimics, miR-766-3p inhibitors, or the corresponding controls using Lipofectamine RNAiMax (Invitrogen, Waltham, MA, USA) according to the manufacturer’s protocol.
## Cell viability and apoptosis assay
Cell viability was measured using the Cell Counting kit-8 (CCK8) (Dojindo, Kumamoto, Japan), according to the manufacturer’s protocol. HIEC-6 cells were treated with LPS at concentrations of 0, 1, 5, 10, 20, and 50 μg/mL for 24 h, and the optimal concentration of LPS was obtained. HIEC-6 cells transfected with lentivirus circFLNA OE, si-circFLNA, or the corresponding control were treated with 50 μg/mL LPS. Absorbance at 450 nm was measured using a Gen5 microplate reader (BioTek, Vermont, USA). Cell apoptosis was detected according to the protocol described in our previous study [32]. A BD FACSCalibur Flow Cytometer (BD Biosciences, CA, USA) was used to evaluate the apoptosis rate according to the manufacturer’s instructions.
## Enzyme-linked immunosorbent assay (ELISA)
Cytokines in the supernatants of HIEC-6 cells and mouse intestinal homogenates were measured using ELISA kits (R&D Systems, Minneapolis, MN, USA), according to the manufacturer’s protocol. The mouse intestinal tissues were homogenized using a homogenizer, and the supernatant was collected after centrifugation at 800 × g for 20 min at 4 °C. The concentrations of interleukin (IL)-6, IL-1β, and tumor necrosis factor alpha (TNF-α) were determined using ELISA kits (R&D Systems). The optical density (OD) at 450 nm was measured using a Gen5 microplate reader (BioTek). A standard curve was plotted before sample concentrations were calculated. The experiments were repeated in triplicate.
## Protein microarray analysis
The protein lysate samples of LPS-treated HIEC-6 cells were analyzed using the Human Cytokine Antibody Array 4000 (RayBio, GA, USA), according to the manufacturer’s instructions. Briefly, the array slide was incubated with 65 µL of LPS-treated HIEC-6 cell lysate overnight at 4 °C and equilibrated to room temperature on the following day. Then, the array slide was incubated for 2 h after extensive washing with an array-specific biotinylated antibody cocktail. The slide was incubated with Cy3-equivalent dye-conjugated streptavidin for 1 h. Finally, an InnoScan 300 microarray scanner (Innopsys, IL, USA) was used to obtain the images. To minimize false-positive hits, each sample was screened twice, and only the hits that appeared on both screenings were analyzed.
## Western blot analysis
Western blot analysis was performed according to the protocol in our previous study [32]. The following antibodies were purchased as indicated: anti-Fas, anti-FADD, anti-Caspase-8, anti-Caspase-3, anti-occludin, anti-ZO-1 (Cell Signaling Technology, MA, USA), and anti-β-actin (Sigma-Aldrich).
## Bioinformatic analysis
The target miRNAs interacting with circFLNA were predicted using the publicly available databases circInteractome (https://circinteractome.nia.nih.gov/index.html) and circBank (http://www.circbank.cn/). The miRNAs targeting Fas were predicted using TargetScan (https://www.targetscan.org/vert_80/). The miRNAs presented in all three databases were validated in HIEC-6 cells.
## Dual-luciferase reporter assay
HIEC-6 cells were transfected with either wild-type (WT) or mutant (MUT) circFLNA vector, miR-766-3p mimics, or mimics normal control (NC), and WT or MUT Fas 3′-UTR reporter using Lipofectamine 2000 (Invitrogen). The dual-luciferase reporter assays (Promega, Madison, WI, USA) were performed according to the manufacturer’s instructions. After transfection for 48 h, a GloMax fluorescence reader (Promega) was used to assess luciferase activity.
## RNA immunoprecipitation
The Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, Bedford, MA, USA) was used to perform the RNA Immunoprecipitation (RIP) assays according to the manufacturer’s instructions. Briefly, HIEC-6 cells were transfected with miR-766-3p mimics or mimics NC and then cells were lysed using RIPA buffer (Beyotime Biotechnology, China) after 48 h. Magnetic beads were incubated with anti-AGO2 and anti-rabbit IgG as controls (Cell Signaling Technology) for 30 min. The cell lysates were immunoprecipitated with coated magnetic beads on a table concentrator at 4 °C overnight. The following day, RNAiso Plus was used to isolate the co-precipitated RNA bound to the beads. The expression of circFLNA and miR-766-3p in the isolated RNAs was detected using qRT-PCR.
## Animals and cecal ligation and puncture (CLP) model
C57BL/6 N mice (8–10 weeks old and 20–25 g) were obtained from the Experimental Animal Center of Zhengzhou University. The mice were maintained in a temperature-controlled room (22 °C ± 2 °C) with free access to water and food. Animal experiments were performed according to the principles of the Declaration of Helsinki and were approved by the Institutional Animal Ethical Committee of Zhengzhou University.
The CLP model of abdominal sepsis was established as previously described [33]. In brief, the abdomen was opened by making a tiny incision (1 cm) in the middle of the abdomen after anesthetization with pentobarbital sodium (50 mg/kg), and the cecum was exposed. The cecum was ligated in the middle and perforated twice using a 20-gauge needle. Subsequently, a small amount of feces was squeezed out of the perforation. The abdominal incision was stitched layer-by-layer before returning the cecum to the peritoneal cavity. For mice in the sham group, the abdomen was opened to expose cecum, but no ligation or perforation was performed. When the procedure was complete, saline (50 mL/kg) was subcutaneously injected for resuscitation.
## Animal grouping and specimen collection
In this study, the mice were divided into six groups ($$n = 25$$), and the treatment for each group was as follows. The sham group (1 mL saline was injected via tail vein 24 h before sham surgery), CLP group (1 mL saline was injected via tail vein 24 h before CLP surgery), CLP + si-NC + inhibitor-NC group (10 μg si-NC and 10 μg inhibitor-NC were injected via tail vein 24 h before CLP surgery), CLP + si-NC + miR-766-3p inhibitor group (10 μg si-NC and 10 μg miR-766-3p inhibitor were injected via tail vein 24 h before CLP surgery), CLP + si-circFLNA + inhibitor-NC group (10 μg si-circFLNA and 10 μg inhibitor-NC were injected via the tail vein 24 h before CLP surgery), and CLP + si-circFLNA + miR-766-3p inhibitor group (10 μg si-circFLNA and 10 μg miR-766-3p inhibitor were injected via the tail vein 24 h before CLP surgery). After surgery, 15 mice in each group were randomly allotted to analyze the survival rate over 7 days, and the remaining 10 mice in each group were used to collect blood and intestinal tissues at 48 h. Blood was collected from the hearts of mice using a micro-injector, and serum was obtained after centrifugation. Intestinal tissues were harvested after anesthetization by intraperitoneal injection of pentobarbital sodium. Part of intestinal tissues was instantly frozen in liquid nitrogen and stored at − 80 °C to detect inflammatory factors and extract proteins. The remaining tissues were fixed in $10\%$ formalin to conduct the subsequent histological experiments.
## Detection of intestinal mucosal permeability
The levels of d-lactic acid in the serum were detected using a d-lactic acid assay kit (Megazyme, Wicklow, Ireland) according to the manufacturer’s instructions. The levels of diamine oxidase (DAO) in the serum were measured using an ELISA kit (R&D Systems) according to the manufacturer’s instructions. Absorbance at an excitation wavelength of 450 nm was measured using a Gen5 microplate reader (BioTek). FD-40 (750 mg/kg) was administered by gavage to the mice in each group 18 h after surgery. Venous blood samples were collected from the mesentery 6 h later, and serum was obtained after centrifugation. The absorbance at an excitation wavelength of 450 nm and an emission wavelength of 520 nm was measured using a Gen5 microplate reader (BioTek). These experiments were repeated in triplicate.
## Hematoxylin and eosin staining, immunohistochemistry, and TdT-mediated dUTP-biotin nick end-labeling staining
After fixation in $10\%$ formalin, the intestinal tissues were embedded in paraffin wax and cut into 3 μm slices. The slices were then dehydrated with gradient alcohol, cleaned with xylene, and sealed with resin. Hematoxylin and eosin (H&E) staining was performed on the slices. Finally, images were captured using a light microscope (Nikon, Tokyo, Japan) to detect histopathological changes. The severity of intestinal injury was assessed according to Chiu’s scoring system, as previously described [9].
For immunohistochemistry (IHC), tissue slices were treated with anti-Fas antibody (1:500 dilution; Cell Signaling Technology). The horseradish peroxidase (HRP)-conjugated secondary antibody (Gene Tech, Shanghai, China) was incubated for 30 min at room temperature and used to detect the primary antibody. The images were acquired using a light microscope (Nikon), and Fas staining was quantified using Image-Pro Plus 7 (Media Cybernetics, MD, USA).
Apoptotic cells in the intestinal epithelium sections were detected using TdT-mediated dUTP-biotin nick end-labeling (TUNEL) reagent (Elabscience, Wuhan, China) according to the manufacturer’s instructions. Images were acquired using a fluorescence microscope (Leica Microsystems), and TUNEL-positive cells were quantified using Image-Pro Plus 7 (Media Cybernetics).
## Statistical analysis
Statistical analyses were performed using the SPSS statistical software program version 22 (IBM, IL, USA). Two-tailed Student’s t-test was applied to compare the differences between two groups, and one-way analysis of variance (ANOVA) was used to compare differences among multiple groups. Survival analysis was performed using the Kaplan–Meier method, and differences between the survival curves were assessed using log-rank tests. Statistical significance was set at $P \leq 0.05.$
## Circular RNA expression profiles in intestinal epithelium of individuals with sepsis
Homeostasis disorder of the intestinal epithelium plays an important role in sepsis pathogenesis. Highly purified intestinal epithelial cells located in intestinal crypts from four patients with intestinal perforation and four samples from age- and sex-matched patients without sepsis were captured using LCM (Fig. 1A). To assess the expression profile of circRNAs isolated from purified intestinal epithelial cells, microarray analysis of circRNAs was conducted. Total RNAs were treated with RNase R to digest linear RNAs. Unsupervised hierarchical clustering showed differentially expressed circRNAs (fold-change (FC) > 2 or < 0.5, $P \leq 0.05$) between septic and non-septic intestinal epithelial cells, including 34 upregulated circRNAs and 2 downregulated circRNAs (Fig. 1B). Table 1 shows the most upregulated 12 circRNAs (FC > 2.5, $P \leq 0.05$) in sepsis tissues compared with their expression in non-sepsis tissues. We selected five highly expressed circRNAs (circFLNA, circLARP4, circBNC2, circFAM13B, and circEDIL3) with a high FC > 3.0 ($P \leq 0.001$) for further validation. Fig. 1circRNA expression profiles in intestinal epithelium from sepsis sample. A Intestinal epithelial cells of patients with or without sepsis were obtained from fixed tissue sections using laser capture microdissection. B Heat map of differentially expressed circRNAs between septic and non-septic human intestinal epithelial cells. C Validation of circFLNA and other differentially expressed circRNAs by qRT-PCR in the intestinal mucosa from 20 patients with sepsis and 23 non-sepsis patients. D circFLNA was upregulated in the intestinal mucosa of mice exposed to CLP for 48 h. E HIEC-6 cells were treated with increasing concentrations of LPS for 24 h and cell viability assays were performed using a CCK-8 kit. F circFLNA levels in HIEC-6 cells treated with different concentrations of LPS for 24 hTable 1Differential circRNAs expression in purified intestinal epithelial cells between four abdominal sepsis and four non-sepsis patientscircRNAP valueFold changeRegulationCircRNA typeGene symbolhsa_circ_0001535 < 0.0016.23UpExonicFAM13Bhsa_circ_0092012 < 0.0015.85UpExonicFLNAhsa_circ_0073244 < 0.0014.36UpExonicEDIL3hsa_circ_0003222 < 0.0013.79UpExonicLARP4hsa_circ_0008732 < 0.0013.13UpExonicBNC2hsa_circ_00069900.032.92UpExonicVAPAhsa_circ_00377770.022.84UpExonicALG1hsa_circ_00444360.0052.82UpExonicKAT7hsa_circ_00010320.042.73UpExonicTET3hsa_circ_00674700.0082.63UpExonicSTAG1hsa_circ_00061980.0062.59UpExonicLCORhsa_circ_00127990.0022.53UpExonicDOCK7 The intestinal mucosae obtained from 20 patients with sepsis and twenty-three non-sepsis patients were used to verify the expression of these circRNAs. As shown in Fig. 1C, the expression of circFLNA and circFAM13B was increased in patients with sepsis compared with that in non-septic patients, while the expression of circBNC2 and circEDIL3 was not significantly different between the two groups. Among the two significantly upregulated circRNAs in patients with sepsis, circFLNA expression was upregulated. Furthermore, we also detected the expression of circFLNA in the mouse CLP model and found that circFLNA expression was increased in the intestinal mucosa of the CLP groups compared with that in the sham group (Fig. 1D). Thus, circFLNA was selected for in vitro validation. First, HIEC-6 cells were treated with different concentrations of LPS to construct an in vitro sepsis model. The viability of HIEC-6 cells decreased after treatment with increasing LPS concentrations for 24 h (Fig. 1E). The cell viability was obviously reduced at an LPS concentration of 50 µg/mL and the falling range reached almost $40\%$. Moreover, qRT-PCR analysis revealed that circFLNA expression was increased more significantly at an LPS concentration of 50 µg/mL than at 10 and 20 µg/mL (Fig. 1F). The concentration of LPS used to treat HIEC-6 cells for further experiments was selected as 50 µg/mL.
## CircFLNA is overexpressed in LPS-treated HIEC-6 cells and mainly localized in the cytoplasm
circFLNA, with a spliced mature sequence of 543 bp in length, was generated by reverse splicing of exons 9–15 of the FLNA gene, located at chrX:153592389–153594592 (Fig. 2A). Convergent primers were designed to amplify the linear FLNA mRNA, and divergent primers were designed to amplify circFLNA. To determine the stability of circFLNA, HIEC-6 cells were treated with actinomycin D (an inhibitor of transcription). qRT-PCR assays showed that the circFLNA transcript with a half-life of more than 24 h was more stable than linear FLNA mRNA in HIEC-6 cells (Fig. 2B). In addition, total RNAs extracted from HIEC-6 cells were digested with RNase R prior to qRT-PCR. We noted that circFLNA was more resistant to RNase R than linear FLNA mRNA (Fig. 2C). RNAs extracted from nuclear and cytoplasmic samples was detected by RT-PCR. The results showed that circFLNA was mainly localized in the cytoplasm of HIEC-6 cells, rather than in the nucleus (Fig. 2D). Moreover, FISH assays showed that circFLNA was predominantly expressed in the cytoplasm of LPS-treated HIEC-6 cells compared to controls (Fig. 2E). Taken together, these results indicate that circFLNA was upregulated in LPS-treated HIEC-6 cells and was mainly localized in the cytoplasm. Fig. 2circFLNA is overexpressed in LPS-treated HIEC-6 cells and mainly localized in the cytoplasm. A A schematic illustration shows that exon 9–15 derived from FLNA formed circFLNA. B qRT-PCR assays were performed to detect the expression of circFLNA and linear FLNA mRNA in HIEC-6 cells treated with actinomycin D at a specific time point. C After total RNA treatment with or without RNase R, the expression of circFLNA and linear FLNA mRNA in HIEC-6 cells was detected using qRT-PCR. D A nuclear-cytoplasmic fractionation assay revealed that circFLNA was mainly detected in the cytoplasm. E RNA-FISH assay showed that circFLNA was predominantly localized in the cytoplasm of HIEC-6 cells after treatment with LPS for 24 h. Nuclei stained blue with DAPI. Values are shown as mean ± standard deviation. NS not significant. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## circFLNA inhibits cell viability and promotes apoptosis and inflammation of LPS-treated HIEC-6 cells
To evaluate the biological role of circFLNA in sepsis, interference and overexpression assays were utilized in this study. First, three siRNAs targeting the junction sites of circFLNA were designed, and si-circFLNA#1 displayed the highest interference efficiency (Fig. 3A). The interference efficiency of si-circFLNA#1 was verified in LPS-treated HIEC-6 cells (Fig. 3B). In addition, we found that decreased expression of circFLNA blocked LPS-induced inhibition of cell viability (Fig. 3C). Upregulation of circFLNA was observed after transfection with lentivirus circFLNA OE in HIEC-6 cells (Fig. 3D and E). Accordingly, increased expression of circFLNA aggravated LPS-induced inhibition of cell viability (Fig. 3F). Furthermore, we found that decreased circFLNA expression ameliorated apoptosis (Fig. 3G) and inflammation factor levels (IL-6, IL-1β, and TNF-α) in LPS-treated HIEC-6 cells (Fig. 3H). In contrast, circFLNA OE aggravated apoptosis (Fig. 3I) and inflammation factor levels (IL-6, IL-1β, and TNF-α) in LPS-treated HIEC-6 cells (Fig. 3J). Taken together, these results indicate that circFLNA inhibited cell viability and promoted apoptosis and inflammation in LPS-treated HIEC-6 cells. Fig. 3circFLNA inhibits cell proliferation, as well as promotes apoptosis and inflammation of LPS-treated HIEC-6 cells. A The reduced efficiency of three siRNAs targeting circFLNA was verified by qRT-PCR in HIEC-6 cells. B qRT-PCR assays were used to detect circFLNA expression in LPS-treated HIEC-6 cells transfected with si-circFLNA#1 or the negative control (si-NC). C Cell Counting Kit-8 assays were used to evaluate the proliferation of LPS-treated HIEC-6 cells transfected with si-circFLNA#1 (red line) or si-NC (blue line). D circFLNA expression was detected by qRT-PCR in HIEC-6 cells infected with lentivirus overexpressing circFLNA or with the empty control vector. E qRT-PCR assays were used to detect circFLNA expression in LPS-treated HIEC-6 cells transfected with circFLNA OE or vector. F Cell Counting Kit-8 assays were used to evaluate the proliferation of LPS-treated HIEC-6 cells transfected with circFLNA OE (red line) or vector (blue line). G *Cell apoptosis* was evaluated using flow cytometry in LPS-treated HIEC-6 cells transfected with si-circFLNA#1 or si-NC. H Concentrations of IL-6, IL-1β, and TNF-α in the supernatants of LPS-treated HIEC-6 cells transfected with si-circFLNA#1 or si-NC were measured by ELISA. I Apoptosis was evaluated by flow cytometry in LPS-treated HIEC-6 cells transfected with circFLNA OE or vector. J Concentrations of IL-6, IL-1β, and TNF-α in supernatants of LPS-treated HIEC-6 cells transfected with circFLNA OE or vector were measured using ELISA. Values are shown as mean ± standard deviation *$P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## circFLNA promotes apoptosis and inflammation by regulating Fas in LPS-treated HIEC-6 cells
To determine the mechanisms of circFLNA in promoting HIEC-6 cell apoptosis and inflammation, protein array analysis was performed in LPS-treated HIEC-6 cells with increased circFLNA expression. The results showed that levels of five proteins were decreased (FC < 0.3, $P \leq 0.05$), while those of three proteins were increased (FC > 3, $P \leq 0.05$) in LPS-treated HIEC-6 cells with increased circFLNA expression (Table 2). qRT-PCR assays were performed to verify the mRNA expression of the three upregulated proteins. We found that decreased expression of circFLNA ameliorated the upregulation of Fas (Fas cell surface death receptor) induced by LPS but did not influence the expression of MMP-9 (matrix metalloproteinase-9) and TIM-1 (T-cell immunoglobulin mucin-1) (Fig. 4A). Furthermore, western blot assays confirmed that the protein level of Fas was reduced after transfection with si-circFLNA#1 in LPS-treated HIEC-6 cells, whereas it was elevated in cells transfected with circFLNA OE (Fig. 4B). Fas, also known as CD95, is constitutively expressed on the basolateral surface of IECs, mediates apoptosis after activation by Fas ligand (FasL), and promotes the production of cytokines and chemokines following LPS treatment [34, 35]. Therefore, we aimed to determine whether circFLNA promotes apoptosis and inflammation by increasing Fas expression in LPS-treated HIEC-6 cells. As shown in Fig. 4C, interference of circFLNA by si-circFLNA#1 significantly decreased the mRNA expression of Fas, which was restored after transfection with lentivirus overexpressing Fas in LPS-treated HIEC-6 cells. Moreover, FISH assays demonstrated that circFLNA and Fas were located in the cytoplasm and cytomembrane, respectively, and that both were upregulated in LPS-treated HIEC-6 cells (Fig. 4D). Finally, we found that the restoration of Fas expression significantly abrogated the effect of si-circFLNA on apoptosis (Fig. 4E and F) and inflammation factor levels (IL-6, IL-1β, and TNF-α) in LPS-treated HIEC-6 cells (Fig. 4G). Taken together, these results indicate that circFLNA promoted apoptosis and inflammation by regulating Fas in LPS-treated HIEC-6 cells. Table 2Differentially expressed proteins in LPS-treated Caco2 cells compared with controlsProteinsFold changeFas cell surface death receptor (Fas)4.31Matrix metalloproteinase-9 (MMP-9)4.02Galectin-73.56T-cell immunoglobulin and mucin domain-containing protein-2 (TIM-2)0.29Oncostatin M (OSM)0.27Vascular cellular adhesion molecule-1 (VCAM-1)0.25Insulin-like growth factor binding protein-4 (IGFBP-4)0.17Epithelial cadherin (E-cadherin)0.13Fig. 4circFLNA promotes apoptosis and inflammation by regulating Fas in LPS-treated HIEC-6 cells. A qRT-PCR assays were performed to detect the expression of the three predicted genes in LPS-treated HIEC-6 cells transfected with si-circFLNA#1 or si-NC. B Western blot assays evaluated the protein levels of Fas in LPS-treated HIEC-6 cells transfected with si-circFLNA#1, si-NC, circFLNA OE, or vector. C Fas mRNA expression was detected in LPS-treated HIEC-6 cells that were co-transfected with lentivirus expressing si-circFLNA#1 or Fas. D FISH and immunofluorescence assays were performed separately to detect the expression of circFLNA and Fas in LPS-treated HIEC-6 cells. Note: Green (circFLNA), blue (DAPI, which reflects total cells), and red (Fas). E and F *Cell apoptosis* was evaluated by flow cytometry in LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing si-circFLNA#1 or Fas. G Concentrations of IL-6, IL-1β, and TNF-α in supernatants of LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing si-circFLNA#1 or Fas were measured by ELISA. Values are shown as mean ± standard deviation. NS not significant. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## circFLNA acts as a miRNA sponge of miR-766-3p in sepsis
CircRNAs have been reported to act as miRNA sponges in the cytoplasm [36]. As mentioned above, circFLNA was mainly localized in the cytoplasm and was stably expressed in LPS-treated HIEC-6 cells. Therefore, we investigated whether circFLNA promoted apoptosis and inflammation in LPS-treated HIEC-6 cells by binding to certain miRNAs. We then performed bioinformatic analysis using three databases: CircInteractome (https://circinteractome.nia.nih.gov/index.html), circBank (http://www.circbank.cn/), and TargetScan (https://www.targetscan.org/vert_80/) were used to predict miRNAs. We identified three overlapping miRNAs (hsa-miR-766-3p, hsa-miR-513a-5p, and hsa-miR-1184) that potentially bind to circFLNA and the 3′-untranslated region (UTR) of Fas mRNA (Fig. 5A). The miRNAs predicted by CircInteractome, CircBank, and TargetScan are listed in Supplementary Table S2. As a result, we speculated that circFLNA might sponge miRNAs and promote Fas expression in HIEC-6 cells. qRT-PCR was performed to verify the influence of circFLNA on the expression of hsa-miR-766-3p, hsa-miR-513a-5p, and hsa-miR-1184 in HIEC-6 cells. The results showed that miR-766-3p expression was significantly decreased in HIEC-6 cells transfected with circFLNA OE (Fig. 5B), while it significantly increased with interference of circFLNA (Fig. 5C). In addition, changes in circFLNA expression had no effect on hsa-miR-513a-5p or hsa-miR-1184. The putative binding sites of miR-766-3p on circFLNA and Fas are shown in Fig. 5D.Fig. 5circFLNA act as a miRNA sponge of miR-766-3p in sepsis. A Schematic illustration showing the identification of three miRNAs, hsa-miR-766-3p, hsa-miR-513a-5p, and hsa-miR-1184, as predicted by circBank, circInteractome, and TargetScan. B and C qRT-PCR assays were performed to detect the expression of three miRNAs in circFLNA-overexpressing and -silenced HIEC-6 cells. D The putative binding sites of miR-766-3p on circFLNA and Fas were identified by bioinformatic analysis. E Predicted binding sites between miR-766-3p and wild-type (WT) or mutant (MUT) circFLNA sequences. F Dual-luciferase reporter assays were performed in HIEC-6 cells co-transfected with WT circFLNA or MUT circFLNA and miR-766-3p mimics or NC. G and H The expression of miR-766-3p and circFLNA was detected by qRT-PCR after the RIP assay for AGO2 in HIEC-6 cells transfected with miR-766-3p mimics or NC. I qRT-PCR assays were used to detect the expression of miR-766-3p in LPS-treated HIEC-6 cells transfected with si-circFLNA#1 or NC. J Expression of miR-766-3p in the intestinal mucosa of 20 patients with sepsis and 23 non-sepsis patients was detected by qRT-PCR. K The circFLNA expression and miR-766-3p were negatively correlated in the intestinal mucosa from 40 patients with sepsis (Pearson correlation: − 0.5326, $$P \leq 0.0004$$, R2 = 0.2837) Furthermore, we performed dual-luciferase assays to determine whether circFLNA directly binds to miR-766-3p. Luciferase reporter plasmids containing the complementary seed sequence of circFLNA at the 3′-UTR of circFLNA were constructed (Fig. 5E). The results showed that co-transfection with WT circFLNA vector and miR-766-3p mimics significantly reduced luciferase activity, but not when MUT circFLNA vector was transfected in HIEC-6 cells (Fig. 5F). Previous work has demonstrated that miRNAs regulate mRNA translation in an Argonaute 2 (AGO2)-dependent manner [37]. Therefore, a RIP assay for AGO2 in HIEC-6 cells was performed to confirm the direct binding between miR-766-3p and circFLNA. Relative to IgG immunoprecipitation, circFLNA and miR-766-3p were both enriched in AGO2 immunoprecipitation, indicating that circFLNA is involved in miR-766-3p-mediated mRNA translation (Fig. 5G and H). Additionally, we found that the expression of miR-766-3p decreased following treatment with LPS and could be restored by circFLNA interference (Fig. 5I). We detected the expression of miR-766-3p in the intestinal mucosae obtained from 20 patients with sepsis and 23 non-sepsis patients by qRT-PCR. The results showed that miR-766-3p expression was significantly reduced in patients with sepsis compared with that in non-septic patients (Fig. 5J). We also found that miR-766-3p was negatively correlated with circFLNA in the intestinal mucosae of patients with sepsis ($$n = 40$$, Pearson correlation: -0.5326, $$P \leq 0.0004$$, R2 = 0.2837) (Fig. 5K). In conclusion, circFLNA acts as a miRNA sponge for miR-766-3p in sepsis.
## miR-766-3p inhibits apoptosis and inflammation in LPS-treated HIEC-6 cells by directly targeting Fas
As mentioned above, *Fas is* a potential target gene of miR-766-3p. Western blot assays showed that overexpression of miR-766-3p prevented LPS-induced upregulation of Fas (Fig. 6A). To confirm whether Fas was directly targeted by miR-766-3p, the 3′-UTR of Fas mRNA containing a complementary binding site to miR-766-3p was cloned into a luciferase reporter plasmid (Fig. 6B). The luciferase reporter assay showed that co-transfection with miR-766-3p mimics significantly decreased the luciferase activity of the WT Fas 3′-UTR reporter but not the MUT Fas 3'-UTR reporter in HIEC-6 cells (Fig. 6C). These results indicate that *Fas is* a direct target of miR-766-3p. As shown in Fig. 6D, miR-766-3p mimics significantly reduced the mRNA expression of Fas, which was restored after transfection with lentivirus overexpressing Fas in LPS-treated HIEC-6 cells. Moreover, we found that restoration of Fas expression significantly deteriorated the effect of miR-766-3p mimics on apoptosis (Fig. 6E and F) and inflammation factor levels (IL-6, IL-1β, and TNF-ɑ) in LPS-treated HIEC-6 cells (Fig. 6G). Taken together, these findings indicate that miR-766-3p inhibits apoptosis and inflammation in LPS-treated HIEC-6 cells by directly targeting Fas. Fig. 6miR-766-3p inhibits apoptosis and inflammation by directly targeting Fas in LPS-treated HIEC-6 cells. A The expression of Fas was detected by western blotting in LPS-treated HIEC-6 cells transfected with miR-766-3p mimics or NC. B Schematic diagram showing that the 3′-UTR of Fas mRNA contains a complementary site for the seed region of miR-766-3p. The mutant sequence was used as a negative control for the luciferase reporter assay. C Dual-luciferase reporter assays were performed to detect the influence of miR-766-3p mimics on the luciferase activities of Fas 3′-UTR WT and MUT reporter genes. D qRT-PCR assays were performed to detect the expression of Fas in LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing miR-766-3p mimics or Fas. E and F *Cell apoptosis* was evaluated by flow cytometry in LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing miR-766-3p mimics or Fas. G Concentrations of IL-6, IL-1β, and TNF-α in supernatants of LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing miR-766-3p mimics or Fas were measured by ELISA. Values are shown as mean ± standard deviation. NS not significant. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## CircFLNA promotes Fas-associated apoptosis by targeting miR-766-3p
Given that both circFLNA and miR-766-3p target Fas in the apoptosis and inflammation of LPS-treated HIEC-6 cells (Figs. 4 and 6), we hypothesized that circFLNA might exert its biological effect on Fas by sponging miR-766-3p. Both qRT-PCR and western blot assays showed that co-transfection with the miR-766-3p inhibitor partially abrogated the downregulation of Fas induced by circFLNA interference in LPS-treated HIEC-6 cells (Fig. 7A and B). The Fas-associated apoptosis pathway begins with FasL binding to Fas, and then the adaptor protein, Fas-associated death domain protein (FADD), recruits and activates caspase-8. Consequently, caspase-3 is activated, which contributes to the activation of the protease cascade, leading to apoptosis [38, 39]. In this study, we found that the protein levels of FADD, cleaved caspase-8, and cleaved caspase-3 were decreased after interference with circFLNA, and this effect was blocked by miR-766-3p inhibitor (Fig. 7B). Subsequently, we demonstrated that inhibition of miR-766-3p impaired the si-circFLNA-mediated inhibition of apoptosis and inflammation factor levels (IL-6, IL-1β, and TNF-α) in LPS-treated HIEC-6 cells (Fig. 7C and D). Collectively, these results reveal that circFLNA enhanced Fas expression by targeting miR-766-3p in LPS-treated HIEC-6 cells, followed by increased Fas-associated apoptosis. Fig. 7circFLNA promotes Fas-associated apoptosis by targeting miR-766-3p. A and B Downregulation of Fas mRNA (A) and apoptosis-associated proteins (B) in LPS-treated HIEC-6 cells transfected with si-circFLNA#1 was partially reversed by inhibition of miR-766-3p. C Apoptosis was evaluated by flow cytometry in LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing si-circFLNA#1 or miR-766-3p inhibitor. Histograms represent the proportion of apoptotic cells. D Concentrations of IL-6, IL-1β, and TNF-α in supernatants of LPS-treated HIEC-6 cells that were co-transfected with adenovirus expressing si-circFLNA#1 or miR-766-3p inhibitor were measured by ELISA. Values are shown as mean ± standard deviation. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## CircFLNA interference ameliorates intestinal epithelial injury and inflammation by regulating miR-766-3p in vivo
To elucidate the roles of circFLNA and miR-766-3p in intestinal epithelial injury during sepsis in vivo, a CLP model was constructed with C57BL/6 mice. H&E staining was utilized to detect histopathological changes in the intestinal epithelium, and representative images of each group are shown in Fig. 8A. Chiu’s intestinal injury score was used to quantify the degree of histological injury (Fig. 8B). The images showed that the injection of si-circFLNA ameliorated intestinal injury, and the CLP + si-circFLNA + inhibitor-NC group exhibited a intestinal epithelial structure similar to that of the sham group. Both groups did not show tissue edema, mucosal atrophy and necrosis, villous rupture, or mucus thinning, which appeared in the other four groups. Compared to the CLP + si-circFLNA + inhibitor-NC group, the CLP + si-circFLNA + miR-766-3p inhibitor group exhibited severe intestinal injury. The results showed that the injection of the miR-766-3p inhibitor impaired the effect of si-circFLNA on intestinal injury. Accordingly, the CLP + si-NC + miR-766-3p inhibitor group exhibited more obvious mucosal necrosis, shedding, and villous rupture than the other groups did. Fig. 8Inhibition of circFLNA ameliorates intestinal epithelial injury and inflammation by regulating miR-766-3p in vivo. A Representative images of H&E staining indicate that injection of miR-766-3p inhibitor impaired the improvement effect of si-circFLNA on intestinal injury at 48 h after CLP (H&E staining 200 ×). B Histological injury of the intestines from each group was assessed using Chiu’s intestinal injury score. C, D, and E Levels of D-lactic acid, DAO, and FD40 in the serum of mice from each group. F, G, and H Levels of inflammatory factors, such as IL-6, TNF-α, and IL-1β, in the intestinal mucosa of mice were measured by ELISA. Kaplan–Meier survival curves and log-rank tests were used to analyze the survival rates of mice from each group. Values are shown as mean ± standard deviation. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$ Furthermore, to study the impact of circFLNA and miR-766-3p on intestinal mucosal permeability, the levels of D-lactic acid, DAO, and FD-40 were detected in serum samples obtained from mice 48 h after CLP. The levels of D-lactic acid, DAO, and FD-40 in mouse serum were significantly downregulated after injection of si-circFLNA, whereas injection of miR-766-3p inhibitor impaired the improvement effect of si-circFLNA on intestinal mucosal permeability (Fig. 8C, D, and E). To determine the impact of circFLNA and miR-766-3p on inflammation of the intestinal mucosa, the levels of TNF-α, IL-6, and IL-1β were measured 48 h after CLP. The levels of TNF-α, IL-6, and IL-1β in the intestinal mucosa of mice were significantly decreased after injection of si-circFLNA, whereas injection of miR-766-3p inhibitor blocked the effect of si-circFLNA on intestinal inflammation (Fig. 8F, G, and H). In addition, the Kaplan–Meier survival curves of mice in each group for 0–7 days post CLP surgery were plotted (Fig. 8I). The mice in the sham group survived for 7 days. The survival rate was not statistically different between the sham group and the CLP + si-circFLNA + inhibitor-NC group pre-treated with si-circFLNA ($100\%$ vs. $80\%$, $$P \leq 0.073$$). Compared to the CLP + si-circFLNA + inhibitor-NC group, the CLP + si-circFLNA + miR-766-3p inhibitor group exhibited a lower survival rate ($80\%$ vs. $46.7\%$, $$P \leq 0.047$$). The lowest survival rate was observed in CLP + si-NC + miR-766-3p inhibitor group. The results showed that pre-treatment with the miR-766-3p inhibitor reduced the effect of si-circFLNA on survival. These findings suggest that interference with circFLNA ameliorates intestinal epithelial injury and inflammation by regulating miR-766-3p in vivo.
## CircFLNA promotes Fas-associated apoptosis by targeting miR-766-3p in vivo
To elucidate whether circFLNA and miR-766-3p exert their biological effects depending on Fas in the CLP model, we performed IHC staining to detect Fas expression in the intestinal epithelium. Representative images from each group are shown in Fig. 9A. CLP upregulated the number of Fas-positive cells in the intestinal epithelium. The number of Fas-positive cells was not different between the sham group and the CLP + si-circFLNA + inhibitor-NC group pre-treated with si-circFLNA. The number of Fas-positive cells was higher in the CLP + si-circFLNA + inhibitor-NC group than in the CLP + si-circFLNA + miR-766-3p inhibitor group. The highest expression of Fas was observed in the CLP + si-NC + miR-766-3p inhibitor group. These results show that interference with circFLNA could reduce the expression of Fas, which could be restored by the miR-766-3p inhibitor. It has been reported that Fas-induced apoptosis is involved in epithelial cell loss in the gut [40]. This led us to perform a TUNEL assay to detect the apoptosis rate of the intestinal epithelium (Fig. 9B and C). The images show that the apoptosis rate of the intestinal epithelium was in line with the Fas level. The execution of CLP increased the apoptosis rate of the intestinal epithelium, which could be restored by interference with circFLNA, but worsened when pre-treated with miR-766-3p inhibitor. In addition, western blotting was performed to detect the proteins involved in the Fas/FasL signaling pathway. The expression of Fas, FADD, cleaved caspase 8, cleaved caspase 3, occludin, and ZO-1 proteins are shown in Fig. 9D and E. We found that the protein levels of Fas, FADD, cleaved caspase-8, and cleaved caspase-3 in the intestinal mucosa were decreased after interference with circFLNA, and this effect was blocked by miR-766-3p inhibitor. As important components of the tight junction of the intestinal epithelium, the expression of occludin and ZO-1 is reduced when apoptosis occurs in the intestinal epithelium [41]. In this study, the protein levels of occludin and ZO-1 in the intestinal mucosa were increased after interference with circFLNA and decreased after injection of the miR-766-3p inhibitor. These results indicate that circFLNA promotes Fas-associated apoptosis by targeting miR-766-3p in vivo. Fig. 9CircFLNA promotes Fas-associated apoptosis by targeting miR-766-3p in vivo. A The expression of Fas in the mouse intestinal epithelium was detected by immunohistochemical staining. B and C TUNEL assay was performed to detect apoptosis in the mouse intestinal epithelium. Note: Green (FITC that reflects apoptotic cells); blue (DAPI that reflects total cells). Scale bars: 250 µm. D and E The levels of occludin, ZO-1, and Fas-associated apoptotic proteins in mouse intestinal epithelium were detected by western blot analysis. Values are shown as mean ± standard deviation. * $P \leq 0.05$, **$P \leq 0.01$, and ***$P \leq 0.001$
## Discussion
Intestinal epithelial integrity prevents harmful substances, such as bacteria and toxins, from passing through the intestinal mucosa and entering the blood circulation. Increased apoptosis of intestinal epithelial cells induced by sepsis exacerbates the progression of sepsis [33]. Therefore, exploring the molecular mechanisms of intestinal epithelial apoptosis and identifying promising targets will contribute to the prevention and treatment of abdominal sepsis. In this study, we obtained purified epithelial cells from intestinal crypts using LCM and found that circFLNA was upregulated in the intestinal epithelium after intestinal perforation-induced abdominal sepsis. CircFLNA promoted apoptosis and inflammation of intestinal epithelial cells in both cultured HIEC-6 cells and CLP mouse models. The molecular mechanism analysis of circFLNA revealed that it enhanced the Fas-mediated apoptosis signaling pathway by sponging miR-766-3p. These findings indicated that the circFLNA/miR-766-3p/*Fas axis* plays a significant role in the pathogenesis of intestinal epithelial injury (Fig. 10).Fig. 10Schematic diagram showing that circFLNA promotes intestinal epithelial apoptosis and inflammation through the miR-766-3p/Fas axis. circFLNA acts as a competing endogenous RNA to regulate miR-766-3p, resulting in the enhancement of Fas expression, thereby attenuating intestinal barrier function by promoting intestinal epithelial apoptosis and inflammatory response Diverse subtypes of IECs present in the intestinal epithelium consist of intestinal stem, Paneth, goblet, enteroendocrine, tuft, and microfold cells, and absorptive enterocytes, and are distributed along the crypt–villus axis [42]. In contrast to other cells, ISCs and Paneth cells are located in the intestinal crypts rather than in the villi. ISCs contribute to the constant renewal of the intestinal epithelium and regulate intestinal homeostasis by maintaining stemness [43]. In addition, Paneth cells can resist the invasion of potential pathogens by self-generated antibiotics and participate in the innate antimicrobial response [44]. Despite the vital role of intestinal crypts, few studies have elucidated the ectopic expression of circRNAs in subtype cells of intestinal crypts in abdominal sepsis. In this study, we performed LCM in four patients with intestinal perforation-induced abdominal sepsis and non-sepsis patients to obtain the specific cell types of the crypt epithelium from the entire intestinal mucosa. CircFLNA was found to be upregulated in the intestinal epithelium exposed to abdominal sepsis using circRNA microarray and was validated in patients with intestinal perforation and CLP mouse models.
Many studies have revealed that circRNAs can function as sponges, interact with RNA-binding proteins (RBPs), and regulate gene translation [10]. As the primary function, circRNA decreases the expression of miRNA by sponging it and thereby increases the translation of mRNA, which is targeted by miRNA [11]. circRNAs have been shown to be involved in inflammation, immunosuppression, coagulation dysfunction, and organ dysfunction during sepsis [45]. In this study, we found that circFLNA was upregulated in LPS-treated HIEC-6 cells and promoted apoptosis and inflammation by increasing the expression of the apoptosis-related *Fas* gene. Bioinformatic analyses showed that circFLNA and Fas bind to miR-766-3p at the same sequence. Consequently, we speculated that circFLNA promoted apoptosis and inflammation of HIEC-6 cells through the miR-766-3p/Fas axis. Several assays, including RNA-FISH, RIP, and dual-luciferase reporter assays, demonstrated that the majority of circRNAs were distributed in the cytoplasm and bound with miR-766-3p as an miRNA sponge. qRT-PCR assay showed that miR-766-3p was downregulated in HIEC-6 cells treated with LPS and decreased in the intestinal epithelium of individuals with abdominal sepsis compared with that in non-sepsis samples. miR-766-3p suppresses inflammation in human rheumatoid arthritis and attenuates oxidative injury of chondrocytes but has not been studied in sepsis [46, 47]. The functional assay showed that miR-766-3p could reverse the pro-apoptotic and pro-inflammatory roles of Fas. In addition, we found that circFLNA interference decreased the apoptosis rate and inflammation induced by the miR-766-3p inhibitor in LPS-treated HIEC-6 cells. Taken together, we demonstrated that circFLNA functions as a sponge of miR-766-3p to promote apoptosis and inflammation by upregulating Fas expression in vivo.
The integrity of the intestinal epithelium is critical for defense against environmental and microbial attacks from the gut. Apoptosis of the intestinal epithelium in both patients with abdominal sepsis and mouse CLP models contributed to increased permeability of the intestine and translocation of bacteria from the enteric cavity to the blood [33]. Fas is a member of the TNF receptor family and participates in the extrinsic pathway of apoptosis upon activation by FasL [48]. In the mouse CLP model, circFLNA aggravated intestinal injury and inflammatory response through the Fas-mediated apoptosis pathway by sponging miR-766-3p.
To the best of our knowledge, this is the first study to demonstrate that circFLNA is upregulated in intestinal epithelium during abdominal sepsis using a circRNA microarray. Furthermore, we demonstrated that circFLNA aggravated apoptosis and the inflammatory response through the Fas-mediated apoptosis pathway by sponging miR-766-3p in both LPS-treated HIEC-6 cells and a mouse CLP model. However, the current study has some limitations. First, we validated the expression of circFLNA in only 20 patients with abdominal sepsis and 23 non-sepsis patients because it was challenging to obtain suitable samples. This is not sufficient to analyze the relationship between circFLNA and clinical features, as well as the mortality of individuals with abdominal sepsis. As a result, larger sample sizes are required to validate circFLNA expression. In addition, it would be preferable to examine the expression of circFLNA in serum samples from patients with sepsis to facilitate the early diagnosis of intestinal injury.
## Conclusions
In conclusion, our findings indicate that circFLNA interference can reduce injury and inflammation of the intestinal epithelium. The circFLNA/miR-766-3p/*Fas axis* has potential as a novel therapeutic target for treating intestinal injury in sepsis.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 13 KB)Supplementary file2 (XLSX 22 KB)
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|
---
title: The implication of calf circumference and grip strength in osteoporosis and
bone mineral density among hemodialysis patients
authors:
- Moe Ozawa
- Nobuhito Hirawa
- Tatsuya Haze
- Aiko Haruna
- Rina Kawano
- Shiro Komiya
- Yuki Ohki
- Shota Suzuki
- Yusuke Kobayashi
- Akira Fujiwara
- Sanae Saka
- Masaaki Hanaoka
- Hiroshi Mitsuhashi
- Satoshi Yamaguchi
- Toshimasa Ohnishi
- Kouichi Tamura
journal: Clinical and Experimental Nephrology
year: 2022
pmcid: PMC10023647
doi: 10.1007/s10157-022-02308-8
license: CC BY 4.0
---
# The implication of calf circumference and grip strength in osteoporosis and bone mineral density among hemodialysis patients
## Abstract
### Background
Chronic kidney disease–mineral and bone disorder (CKD–MBD), nutritional status, and uremia management have been emphasized for bone management in hemodialysis patients. Nevertheless, valuable data on the importance of muscle mass in bone management are limited, including whether conventional management alone can prevent osteoporosis. Thus, the importance of muscle mass and strength, independent of the conventional management in osteoporosis prevention among hemodialysis patients, was evaluated.
### Methods
Patients with a history of hemodialysis 6 months or longer were selected. We assessed the risk for osteoporosis associated with calf circumference or grip strength using multivariable adjustment for indices of CKD–MBD, nutrition, and dialysis adequacy. Moreover, the associations between bone mineral density (BMD), calf circumference, grip strength, and bone metabolic markers were also evaluated.
### Results
A total of 136 patients were included. The odds ratios ($95\%$ confidence interval) for osteoporosis at the femoral neck were 1.25 (1.04–1.54, $P \leq 0.05$) and 1.08 (1.00–1.18, $P \leq 0.05$) per 1 cm shorter calf circumference or 1 kg weaker grip strength, respectively. Shorter calf circumference was significantly associated with a lower BMD at the femoral neck and lumbar spine ($P \leq 0.001$). Weaker grip strength was also associated with lower BMD at the femoral neck ($P \leq 0.01$). Calf circumference or grip strength was negatively correlated with bone metabolic marker values.
### Conclusion
Shorter calf circumference or weaker grip strength was associated with osteoporosis risk and lower BMD among hemodialysis patients, independent of the conventional therapies.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10157-022-02308-8.
## Introduction
Osteoporosis is one of the major problems in the aging society, as it not only impairs the quality of life because of fractures but also increases the risk of mortality [1]. The frequency of femoral neck fractures is approximately fivefold to sixfold higher in dialysis patients than in healthy subjects [2]. In addition, hemodialysis patients with fractures have an approximately fourfold higher mortality risk [3]. Patients with chronic kidney disease (CKD) are afflicted with several bone metabolism-related conditions, one of which is the CKD-mineral and bone disorder (CKD–MBD) [4]. Typically, CKD causes secondary hyperparathyroidism with increased fibroblast growth factor 23, parathyroid hormone (PTH), and serum phosphorus (P), which affects bone metabolism. Moreover, uremic substances are detrimental to the bone and reduce elastic mechanical properties [5]. For these disease-specific conditions, the Kidney Disease Improving Global Organization and The European Renal Association–European Dialysis and Transplant Association have indicated the importance of controlling CKD–MBD, nutrition, and dialysis adequacy in the bone management of dialysis patients [6, 7].
On the other hand, sarcopenia, which is an equally important problem in an aging society, is exacerbated by CKD because of uremic toxins, oxidative stress, chronic inflammation, and malnutrition [8]. Approximately, $20\%$ of dialysis patients have been reported to have sarcopenia [9]. Thus, hemodialysis patients have specific bone and muscle pathologies.
Bone mineral density (BMD) management is clinically important in osteoporosis and fracture prevention because osteoporosis is diagnosed through bone mass, and low BMD is a risk factor for fractures in hemodialysis patients [10]. Although previous studies have reported the association between low BMD and loss of appendicular skeletal muscle mass in the general population [11, 12], literature elucidating whether a similar relationship also applies to hemodialysis patients is limited. Furthermore, it is unclear whether managing CKD–MBD, nutrition, and uremia alone is sufficient to prevent osteoporosis in hemodialysis patients. In this study, we investigated whether the association between osteoporosis and muscle mass or strength was independent of conventional management to explore the importance of muscle mass and strength for preventing osteoporosis among hemodialysis patients.
## Study design and participants
This is a single-center, cross-sectional study. We screened patients aged over 20 years, who underwent hemodialysis three times a week at Kamiooka Jinsei Clinic (Yokohama, Japan) between July 2020 and April 2021. 200 individuals, who had a history of hemodialysis longer than 6 months and consented to this study, were selected. Among these 200 candidates, we excluded 64 patients [1] who had a history of lower-limb amputation, paralysis of limbs, use of steroids, or bone metastasis of cancer; [2] who were currently undergoing peritoneal dialysis; [3] who were currently receiving anti-osteoporosis drugs; or [4] whose record included a missing value required in the study. Finally, 136 patients were included in our analysis.
## Bone mineral density
Mean BMD at each position of the lumbar spine (L2–4) and femoral neck was measured by dual-energy X-ray absorptiometry (DXA, Aria Chorale; GE Healthcare Japan Corporation, Tokyo, Japan). For measurement of the lumbar spine, vertebrae with focal changes (i.e., sclerotic changes) or artifacts were excluded. The mean BMD of two or more vertebrae, their T-score, and young adult mean (YAM) were evaluated. Osteoporosis was defined as T-score ≤ − 2.5 in accordance with the definition of the World Health Organization (WHO) [13].
## Muscle mass and strength
We measured calf circumference and grip strength [14]. For calf circumference, the circumference of the thickest part of the lower leg was measured with a measuring tape, and the mean value of both sides was calculated. Grip strength was measured using a digital grip strength system (jammer type, MG-4800; MORITOH Co., Aichi, Japan), with the elbow joint bent at a 90° angle in the sitting position. The measurements were taken four times, alternating left and right twice, and the maximum value was obtained.
## Nutritional indices
We calculated two nutritional indices, Nutritional Risk Index for Japanese Hemodialysis Patients (NRI-JH) and Geriatric Nutritional Risk Index (GNRI), to assess the nutritional state of each participant [15, 16].
## Bone metabolic markers
We measured bone-specific alkaline phosphatase (BALP) and total type I procollagen N-terminal propeptide (P1NP) as bone formation markers and serum tartrate-resistant acid phosphatase 5b (TRACP-5b) as bone resorption marker [17].
## Others
Blood samples were obtained on the first dialysis day of the week, 2 days after the previous dialysis day. The behavioral characteristics and clinical history were obtained using a questionnaire and medical records. For details of our methods, see Supplementary methods
## Statistical analyses
All analyses were performed by EZR on R Commander version 1.55. Significance was defined as $P \leq 0.05$ using two-sided tests.
First, we estimated the odds ratio (OR) for osteoporosis per 1 cm shorter calf circumference or 1 kg weaker grip strength using logistic regression models. Model 1 was unadjusted. Model 2 was adjusted for age, sex, history of diabetes, current smoking status, and habitual alcohol drinking. Model 3 was adjusted for the covariates included in Model 2 as well as serum hemoglobin (Hb), NRI-JH score, intact PTH (iPTH), and Kt/V. Covariates were selected a priori [18, 19]. Since we expected calf circumference and grip strength to be strongly correlated with BMI, we did not select BMI in our main models to avoid multicollinearity. Instead, we analyzed models that included height or DW as sensitivity analyses to ensure that the results did not change when taking body size into account. To confirm the linear relationship between calf circumference or grip strength and the risk for osteoporosis, we estimated adjusted ORs using the covariates in Model 3 between the tertiles based on calf circumference [i.e., Low ≤ 33.4, 33.5 ≤ Medium ≤ 36.5, 36.6 ≤ High (cm)] or grip strength [i.e., Low ≤ 22.3, 22.8 ≤ Medium ≤ 30.9, 31.2 ≤ High (cm)] using the highest group as a reference.
Second, we estimated the Pearson’s product–moment correlation coefficients and $95\%$ confidence interval (CI) for the correlation between BMD and calf circumference or grip strength.
Third, we used multiple linear regression models to calculate standardized β and $95\%$ CI for the association of BMD with calf circumference or grip strength. The linear models were adjusted for the same covariates as in the logistic regression models.
Fourth, we assessed the association between bone metabolic markers and calf circumference, grip strength, or BMD by scatter plotting and correlation tests.
## Sensitivity analyses
We performed the following sensitivity analyses by modifying some of the covariates included in Model 3: [1] additionally adjusted for height or dry weight when assessing calf circumference or grip strength, respectively; [2] used GNRI score instead of NRI-JH score; [3] used serum albumin (Alb) instead of NRI-JH score; [4] used corrected serum calcium (Ca) and P levels instead of iPTH; [5] used CKD–MBD drug (i.e., vitamin D receptor activators, phosphate binders, or calcimimetics) instead of iPTH; [6] used log-transformed hemodialysis duration instead of Kt/V.
## Subgroup analyses by iPTH or sex
To check the heterogeneity in the association between BMD and calf circumference or grip strength by the levels of iPTH, we performed the subgroup analysis between the low- and high-iPTH groups (i.e., iPTH < 147.5 pg/mL, iPTH ≥ 147.5 pg/mL according to the median value) and included multiplicative interaction terms in the regression models. We also performed subgroup analyses of the female and male groups.
## Clinical characteristics
The final analytic sample included 136 patients. Table 1 shows the baseline characteristics. The mean ± standard deviation (SD) of age was 67.4 ± 12.7 years and $25.0\%$ of the subjects were female. The levels of BMD at the femoral neck and lumbar spine were 0.8 ± 0.1 g/cm2 and 1.2 ± 0.3 g/cm2, respectively. The mean calf circumference was 35.3 ± 4.0 cm and the grip strength was 26.9 ± 9.7 kg. Table 1Characteristics of participantsOverall group($$n = 136$$)Clinical and behavioral characteristics Age, years67.4 ± 12.7 Female, n (%)34 (25.0) Height, m1.6 ± 0.1 Dry weight, kg62.6 ± 14.2 BMI *, kg/m223.1 ± 4.4 Current smoking, n (%)16 (11.8) Habitual drinking, n (%)38 (27.9)Hemodialysis condition Hemodialysis duration, years7.5 (3.3, 11.6) Kt/V1.4 ± 0.3Laboratory data Hemoglobin, g/L111.1 ± 10.8 Serum albumin, g/L37.2 ± 3.0 *Corrected serum* calcium †, mmol/L2.3 ± 0.2 Serum phosphorus, mmol/L1.7 ± 0.4 IPTH, pg/mL156.8 ± 84.3Cause of end-stage renal disease Glomerulonephritis, n (%)45 (33.1) Diabetic nephropathy, n (%)58 (42.6) Nephrosclerosis, n (%)15 (11.0) Polycystic kidney disease, n (%)8 (5.9) Unknown or others, n (%)10 (7.4)History of complications Diabetes, n (%)63 (46.3) Parathyroidectomy, n (%)5 (3.7) CVD event ‡, n (%)47 (34.6)Medications Vitamin D receptor activators, n (%)101 (74.3) Phosphate binders, n (%)119 (87.5) Calcimimetics, n (%)49 (36.0)Bone mass Femoral neck BMD, g/cm20.8 ± 0.1 T-score − 1.9 ± 1.1 YAM, %77.3 ± 14.0 Lumbar spine BMD, g/cm21.2 ± 0.3 T-score0.3 ± 1.7 YAM, %104.9 ± 23.3Measurements of muscle mass and strength Calf circumference, cm35.3 ± 4.0 Grip strength, kg26.9 ± 9.7Nutritional indices NRI-JH score3.1 ± 3.0 GNRI score104.8 ± 10.7Values for continuous variables are expressed as mean ± standard deviation for unskewed variables and median (25th and 75th percentiles) for skewed variables. Values for categorical variables are expressed as percentages. BMD was calculated using dual-energy X-ray absorptiometry. T-score was calculated as the standard deviation from the mean BMD of Japanese young adults aged 20–29 years for the femoral neck and 20–44 years for the lumbar spine. YAM was calculated as a percentage of the subject’s BMD relative to the average BMD value of Japanese young adults*Calculated as follows: dry weight / (height, m)2†Corrected as follows (expressed in mmol/L): calcium (g/dL) + [4.0 − serum albumin (g/dL)], if albumin < 4.0 g/dL‡Defined as angina requiring cardiac catheterization, myocardial infarction, or strokeBMD bone mineral density, BMI body mass index, CVD cardiovascular disease, DM diabetes mellitus, GNRI Geriatric Nutritional Risk Index, iPTH intact parathyroid hormone, NRI-JH Nutritional Risk Index for Japanese Hemodialysis Patients, YAM young adult mean
## Risk for osteoporosis among hemodialysis patients with decreased calf circumference or grip strength
Forty-five patients, consisting of $\frac{23}{34}$ ($67.6\%$) women and $\frac{22}{102}$ ($21.6\%$) men, were diagnosed with osteoporosis at the femoral neck. At the lumbar spine, six patients (all women) were diagnosed with osteoporosis. As shown in Table 2, when the risk for osteoporosis diagnosed at the femoral neck was assessed, the estimated OR ($95\%$ CI) for osteoporosis per 1 cm shorter calf circumference was 1.25 (1.04–1.54, $P \leq 0.05$). The estimated OR for osteoporosis per 1 kg weaker grip strength was 1.08 (1.00–1.18, $P \leq 0.05$). In the analysis with calf circumference as a variable (model 3), being female was a significant risk factor for osteoporosis. Furthermore, when calf circumference was divided into three groups according to the tertiles in Fig. 1, the shortest group (Low) and the intermediate group (Medium) were at significantly higher risk for osteoporosis compared to the longest group (High) [OR 6.38 (1.46–32.53), $P \leq 0.05$; OR 3.92 (1.07–17.03), $P \leq 0.05$, respectively]. For grip strength, although not significant, the Low and Medium groups showed higher ORs compared to the High group in terms of the point estimates [OR 4.80 (0.96–28.60), $$P \leq 0.06$$; OR 3.70 (0.96–18.38), $$P \leq 0.06$$, respectively]. No evidence was found to suggest that there was a non-linear relationship between decreased calf circumference or grip strength and the risk for osteoporosis. Table 2Risk for osteoporosis diagnosed at the femoral neck with decreased calf circumference or grip strengthOdds ratio for osteoporosisCalf circumferenceper 1 cm shorterGrip strengthper 1 kg weakerModel 1 (unadjusted)1.36 (1.20, 1.57) **1.14 (1.09, 1.21) **Model 21.22 (1.03, 1.46) *1.08 (1.01, 1.17) *Model 31.25 (1.04, 1.54) *1.08 (1.00, 1.18) *Unadjusted and adjusted odds ratios ($95\%$ confidence interval) for osteoporosis associated with one-unit decrement of calf circumference or grip strength are shown. Osteoporosis was defined as T-score ≤ − 2.5 at the femoral neck. Model 1 was unadjusted. Model 2 was adjusted for age, sex, history of diabetes, current smoking status, and habitual alcohol drinking. Model 3 was adjusted for the covariates included in Model 2 as well as hemoglobin, NRI-JH score, iPTH, and Kt/V. Exposures were included in models separately*$P \leq 0.05$; **$P \leq 0.001$iPTH intact parathyroid hormone, NRI-JH Nutritional Risk Index for Japanese Hemodialysis PatientsFig. 1Risk for osteoporosis in tertiles based on calf circumference. Adjusted estimated odds ratios ($95\%$ confidence intervals) for osteoporosis in tertiles based on calf circumference [i.e., Low ≤ 33.4 vs. 33.5 ≤ Medium ≤ 36.5 vs. 36.6 ≤ High (cm)] are shown. The estimated odds ratios were adjusted for age, sex, history of diabetes, current smoking status, habitual alcohol drinking, hemoglobin, NRI-JH score, iPTH, and Kt/V. iPTH = intact parathyroid hormone; NRI-JH = nutritional risk index for *Japanese hemodialysis* patients. * $P \leq 0.05$
## Association between BMD and muscle mass and strength
As shown in Fig. 2, calf circumference was significantly correlated with BMD at both the femoral neck [r ($95\%$ CI) = 0.53 (0.40–0.64), $P \leq 0.001$] and the lumbar spine [0.41 (0.26–0.54), $P \leq 0.001$]. Similarly, grip strength showed correlations with BMD at both the femoral neck [0.56 (0.43–0.66), $P \leq 0.001$] and the lumbar spine [0.32 (0.16–0.46), $P \leq 0.001$] (Fig. 3).Fig. 2Scatter plots of the relationships between BMD and calf circumference. Scatter plots of the relationships between BMD at the a femoral neck or b lumbar spine and calf circumference among hemodialysis patients are shown. Each circle represents an individual value. The black lines represent simple linear regression models. The P values were calculated for Pearson’s product–moment correlation coefficients (r-values). BMD bone mineral densityFig. 3Scatter plots of the relationships between BMD and grip strength. Scatter plots of the relationships between BMD at the a femoral neck or b lumbar spine and grip strength among hemodialysis patients are shown. Each circle represents an individual value. The black lines represent simple linear regression models. The P-values were calculated for Pearson’s product–moment correlation coefficients (r-values). BMD = bone mineral density In the unadjusted model of linear regression analyses (Table 3), calf circumference and grip strength were associated with BMD at both the femoral neck and lumbar spine. After multivariable adjustment including the indices for managing CKD–MBD, nutrition, and dialysis adequacy (i.e., iPTH, NRI-JH score, and Kt/V), shorter calf circumference was significantly associated with lower BMD at both the femoral neck [β ($95\%$ CI) = 0.36 (0.18–0.55), $P \leq 0.001$] and the lumbar spine [0.37 (0.16 to 0.56), $P \leq 0.001$]. Weaker grip strength was also associated with lower BMD at the femoral neck [0.32 (0.11–0.53), $P \leq 0.01$]. Women, non-DM patients, and older age showed significant differences in the association with BMD in our models, while iPTH, NRI-JH score, and Kt/V did not. Table 3Association between BMD and calf circumference or grip strength among hemodialysis patientsStandardized bCalf circumferenceGrip strengthFemoral neck Model 1 (unadjusted)0.53 (0.38, 0.67) **0.55 (0.41, 0.70) ** Model 20.37 (0.20, 0.54) **0.36 (0.16, 0.56) ** Model 30.36 (0.18, 0.55) **0.32 (0.11, 0.53) *Lumbar spine Model 1 (unadjusted)0.41 (0.25, 0.56) **0.32 (0.16, 0.48) ** Model 20.36 (0.18, 0.54) **0.15 (-0.07, 0.36) Model 30.37 (0.17, 0.56) **0.09 (-0.14, 0.32)Unadjusted and adjusted standardized b values ($95\%$ confidence interval) for BMD associated with calf circumference or grip strength are shown. Model 1 was unadjusted. Model 2 was adjusted for age, sex, history of diabetes, current smoking status, and habitual alcohol drinking. Model 3 was adjusted for the covariates included in Model 2 as well as hemoglobin, NRI-JH score, iPTH, and Kt/V. Exposures were included in the models separately*$P \leq 0.01$; **$P \leq 0.001$BMD bone mineral density, iPTH intact parathyroid hormone, NRI-JH Nutritional Risk Index for Japanese Hemodialysis Patients
## Sensitivity analyses and subgroup analyses
We conducted six sensitivity analyses including body size correction, other nutritional indices, and CKD–MBD drug, the results of which were similar in terms of the point estimates (Supplementary Tables S1 and S2).
Furthermore, in the subgroup analysis by iPTH levels, calf circumference was significantly associated with BMD in both groups (Supplementary Table S3). No evidence was found to suggest that iPTH levels interacted in the association between BMD and calf circumference or grip strength (all P for interaction > 0.61). Subgroup analysis by sex also showed a significant association between calf circumference and BMD in both groups (Supplementary Table S4).
## Correlations between bone metabolic markers and calf circumference or grip strength
The scatter plots of bone metabolic markers and calf circumference or grip strength are shown in Fig. 4 and Supplementary Fig. S1, respectively. Calf circumference was negatively correlated with log-transformed BALP [r ($95\%$ CI) = − 0.32 (− 0.47 to − 0.16), $P \leq 0.001$], total P1NP [− 0.21 (− 0.36 to − 0.04), $P \leq 0.05$], or TRACP-5b [− 0.31 (− 0.45 to − 0.14), $P \leq 0.001$]. Grip strength was also inversely correlated with log-transformed BALP [− 0.33 (− 0.47 to − 0.17), $P \leq 0.001$], total P1NP [− 0.28 (− 0.43 to − 0.12), $P \leq 0.001$], or TRACP-5b [− 0.33 (− 0.48 to − 0.18), $P \leq 0.001$]. Furthermore, in subgroup analysis using the median iPTH as a cutoff, calf circumference was negatively correlated with log-transformed BALP and TRACP5b in both subgroups (Supplementary Table S5). These metabolic markers were also inversely correlated with BMD (Supplementary Fig. S2).Fig. 4Scatter plots of the relationships between bone metabolic markers and calf circumference. Scatter plots of the relationships between log-transformed a BALP, b total P1NP, or c TRACP-5b and calf circumference are shown. Each circle represents an individual value. The black lines represent simple linear regression models. The P values were calculated for Pearson’s product–moment correlation coefficients (r-values). BALP = bone-specific alkaline phosphatase; P1NP = type I procollagen N-terminal propeptide; TRACP-5b = tartrate-resistant acid phosphatase 5b
## Discussion
In the general Japanese population, the prevalence of osteoporosis diagnosed at the femoral neck is reported to be $22.2\%$ in women and $7.0\%$ in men aged 60–69 years [20]. In this study of hemodialysis patients, osteoporosis diagnosed was approximately three times higher than in the general population. Furthermore, in hemodialysis patients, calf circumference and grip strength were significantly associated with the risk for osteoporosis. These associations were independent of the nutritional state (i.e., NRI-JH, GNRI, and serum Alb), CKD–MBD indices (i.e., serum Ca, serum P, iPTH, and use of vitamin D, phosphate binders, or calcimimetics), and dialysis adequacy (i.e., Kt/V). Calf circumference is related to appendicular skeletal muscle mass, and calf circumference and grip strength are used as surrogate markers for the diagnosis of sarcopenia [14, 21]. Hence, our findings may suggest a close association between sarcopenia and osteoporosis in hemodialysis patients.
Similarly, in a multivariate analysis, shorter calf circumference or weaker grip strength were also associated with low BMD. We assessed the heterogeneity in these associations between sexes [22], and the association between muscle mass and BMD was significant in both sexes. We also demonstrated that calf circumference and BMD were inversely correlated with both bone formation and resorption markers. In this study, lower muscle mass and BMD were associated with a higher bone turnover as elevations in both bone formation and resorption markers indicate increased bone metabolism [23]. Because iPTH promotes bone resorption and affects bone rotation [4], this association was also examined in the subgroup analysis of iPTH. The results showed that calf circumference was significantly correlated with BALP or TRACP-5b in both groups. Several reports have already shown that the elevation of these resorption and formation markers was strongly associated with a rapid decrease in BMD and risk for fractures [24]. Thus, our findings indicate that the loss of muscle mass may be associated with a rapid BMD decline due to a high bone turnover and thus require early intervention.
Recently, it has been clarified that muscle can endocrinologically affect bone metabolism via myokines. Myostatin, which promotes bone resorption, is negatively correlated with muscle mass, and elevated myostatin levels may be related to lower BMD in sarcopenia [25]. Additionally, irisin, whose secretion is increased through exercise, inhibits bone resorption via the suppression of receptor activator of nuclear factor κB ligand expression [26]. Therefore, maintaining muscle mass may suppress bone resorption and preserve BMD through these myokines.
In a cross-sectional study of hemodialysis patients, Tominaga et al. and Ito et al. showed that grip strength was not associated with BMD, whereas muscle mass was significantly associated with BMD [21, 27]. In a study of 131 patients undergoing hemodialysis, Lee et al. reported that upper arm circumference and skeletal muscle mass index were lower in the osteopenia and osteoporosis groups than in the normal group [28]. Although these previous studies reported the association between low muscle mass or strength and low BMD or osteoporosis in hemodialysis patients, the association has not been adequately investigated, adjusting for factors (CKD–MBD, nutrition, and uremia) that have traditionally been considered risk factors for osteoporosis. Our results clearly demonstrated this association in hemodialysis patients and indicate the possibility that maintenance or appendicular muscle training may be needed to prevent osteoporosis, in addition to the conventional management of CKD–MBD, nutrition, and dialysis adequacy. Furthermore, we found that lower muscle mass was associated with higher levels of bone metabolic markers and lower BMD in hemodialysis patients. Because dialysis patients are sometimes unable to perform sufficient exercise because of their complications (e.g., heart failure and cerebral infarction), they will have an increased risk of a low BMD. Therefore, a bone resorption inhibitor may be useful for patients who have difficulty performing proper exercise.
In our multiple regression models, the association between grip strength and BMD at the lumbar spine did not reach statistical significance. Bone mass at the lumbar spine of hemodialysis patients was high, despite the exclusion of spines with localized changes on imaging findings. Aortic calcification, a common complication among dialysis patients, affect BMD values at the lumbar spine assessed using DXA [29]. Thus, the femoral neck may be more suitable than the lumbar spine in the assessment of BMD in hemodialysis patients. Also, older age, women, and non-DM patients were significantly associated with osteoporosis risk or lower BMD in our multivariate model. Aging and women are known risk factors for osteoporosis and lower BMD [30], consistent with our results. It has been reported that osteocalcin levels, a marker of bone formation, are decreased in DM patients, and that osteocalcin increases as blood glucose improves [31]. On the other hand, large studies reported that DM patients have rather higher BMD than non-DM patients [32, 33]. The mechanism by which BMD is higher in patients with DM is still unknown; however, it is hypothesized that insulin promotes bone formation by interacting with IGF-1 receptor [34]. In the present study, decreased calf circumference or grip strength was associated with the risk for osteoporosis or lower BMD, even when adjusted for the presence of DM.
This study has several limitations. First, we could not measure muscle mass directly, which might be helpful for the precise diagnosis of sarcopenia. Second, the causal relationship between muscle mass and BMD could not be revealed because of the observational nature of this study. However, it is reasonable to assume that exercise therapy may improve BMD in dialysis patients because it has already been shown that adequate exercise increases BMD in healthy subjects [35]. Prospective interventional studies are needed to confirm this in the future.
## Conclusion
Shorter calf circumference and weaker grip strength were associated with osteoporosis risk and lower BMD in hemodialysis patients, independent of the management of CKD–MBD, nutrition status, and dialysis adequacy. In addition, BMD decline with loss of muscle mass may require earlier intervention due to higher bone turnover. To prevent osteoporosis in hemodialysis patients, clinicians should pay attention to calf circumference and grip strength, in addition to the conventional management.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 654 KB)
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|
---
title: TCR repertoire landscape reveals macrophage-mediated clone deletion in endotoxin
tolerance
authors:
- Juanjuan Zhao
- Li Jia
- YiJing Tao
- Xu Zhao
- Jing Yang
- Yanxin Lu
- Yaping Yan
- Ling Mao
- Lin Hu
- Jia Lu
- MengMeng Guo
- Chao Chen
- Ya Zhou
- Zhenke Wen
- Zhixu He
- Lin Xu
journal: Inflammation Research
year: 2023
pmcid: PMC10023648
doi: 10.1007/s00011-022-01685-w
license: CC BY 4.0
---
# TCR repertoire landscape reveals macrophage-mediated clone deletion in endotoxin tolerance
## Abstract
### Background
Endotoxin tolerance (ET) is a protective mechanism in the process of sepsis, septic shock, and their sequelae including uncontrolled inflammation. Accumulating evidence has shown that peripheral T cells contribute to the induction of ET. However, what and how T-cell development contributes to ET inductions remain unclear.
### Methods
Mice were intraperitoneally injected with LPS at a concentration of 5 mg/kg to establish an LPS tolerance model and were divided into two groups: a group examined 72 h after LPS injection (72-h group) and a group examined 8 days after LPS injection (8-day group). Injection of PBS was used as a control. We performed high-throughput sequencing to analyze the characteristics and changes of CD4+SP TCRβ CDR3 repertoires with respect to V direct to J rearrangement during the ET induction. Moreover, the proportion and proliferation, as well as surface molecules such as CD80 and CD86, of F$\frac{4}{80}$+ macrophages were analyzed using FCM. Furthermore, ACT assay was designed and administered by the tail vein into murine LPS-induced mouse model to evaluate the role of F$\frac{4}{80}$+ macrophages on the development of CD4+SP thymocytes in ET condition.
### Results
We found that the frequency and characteristics of the TCRβ chain CDR3 changed obviously under condition of ET, indicating the occurrence of TCR rearrangement and thymocyte diversification. Moreover, the absolute numbers of F$\frac{4}{80}$+ macrophages, but not other APCs, were increased in thymic medulla at 72-h group, accompanied by the elevated function-related molecules of F$\frac{4}{80}$+ macrophages. Furthermore, adoptively transferred OVA332-339 peptide-loaded macrophages into Rag-1−/− mice induced the clone deletion of OVA-specific CD4+SP, thereby ameliorating the pathology in lung tissue in LPS challenge.
### Conclusions
These data reveal that the frequency and characteristics of the TCRβ chain CDR3 undergo dynamic programming under conditions of LPS tolerance. Furthermore, the peripheral macrophages may be a key factor which carry peripheral antigen to thymic medulla and affect the negative selection of T-cell population, thereby contributing to the formation of ET. These results suggest that the clone selection in thymus in ET may confer protection against microbial sepsis.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00011-022-01685-w.
## Introduction
Sepsis, septic shock, and their sequelae including uncontrolled inflammation are the leading causes of death in intensive care units, with limited therapeutic options. Pathophysiological adaptations to regulate over-exuberant inflammation serve as an important mechanism for host protection against endotoxin shock. One of these protective mechanisms is endotoxin tolerance (ET) [1, 2]. Long-term exposure to lipopolysaccharide (LPS) or injection of sub-lethal doses of LPS in animals can induce tolerance of endotoxin that reprograms the inflammatory response, resulting in cells or organisms entering into a transient unresponsive state where they are unable to respond to further challenges with endotoxin. In recent years, the studies about ET mainly focus on the innate cells, such as macrophages, and innate molecule TLR4-related signaling pathways [3]. Meanwhile, some factors, such as TLR2, Gi protein, and PKC, are also responsible for the induction of ET. Interesting, a growing number of studies reported that adaptive immune cells, such as T cells, were also contributed to ET induction. T-cell depletion mediates the decreased sensitivity to LPS in TGF-β−/− mice or SCID mice, which prolongs the survival of several autoimmune disease mice [4, 5]. Moreover, pathologic CD4+T cells contribute to exaggerated immune activation toward LPS challenge that impairs the induction of ET [6]. These research works supported that T cells play a critical role for the formation of ET.
It is deserved to note that the development of T cells is closely related to the formation of central tolerance and contributes to the process of various diseases. For example, the proportion of thymocytes is abnormal under conditions of Leishmania infection combined with malnutrition [7]. In addition, the migration of T cells to peripheral immune organs increases during acute infection with Trypanosoma cruzi, which affects thymocyte development [8]. Similarly, a study has reported that a reduction in thymic emigrants contributes to the development of coronary heart disease, which may be related to the destruction of immune tolerance caused by T-cell confusion and thymus degeneration [9]. Interestingly, our recent studies showed that the total numbers and function of thymocytes were changed under endotoxin tolerance induction [10]. These findings raise an interesting question: what and how T-cell development contributes to ET induction and urged a re-evaluation as the new mechanism of ET.
In the present study, we aimed to evaluate the role of thymocyte development in ET establishment from the perspective of central tolerance based on the high-throughput analysis of T-cell receptor (TCR) repertoire. We found that there was the change of CD4+SP TCRβ CDR3 repertoires during the induction of ET. Of note, peripheral macrophages may be as a key factor which carry peripheral antigen to thymic medulla and interaction with thymocytes contributes to negative selection of T-cell population, subsequently participating in the formation of ET. These key points may be helpful for the development of therapeutic approaches of uncontrolled inflammation in Sepsis.
## Mice and model
C57BL/6 wild-type (WT) mice, Rag1−/− mice, and OT-II mice (female, 8–10 weeks of age) [11] were housed under specific pathogen-free (SPF) conditions at Zunyi Medical University, according to the guidelines for the Care and Use of Laboratory Animals (Ministry of Health, China, 1998). The experimental procedures were approved by the Zunyi Medical University Laboratory Animal Care and Use Committee (permit number SYXK2013068).
LPS tolerance model: female WT mice at 8 to 10 weeks of age were intraperitoneally injected with LPS at a concentration of 5 mg/kg to establish an LPS tolerance model, and two groups were established: a group examined 72 h after LPS injection (72-h group) and a group examined 8 days after LPS injection (8-day group). PBS was injected for the control group (control group). Thymus tissue was obtained from all mice at the indicated times.
ACT model: Bone marrow-derived monocytes (BMDMs) purified from WT mice (female 8–10 weeks) were cultivated 7 days with GM-CSF (20 ng/ml). Then, these cells were labeled with CFSE (5 μM). 2 × 106 cells were adoptively transferred into syngenic WT mice through tail vein. 24 h later, mice were treated with 5 mg/kg LPS. Next, the distribution of CFSE+ macrophages was observed by image assay at the indicated point times.
Attack experiment of high-dose LPS model: Bone marrow-derived from OT-II mice was adoptively transferred into syngenic Rag1−/− mice through tail vein. 24 h later, mice were injected with OVA332-339-loaded macrophages (2 × 106 cells) i.v. and then treated with 0.5 mg/kg LPS i.p.. The distribution of OVA-specific CD4+ T cells was observed and analyzed by FCM using Tetramer technique. 72 h later, mice were retreated with 2 mg/kg LPS. Thymus tissue was obtained from all mice at the indicated times.
## High-throughput sequencing
Next-generation sequencing of TCR was carried out as previously described [12]. We obtained thymic CD4+SP cells from mice in different groups using MACS. Briefly, DNA was extracted from these thymic CD4+ SP cells using DNeasy Blood &Tissue kit (Qiagen), quantified using a Qubit Fluorometer (Thermo) and amplified by multiplex-PCR of rearranged variable, diverse, joining (VDJ) segments of the TCR genes, which encode the hypervariable CDR3 domain. The products were size selected using Pronex beads (Promega) and subsequently sequenced on a MiSeq (Illumina). The length and polymorphism of CDR3 were analyzed with GeneMapper 4.1 software (Thermo).
## Histopathology
Indicated tissues were fixed in $4\%$ paraformaldehyde, embedded in paraffin, and cut into 3.5-μm-thick sections. Sections were stained with H&E, and images were taken with an Olympus IX71 microscope. Two investigators blinded to group assignments analyzed the samples and determined the injury levels.
## Immunofluorescence (IF)
Sections were hydrated and rinsed with PBS three times (5 min each) and then blocked with $10\%$ normal goat serum at room temperature for 10 min and incubated with rabbit anti-mouse antibodies at appropriate dilution in TBS overnight at 4 °C. The primary antibodies used were as follows: FITC-UEA-1 (1:50; Vector Laboratories; no. FL-1061–2) and F$\frac{4}{80}$ (1:250; Abcam; no. ab204467). PBS instead of primary antibody served as a control. Then, the slices were rinsed with cold PBS three times (5 min each). Finally, the sections were mounted with Slow Fade Gold Antifade Reagent with DAPI and examined by fluorescence microscopy.
## Flow cytometry
Cytokines, transcriptional factors, and surface markers of various immune cells were evaluated by flow cytometry (FCM) with Beckman Gallios (Beckman Coulter, Inc.). FCM was performed on Beckman Gallios (Beckman Coulter, Inc.) with CellQuest Pro software using directly conjugated mAbs against the following markers: F$\frac{4}{80}$-Percp-Cy5.5 (45–4801-82), MHC-II-APC (no. 17–5320-82), CD86-Percp-Cy7 (no. 25–0862-82), CD80-PE (no. 12–0801-82), CD11c-PE (no. 12–0114-82), CD19-Percp-Cy7 (no.25–0193-82), NK1.1-APC (no. 17–5941-81), CD4-Percp-Cy5.5 (no. 12–0041-82), CD62L-PE (no. 12–0621-81), CD69-APC (no. 17–0691-82), IFN-γ-Percp-Cy5.5 (no. 45–7311-82), IL-4-APC (no. 17–7041-82), and Ki67-PE (no. 12–5698-82), with corresponding isotype-matched controls (eBioscience). For intracellular staining, cells were first surface stained for activated markers and then fixed, permeabilized with IC Fixation & Permeabilization buffer (eBioscience, 88–8823-88), and stained with antibodies against IFN-γ, IL-4, Ki67 (eBioscience), respectively, at 4 ℃ for 30 min in dark. After washing twice, stained cells were analyzed with a Beckman coulter flow cytometer.
The live status of cells was first analyzed by trypan blue staining. Under the condition of the percentage of live cells was above $90\%$, cells then were analyzed in following experiments. The absolute numbers of thymocytes were counted by cell counting chamber. The percentage of various subpopulations including F$\frac{4}{80}$+ macrophages, CD19+ B cells, NK1.1+ cells, and other cells in thymocytes were analyzed using flow cytometry (Beckman Coulter, USA), and then, the absolute numbers of these cell populations were calculated. Finally, the percentages of CD86, CD80, and MHC class II were analyzed based on the gating on F$\frac{4}{80}$+ macrophages (Sfig.3).
## Statistical analyses
The data were analyzed with GraphPad Prism 7.0 and are presented as the mean ± SD. Student’s t test was used when two conditions were compared, and analysis of variance with Bonferroni or Newman–Keuls correction was used for multiple comparisons. Probability values of < 0.05 were considered significant; two-sided tests were performed.
## The frequency and characteristics of CD4+SP TCRβ CDR3 repertoires undergo dynamic programming during the ET induction
TCR repertoires can characterize the features of the host T-cell immune status [13]. In particular, dynamic analysis of the TCR repertoires of T cells is valuable for estimating the immune reconstitution in the host with different situations, which play an important role in the assessment of multiple factors such as tumor, inflammatory, and vaccine-mediated immune response and the development of future therapeutics [14, 15]. Our previous results showed that the total numbers and function of thymocytes, especially CD4+SP, were changed significantly under ET conditions [10], Therefore, in the present study, we performed high-throughput sequencing to analyze the characteristics and changes of CD4+SP TCRβ CDR3 repertoires with respect to V direct to J rearrangement during the ET induction. We found that the total and in frame sequence amounts of V–J rearrangement in the CDR3 repertoire in 72 h of post-LPS i.p. group were significantly reduced, but the proportion of clonotype exhibited increased, compared with those in control group and 8 days of post-LPS i.p. group (Fig. 1A and B). Furthermore, a Gaussian CDR3 length distribution pattern was also observed during ET induction. The amino acids’ (AA) length of TCR beta chain V–J rearrangements in the TCRβ CDR3 repertoire was between 6 and 23 aa, and the highest peak was 12 aa. In AA usage of TCRβ chain V–J rearrangements in the CDR3 repertoire, the usage of I and V amino acids decreased in 72 h of post-LPS i.p. group; In contrast, R, D, and K were the dominant amino acids in 8 days of post-LPS i.p. group. In addition, W, Y, and V were also reduced in V–J rearrangements in the CDR3 repertoire of 8 days after LPS treatment (Sfig.1A-D).Fig. 1The change of thymic CD4+SP TCRβ chain in endotoxin tolerance. The C57BL/6 WT mice (female $$n = 9$$) were treated with 5 mg/kg LPS i.p.. At indicated time points (0 day, 72 h, and 8 days), the CDR3 repertoires of thymic CD4+SP cells purified by MACS were analyzed using high-throughput sequencing, respectively. A The total sequences, unique sequences, and in frame sequences, and clonotype distribution plots B, AA usage of TCR beta chain V–J rearrangement and CDR3 repertoire with TRBV and TRBJ pairing were analyzed C–F TRBV, TRBJ usage, and TRBV–TRBJ gene pairing of the TCRβ chain V–J rearrangements in the CDR3 repertoire were also analyzed (Fig. 1C–F). At 72 h of post-LPS i.p., the preferential use of TRBV in the CD4+SP TCRβ chain V–J rearrangement in the CDR3 repertoire involved TRBV01-04 and TRBJ02-02 (Fig. 1F). The top 3 of TRBV-TRBJ preferential gene pairings were TRBV13-02/TRBJ02-06, TRBV13-03/TRBJ01-02, and TRBV10-01/TRBJ01-02, compare with control group and 8 days of post-LPS i.p. group (Fig. 1F). These data suggested that the frequency and characteristics of the TCRβ chain CDR3 undergo dynamic programming under conditions of LPS tolerance, indicating that there were clonal selection or deletion of T-cell repertoire with a specific TCRβ chain V–J rearrangement in the CDR3 repertoire during ET induction.
## The peripheral macrophages migrated into thymus in ET condition
The professional antigen-presenting cells (APC) are a class of adjuvant cells that processing and presenting antigen information to affect T-cell development in thymus, including dendritic cells (DCs), monocytes/macrophages, and B lymphocytes [16, 17]. Recent studies have shown that the professional APCs directional migration is not only important for immune response and regulation, but also cause the developing antigen-specific T cell to clonal deletion to take part in the process of negative selection for maintaining central tolerance in the thymus medulla region [18]. Herein, we further showed that, compared with that in control group, the absolute numbers of F$\frac{4}{80}$+ macrophages were increased in 72 h of post-LPS i.p. group. ( Fig. 2A), not other APCs, such as CD11c+ DCs, CD19+ B cells, and NK1.1+ cells (Sfig.2A-E). FCM analysis showed that, compared with the control group, the expression level of CD80 on F$\frac{4}{80}$+ macrophages increased, even the levels of CD86 and MHC class II increased mildly at 72 h of post-LPS i.p. ( Fig. 2B and C). Immunofluorescence data further confirmed that F$\frac{4}{80}$+ macrophages were increased in thymus at 72 h of post-LPS i.p., which mostly were located in thymic medulla (Fig. 2D). Unexpectedly, compared with control group, the apoptosis of F$\frac{4}{80}$+ macrophages were obviously increased at 72 h of post-LPS i.p., whereas the proportion was decreased at 8 days of post-LPS i.p. ( Fig. 2E). Meanwhile, the proliferation of F$\frac{4}{80}$+ macrophages did not change significantly (Fig. 2E). In additions, the absolute numbers of F$\frac{4}{80}$+ macrophages in peripheral immune organ spleen were prominently decreased (data not shown). These data indicate that the migration of peripheral macrophages into thymus, but not resident macrophages, might be main responsible for the macrophage population enrichment in thymus in ET condition. Fig. 2The change on macrophages in thymus in endotoxin tolerance. The C57BL/6 WT mice (female $$n = 9$$) were treated with 5 mg/kg LPS i.p.. At indicated time points (0 day, 72 h and 8 days), the percentage and cell numbers of F$\frac{4}{80}$+ macrophages A were analyzed and calculated. The gating strategy was shown in supplementary Fig. 3. B–C The activation-associated molecules CD80, CD86, and MHC-II, as well as the proliferation-related molecule Ki-67 and apoptosis E of F$\frac{4}{80}$+ macrophages in thymus were detected by FCM and calculated, respectively. D The localization of F$\frac{4}{80}$+ macrophages was detected by Immunofluorescence confocal. UEA-1 (green) immunostaining of medulla, DAPI (blue), and F$\frac{4}{80}$+ macrophages (red). White line indicates the region between thymic medulla and cortex. The values are the means ± SD ($$n = 9$$). * $P \leq 0.05$, **$P \leq 0.01$ (color figure online) To confirm the migration of peripheral macrophages into thymus in ET condition, we adoptively transferred CFSE labeling F$\frac{4}{80}$+ macrophages into normal mice through tail vein. Next, 24 h later, these mice were challenged with 5 mg/kg LPS i.p.. In vivo fluorescence imaging and FCM analysis showed that the infiltrating CFSE+ macrophages in thymus tissues markedly increased at 72 h and decreased at 8 days of post-LPS i.p. compared with the 0 day of post-LPS i.p. ( Fig. 3A–D). Moreover, these cells dominantly located in thymic medulla (Fig. 3E). Furthermore, the expression levels of CD86 and CD80 on CFSE+ macrophages increased at 72 h of post-LPS i.p., even the level of MHC class II did not change at 72 h and 8 days of post-LPS i.p. ( Fig. 3B–C). These data suggested that macrophages, but not other APCs, might migrate into thymus and affect the T-cell development in ET condition. Fig. 3The peripheral macrophages migrate into thymus in endotoxin tolerance. Bone marrow-derived monocytes (BMDMs) purified from WT mice were cultivated 7 days with GM-CSF (20 ng/ml). Then, cells were labeled with CFSE. 2 × 106 cells were adoptively transferred into syngenic WT mice through tail vein. 24 h later, mice were treated with 5 mg/kg LPS. Next, the distribution of CFSE+ macrophages was observed by image assay A. The proportion of CFSE+ macrophages, and the activation-related molecule CD80, CD86, and MHC-II of CFSE+ macrophages in thymus were analyzed by FCM and calculated B-D. E The localization of CFSE+ macrophages was detected by Immunofluorescence confocal. One representative data of three independent experiments were shown. ** $P \leq 0.01$
## The peripheral macrophages mediated negative selection of T-cell population
TCR gene, especially CDR3 region, happens rearrangement in T-lymphocytes produce some autoreactive TCR which could interact high affinity with APCs presenting autoantigen–MHC complex to start the apoptosis program results in clone deletion. Our above data showed that there were change of TCRβ CDR3 repertoire of CD4+SP cells in the condition of ET. Next, to further explore whether peripheral macrophages migrated into thymus and affected T-cell development, we further adoptively transferred OVA332-339 peptide-loaded macrophages into Rag-1−/− mice (the mice were pre-transferred with bone marrow cells from OT-II mice). After 24 h, these mice were injected with 0.5 mg/kg LPS i.p. ( Fig. 4A). Data showed that the proportion of OVA-specific CD4+SP in thymus were obviously increased at 72 h and decreased at 8 days of post-LPS treatment in control unloaded macrophage transferred group (Fig. 4B), which were similar to our previous results [10]. Importantly, the proportion of thymic OVA-specific CD4+SP were obviously decreased at 72 h of post-LPS treatment in the OVA332-339 peptide-loaded macrophage transferred group (Fig. 4B, C). Meanwhile, the expression level of CD62L on these thymic OVA-specific CD4+SP decreased and the expression level of CD44 increased, even the expression of CD69 did not change (Fig. 4B, C). These data indicated that there were clonal deletion on OVA-specific CD4+SP in thymus during ET induction. Fig. 4The peripheral macrophages mediate negative selection of T-cell population. A Bone marrow cells derived from OT-II mice were adoptively transferred into syngenic Rag1−/− mice through tail vein. 24 h later, mice were injected with OVA332-339-loaded macrophages (2 × 106 cells) i.v. and then treated with 0.5 mg/kg LPS i.p.. The distribution of OVA-specific CD4+ T cells and its activation-related molecule CD62L, CD44 and CD69, was observed and analyzed by FCM using Tetramer technique B–C. The values are the means ± SD ($$n = 9$$). * $P \leq 0.05$, **$P \leq 0.01$
## Macrophage-mediated clone deletion ameliorates the pathology of LPS-induced sepsis
To further explore the possible effect of clonal deletion of OVA-specific CD4+SP on ET induction, we observed the pathological damage of lung in recipient mice after a lethal dose of LPS administration. Expectedly, data showed that there was ameliorated pathology in lung tissue in the OVA332-339 peptide-loaded macrophage transferred group (Fig. 5A and B). Consistently, the expression levels of pro-inflammatory factors such as TNF-α, IL-1β, and IFN-γ in lung tissue also decreased significantly (Fig. 5C); conversely, the expression levels of anti-inflammatory factors, such as IL-10 and TGF-β, increased obviously, even though IL-4 only tended to be upregulated (Fig. 5C). Further analysis showed that the proportion of peripheral OVA-specific CD4+T cells also decreased (Fig. 5D). Finally, even though the expression level of CD62L did not change, the expression level of CD69 increased significantly in OVA-specific CD4+T cells, displaying a hyperactivation status. However, the expression of IFN-γ in these CD4+T cells was markedly reduced (Fig. 5E). Collectively, our data demonstrated that peripheral macrophages carry peripheral antigen to thymic medulla, and then, interaction with thymocytes contributes to negative selection of T-cell population, subsequently participating in the formation of ET and affecting lung injury development. Fig. 5Macrophage-mediated clone deletion ameliorates the pathology of LPS-induced sepsis. Bone marrow cells derived from OT-II mice were adoptively transferred into syngenic Rag1−/− mice through tail vein. 24 h later, mice were injected with OVA332-339-loaded macrophages (2 × 106 cells) i.v. and then treated with 0.5 mg/kg LPS i.p.. 72 h later, mice were retreated with 2 mg/kg LPS i.p.. The pathology of lung tissue was detected A and the expression levels of inflammatory cytokines TNF-α, IL-1β, IFN-γ, IL-10, IL-4, and TGF-β were analyzed by real-time PCR and calculated C. The proportion and functionally associated molecules CD62L, CD69, and IFN-γ in OVA-specific CD4+ T cells in thymus were analyzed by FCM and calculated D–E. The values are the means ± SD ($$n = 9$$). * $P \leq 0.05$, **$P \leq 0.01$
## Discussion
A large number of recent studies have shown that the secretion of inflammatory cytokine and mortality are decreased in the state of ET [19, 20]. Moreover, the proportion and function of T cells play a decisive role for the formation of ET [10, 21, 22]. However, what and how T-cell development contributes to ET remain to be fully elucidated. TCR repertoires can characterize the features of the host T-cell immune status. For example, Simpson [23] showed that the TCR repertoire of cytomegalovirus (CMV)-specific CD8+ T cells to establish if TCR tissue-specificity was shared among viruses that chronically replicate. Rowntree’s TCR sequencing data showing reduced clonal expansion in unvaccinated children who seroconverted had comparable Spike-specific but lower ORF1a- and N-specific memory T-cell responses compared with adults [24]. Similarly, in the present study, our data showed that the frequency and characteristics of the TCRβ chain CDR3 undergo dynamic programming under conditions of LPS tolerance, indicating that there were clonal selection or deletion of T-cell repertoire with a specific TCRβ chain V–J rearrangement in the CDR3 repertoire during ET induction. Consistently, our recent study showed that the total numbers and function of thymocytes were changed under ET induction. Therefore, our current findings further support the fact that there is the change on T-cell development in ET condition.
A large number of recent studies have shown that the professional APC, including DCs, macrophages and B lymphocytes, directional migration is not only important for immune response and regulation, but also could migrate to the thymus to participate in the development of thymocytes. Such as, Bonasio [25] found that circulating DCs through kinds of adhesion molecules migrate to the thymus medulla, causing the developing antigen-specific T cell to clonal deletion to take part in the process of negative selection for maintaining central tolerance. Herein, we found that the proportion and absolute numbers of F$\frac{4}{80}$+ macrophages, not other CD19+ B cells and DCs, were increased at 72 h of post-LPS i.p. Moreover, the expression level of CD80 on these F$\frac{4}{80}$+ macrophages were obviously increased. Importantly, the increased F$\frac{4}{80}$+ macrophages in thymus were mostly located in thymic medulla, indicating that macrophages might be the main responsible for the change on T-cell development in ET. In addition, we noticed that the proportion and the absolute numbers of F$\frac{4}{80}$+ macrophages in spleen were prominently decreased. Thus, combining these data indicated that the migration of peripheral macrophages into thymus, but not resident macrophages, might be main responsible for the macrophage population enrichment in thymus in ET condition.
Post-positive selection SP migrate into the thymic medulla to undergo negative selection to acquire autoimmune tolerance, suggesting that thymus macrophages are strongly linked to the establishment of the central tolerance in LPS-induced acute inflammation. In the present study, we further reconstructed the thymus development system of Rag-1−/− mice and found that OVA-loaded macrophages could not only migrate into the thymus but also affect the development of OVA-specific CD4+T cells in ET condition. Importantly, we revealed that there was ameliorated pathology of lung tissue after a lethal dose of LPS administration in the OVA-loaded macrophage transferred group. In line with these findings, we further found that the proportion and IFN-γ secretion of peripheral OVA-specific CD4+ T cells also decreased. Collectively, our data indicated that peripheral macrophages carry peripheral antigen to thymic medulla, and then, interaction with thymocytes contributes to negative selection of T-cell population, subsequently participating in the formation of ET and affecting lung injury development.
## Conclusion
In all, we found that there was the change of CD4+SP TCRβ CDR3 repertoires during the induction of ET. Of note, peripheral macrophages may be as a key factor which carry peripheral antigen to thymic medulla and interaction with thymocytes contributes to negative selection of T-cell population, subsequently participating in the formation of ET. These findings might provide a new basement for the explorations on the establishment of ET and would have a profound understanding to the protective mechanism for some acute disorders and benefit the outcome of related clinical diseases.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (PDF 652 KB)
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|
---
title: 'Postnatal growth and body composition in extremely low birth weight infants
fed with individually adjusted fortified human milk: a cohort study'
authors:
- Tania Perrin
- Pierre Pradat
- Julie Larcade
- Marion Masclef-Imbert
- Blandine Pastor-Diez
- Jean-Charles Picaud
journal: European Journal of Pediatrics
year: 2023
pmcid: PMC10023649
doi: 10.1007/s00431-022-04775-3
license: CC BY 4.0
---
# Postnatal growth and body composition in extremely low birth weight infants fed with individually adjusted fortified human milk: a cohort study
## Abstract
This cohort study aimed to evaluate the impact of an individualised nutritional care approach combining standardised fortification with adjustable fortification on postnatal growth and body composition in extremely low birth weight (ELBW) infants. We included ELBW infants admitted to a neonatal intensive care unit and still hospitalised at 35 weeks postmenstrual age (PMA). The fortification of human milk was standardised (multicomponent fortifier) between 70 mL/kg/day and full enteral feeding, and then individualised using adjustable fortification. When weight gain was below 20 g/kg/day, protein or energy was added when serum urea was below or above 3.5 mmol/L, respectively. Postnatal growth failure (PNGF) was defined as being small for gestational age at discharge and/or when the Z-score loss between birth and discharge was higher than 1. Body composition was assessed between 35 and 41 weeks of PMA. Among the 310 ELBW infants included, the gestational age of birth was 26.7 ± 1.8 weeks, and the birth weight was 800 ± 128 g. The mean Z-score difference between birth and discharge was moderately negative for the weight (−0.32), more strongly negative for length (−1.21), and almost nil for head circumference (+ 0.03). Only $27\%$ of infants presented PNGF. At discharge, fat mass was 19.8 ± $3.6\%$. Multivariable analysis showed that the proportion of preterm formula received and gestational age at birth were independently associated with the percentage of fat mass.
Conclusion: The individualised nutritional care approach applied herein prevented postnatal weight loss in most infants, limited length growth deficit, and supported excellent head circumference growth. What is Known:• At least half of extremely low birth weight infants are small for gestational age at discharge and postnatal growth deficit has been associated with impaired neurocognitive and renal development.• Human milk is the main milk used in neonatology and, although fortification of human milk is a standard of care, there is no consensus regarding the optimal fortification strategy to be adopted. What is New:• Using an approach combining standardised fortification followed by individualised adjustable fortification limited postnatal growth deficit for body weight and head circumference. Postnatal growth failure is not a fatality in extremely low birth weight infants.• Each additional gestational age week at birth resulted in a decrease in fat mass percentage at discharge, which was higher than in foetuses of the same gestational age, likely representing a necessary adaptation to extra-uterine life.
## Introduction
Postnatal growth failure, which is associated with impaired neurocognitive and renal development [1–4], was observed in nearly $100\%$ of very low birth weight infants at the end of the 1990s and still occurs in more than half of these infants [5]. In the Swedish EXPRESS cohort of ELBW infants, the median Z-scores for body weight were −0.66 at birth and −1.84 at 36 weeks PMA. Importantly, $44\%$ of these infants had a Z-score below -2 standard deviation (SD) at discharge [6]. Although length growth deficit is also very common, the vast majority of children gradually catch up between the ages of 2 and 8 years and are within normal height ranges as adults [7, 8]. A deficit in fat-free mass (FFM) at discharge has been associated with suboptimal neurological outcomes, and the proportion of fat mass (FM) is known to be higher in premature infants compared to foetuses of the same gestational age (GA) [9, 10].
Human milk fortification strategies used in neonatal units worldwide are highly variable [11, 12]. Individualised fortification, whether adjustable or targeted, achieves better postnatal growth than standardised fortification [13, 14]. Adjustable fortification consists in modulating protein enrichment according to serum urea levels, while targeted fortification is based on the analysis of breast milk to adjust protein or energy content [11, 13]. The latter is time- and resource-consuming and its superiority over adjustable fortification have not been demonstrated [14].
The aim of this study was to evaluate the frequency of postnatal growth deficit and assess body composition at discharge in ELBW infants using an individualised nutritional care approach combining standardised fortification followed by adjustable fortification adapted to weight gain and serum urea.
## Population and methods
This single-centre retrospective observational study included infants born with a birth weight less than 1000 g, admitted within the first 24 h of life to the neonatal intensive care unit of the Croix-Rousse University Hospital in Lyon (France), and still hospitalised at 35 weeks PMA. Infants with serious congenital malformations were excluded.
Data were prospectively recorded in the patient’s electronic files (ICCA, Philips, Boblingen, Germany). Daily protein and energy intakes were assessed on the first day of each week of life and compared to recommended intakes (protein: < 1 kg: 4.0–4.5 g/kg/day, 1–1.8 kg: 3.5–4.0 g/kg/day, energy: 110–135 kcal/kg/day) [15]. Serum urea was measured weekly. Bronchopulmonary dysplasia (ventilatory support or oxygen therapy at 36 weeks PMA), intraventricular haemorrhage grade 3 or 4, periventricular leukomalacia, retinopathy of prematurity stage ≥ 3, and necrotising enterocolitis stage ≥ 2 were collected.
Body weight was measured daily during the first week of life, and then weight, crown-heel length, and head circumference (HC) were measured weekly. The length was measured using a rigid measuring board suitable for premature newborns (Premie Stadiometer, Ellard instrumentation, Monroe, USA). Anthropometric data were expressed in absolute values and Z-scores, and differences in Z-scores between birth and discharge were calculated [16]. Infants were considered to be small for GA (SGA) when the Z-score for body weight was ≤ −1.28 (10th percentile equivalent) [16]. PNGF was considered when the Z-score loss between birth and discharge was higher than 1 or when the Z-score for body weight at discharge was ≤ −1.28. Air displacement plethysmography (PEA POD®, Cosmed France, Brignais, France) was performed between 35 and 41 weeks PMA. Both FM% and absolute values of FFM were collected. Since at 35 to 41 weeks, infants were 2 to 4 months old, the data from infants born at 35–41 weeks as well as the data obtained in 2-month-old term infants were used as a reference [10].
Parenteral nutrition was started within the first 2 h of life, was individualised as soon as possible (within 48 h), and continued until the enteral ration reached 120 mL/kg/day. Enteral nutrition started on the first day of life using donor human milk. Then, the mother’s own milk was introduced as soon as possible, when available. It was pasteurised up to 32 weeks of corrected age [17]. Enteral nutrition increased daily from 15 to 20 mL/kg/day, up to 160 mL/kg/day. Energy supplement started at the end of parenteral nutrition and continued until milk intake reached 160 mL/kg/day: Liquigen® 4 g/100 mL (Nutricia, Saint Ouen, France). Fortification of human milk was started when enteral nutrition reached 70 mL/kg/day. Initially, all infants received a standardised fortification with a powder multicomponent fortifier: Fortipre® 4 g powder/100 mL (Nestlé, Noisiel, France) or Fortema® 3 g powder/100 mL + Nutriprem® 0.5 g powder/100 mL (Bledina-Danone, Limonest, France) (Table 1) [11]. Weight gain calculation and serum urea assessment were performed weekly. When weight gain was insufficient, i.e. < 20 g/kg/day, enteral intake was increased to 180 mL/kg/day. If it remained insufficient after 1 week, the fortification was individualised. Individualised adjustable fortification consisted of the addition of a protein supplement (Nutriprem® 1 g/100 mL) if serum urea was low (< 3.5 mmol/L) or an energy supplement (Liquigen®: 4 g/100 mL) if the serum urea was normal (> 3.5 mmol/L). Additional protein was reduced if serum urea was above 6.5 mmol/L. Fortification of human milk was maintained until body weight reached 1800 g. When the mother had not—or not enough—milk, donor human milk was used to complete or replace the mother’s own milk, and then, when body weight was equal to 1800 g, donor human milk was replaced by a preterm formula. Table 1Composition of multicomponent fortifiers and protein supplement (per 100 g powder)MCF1MCF2PsEnergy, kcal347435338Protein, g25.235.582.1Carbohydrate, g62.2322.2Sodium, mg89.27.8Calcium, mg14.918.95.2Phosphorus, mg8.7115.2MCF1: Fortipre® (Nestlé, Noisiel, France), MCF2: Fortema® (Bledina-Danone, Limonest, France), Ps: Nutriprem® (Nutricia, Saint Ouen, France)MCF multicomponent fortifier, Ps protein supplement For statistical analysis, continuous variables were described by their means and SD, and comparisons were performed using Welch’s t-test or the nonparametric Mann–Whitney test. Categorical variables were described by the number of occurrences and percentages, and comparisons were performed using the chi-square test or Fisher’s exact test, as appropriate. Univariable and multivariable logistic regression analyses were performed to identify factors potentially associated with PNGF. Variables with $p \leq 0.05$ in univariable analysis were retained in the multivariable model. Results are presented as odds ratios and their $95\%$ confidence intervals [$95\%$CI]. The search for factors potentially associated with FM% was carried out using univariable and multivariable linear regression analyses. Variables with $p \leq 0.05$ in univariable analysis were retained in the multivariable model. Results are presented as an estimate with [$95\%$CI]. The alpha-risk significance level for all analyses was set at 0.05. All analyses were performed using R software version 3.5.3 (R Foundation for Statistical Computing, Vienna, Austria).
The study was approved by the ethics committee Comité de Protection des Personnes Sud-Est IV (IRB: 00009118), and the institutional review board (Comité scientifique et éthique des Hospices Civils de Lyon, n°22_608) was registered in Clinicaltrials.gov (NCT02686801).
## Results
Between April 1, 2014, and December 31, 2019, 310 infants among the 490 infants with a birth weight less than 1000 g admitted to the neonatal intensive care unit were included. Body composition was assessed in $\frac{112}{310}$ ($36\%$) infants (Fig. 1). At birth, mean ± SD GA was 26.7 ± 1.8 weeks with a minimum of 23 weeks. The mean birth weight was 800 ± 128 g with a minimum of 440 g (Table 2).Fig. 1Flow chartTable 2Characteristics of 310 extremely low birth weight infants at birth and during hospitalisationCharacteristicsTotalAntenatal steroids, n (%)300 [97]Gestational age at birth, weeks26.7 (± 1.8)Male sex, n (%)140 [45]Birth weight, g800 (± 128)Small for gestational age, n (%)104 [33]Bronchopulmonary dysplasia, n (%)228 [74]Postnatal steroids, n (%)117 [37]Periventricular leukomalacia, n (%)5 [2]Intraventricular haemorrhage grades 3 and 4, n (%)16 [5]Retinopathy of prematurity stage ≥ 3, n (%)10 [3]*Necrotising enterocolitis* ≥ 2, n (%)4 (1.3)Parenteral nutrition duration, days19 (± 11)Gestational age at discharge, weeks38 (± 1.5)Length of stay, days79 (± 16)Values are expressed as number (percentage) or mean (± 1 standard deviation) Recommended protein intakes were reached before the end of the first week of life. Recommended energy intakes were reached at the end of the second week and were exceeded between the third and eighth week of life, bringing the protein-energy ratio slightly lower than recommended (Fig. 2). Mean serum urea levels were 4.2 ± 3.3 mmol/L at 1 month of life, 3.8 ± 2.4 mmol/L at 6 weeks of life, and 3.5 ± 1.9 mmol/L at discharge. The threshold value of 6.5 mmol/L was exceeded in $14.8\%$ of infants at 1 month, $8.2\%$ at 6 weeks, and $6.0\%$ at discharge. Fig. 2Weekly protein intake a, energy intake b, and protein to energy ratio c in 310 extremely low birth weight infants between birth and discharge, expressed as mean and standard deviation. Grey zones represent recommended intakes (Ref. 15) At birth, the mean Z-score was −0.39 for body weight, −0.44 for length, and −0.26 for HC. The mean initial weight loss was 10 ± $4\%$ of birth weight (Table 3). Thereafter, weight gain in both boys and girls closely followed the reference curves (Fig. 3). At 1 month of life, 62 infants ($20.0\%$) had a body weight < 10th percentile. The mean Z-score difference between birth and discharge was almost nil for HC (0.03 ± 1.12), moderately negative for the weight (−0.32 ± 0.75), and more strongly negative for length (−1.21 ± 0.92) (Fig. 4). Overall, 84 infants ($27.1\%$) had a body weight < 10th percentile and 26 ($8.4\%$) had a Z-score for body weight below the 3rd percentile at discharge. A total of 114 infants ($36.8\%$) presented PNGF (Table 3). The multivariable analysis identified SGA at birth and the use of postnatal steroids as independent risk factors for PNGF. The proportion of total milk intake as the preterm formula was a protective factor (Table 4).Table 3Anthropometric data and postnatal growth in 310 extremely low birth weight infantsBody weight Birth weight, g800 (± 128)Z-score birth weight−0.39 (± 0.98)Initial weight loss, % of birth weight10 (± 4)Weight at discharge, g2790 (± 513)Weight Z-score at discharge, SD−0.71 (± 0.94)Weight ΔZ-score between birth and discharge−0.32 (± 0.75)Loss in body weight Z-score from birth to discharge ≥ 1SD, n (%)51 [17]Weight at discharge < 10th percentile, n (%)84 [27]Postnatal growth failure* for body weight, n (%)114 [37]Crown-heel lengthLength at birth, cm33.2 (± 2.2)Length Z-score at birth, SD−0.44 (± 1.15)Length at discharge, cm45.1 (± 2.7)Length Z-score at discharge−1.65 (± 1.02)Length ΔZ-score between birth and discharge−1.21 (± 0.92)Loss in length Z-score from birth to discharge ≥ 1SD, n (%)183 [59]Length at discharge < 10th percentile, n (%)188 [61]Postnatal growth failure* for length, n (%)246 [80]Head circumferenceHead circumference at birth, cm23.7 (± 1.5)Head circumference Z-score at birth, SD−0.26 (± 1.05)Head circumference at discharge, cm33.5 (± 1.8)Head circumference Z-score at discharge, SD−0.23 (± 1.12)Head circumference ΔZ-score between discharge and birth, SD0.03 (± 1.10)Loss in head circumference Z-score from birth to discharge ≥ 1SD, n (%)42 [14]Head circumference at discharge < 10th percentile, n (%)53 [17]Postnatal growth failure* for head circumference, n (%)76 [25]* Loss in Z-score for given anthropometric parameter, from birth to discharge, equal or higher than 1 standard deviation (SD) and/or Z-score for body weight at discharge less than −1.28 (10th percentile equivalent)Fig. 3Postnatal weight gain (g/kg/day) in 310 extremely low birth weight infants (a boys; b girls). The grey lines represent the Fenton weight gain curves, and the black lines represent the mean weight gain of the present cohort (solid line) + / −1 standard deviation (dotted lines)Fig. 4Differences in Z-score for body weight, crown-heel length, and head circumference between birth (light grey) and discharge (dark grey) in 310 extremely low birth weight infants. Expressed as boxplot (median, P25, P50, min, max)Table 4Risk factors of postnatal growth failure in 310 extremely low birth weight infantsUnivariable analysisMultivariable analysisRisk factorsOR [$95\%$CI]pOR [$95\%$CI]pGestational age at birth, weeks1.32 [1.15; 1.52] < 0.001/*Sex (female vs male)1.03 [0.65; 1.63]0.909/Antenatal steroids0.71 [0.19; 2.71]0.620/Z-score birth weight0.59 [0.46; 0.77] < 0.001/Small for gestational age5.12 [3.05; 8.57] < 0.0016.91 [3.80; 12.6] < 0.001Protein intake at day 7, 0.5 g/kg/day0.56 [0.26; 1.22]0.145/Protein intake at day 35, 0.5 g/kg/day0.59 [0.35; 0.99]0.0490.94 [0.34; 2.58]0.898Energy intake at day 7, 10 kcal/kg/day0.88 [0.77; 1.02]0.081/Energy intake at day 35, 10 kcal/kg/day0.90 [0.83; 0.97]0.0090.92 [0.79; 1.07]0.274Postnatal steroids1.90 [1.18; 3.05]0.0082.64 [1.49; 4.68] < 0.001Parenteral nutrition duration, day1.03[1.01; 1.05]0.0121.02 [1.00; 1.05]0.074Preterm formula, $25\%$ total enteral intake0.77 [0.62; 0.96]0.0230.76 [0.59; 0.98]0.036OR odds ratio, $95\%$ CI $95\%$ confidence interval*excluded from the multivariable model because of strong collinearity with small for gestational age Body composition assessment could not be performed in 198 infants, mainly because of ventilatory support (Fig. 1). The infants who underwent a body composition assessment had similar characteristics to the others, except for their birth weight, which was significantly higher. They were also less sick and had less PNGF (Table 5). Measurement of body composition was performed at a mean of 67 ± 15 days of life, i.e., 38 ± 1 weeks PMA. FM% was 19.8 ± $3.6\%$ of body weight, and FFM was 2314 ± 389 g (Fig. 5). FM% tended to be lower in infants that were SGA at birth than in non-SGA infants (18.8 ± $3.6\%$ vs. 20.1 ± $3.4\%$ $$p \leq 0.093$$). FM% was significantly higher in infants with optimal growth compared to those with PNGF (20.2 ± $3.2\%$ vs. 18.1 ± $4.5\%$, $$p \leq 0.036$$). Factors influencing FM% were the proportion of milk intake as preterm formula and GA at birth. Those influencing FFM were GA at birth and sex (Table 6).Table 5Characteristics of extremely low birth weight infants with or without PEA POD®CharacteristicsWith PEA POD®$$n = 112$$Without PEA POD®$$n = 198$$pAntenatal steroids, n (%)110 [98]190 [96]0.496Gestational age at birth, weeks27.9 (± 1.8)26.6 (± 1.8)0.096Male sex, n (%)45 [40]95 [48]0.184Birth weight, g830 (± 116)783 (± 132)0.003Birth length, cm33.9 (± 2)32.8 (± 2.2)1.118Birth head circumference, cm23.9 (± 1.5)23.6 (± 1.5)0.104Small for gestational age, n (%)31 [28]73 [37]0.1Postnatal steroids, n (%)17 [15]100 [51] < 0.001Bronchopulmonary dysplasia, n (%)64 [57]164 [83] < 0.001Periventricular leukomalacia, n (%)0 [0]5 [3]0.163Intraventricular haemorrhage stage 3 and 4, n (%)4 [4]12 [6]0.341Retinopathy of prematurity stage ≥ 3, n (%)5 [5]5 [3]0.505Necrotising enterocolitis stage ≥ 2, n (%)4 [2]0 [0]0.3Parenteral nutrition duration, days17 (± 9)20 (± 13)0.128ΔZ-score weight between discharge and birth−0.15 (± 0.75)−0.41 (± 0.72)0.003Postnatal growth failure, n (%)15 [13]69 [35] < 0.001Fig. 5Fat mass (%) and fat-free mass (g) at discharge in 112 extremely low birth weight infants of the present cohort (dark black) compared to term infants at birth (white) and term infants at 2 months of age (grey). Reference values for term infants were obtained from ref. 10Table 6Factors associated with fat mass percentage (a) and fat-free mass (b) at discharge in 112 extremely low birth weight infants. Results are expressed as estimates with their $95\%$ confidence intervala. FactorsUnivariablePMultivariablePGestational age at birth, weeks−0.89 [−1.3; −0.5] < 0.001−0.87 [−1.3; −0.45] < 0.001Sex (female vs. male)0.34 [−1.0; 1.7]0.623/Antenatal steroids−0.35 [−5.5; 4.8]0.893/Small for gestational age−1.5 [−3.0; 0.1]0.060/Protein intake at day 7, 0.5 g/kg/day0.02 [−2.20; 2.20]0.986/Protein intake at day 35, 0.5 g/kg/day−1.70 [−3.10; −0.19]0.027−0.61 [−2.00; 0.77]0.382Energy intake at day 7, 10 kcal/kg/day0.11 [−0.29; 0.51]0.591/Energy intake at day 35, 10 kcal/kg/day−0.01 [−0.25; 0.23]0.937/Postnatal steroids2.7 [0.86; 4.5]0.0041.49 [−0.13; 3.10]0.071Parenteral nutrition duration, day0.09[0.02; 0.17]0.0150.03 [−0.04; 0.09]0.361Preterm formula, $25\%$ total enteral intake0.45 [0.45; 1.60] < 0.0011.11 [0.60; 1.6] < 0.001b. FactorsUnivariablePMultivariablePGestational age at birth, weeks−130 [−166; −94] < 0.001−78 [−127; −0.29]0.002Sex (female vs. male)−185 [−330; −40]0.013−168 [−294; −42]0.009Antenatal steroids−214 [−765; 336]0.442/Small for gestational age−370 [−522; −219] < 0.001−140 [−316; 36]0.118Protein intake at day 7, 0.5 g/kg/day−39 [−82; 3.10]0.069/Protein intake at day 35, 0.5 g/kg/day−182 [−345; −20]0.028−65 [−215; 84]0.387Energy intake at day 7, 10 kcal/kg/day0.11 [−0.29; 0.51]0.591/Energy intake at day 35, 10 kcal/kg/day−14 [−40; 13]0.310/Postnatal steroids232 [33; 431]0.023−11 [−191; 170]0.907Parenteral nutrition duration, day9.3 [1.0; 18]0.0282.29 [−5.9; 9.8]0.544Preterm formula, $25\%$ total enteral intake41 [−25; 107]0.222/Results are expressed as estimates with their $95\%$ confidence intervals
## Discussion
In the present cohort of very high-risk ELBW infants, the individualised nutritional care approach applied prevented postnatal weight loss in most infants, limited length deficit, and supported excellent HC growth.
Protein intakes were close to the recommended intakes [15]. The recommended total energy intake, which may be difficult to achieve in such extremely immature infants, was reached faster than previously reported [15, 18]. Although the protein-to-energy ratio was slightly lower than recommended due to the high energy intake, the former remained higher than previously reported [19]. The high energy intake observed is likely due to the fact that the energy supplementation required after cessation of parenteral nutrition was not stopped as soon as recommended, reflecting the difficulties in fully adhering to protocols in clinical practice. However, such intakes supported early postnatal growth, as only a fifth of infants were SGA at 1 month of life, compared to the $75\%$ previously reported, representing a significant improvement in the prevention of initial growth deficit originally described by Embleton et al. [ 18, 20]. Good protein utilisation was reflected by rather low serum urea. These results suggest that the slightly excessive energy intake relative to the protein intake avoided the restriction of protein utilisation which could be related to a lack of energy. Although such intakes allowed good postnatal growth in most infants, they also likely favoured high FM% at discharge. These data advocate for close monitoring of protein and energy intakes, but also that of the protein-to-energy ratio.
The postnatal growth observed in this cohort closely followed that of foetal growth, at least for body weight and HC. The present individualised nutritional care approach helped to avoid the postnatal weight deficit as demonstrated by a Z-score loss much lower than previously reported (−1), despite the fact that the infants herein were less mature [20]. This deficit was also lower than that reported more recently by Cormack et al. ( −0.48) in a similar population and even lower than the −0.7 to −1 Z-score loss recently proposed as acceptable [11, 21]. In the present cohort, there were four times less infants with a Z-score for body weight below −2 at discharge than in the EXPRESS cohort [6]. Moreover, less than a third of infants herein had a Z-score for body weight below −1.28 at discharge, which is lower than previously reported in even more mature very low birth weight infants [23]. The observed absence of postnatal HC deficit in the majority of infants herein is also noteworthy, as such a deficit has been associated with suboptimal neurological outcomes [1, 24]. In a similar population, Cormack et al. reported a higher Z-score loss of 0.82 [22]. In the EPICure cohort, ELBW infants with a significant deficit in postnatal HC growth had HC below reference values as adults [25]. Although there is no strong evidence supporting that having an HC close to the mean for GA at discharge is associated with better neurodevelopment, it seems rather reassuring for the future of these high-risk infants. The length deficit observed herein was similar to the −1.5 and −1.16 previously reported [21, 26]. Surprisingly, length data are quite rarely reported in studies assessing postnatal growth in ELBW infants [20, 27], and very few authors reported an improvement in the Z-score for length during hospitalisation [28, 29]. It is well known that the final height of premature infants is approximately 1SD lower than that of term infants [30]. Furthermore, since postnatal length growth deficit can be associated with long-term consequences such as osteoporosis, large deficits in length should be avoided as much as possible [31]. The few studies that found such positive length kinetics underlined the central role of protein intake [28, 29]. This represents another reason for optimising protein intakes and protein-to-energy ratio. In summary, and in contrary to previously reported studies, the individualised nutritional care approach used in the present cohort helped limit postnatal growth deficits [32–34].
Of note, PNGF might be a more relevant marker of growth deficit than just SGA at discharge [35]. When using PNGF, the postnatal growth deficits in weight, length, and HC were present in more infants than when using SGA at discharge. Neonatologists should aim to reduce the risk of PNGF rather than SGA at discharge. This study confirmed that SGA at birth and postnatal steroids are independent risk factors for PNGF and found that the proportion of milk ingested as the preterm formula was a significant protective factor of PNGF. This could be due to the fact that preterm formula, which is used to supplement or replace absent or insufficient breast milk provides, a more stable nutritional supply than fortified breast milk.
Currently, there is no consensus regarding the body composition objective at the end of hospitalisation. Due to the metabolic adaptation to extrauterine life needed to increase energy storage and improve thermoregulation, the foetal body composition cannot serve as a reference [36]. Given that full-term neonates have an FM% of around $10\%$ at birth and $25\%$ at 2–3 months of life [11, 37] and that the FM% at discharge in the present ELBW cohort was $20\%$, the objective could be between that of a 36–40 week foetus and that of a full-term infant aged 2–3 months. This relatively high FM% at discharge is similar to that observed in smaller cohorts with similar postnatal weight change but higher than the $15\%$ reported in less immature infants with a more favourable postnatal weight change [37, 38]. Contrary to what has been reported, the results herein showed that each additional GA week at birth resulted in a decrease in FM% [39]. Thus, the more immature the infant, the higher the FM% at discharge, which could reflect an increase in fat storage due to the difficulty in maintaining well-balanced protein and energy intakes throughout hospitalisation. Nevertheless, FM% has been shown to normalise within a few months after discharge [10]. Such a transient excess in FM% could thus be useful for ELBW infants, as it may represent the “price to pay” to avoid postnatal growth deficits, particularly regarding HC.
A deficit in FFM at discharge has been associated with neurological impairment at 2 years of age [9, 40]. Herein, FFM was lower than in term infants (2.8–2.9 kg), confirming the data published by Hamatschek et al. ( mean of 2.5 kg), but was 300 g higher than that reported in a very low birth weight cohort [10, 39]. However, as the FFM is expressed in absolute value, it depends directly on body weight, and it is therefore difficult to compare studies, in which nutritional care and body weight at discharge varies greatly.
A limitation of this study is its single-centre design, although this did not prevent the data from a significant number of ELBW infants to be analysed. Furthermore, it avoided the impact of potential inter-centre differences in practices other than nutritional management, which could impact postnatal growth. Moreover, only a subgroup of infants could benefit from the body composition assessment herein. They had less bronchopulmonary dysplasia and therefore, less postnatal steroid treatment. However, even though the most severely ill infants did not undergo a body composition measurement, those who had it still represented a population of very high-risk ELBW infants.
In conclusion, an individualised nutritional care approach using standardised fortification followed by adjustable fortification limited body weight and HC postnatal growth deficits. FM% was higher than that of foetuses of the same GA, possibly representing a necessary adaptation to extrauterine life. Further studies are still needed to determine the growth and body composition objectives in ELBW infants according to their impact on later development.
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|
---
title: The SGLT2 inhibitor empagliflozin improves cardiac energy status via mitochondrial
ATP production in diabetic mice
authors:
- Jungmi Choi
- Naoki Matoba
- Daiki Setoyama
- Daiki Watanabe
- Yuichiro Ohnishi
- Ryuto Yasui
- Yuichirou Kitai
- Aki Oomachi
- Yutaro Kotobuki
- Yoichi Nishiya
- Michael Paul Pieper
- Hiromi Imamura
- Motoko Yanagita
- Masamichi Yamamoto
journal: Communications Biology
year: 2023
pmcid: PMC10023657
doi: 10.1038/s42003-023-04663-y
license: CC BY 4.0
---
# The SGLT2 inhibitor empagliflozin improves cardiac energy status via mitochondrial ATP production in diabetic mice
## Abstract
Empagliflozin, a sodium-glucose co-transporter 2 inhibitor developed, has been shown to reduce cardiovascular events in patients with type 2 diabetes and established cardiovascular disease. Several studies have suggested that empagliflozin improves the cardiac energy state which is a partial cause of its potency. However, the detailed mechanism remains unclear. To address this issue, we used a mouse model that enabled direct measurement of cytosolic and mitochondrial ATP levels. Empagliflozin treatment significantly increased cytosolic and mitochondrial ATP levels in the hearts of db/db mice. Empagliflozin also enhanced cardiac robustness by maintaining intracellular ATP levels and the recovery capacity in the infarcted area during ischemic-reperfusion. Our findings suggest that empagliflozin enters cardiac mitochondria and directly causes these effects by increasing mitochondrial ATP via inhibition of NHE1 and Nav1.5 or their common downstream sites. These cardioprotective effects may be involved in the beneficial effects on heart failure seen in clinical trials.
The SGLT2 inhibitor, Empagliflozin, is shown to exert cardioprotective effects by increasing mitochondrial ATP in a mouse model of diabetes.
## Introduction
Sodium-glucose cotransporter 2 (SGLT2) inhibitors are a unique class of oral antidiabetic medications that reduce glucose reabsorption in the renal proximal tubules, thereby enhancing urinary glucose excretion1. In the EMPA-REG OUTCOME trial, a large randomized controlled clinical trial, the highly selective SGLT2 inhibitor empagliflozin significantly reduced the risk of three-point major adverse CV events, cardiovascular death, heart failure hospitalization and composite renal outcomes in patients with type 2 diabetes (T2D) with established cardiovascular disease2.
Multiple hypotheses have been proposed to explain the beneficial effects of SGLT2 inhibitors3,4, which can be multifactorial and acute/chronic5; the most common one is via an effect on diuresis/natriuresis, but the mechanisms involved in these impressive cardiac benefits are incompletely understood. Several research groups have suggested the potential efficacy of empagliflozin on cardiac energetics. Changes in cardiac energy production play a critical role in the pathophysiology of heart failure. The failing heart faces an energy deficit primarily because of a decrease in mitochondrial oxidative capacity, which is partly compensated for by an increase in ATP production from glycolysis occurring in the cytoplasm6. Several studies have shown that empagliflozin improved cardiac energy production in animal experiments7,8. Indeed, a single dose of empagliflozin in fasting diabetic db/db mice was associated with an improvement in cardiac energetics, which is also associated with an increase in ketone levels7. Similarly, Verma et al. reported that chronic administration of empagliflozin enhanced ATP production in the heart of db/db mice, although they argued that this was due to an increase in glucose and fatty acid oxidation rather than the utilization of ketone bodies8. To investigate the mechanism underlying the improvement in cardiac energy status with empagliflozin, real-time and accurate monitoring of the pathways and amount of energy production in vivo is needed.
Recently, using a modified Ateam (GO-ATeam9), which can directly quantify intracellular ATP levels via Fluorescence Resonance Energy Transfer (FRET) in cytosol and mitochondria, we generated transgenic mice to monitor subcellular ATP levels in the whole body, organ and cells, as well as in the beating heart (cytoATP-Tg for cytosol; mitoATP-Tg for mitochondria; manuscript submitted). These mice enabled us to visualize ATP levels in real time and in a living state by measuring the FRET/GFP ratio and proved ideal for studying drugs that affect energy metabolism.
In the present study, we crossed cytoATP-Tg or mitoATP-Tg mice with a mouse model of T2D and assessed the effects of empagliflozin on the cardiac energy status of those mice. In addition, several studies have demonstrated the beneficial effects of empagliflozin on myocardial infarction (MI) in diabetic animal models10,11, prompting us to investigate the real-time ATP change in cardiac energy production in an ischemic-reperfusion model of MI. Furthermore, to gain insight into the direct effects of empagliflozin on the myocardium and its underlying molecular pathway, we also conducted experiments using isolated cardiomyocytes.
## Chronic treatment with empagliflozin reverses the energy reduction due to diabetic heart disease in mice with type 2 diabetes mellitus
Using ATeam mice, we first investigated whether or not it was possible to visualize the decrease in cardiac ATP levels when T2D is present and the attenuation of the decrease by empagliflozin. After eight-week administration of empagliflozin, blood glucose levels in db/db mice were significantly decreased compared to controls (Supplementary Fig. 1a, b). Blood concentrations of ketone bodies were increased in db/db mice compared to wild-type mice, but no significant difference was observed between the empagliflozin administration db/db group and the control db/db group (Supplementary Fig. 1c, d). In addition, body weight changes of empagliflozin-treated db/db mice were not significantly different from the weight change in the control db/db group (Supplementary Fig. 1e). The systolic blood pressure also did not differ markedly among the three groups (Supplementary Fig. 1f, g).
Next, we examined the ATP dynamics in the hearts of mice administered empagliflozin for 10 weeks by measuring the FRET/GFP ratio using a fluorescence microscope (Fig. 1a). First, we investigated the effect of autofluorescence in the heart on the measurement of FRET (ET$\frac{470}{40}$ nm, D$\frac{570}{40}$ nm) and GFP (ET$\frac{470}{40}$ nm, D$\frac{515}{30}$ nm). The autofluorescence intensity of FRET and GFP in the hearts were 2.97 ± 0.006 and 5.45 ± 0.01, respectively (Supplementary Fig. 2i–m). In cytoATP-Tg mice, the fluorescence intensity of FRET and GFP in the hearts were 267.87 ± 0.71 and 162.16 ± 0.26, respectively (Supplementary Fig. 2a–d, m). These results indicate that the effect of autofluorescence in the heart was small enough to be negligible (Supplementary Fig. 2m).Fig. 1Chronic treatment with empagliflozin reverses cytosolic ATP loss in the heart of T2D model mice.a Schematic illustration of the treatment regimen (EMPA empagliflozin). b–d Representative fluorescence images of a live heart from each condition. Warmer colors indicate higher ATP concentrations. Scale bar: 2 mm. e FRET/GFP ratios indicating the amount of ATP in the heart under each condition (+/+ [control]: $$n = 10$$, db/db [control]: $$n = 20$$, db/db [EMPA]: $$n = 20$$). * $$p \leq 0.000.$$
The FRET/GFP ratio (ATP level) in the heart was significantly reduced to 1.44 ± 0.01 (about 1.03 mM) in db/db mice compared to 1.65 ± 0.01 (about 1.21 mM) in wild-type mice (Fig. 1b, c, e). This ATP reduction in db/db mice was not observed after 10 weeks of empagliflozin administration (1.64 ± 0.01, Fig. 1d, e).
## Chronic treatment with empagliflozin is cardioprotective by maintaining ATP levels in the infarcted region in type 2 diabetic hearts
The increase in ATP levels induced by empagliflozin implies an improvement in energy maintenance in the heart. We therefore hypothesized that the beneficial effect of empagliflozin on an ischemia/reperfusion model10, which is a severely stressed condition, might be mediated by the maintenance of the ATP levels in the heart. Thus, we investigated the effect of empagliflozin on the temporal changes in ATP levels in the infarcted myocardium, particularly during ischemic-reperfusion.
db/db; cytoATP-Tg mice were treated with control or empagliflozin for 10 weeks, starting at 7 weeks old, and at 17 weeks old, they underwent the ischemic/reperfusion procedure, and the ATP levels at the infarct sites of the heart were measured over time (Fig. 2a). Specifically, ischemia was induced by ligating the left anterior descending artery while monitoring the ECG after opening the chest with respiration maintained with a ventilator. Reperfusion was then initiated by removing the thread that had ligated the left anterior descending artery 30 min after the start of ischemia. In the db/db control group, a reduction in the ΔFRET/GFP ratio of about –0.28 (indicating a decreased ATP level) was induced in the infarct region compared to before ligation after 20 min of ligation (Fig. 2b, c, h). In the empagliflozin group, the ΔFRET/GFP ratio in the myocardial infarct region decreased by about –0.06 (Fig. 2e, f, h). This indicates that empagliflozin-treated mice had a higher ATP level in the infarcted area than the db/db control group. In the db/db control group, the ΔFRET/GFP ratio of the myocardial infarct scar gradually recovered up to 30 min after recanalization, and the ΔFRET/GFP ratio recovered to about –0.21 (Fig. 2d, h). In the empagliflozin-treated group, ATP levels were restored to the baseline levels in the infarct region. ( Fig. 2g, h). These results indicate that not only was the decrease in the ATP levels in the myocardial infarct region suppressed in the empagliflozin group compared to the db/db control group, but empagliflozin also maintained and restored energy, with a quick ATP recovery to the baseline levels after recirculation compared to the db/db control group (Fig. 2h). This difference suggests that empagliflozin confers “robustness” in ATP maintenance/production in the diabetic heart, and that this may contribute to the protective effects of empagliflozin against the ischemic-reperfusion injury as reported previously. Fig. 2Chronic treatment with empagliflozin is cardioprotective by maintaining cytosolic ATP levels in the infarcted region of T2D hearts.a Schematic illustration of the treatment regimen (EMPA empagliflozin). b–g Fluorescence images of a live heart from each condition (EMPA empagliflozin). Arrowheads show the sites of punctuation with a suture needle to induce ischemia. Dotted lines show the infarct regions. Warmer colors indicate higher ATP concentrations. Scale bar: 2 mm. h Time course changes in FRET/GFP ratios in the ischemic region during ischemia and reperfusion (db/db [control]: $$n = 6$$, db/db [EMPA]: $$n = 9$$; ischemia 20 min: *$$p \leq 0.001$$, ischemia 30 min: *$$p \leq 0.000$$, just after reperfusion: *$$p \leq 0.000$$, 15 min after reperfusion: *$$p \leq 0.001$$, 30 min after reperfusion: *$$p \leq 0.001$$).
## Chronic treatment with empagliflozin reverses the decrease in mitochondrial ATP levels in the hearts of T2D model mice
Since chronic administration of empagliflozin suppressed the decrease in cytosolic ATP levels in diabetic heart, we decided to measure the ATP levels in mitochondria, the major source of ATP production. In mitoATP-Tg mice, the fluorescence intensity of FRET and GFP in the heart was 392.6 ± 0.84 and 267.4 ± 0.30, respectively (Supplementary Fig. 2e–h, m). These results indicate that in mitoATP-Tg mice, the effect of autofluorescence in the heart was small enough to be negligible (Supplementary Fig. 2m).
db/db; mitoATP-Tg mice were treated with control or empagliflozin for 10 weeks, starting at 7 weeks old, and the amount of ATP in the heart mitochondria was measured at 17 weeks old (Fig. 3a). The FRET ratio was around 1.74 ± 0.01 in the wild-type mice but was significantly reduced to around 1.42 ± 0.01 in db/db with control treatment (Fig. 3b, c).Fig. 3Chronic treatment with empagliflozin reverses mitochondrial ATP loss in the heart of T2D model mice.a Schematic illustration of the treatment regimen (EMPA empagliflozin). b–d Representative fluorescence images of a live heart from each condition (EMPA empagliflozin). Warmer colors indicate higher ATP concentrations. Scale bar: 2 mm. e FRET/GFP ratios indicating the mitochondrial ATP level in the heart under each condition (+/+ (control): $$n = 11$$, db/db (control): $$n = 10$$, db/db (EMPA): $$n = 17$$). * $$p \leq 0.000$$).
We then measured the amount of ATP in the cardiac mitochondria in a mouse model of T2D treated with empagliflozin. The mitochondrial ATP in the heart increased compared to the db/db control group, measuring about 1.75 ± 0.01 (FRET/GFT ratio), which is almost the same as that observed in the wild-type mice (Fig. 3d, e). These results indicate that empagliflozin suppressed the decrease in the mitochondrial ATP level in diabetic hearts.
## Single treatment of empagliflozin promotes an increase in mitochondrial ATP levels
Next, we examined the timing of empagliflozin-induced changes in mitochondrial energy production.
In 8-week-old db/db; mitoATP-Tg mice, we found that the ATP level in the mitochondria was already lower than in wild-type mice (Fig. 4b). Therefore, we orally administered 30 mg/kg body weight of empagliflozin after 4 h of fasting and measured the ATP levels in the cardiac mitochondria of the mice in vivo 3 h later (Fig. 4a). Glucose and ketone concentrations in the blood were measured before measuring the ATP levels in the heart. Glucose levels were significantly lower in the empagliflozin group (approximately 135 mg/dL) than in the db/db control group (approximately 422 mg/dL) (Supplementary Fig. 3a). In contrast, the ketone concentration was significantly higher in the empagliflozin group (approximately 6.9 mmol/L) than in the db/db control group (approximately 2.35 mmol/L) (Supplementary Fig. 3b). At the time of observation, the mice treated with empagliflozin showed elevated mitochondrial ATP levels in the heart (Fig. 4b–d). At this time, empagliflozin was detected and quantified in mitochondria in the heart and other organs, except for the brain (Fig. 4h). These results suggest that empagliflozin entered cardiac mitochondria and increased the ATP levels in vivo as early as 3 h after administration. However, since the blood ketone concentration, which is considered an energy source for the heart, also increased during this period (Supplementary Fig. 3b), we were unable to determine whether the increase in the cardiac mitochondrial ATP level was a direct effect of empagliflozin or a secondary effect due to an increase in ketone concentrations. Fig. 4Single treatment with empagliflozin increases mitochondrial ATP levels in the heart of T2D model mice.a Schematic illustration of the treatment regimen (EMPA empagliflozin). b, c Representative fluorescence images of a live heart from each condition (EMPA empagliflozin) with or without increased ATP. Warmer colors indicate higher ATP concentrations. Scale bar: 2 mm. d FRET/GFP ratio indicating the amount of ATP in the heart under each condition (db/db [control]: $$n = 6$$, db/db [EMPA]: $$n = 8$$). * $$p \leq 0.013.$$ e, f Representative images of mitochondrial ATP concentrations before (e) and 1 h after (f) empagliflozin 1000 nM administration. Scale bar: 25 μm. g Graph of the FRET/GFP ratio in mature cardiomyocytes with control ($$n = 22$$), empagliflozin 10 nM ($$n = 16$$), empagliflozin 100 nM ($$n = 14$$), empagliflozin 500 nM ($$n = 21$$) and empagliflozin 1000 nM ($$n = 27$$) (*$$p \leq 0.0010$$, 0.0058, 0.0000, 0.0000, respectively). h Biodistribution of empagliflozin in mitochondria in various tissues at 3 h after empagliflozin administration ($$n = 10$$). Heart: 0.56 ± 0.029, kidney: 3.95 ± 0.581, liver: 3.85 ± 0.216, small intestine: 1.15 ± 0.315, brain: 0.0 ± 0.000 nmol/mg.
Therefore, we isolated mature cardiomyocytes from 8-week-old db/db; mitoATP-Tg mice and examined the mitochondrial ATP levels in the same cells before and 1 h after the addition of empagliflozin. The results showed that the FRET/GFP ratio was 1.05 ± 0.019 in the control group, 1.25 ± 0.020 in the group with 10 nM empagliflozin, 1.22 ± 0.020 in the group with 100 nM empagliflozin, 1.31 ± 0.015 in the group with 500 nM empagliflozin and 1.40 ± 0.013 in the group with 1000 nM empagliflozin (Fig. 4e–g, $$p \leq 0.0010$$, 0.0058, 0.0000, 0.0000 vs. db/db control). At this time, the ATP level in the cytoplasm was not changed (Supplementary Fig. 4a–e). This result indicates that empagliflozin directly increased the ATP level in mitochondria in cardiomyocytes apart from an increase in blood ketone concentrations.
## Empagliflozin maintains mitochondrial ATP levels in mature cardiomyocytes even under hypoxic conditions, demonstrating its cardioprotective effect
Next, we examined the effect of empagliflozin on the temporal changes in mitochondrial ATP levels while trying to mimic MI.
Therefore, we isolated mature cardiomyocytes from 18-week-old db/db; mitoATP-Tg mice and examined the mitochondrial ATP levels in the cells over time during hypoxia exposure and oxygen recovery (Fig. 5a). The results showed that a significant reduction in the ΔFRET/GFP ratio of about –0.11 ± 0.01 (indicating a decreased ATP level) was induced in mitochondria after 30 min of hypoxia compared to before the exposure (Fig. 5b, c, h). At 30 min after the induction of hypoxia, the cardiomyocytes were allowed to recover to high-oxygen conditions. However, the ΔFRET/GFP ratio had scarcely recovered even 30 min after exposure to an oxygen-rich environment, reaching about –0.09 ± 0.01 (Fig. 5d, h). We also conducted the same experiments with mature cardiomyocytes treated with empagliflozin for 1 h. In the empagliflozin group, the ΔFRET/GFP ratio in mitochondria decreased by about 0.03 ± 0.02 after 30 min of exposure to low-oxygen conditions (Fig. 5e, f, h). Furthermore, mitochondrial ATP levels were restored to the baseline levels (0.02 ± 0.01) by 30 min after exposure to high-oxygen conditions (Fig. 5g, h). These results indicate that not only was the decrease in mitochondrial ATP levels during low-oxygen exposure suppressed in the empagliflozin group compared to the control group, but empagliflozin also maintained and restored energy, with an increased rate of mitochondrial ATP recovery after exposure to an oxygen-rich environment compared to the control group (Fig. 5h).Fig. 5Single treatment of empagliflozin is cardioprotective by maintaining mitochondrial ATP levels in hypoxia in mature cardiomyocytes.a Schematic illustration of the treatment regimen (EMPA empagliflozin). b–g Fluorescence images of cardiomyocytes from each condition (EMPA empagliflozin). Warmer colors indicate higher ATP concentrations. Scale bar: 25 μm. h Time course changes in FRET/GFP ratios in the cardiomyocytes under low- and high-oxygen conditions (db/db [control]: $$n = 44$$, db/db [EMPA]: $$n = 40$$; 30 min after exposure to low-oxygen conditions: *$$p \leq 0.020$$, 30 min after exposure to high-oxygen conditions: **$$p \leq 0.000$$).
## Empagliflozin promotes increased mitochondrial membrane potential and mitochondrial ATP levels via Na+/H+ exchanger and Nav1.5
It was shown that empagliflozin may directly promote an increase in mitochondrial ATP levels in cardiomyocytes, but the mechanism of action is unclear. Therefore, we investigated whether or not the increase in mitochondrial ATP levels was accompanied by an increase in mitochondrial membrane potential, which is of critical importance in maintaining the function of the respiratory chain to produce ATP.
The mitochondrial membrane potential was measured after adding vehicle or empagliflozin to mature cardiomyocytes isolated from 8-week-old wild-type mice, and 1 h later, JC-1 was applied to the cells (0.30 ± 0.004: $$n = 120$$, 0.33 ± 0.010: 135 each). The results showed that the mitochondrial membrane potential was significantly increased in the cardiomyocytes treated with empagliflozin (Fig. 6a–c, $$p \leq 0.03$$).Fig. 6Empagliflozin promotes mitochondrial activity via the NHE1 and Nav1.5 pathways.a, b Measurement of mitochondrial membrane potential using JC-1. Representative cellular images from each condition (EMPA empagliflozin). Warmer colors indicate higher mitochondrial membrane potential. Scale bar: 25 μm. c Graph of the mitochondrial membrane potential of mature cardiomyocytes with control ($$n = 120$$), empagliflozin 10 nM ($$n = 134$$). * $$p \leq 0.021.$$ d Graph of the FRET/GFP ratio in mature cardiomyocytes isolated from 8-week-old mitoATP-Tg mice 1 h after addition of control ($$n = 67$$), 300 nM empagliflozin ($$n = 54$$), 10 μM cariporide ($$n = 49$$), cariporide and empagliflozin ($$n = 49$$), 30 μM ranolazine ($$n = 65$$), ranolazine and empagliflozin ($$n = 53$$) or 10 μM MA-5 ($$n = 163$$) (**$$p \leq 0.0012$$, 0.0001, 0.0000, 0.0006, 0.0011, *$$p \leq 0.046$$, respectively).
Empagliflozin reportedly inhibits not only SGLT2 but may also directly inhibit Na+/H+ exchanger 1 (NHE1)12,13 and Nav1.514. To investigate the possibility that these ion channels influence mitochondrial ATP levels in mature cardiomyocytes, we next performed experiments in which we added each inhibitor to mature cardiomyocytes isolated from 8-week-old +/+; mitoATP-Tg mice and incubated them with either vehicle or empagliflozin. The ATP levels in the mitochondria were measured 1 h after the addition of 300 nM empagliflozin. The FRET ratio was 1.37 ± 0.005 in control and was significantly increased to 1.39 ± 0.007 in MA-5-treated cardiomyocytes, which facilitates mitochondrial ATP production via ATP synthase oligomerization (Fig. 6d, $$p \leq 0.046$$). In empagliflozin-treated cardiomyocytes, the FRET/GFP ratio was significantly increased to 1.42 ± 0.006 (Fig. 6d, $$p \leq 0.0012$$). To investigate the effect of cariporide and ranolazine, inhibitors of NHE1 and Nav1.5, respectively, we measured the ATP level in mitochondria of cardiomyocytes 1 h after the addition of cariporide. The FRET/GFP ratio was significantly increased to 1.42 ± 0.006 after the addition of cariporide and to 1.42 ± 0.006 after the addition of ranolazine (Fig. 6d, $$p \leq 0.0001$$, 0.006 each). We then examined whether or not the addition of empagliflozin to these inhibitors had an add-on effect. Consequently, the FRET/GFP ratio was 1.42 ± 0.004 when cariporide plus empagliflozin was added, showing no significant difference from cariporide alone (1.42 ± 0.006) or empagliflozin alone (1.42 ± 0.006) (Fig. 6d, $$p \leq 0.89$$ and 0.36, respectively). The addition of ranolazine plus empagliflozin resulted in a FRET ratio of 1.42 ± 0.006, showing no significant difference from the addition of ranolazine alone (1.42 ± 0.006) or empagliflozin alone (1.42) (Fig. 6d, $$p \leq 0.91$$, 0.98 each). These results, combined with the previous findings that empagliflozin directly inhibits NHE1 and Nav1.5, suggest that empagliflozin may enhance ATP levels in mitochondria via the inhibition of these ion channels or common downstream sites of these channels.
## Discussion
The current study was performed to evaluate the effect of empagliflozin on cardiac energy metabolism in T2D mice in order to directly quantify cytosolic and mitochondrial ATP levels. The hearts of empagliflozin-treated mice had significantly higher ATP levels in the cytoplasm and mitochondria after 10 weeks of treatment than control mice (db/db, 17 weeks old). Empagliflozin attenuated the decrease in and enhanced recovery of ATP levels at the ischemic site in an ischemia-reperfusion model of MI. Furthermore, the mitochondrial ATP level was increased 3 h after a single administration of empagliflozin. These results prompted us to examine cardiomyocytes derived from the model mice, and the direct effects of empagliflozin on the mitochondria of cardiomyocytes were demonstrated.
## Dynamics of ATP in the heart visualized by GO-ATeam
Empagliflozin has been shown to increase ATP in the heart of db/db mice7,8. However, since the measurement was performed by extracting tissues after sacrificing animals, it was difficult to follow changes over time and monitor ATP production in intracellular organelles. In the present study, we used GO-ATeam mice, which can be used to visualize ATP dynamics in vivo and thereby evaluate the effect of empagliflozin on myocardial energy metabolism in diabetic mice. In contrast to findings with the conventional method, we found that empagliflozin increased ATP in mitochondria as early as 3 h after treatment. We also showed in a time-dependent fashion that empagliflozin attenuated the reduction in cytosolic ATP level and also enhanced the recovery of ATP at the infarct site during the ischemic-reperfusion period, suggesting the robustness of myocardial energy metabolism, as discussed below. In vitro studies demonstrated the differential effects of empagliflozin in cytosol or mitochondria on ATP production. Thus, GO-ATeam mice enabled us to visualize ATP levels in real time, in a living state, at organellar levels, making it a powerful tool for investigating energy metabolism under various perturbed conditions (e.g., drug administration, gene knockout, disease conditions) in a variety of organs/cells.
## Energetic remodeling in the diabetic heart by empagliflozin
Encouraged by the finding that empagliflozin increased mitochondrial ATP in db/db mice, which suggested the robustness of its effect on the cardiac energy state, we monitored the changes in cytosolic ATP concentrations in the ischemic region of the ischemia-reperfusion model after 10 weeks of empagliflozin administration. The changes in the cytoplasmic ATP levels during ischemia and reperfusion were clearly ameliorated in the empagliflozin-treated group, thus demonstrating that the energy production had become robust in vivo. In vitro studies using isolated cells, by contrast, revealed that 1-h treatment with empagliflozin significantly increased mitochondrial ATP but not cytoplasmic ATP. Furthermore, it also significantly increased mitochondrial ATP in wild-type-derived cardiomyocytes. Furthermore, we showed that empagliflozin alleviated the changes in mitochondrial ATP levels in cardiomyocytes during low-oxygen exposure, suggesting that the energy production had become robust in vitro. These in vivo/in vitro results suggest that empagliflozin not only quickly increases ATP production in mitochondria, but also increases ATP production in the entire cell in the long term. Such qualitative changes in myocardial energy metabolism, which we call “remodeling”, may underlie the cardiovascular benefits of empagliflozin seen in clinical trials and support the ongoing clinical trial evaluating the effect of empagliflozin on patients after acute MI (EMPACT-MI)15.
## Mechanism underlying the effects of empagliflozin at the cellular and molecular levels
Since SGLT2, the molecular target of empagliflozin, is expressed specifically in the proximal tubules of the kidney but not in the heart16,17, the primary target tissue and target molecule for its cardioprotection has long been discussed. In the present study, we showed that empagliflozin rapidly reached the cardiac mitochondria in vivo. Furthermore, in vitro studies revealed that empagliflozin immediately increased mitochondrial ATP in isolated cardiomyocytes, suggesting that empagliflozin has a direct effect on the myocardium. In addition, our findings also showed that the cytosolic ATP levels did not increase in vitro, while the mitochondrial ATP levels and membrane potential both increased. These in vivo/in vitro results suggest that the target in cardiomyocytes may reside in the mitochondria.
Some researchers have proposed the possibility of NHE1 and Nav1.5 as off-targets of empagliflozin in the heart13,14. In our study, we found that both cariporide and ranolazine, inhibitors of NHE1 and Nav1.5, respectively, increased mitochondrial ATP production. Empagliflozin, however, did not induce any additional benefit, suggesting that empagliflozin may increase mitochondrial ATP by inhibiting both NHE1 and Nav1.5. This conclusion is consistent with reports that NHE1 is expressed in mitochondrial membranes, with its inhibition maintaining the membrane potential18, and that Nav1.5 is functionally linked to mitochondria in cardiomyocytes19. However, this should be interpreted with caution: first, a report was recently published arguing that empagliflozin does not inhibit NHE120. If this is the case, to reconcile these findings with our own, the mechanism underlying the effects of empagliflozin may involve the common pathway of both inhibitors, i.e., reduction in Ca2+ overload by decreasing the intracellular Na+ concentration. Second, cariporide and ranolazine used as tool compounds in this study may not have been specific to each target21–23, and the concentrations used are in the micromolar range; therefore, we cannot exclude the possibility that ATP is induced by their off-target effects. Further studies are needed to identify bona-fide molecular targets of empagliflozin in the heart.
## Strengths and limitations
Several strengths and limitations associated with the present study warrant mention. First, our ATeam mouse is a very useful tool for visualizing energy metabolism in vivo, even at organellar levels, which is not limited to the heart. It should be pointed out, however, that this assay only measured cardiac surface cells, so the results need to be validated using two-photon microscopy or fiber microscopy, which can measure deeper areas. Second, we clearly showed the energetic effects of empagliflozin in the failing heart of diabetic db/db mice, which recapitulate the features of cardiomyopathy and heart failure24,25. However, given that there are many etiologies of heart failure and the clinical evidence indicating that empagliflozin is effective against heart failure with or without diabetes26,27, to what extent our findings can be applied remains to be seen. Third, our in vitro experiments were conducted with cardiomyocytes isolated from the heart, so the molecular mechanism underlying ATP enhancement by empagliflozin that we postulated here is not necessarily the same as that seen in the in vivo native environment.
#### mitoATP-Tg mice with empagliflozin
Seven-week-old db/db; cytoATP-Tg/mitoATP-Tg or control (+/+; cytoATP-Tg/mitoATP-Tg) mice were fed a diet with/without 30 mg/kg empagliflozin for 10 weeks. The dose of empagliflozin was chosen based on the data from previously published pre-clinical studies7,10.
Prior to starting the experiments, baseline urine and blood samples were collected. The body weight and systolic blood pressure in awake mice were measured. The body weight was measured 8 weeks after the start of drug treatment, and the systolic blood pressure was measured 8 weeks after the start of drug treatment. Blood samples were collected from the tail vein of mice for measurement of non-fasting blood glucose and ketone levels at eight weeks after the start of drug treatment, and the blood pressure was measured at 8 weeks after the start of drug treatment. After 8 weeks of drug treatment, mice were housed in metabolic cages to collect 24-h urine. At the end of the 10-week treatment, 17-week-old mice were anaesthetized with isoflurane, and the chest was opened surgically. The beating heart was observed under a fluorescent microscope for ATP imaging in vivo.
Regarding the observation method, in brief, the object was exposed to an excitation light (ET$\frac{470}{40}$) using a fluorescence microscope (Leica M165FC; Leica Microsystems, Germany), and the absorbed light was separated into GFP (D$\frac{515}{30}$m, Chroma Technology, USA) and RFP (D$\frac{575}{40}$m, Chroma Technology, USA) by a Dual-View (DM540; Nippon Roper, Japan), and the image was captured by a cMOS camera (ORCA-Flash4.0; Hamamatsu Photonics, Japan) to obtain dual-images simultaneously.
Analyses were performed using the MetaMorph software program (Molecular Devices, USA) and displayed as IMD images. For cytosolic ATP, since we were able to make a calibration curve showing the relationship between the FRET/GFP ratio and ATP level, we were able to estimate the ATP level from the FRET/GFP ratio (paper submission). However, for mitochondrial ATP, only the FRET/GFP ratio was described, as it is technically difficult to make a calibration curve.
All male mice used were 8 weeks old at the start of each experiment and were housed individually or in groups of 2–3 per cage at a temperature of 22 ± 1 °C with a 12-h light–dark cycle and ad libitum access to food and water. All procedures were performed in accordance with the guidelines of the Laboratory Animals Care and Use Committee (No. 20073, 21053, 22029). Efforts were made to minimize the number of animals used and to limit their suffering.
## Operation of ischemia and reperfusion in the heart
After anesthesia with isoflurane, overnight-fasted 17-week-old db/db; cytoATP-Tg mice were intubated and artificially ventilated. After opening the chest using electrocautery, ischemia was induced by ligating the left anterior descending artery with a suture needle and surgical thread (L6-80 n3; Natsume Seisakusho, Japan) under a microscope while monitoring the electrocardiogram (ECG). Reperfusion was performed by removing the suture 30 min after the creation of the infarction while monitoring the ECG. In vivo ATP imaging was performed using a fluorescence microscope (Leica M165FC) at six time points: before ligation (0 min), after ligation (20 and 30 min), just after reperfusion, and 15 and 30 min (45 and 60 min, respectively) after reperfusion.
### mitoATP-Tg mice with empagliflozin
Eight-week-old db/db; mitoATP-Tg or control mice received a single dose of empagliflozin (30 mg/kg body weight) via oral gavage after 4 h of fasting. After another 3 h of fasting, the mitochondrial ATP level of the heart was monitored in vivo under fluorescent microscopy.
## Isolation of mature cardiomyocytes
We isolated mature cardiomyocytes from the indicated genotype mice using a Langendorff perfusion system (Radnoti, USA). To investigate the effect of empagliflozin, cariporide, and ranolazine on cardiomyocyte energetics in mitochondria or cytosol, we studied the mitochondrial or cytosolic ATP levels of cardiomyocytes with/without empagliflozin (10, 100, 300, 1000 nM), cariporide (10 μM), ranolazine (30 μM) or MA-5 (10 μM). To investigate the effect of empagliflozin on the mitochondrial membrane potential of cardiomyocyte, we used the JC-1 with empagliflozin (300 nM). Two-photon microscopy (Leica SP8MP) was used to observe fluorescence.
## The quantification of empagliflozin in mitochondria
A single oral dose of empagliflozin was administered to 8-week-old wild-type mice, and 3 h later, they were sacrificed, with the heart, kidney, liver, small intestine and brain tissue collected. The tissues were homogenized using a homogenizer (Nippi Inc.) containing mitochondria buffer (250 mM sucrose, 20 mM HEPES-KOH, 5 mM KH2PO4, 50 μM MgCl2, $0.2\%$ BSA, pH 7.5) and lysed by being passed through a 1-mL syringe with a 27-G needle 10 times. Homogenized lysates were next centrifuged at 800 × g for 5 min at 4 °C, and supernatants were further centrifuged at 6000 × g for 15 min at 4 °C. The resulting pellets containing mitochondria were resuspended in a mitochondria buffer. Protein concentrations were determined using the BCA protein assay (Thermo Fisher Scientific) according to the manufacturer’s instructions.
## Hypoxic treatment of cardiomyocytes
Cardiomyocytes were isolated from 18-week-old db/db; mitoATP-Tg, treated with empagliflozin (1000 nM) or DMSO in Tyrode solution for 1 h, and then transferred to Tyrode saturated with $95\%$ O2, $5\%$ CO2 for imaging of the mitochondrial ATP level using two-photon microscopy (0 min). Subsequently, hypoxia treatment was performed by switching from $95\%$ O2, $5\%$ CO2 state to $10\%$ O2, $5\%$ CO2, $85\%$ N2 state, and the mitochondrial ATP level was imaged by two-photon microscopy after 20 and 30 min. Further imaging was performed 15 and 30 min after switching again to $95\%$ O2 and $5\%$ CO2 (45 and 60 min, respectively).
## Statistical and reproductivity
Data are expressed as the mean ± SEM. Statistical analyses were performed using the unpaired two-tailed Student’s t-test to compare two groups and an analysis of variance (ANOVA) (*$p \leq 0.05$; **$p \leq 0.01$; NS, not significant). All data were normally distributed, and variance was similar between groups. Biological replicates were derived using different samples derived from different mice. The results represent data from at least three independent experiments. We estimated the required sample size considering the variation and mean of the samples. We used the fewest animals required to draw statistically valid conclusions. Our protocol required excluding mice if we observed an abnormal size, weight, or both or disease symptoms before performing experiments. However, this was unnecessary, as all mice were phenotypically normal and healthy.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review File Supplementary Figures Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04663-y.
## Peer review information
Communications Biology thanks Gary Lopaschuk and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Gabriela Da Silva Xavier and Karli Montague-Cardoso. Peer reviewer reports are available.
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|
---
title: Primary cilia control cellular patterning of Meibomian glands during morphogenesis
but not lipid composition
authors:
- Céline Portal
- Yvonne Lin
- Varuni Rastogi
- Cornelia Peterson
- Samuel Chi-Hung Yiu
- James W. Foster
- Amber Wilkerson
- Igor A. Butovich
- Carlo Iomini
journal: Communications Biology
year: 2023
pmcid: PMC10023665
doi: 10.1038/s42003-023-04632-5
license: CC BY 4.0
---
# Primary cilia control cellular patterning of Meibomian glands during morphogenesis but not lipid composition
## Abstract
Meibomian glands (MGs) are modified sebaceous glands producing the tear film’s lipids. Despite their critical role in maintaining clear vision, the mechanisms underlying MG morphogenesis in development and disease remain obscure. Cilia-mediate signals are critical for the development of skin adnexa, including sebaceous glands. Thus, we investigated the role of cilia in MG morphogenesis during development. Most cells were ciliated during early MG development, followed by cilia disassembly during differentiation. In mature glands, ciliated cells were primarily restricted to the basal layer of the proximal gland central duct. Cilia ablation in keratine14-expressing tissue disrupted the accumulation of proliferative cells at the distal tip but did not affect the overall rate of proliferation or apoptosis. Moreover, impaired cellular patterning during elongation resulted in hypertrophy of mature MGs with increased meibum volume without altering its lipid composition. Thus, cilia signaling networks provide a new platform to design therapeutic treatments for MG dysfunction.
Meibomian gland (MG) cells are ciliated in early stages of development and primary cilium is required to regulate early MG cell patterning within developing glands, with cilia-mediated signaling pathways identified as therapeutic targets.
## Introduction
Meibomian glands (MGs) are holocrine glands located within the tarsal plates of the upper and lower eyelids. These modified sebaceous glands (SGs) are composed of clusters of secretory acini that discharge their secretion into several shorter ductules branching from the central duct of the gland. The secretory product, meibum (composed of lipids, proteins and nucleic acids of the whole cell), is ultimately released at the eyelid margin. The meibum subsequently spreads onto the ocular surface, as the outermost layer of the tear film, with each blink1,2. This lipid-rich layer plays crucial protective roles for the ocular surface, as it functions as a lubricant for the eyelids during blinking, prevents tear overflow onto the lids, and reduces tear evaporation1,3.
Defective MGs lead to meibomian gland dysfunction (MGD), defined as “a chronic, diffuse abnormality of the MGs, commonly characterized by terminal duct obstruction and/or qualitative/quantitative changes in the glandular secretion”4. Reduced lipid secretion may contribute to tear film instability and facilitate entry into the vicious cycle of dry eye disease (DED), among the most commonly encountered ophthalmic diseases with a global prevalence ranging from 5 to $50\%$5. DED is subdivided into two primary and non-mutually exclusive categories: aqueous deficient dry eye (ADDE) and evaporative dry eye (EDE)6. MGD is considered the leading cause of EDE and DED6–8. The recent development of therapeutic solutions targeting MGD includes ocular lubricants, eyelid-warming devices and intense pulsed light, primarily focusing on relieving MG obstruction or replacing lipids9. However, there is an unmet need for treatments to prevent MG atrophy and stimulate lipid production. This deficiency in current effective pharmacological targets is due primarily to the very limited knowledge about molecular networks underlying MG development and renewal that could be the target for an efficient and long-lasting treatment of MGD.
Human MG formation occurs during embryonic development between the third and the seventh month of gestation, corresponding to the sealed-lid phase of eyelid development1. In mice, MG development begins at embryonic day 18.5 (E18.5) and continues postnatally10. As in humans, MG development in mice occurs during the sealed-lid phase of eyelid development, which is indispensable for MG development11,12.
Although it has been suggested that MG development shares similarities with the development of the pilosebaceous unit comprising a hair follicle and its associated SGs, the basic mechanisms underlying MG development and renewal remain poorly understood. Like hair follicles, MGs develop from the ectodermal sheet, which invaginates into the mesoderm to form an anlage. Then, similar to the hair anlage of eyelashes, the meibomian anlage develops lateral outgrowths that later differentiate into ductules and sebaceous acini13. In murine development, an epithelial placode forms at E18.5, followed by invagination in the mesenchyme and elongation of the placode, branching of the MGs beginning around postnatal day 5 (P5), and acquisition of their mature morphology by P1510.
Primary cilia are microtubule-based cellular organelles that originate from the basal body and extend from the plasma membrane. Intraflagellar transport (IFT), a bidirectional movement of protein particles along the axoneme, ensures the appropriate assembly and maintenance of cilia14–18. Dysfunction of the primary cilium produces a heterogeneous group of diseases termed ciliopathies, some of which induce severe developmental defects, highlighting the crucial role of the primary cilium in tissue development19. The primary cilium plays essential roles in the development of ectoderm-derived tissues, including the skin, the corneal epithelium, and the pilosebaceous unit20,21. In particular, the primary cilium modulates corneal epithelial thickening through the regulation of cell proliferation and vertical migration22. In the skin, primary cilia limit the hyperproliferation of keratinocytes in the epidermis23,24. Moreover, primary cilia ablation leads to hair follicle morphogenesis arrest24–30 and dysregulation of the hair growth cycle31. Patients affected by Bardet-Biedl syndrome, an autosomal recessive ciliopathy, suffer from several cutaneous conditions, including keratosis pilaris and seborrheic dermatitis32. Interestingly, primary cilia ablation induces hyperplasia of SG lobules, indicating a cilia-dependent regulatory role in SG development23. However, the pathogenesis of these cilia-associated cutaneous conditions and the mechanisms underlying the abnormal enlargement of SGs remain unknown. Although the role of the primary cilium has been investigated in various ectoderm-derived tissues, its role in MG development, maintenance and function remains unknown.
In this study, we show that MG cells are ciliated in the early stages of development, and meibocytes lose their primary cilium as they differentiate. We demonstrate that the primary cilium is required for regulating the central duct diameter and overall size of MGs. We propose a mechanism by which primary cilia determine the early MG cell patterning by controlling the spatial distribution of proliferating and dying cells within developing glands. These findings suggest cilia-mediated signaling pathways as potential therapeutic targets to counteract MGD.
## Developmental loss of primary cilia in K14-expressing tissues leads to an abnormal increase in MGs size and lipid content
To determine the involvement of primary cilia in MG morphogenesis, we generated the conditional knockout K14-Cre;Ift88fl/fl (here referred to as cKO). In this mouse, the *Ift88* gene, encoding for a subunit of the IFT machinery required for cilia assembly and maintenance33, is excised in all epithelial cells expressing Keratin 14 (K14), including MG tissues. The expression of the K14-Cre recombinase was followed by using the mT/mG reporter mouse line34 (Supplement Fig. 1). In the K14-Cre;Ift88floxed;mT/mG transgenic line, the Cre-dependent excision of a cassette expressing the red-fluorescent membrane-targeted tdTomato (mT) drove the expression of a membrane-targeted green fluorescent protein (mG) in K14-expressing tissues (Supplement Fig. 1). To monitor primary cilia ablation in MGs, we immunodetected ARL13B, a protein associated with the ciliary membrane35,36. At P3, cilia were present on virtually all MG cells of the control mice. In contrast, cilia were absent or very short in MG cells of cKO mice (Supplement Fig. 1). As previously described, the external appearance of newborn cKO mice was generally similar to that of control mice22,23. Because defects in eyelid fusion and opening during development can affect MG morphogenesis, we analyzed in detail these processes11. Eyelid fusion and opening in both cKO and control mice occurred around E15.5 and P13, respectively, confirming the finding of previous studies22,23. However, we noticed the presence of multifocal, white, granular to chalky seborrheic debris along the eyelid margins of most adult cKO mice, which was not observed in control mice (Fig. 1a).Fig. 1Primary cilium ablation leads to larger MGs.a Representative pictures of control and cKO adult (6 months) eyes. Arrow indicates a white deposit, only observed in cKO mice. b Representative images of tarsal plates stained with ORO at P6 and P8. Boxed regions indicate the areas shown at higher magnification. Scale bar, 200 µm; N, nasal; T, temporal. c The number of MGs and MG size were quantified at P6 and P8 ($$n = 20$$ controls and 5 cKO mice at P6; $$n = 13$$ controls and 9 cKO mice at P8). Per mouse, MG area was determined by averaging the MG area of all individual MGs in the upper and lower eyelids. Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test. ns, nonsignificant, P ≥ 0.05. d Representative images of tarsal plates stained with ORO at P21. Boxed regions indicate the areas shown at higher magnification. Scale bar, 200 µm; N, nasal; T, temporal. e Representative images of control and cKO MGs stained with HE at P6 and P21. Boxed regions indicate the areas shown at higher magnification. Scale bar, 200 µm.
Oil red O (ORO) staining of the tarsal plate revealed that the number of MGs per eyelid (upper and lower) was similar in control and cKO mice at P6 and P8 (Fig. 1b, c). However, stained areas of the cKO MGs were $29\%$ and $21\%$ larger than those of control mice at P6 and P8, respectively (Fig. 1c). Mature MGs, as seen in mice at P21, appeared very close to each other in both cKO and control mice, challenging the accurate outlining of individual glands and thus the measurement of their surface (Fig. 1d). However, MGs appeared more densely distributed in the tarsal plates of the cKO than in those of control mice. Unstained foci were visible between the proximal region of adjacent MGs in control, but not in the cKO (arrows in Fig. 1d). Moreover, no significant differences in MG dimensions were observed between males and females of the same genotype (Supplement Fig. 2), and both sexes were pooled for the entire study. Despite differences in MG size, histological analysis revealed that the morphology of basal and differentiating meibocytes was similar in both mutant and control, and no sign of duct obstruction was observed in mutant mice (Fig. 1e).
Given the significant expansion of the mutant MGs, we asked whether the lipid production and composition were altered in the cilia mutant. Lipid profiles of the tarsal plate extracts were assessed by high-resolution mass spectrometry (MS) in combination with isocratic and gradient reverse-phase ultra-high performance liquid chromatography. Approximately 150 analytes with unique combinations of retention times and mass-to-charge (m/z) ratios were identified. Representative mass spectra of a control wild-type sample are shown in Fig. 2a–c. The principal component analysis (PCA) produced no obvious clustering of the control or cKO samples (Fig. 2d), implying that their chemical compositions were similar. However, analysis of samples for a set of 15 major lipid species from the wax ester (WE) and the cholesteryl ester (CE) families showed approximately a two-fold increase in the amount of produced lipids in the cKO mutant compared to control mice (Fig. 2e). Collectively, primary cilia ablation in the MGs led to the expansion of MGs size and a two-fold increase in lipid production without affecting the overall maturation process of meibocytes and, therefore, the lipid composition of the meibum. Fig. 2Reverse phase liquid chromatography/high-resolution time of flight atmospheric pressure chemical ionization mass spectrometry (LC/MS) analysis of mouse meibomian lipids conducted in the positive ion mode revealed close similarity in the lipidomes of control and cKO mice, but a two-fold increase in the total lipid content of the latter.a An analytical signal of free cholesterol (Chl) and cholesteryl esters (CEs) from a control mouse. b An observation spectrum of the pool of meibomian wax esters (WEs) from a control mouse. ( c) An observation spectrum of the pools of triacylglycerols (TAGs), α,ω-diacylated diols (DiADs), and cholesteryl esters of (O)-acylated ω-hydroxy fatty acids (Chl-OAHFAs) from a control mouse. d A scores plot generated using Principal Component Analysis (PCA) for the control (C, yellow dots) and cKO (M, green dots) LC/MS data demonstrated a strong overlap of the control and mutant samples of meibomian lipids with no clear clustering of the samples of different types, indicating their close biochemical compositions. e However, primary cilium ablation led to a higher overall lipid production in the tarsal plates of cKO mice compared with the lipid content of control mice ($$n = 6$$ for Ctrl and $$n = 6$$ for cKO). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test.
## Primary cilia progressively disassemble during MG development and meibocyte maturation
To gain insights into the role of cilia in MG size control, we sought to determine the spatiotemporal distribution of primary cilia in the developing and mature MGs of the Arl13b-mCherry;Centrin2-GFP transgenic mouse (Fig. 3). This mouse expresses ARL13B, a cilia-membrane-associated protein, fused to the monomeric red fluorescent protein mCherry, and Centrin2, a centriolar protein, fused to GFP resulting in red and green fluorescently-labeled cilia and basal bodies, respectively35–38. Because the size and morphology of MGs varies according to their position within the tarsal plate (Fig. 1b), we numbered MGs starting from the biggest gland at the temporal side of the eyelid (Fig. 3a). Throughout this study we analyzed glands preferentially located at a similar middle position in the tarsal plate, as illustrated in Fig. 3a, b, in mutant and control mice. Fig. 3Most MG cells were ciliated in early stages of MG development, but meibocytes lost their cilium as they matured.a To facilitate the comparison of similar MGs, MGs were numbered from the temporal side (MG#1) to the nasal side (MG#11). The boxed area indicates the region in which MGs were studied for all the following experiments b Whole mount tarsal plate at P3 imaged by confocal microscopy. MGs were stained with an antibody against K14 (in white). Hair follicles (marked with *) were also stained but were easily distinguishable from MGs due to the presence of a hair shaft. The boxed region indicates the MG shown at higher magnification in c and d. Scale bar, 100 µm. c 3D reconstruction with Imaris of MG#7 In Arl13b-mCherry;Centrin2-GFP mice, mCherry labels primary cilia in red, and GFP labels basal bodies in green. At P3, cilia were visible all along the MG. Scale bar, 15 µm. d Optical section picked in the center of MG#7. Basal bodies (in green) and primary cilia (in red) were localized on the apical side of MG basal cells (outlined with a yellow dotted line). Scale bar, 15 µm. e Representative MG longitudinal section at P6. MGs (outlined by a yellow dotted line) were stained with an antibody against K14 (in white), nuclei were stained with DAPI (in blue), basal bodies were labeled with GFP (in green) and primary cilia were labeled with mCherry (in red). MG cells are ciliated all along the MG, including in the distal tip of the gland (i), the forming acini (ii) and the forming central duct (iii). Scale bar, 15 µm. f Representative longitudinal section of a morphologically-mature MG at P25. MGs (outlined by a yellow dotted line) were stained with an antibody against K14 (in white), nuclei were stained with DAPI (in blue), basal bodies were labeled with GFP (in green) and primary cilia were labeled with mCherry (in red). No primary cilia were visible in the acini (i), but primary cilia (arrows) were still present in ductules (ii) and the central duct (iii). Scale bar, 100 µm; Ac, acini; CD, central duct. g Quantification of the percentage of ciliated cells throughout development ($$n = 3$$ for each age). Data were presented as mean ± SD. Statistical significance was assessed using Kruskal-Wallis test. For clarity, only statistically significant differences are indicated on the graph. h Spatial distribution of ciliated cells within MGs at P12 and P25. The percentage of ciliated cells was quantified specifically in the acini and in the central duct of MGs at P12 and P25 ($$n = 3$$ for each age). Data were presented as mean ± SD. Statistical significance was assessed using Mann Whitney test (Ctrl vs. cKO) and Wilcoxon signed rank test (acini vs. duct). ns, non-significant, P ≥ 0.05.
At P3, more than $70\%$ of MG cells displayed primary cilia emanating from the apical side of basal cells into the center of the developing gland (Fig. 3c, d, g). A similar proportion of MG cells were ciliated during early MG branching at P6 and P8 (Fig. 3e, g). At P6, primary cilia were visible in the elongating distal tip, the developing duct, and the budding acini; however, basal bodies of mature meibocytes located at the center of the central duct did not display cilia (Fig. 3e, yellow arrowheads). As MGs continued to mature, the percentage of ciliated cells progressively decreased to $30\%$ and $12\%$ at P12 and P25, respectively (Fig. 3g). The percentage of ciliated cells was similar in the duct and acini of MGs at P12 (Fig. 3h); however, at P25, primary cilia were absent in acini (Fig. 3f, h). At P25, most primary cilia were detected on the basal cells of the proximal region of the central duct. Still, some were also occasionally visible on the basal cells of the connecting ductules (Fig. 3f). Thus, primary cilia localized to basal cells of the developing MGs and disassembled as MG development progressed and meibocytes matured. However, cilia persisted on basal cells of the central duct of mature glands. Altogether, these results suggest a role of cilia during an early developmental step of MG morphogenesis primarily involving dividing and undifferentiated cells.
## Primary cilium ablation leads to the mislocalization of proliferating and apoptotic cells along the proximal-distal MG axis
Several studies have shown that primary cilium resorption is required for cell proliferation and have demonstrated either reduction or loss of primary cilia in various epithelial neoplasms (reviewed in39–41). Thus, we examined whether the abnormal expansion in size of MGs of the cilia mutant was due to an increase in cell proliferation rates. Proliferation rates were determined by counting the number of EdU-positive cells 6 h after EdU injection and normalized to the total number of cells identified by DAPI nuclear staining. To accurately assess the gland proliferation rates, we counted EdU-positive and DAPI-positive cells on serial sections of the eyelid, covering the total MGs thickness for each gland. Cell proliferation rates were assessed at P4, P6, and P21 when MGs were elongating, beginning to branch, and reaching their mature morphology, respectively10. The overall percentage of dividing cells in MGs of both mutant and control mice was ~$40\%$ at P4, decreased to ~$25\%$ at P6, and reached baseline values of ~$5\%$ at P21 with no significant differences detected between control and cKO mice at any of the time points analyzed (Fig. 4a–d).Fig. 4Primary cilium ablation did not change the overall rates of proliferation and dying cells in MGs but led to an abnormal distal/proximal localization of the proliferating and dying cells within MGs.a–c Cell proliferation was assessed by EdU staining in MGs at P4, P6 and P21 in control (a, b, and c) and cKO (a’, b’, and c’) mice, respectively. Scale bar, 100 µm; *, hair follicle; Elm, eyelid margin; d, distal; p, proximal. d–e Proliferation rates were quantified at P4, P6 and P21 in the full MGs (d), and specifically in the proximal (p) half (from the eyelid margin to the center of the gland) and the distal (d) half (from the middle to the tip of the gland) of MGs e. Proliferation rates were determined by normalizing the number of EdU-positive nuclei to the total number of nuclei stained by DAPI ($$n = 4$$/group). Data were presented as mean ± SD. Statistical significance was assessed using the Mann Whitney test (Ctrl vs. cKO) and Wilcoxon signed rank test (distal vs. proximal). ns, non-significant, P ≥ 0.05. ( f) Cell death was assessed by TUNEL staining in MGs at P21 in control (f) and cKO (f’) mice, respectively. Scale bar, 100 µm; *, hair follicle; Elm, eyelid margin; d, distal; p, proximal. ( g–h Cell death rates were quantified at P21 in the full MGs (g) and specifically in the central duct (h). Cell death rates were determined by normalizing the number of TUNEL-positive nuclei to the total number of nuclei stained by DAPI ($$n = 3$$/group). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test (total, Ctrl vs. cKO) and Wilcoxon signed rank test (distal vs. proximal). ns, non-significant, P ≥ 0.05.
Furthermore, we examined the distribution of proliferating cells along the proximal-distal axis of the glands. In control mice, $52\%$ and $28\%$ of the cells were dividing in the distal half of the gland at P4 and P6, respectively. In contrast, only $32\%$ and $22\%$ of cells were proliferating in the proximal half of the gland in control mice at P4 and P6, respectively. Thus, during the development of control MGs, proliferating cells were more abundant in the distal region than proximal (Fig. 4a, b, e). In contrast, proliferating cells were distributed uniformly along the length of the glands in the mutant (Fig. 4a’, b’, e). By P21, the distal enrichment of proliferative cells along the proximo-distal axis was no longer visible (Fig. 4c, e). Thus, during MG development, primary cilia ablation did not alter the overall cell proliferation rates but, instead, had an impact on preferentially concentrating the dividing cells to the distal half of the glands. However, the number of basal bodies associated with a primary cilium was not significantly different between the distal and proximal halves of MGs at P3, P6, and P8 (Supplement Fig. 3).
Next, we assessed whether the perturbation of the gland architecture, observed with cilia ablation, similarly altered the number or distribution of apoptotic cells. We quantified the percentage of apoptotic cells in mature MGs at P21 by TUNEL and DAPI staining. The overall percentage of TUNEL positive (TUNEL +) cells detected in the ductules, acini and the gland central duct, where the majority of apoptotic cells localized, was similar in both control and cKO mice (Fig. 4f, g). Although we didn’t detect spatial segregation of apoptotic cells in the MG of both control and cKO mice (Fig. 4g), when we considered only the gland central duct, a higher percentage of TUNEL + cells were localized to the distal half than to the proximal half in control mice (Fig. 4h). In contrast, in the central duct of cKO mice, apoptotic cells were uniformly distributed between the distal and proximal half of the glands (Fig. 4h). Thus, the ablation of primary cilia does not affect rates of cell death, but instead affects the localization of TUNEL + cells within the MG central duct. Collectively, these results indicate that the absence of cilia affects the distribution of dividing and apoptotic cells rather than the overall rates of proliferation and cell death, which subsequently alters the architecture and size of mutant MGs.
## Primary cilia orchestrate cell patterning during early MG development, control the central duct width and overall MG size but not MG branching
To determine how the absence of cilia affects MG morphogenesis and leads to abnormally larger glands, we examined the formation of cell patterns during early morphogenetic steps of MG development. The mG reporter expressed upon Cre-dependent homologous recombination in the mT/mG mouse allowed us to follow MG morphogenesis at the cell resolution in both mutant and control. At P1, when the epithelial invagination from the eyelid margin elongates into the eyelid mesenchyme, the meibomian anlages of the mutant displayed a similar size and overall shape of those observed in the control (Fig. 5a, b). However, while the cells of the invaginating epithelial anlage in control appeared well organized in one layer of basal cells surrounding a layer of suprabasal cells, in the mutant, this cellular pattern appeared less defined, and the basal and inner layers not clearly recognizable (Fig. 5a, b). As morphogenesis progressed, by P3 MG, primordia of the mutant were shorter but wider than those of the control (Fig. 5c–g). Moreover, the ratio of the number of basal cells compared to the number of cells in the center of the gland was significantly reduced in cKO mice, indicating that there were more cells in the central part of cKO MGs (Fig. 5h, i).Fig. 5Primary cilium regulates MG elongation.a–b Representative overview of whole mount tarsal plates at P1 imaged by confocal microscopy. MGs were visualized by the mG fluorescent reporter. Boxed regions indicate MGs shown at higher magnification, for which serial optical sections are displayed. Scale bar, 50 µm. c–d A representative overview of whole mount tarsal plates at P3 imaged by confocal microscopy. MGs were visualized by the mG fluorescent reporter. Boxed regions indicate MGs shown at higher magnification in e. Scale bar, 50 µm. MG length (f) and MG width (g) of cKO mice normalized to control ($$n = 5$$ mice/group). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test. h Basal cells (in purple) and central cells (in blue) were manually color-coded and then counted. i The ratio between basal cells and central cells was calculated ($$n = 5$$ mice/group). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test.
As MGs continue to develop, cells within the central cord of the epithelium expand at P6, the central duct begins to form, and lateral branches appear10. At P6 and P21, the width of the mutant central duct was $10\%$ and $30\%$ larger than the control, respectively (Fig. 6a–d). Because cell proliferation, apoptosis, and cell size remained unchanged in the MGs of both mutant and control, we reasoned that, at least during early developmental stages, the overall mass of the glands in both genotypes would remain similar. Using two-photon microscopy on whole mount samples of tarsal plates, we found no significant differences in the overall average volume between mutant and control MGs at P1, P3, or P4 (Fig. 6e, f). In contrast, at P8, the average volume of the mutant glands was nearly double that of the control glands (Fig. 6g, h). Finally, the number of acini was not significantly different between both genotypes (Fig. 6i). Thus, primary cilia promote the segregation of proliferating cells at the distal tip of the growing MGs, which in turn ensures a balanced growth in length and width of the MG duct (Fig. 6j). To determine whether ablation of cilia perturbs the outcome of known cilia-mediated signaling cascades, we examined the expression levels of selected genes known to be transcriptional targets or essential components of the Hedgehog (Hh), Wnt or Notch pathways in the tarsal plate of adult cKO and control mice by quantitative real-time PCR (RT-qPCR). The expression levels of genes associated with the Wnt and Notch signaling cascades detected in the mutant were indistinguishable from those found in the control (Fig. 6k). In contrast, we detected a significant reduction of mRNA levels of the *Gli1* gene, a transcription factor and a transcriptional target of the Hh pathway, in the mutant compared to the control (Fig. 6k). However, expression levels of additional genes of the Hh pathway, including the Hh ligands Desert (Dhh), Indian (Ihh) and Sonic hedgehog (Shh) and transcriptional targets, including CyclinD and Ptch1, remained unchanged in both. Future studies involving single-cell analysis will further our understanding of the role of primary cilia in MG morphogenesis and renewal. Fig. 6Primary cilium ablation induces dilation of the MG central duct and volume but does not affect MG branching.a–d Representative MG longitudinal sections at P6 (a) and P21 (c) and quantification of central duct diameter at P6 (b) and P21 (d). MGs were visualized by the mG fluorescent reporter, and MG central duct diameter (outlined by white dotted lines) was measured ($$n = 5$$ mice/group). Scale bar, 100 µm. e Representative overview of whole mount tarsal plates at P3 imaged by 2-photon. Scale bar, 100 µm. f MG volume at P1, P3, and P4 was quantified after 3D reconstruction of z-stacks using Imaris ($$n = 4$$ mice/group at P1, $$n = 5$$ mice/group at P3 and $$n = 3$$ mice/group at P4). Data were presented as mean ± SD. Statistical significance was assessed using the Kruskal-Wallis test. ns, non-significant, P ≥ 0.05. g Representative overview of whole mount tarsal plates at P8 imaged by 2-photon. Scale bar, 100 µm. h MG volume at P8 was quantified after 3D reconstruction of z-stacks using Imaris ($$n = 4$$ mice/group). Data were presented as mean ± SD. Statistical significance was assessed using the Kruskal-Wallis test. ns, non-significant, P ≥ 0.05. i MG branching was assessed by counting the number of acini per gland at P8 ($$n = 5$$ controls and $$n = 4$$ cKO). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test. ns, non-significant, P ≥ 0.05. ( j) Model of MG morphogenesis and cell patterning formation occurring during early development (from P1 to P3) in cKO and control. k RT-qPCR analysis of Hh (Dhh, Ihh, Shh, CyclinD, Gli1, Ptch1), Notch (Hes1, Hey1, Maml1, Notch1), Wnt (Axin2) target genes in isolated tarsal plates of cKO and control shows significant downregulation of Gli1 expression in mutant tissue relative to control at adulthood ($$n = 7$$ controls and $$n = 5$$ cKO). Data were presented as mean ± SD. Statistical significance was assessed using the Mann-Whitney test. ns, non-significant, P ≥ 0.05.
## Discussion
The primary function of the MGs is to secrete the meibum, a lipid layer that protects the ocular surface from hazardous environmental factors and desiccation1,42,43. Several factors can lead to functional defects of MGs for which there are no effective long-term treatments. Despite their critical role in maintaining clear vision, the molecular mechanisms and signaling networks underlying the development and maintenance of MGs remain poorly understood. Our study unveils the critical role of primary cilia in controlling the size of MGs and the amount of meibum produced. These findings shed light on the fundamental mechanisms of MG development and maintenance with important implications for designing MGD treatments. Here we have shown that a mutant mouse lacking cilia, via ablation of the *Ift88* gene in K14-expressing tissue, develops abnormally large MGs, which contain twice as much lipids as usual. However, gland enlargement was not achieved by alterations of proliferation or apoptosis rates. Instead, we demonstrated that the ablation of cilia in developing glands altered the cellular organization and the localization of dividing cells along their proximal-distal axis, modifying their cellular patterning and resulting in increased gland dimensions.
Using a transgenic mouse model with fluorescently labeled cilia and basal body, we have shown that primary cilia were present on meibocytes primarily during early MG development. Thus, to explain how developmental processes in cilia-deficient K14-expressing-tissue led to larger MGs in adult mice without changes in proliferation or apoptosis rates, we have examined morphogenesis and cell patterning of the developing MGs in control and cKO mice. We found that at P1, the cellular pattern of control glands budding from the fused eyelid margin appeared organized in one bordering layer of basal cells and one layer of suprabasal cells at the center of the elongating bud. In contrast, this cell pattern was perturbed in the MG buds of the cilia mutant, where cells appeared randomly organized with poorly discernible layers (Fig. 5). At P3, the solid epithelial cord continued to invaginate into the mesenchyme of the tarsal plate, and the number of central/suprabasal layers increased to 2 or 3. During the elongation phase between P4 and P6, the majority of the dividing cells localized at the distal tip of the developing glands of the control but were uniformly distributed along the length of the glands in the mutant. Thus, cilia of the MGs could be essential in sensing mitogenic signals required for proximo-distal elongation. We propose that failure to spatially segregate dividing cells in the growing gland could interfere with the elongation process at the distal tip of the glands. Because no difference in proliferation was detected, slower growth at the distal tip would consequently lead to an abnormal lateral expansion of the invaginating epithelial cords. Indeed, we found that the number of central/suprabasal cell layers abnormally increased to 4 or 5 in the gland of the cilia mutant, and as a result, developing glands were both wider and shorter than the control glands. Consistent with this possibility, the total volume of the glands remained similar in both mutant and control up to at least P4 (see the model in Fig. 6j). At P8, the volume of the mutant MGs increased significantly compared to the control; however, the gland length was similar in both. Thus, we hypothesize that as the glands grow, inhibitory signals could hinder their elongation, which eventually halts at full morphological maturation of the glands (P15). Therefore, while control MGs stopped elongating, mutant glands continued to grow and eventually reached the length of control glands. However, because the central duct was larger in the mutant glands, the overall size and lipid content of the mutant MGs were increased relative to the control. Similar dynamics could also occur during the genesis of the ductules. Future studies to elucidate the role of primary cilia in determining the localization of dividing cells at the distal tip of the developing MG may reveal a novel role for cilia in tissue morphogenesis.
Unlike SGs, MGs are not structurally linked to the hair follicle1. However, it is unclear whether the hair follicles of the eyelashes, which intercalate the MGs affect their development or homeostasis. In the skin, ablation of ciliogenic proteins in K14-expressing tissue leads to degeneration of the hair follicles via inactivation of the Hh signaling pathway23,29,44. However, defects in hair follicle formation and maintenance were evident only three weeks after birth, a point at which the MGs have already reached their mature configuration23. Thus, we can exclude any indirect role of the hair follicles on cilia-dependent defects affecting the MGs as reported in this study. It is widely accepted that the ciliary compartment is required for the propagation of the Hh signaling pathway and that ablation of Ift88 inhibits Hh responsiveness in several tissues45,46. Accordingly, we detected reduced mRNA levels of the transcription factor and transcriptional target gene Gli1 in the mutant tarsal plate when compared to the control. Studies in the skin have shown that the Hh pathway is critical for the development of SG, which share several basic features with the MGs47. Thus, our results showing abnormal enlargement of cilia-deficient MGs via Ift88 ablation may be inconsistent with Hh mediating MG development. Interestingly, however, ablation of cilia in K14-expressing cells compromised hair growth and maintenance via depletion of the Hh signaling but also resulted in enlarged and multilobulated SG in the mouse tail23. Thus, this evidence suggested an unusual and complex involvement of cilia in the cilia-Hh signaling axis in SGs and MGs. A thorough investigation of this interaction could reveal fundamental mechanistic insights in designing therapeutic strategies to address both skin-related conditions associated with ciliopathies, such as keratosis pilaris and seborrheic dermatitis, and MGD32.
Here we have shown that meibocytes lose their primary cilium as they mature. Similar to other epithelia, including the epidermis and the corneal epithelium, cilia were present on basal cells but disassembled as cells differentiated and moved apically22–24. In mature glands, ciliated cells were restricted to the proximal region of the central duct and at the basal layer of the ductules and acini. Intriguingly, it was demonstrated that in adult MGs, slow-dividing cells, which reflect a stem cell characteristic, localize to the ductules at the point where the central duct transitions to the acini48. Thus, it would be interesting to determine whether slow-cycling cells are ciliated. In the skin, primary cilia play a Hh-independent interfollicular role in the stratification of the epidermis at homeostasis23,24. Ablation of Ift88 increased rates of proliferation and led to an expansion of the basal layer with basal-like cells. However, hyperproliferation did not lead to the formation of tumors or blistering23. This is in contrast with our finding indicating that the expansion of MG does not involve an increase in proliferation rate. However, it must be noted that ablation of Ift88 in highly proliferating corneal epithelium during development or repair had no effect on proliferation rates22. Suggesting that cell proliferation could be a secondary and indirect effect of cilia ablation. Interestingly, in the skin and cornea primary cilia of epithelial basal cells modulated the Notch pathway independently of cell proliferation22,24. Although our results from RT-qPCR of tarsal plate tissue suggest that the primary cilium in MGs of adult mice are not required to transduce the Notch signaling, it is possible that potential differences in mRNA levels of Notch target genes between mutant and control are under the detectable margin of this approach. Indeed, we have shown that only a small number of cells remain ciliated in mature MGs. Thus, future studies at the single-cell level could provide a more comprehensive understanding of cilia involvement in Notch and MG maintenance. Interestingly, it was shown that suppression of the Notch signaling pathway in progenitor cells of SGs led to atrophy of the gland49. In contrast, Notch ablation outside the stem cell compartment drove SG expansion49. In another study, ablation of Notch1 in K14-expressing tissue led to the formation of cyst-like structures replacing the MGs50. Thus, it would be interesting to determine a possible involvement of the primary cilium in the transduction of the Notch pathway at the single cell level in relation to stem cell niche maintenance in MGs51.
Lipid analysis has shown that ablation of cilia during MG development led to a two-fold increase in lipid content. Although we have demonstrated the involvement of cilia in regulating MG size, it remains unclear whether the cilium also plays a more direct role in meibum production. The Peroxisome proliferator-activated receptor-γ (PPARγ) is a member of the nuclear receptor family of ligand-activated transcription factors implicated in regulating adipocyte and sebocyte differentiation as well as lipogenesis52,53. Moreover, PPARγ is also involved in meibocyte differentiation in vivo and in vitro and is required for the upregulation of genes implicated in lipid production54–56. Recent studies have shown that in the context of injury-induced adipogenesis, ablation of cilia in fibro/adipogenic progenitors (FAP) via Ift88 deletion inhibited the production of PPARγ and FAP differentiation into adipocytes57,58. These studies would argue against a direct involvement of the MG cilia in PPARγ-dependent lipogenesis or meibocyte differentiation since cilia ablation in MGs led to an increase in lipid amount and an expansion of meibocyte mass. However, unlike most cell types, primary cilia of FAPs and cells in the developing limb bud inhibit GLI1 and PATCHED1 expression by promoting GLI3 repressor formation. Consequently, the ablation of cilia led to an increase, rather than a decrease, of the Hh signaling activity59. Several studies have shown the complex role of the Hh pathway in adipogenesis and PPARγ regulation60–63. Thus, future investigation addressing the role of GLI proteins and, more broadly, the role of the Hh pathway in MGs development and homeostasis will provide critical mechanistic insights into our understanding of meibocyte differentiation, renewal, and meibogenesis.
To date, treatment options for MGD and DED are limited. Physical treatments seeking to increase the quality and quantity of meibum did not achieve satisfactory long-term results64–66. Other approaches aiming to substitute the lipid layer with topical application of lipid-containing artificial tears and emulsions are challenging given the complex structure and composition of the lipid layer of the ocular surface42,67 Thus, expanding treatment strategies by directly targeting MG renewal and lipid production have the potential to not only relieve ocular surface discomfort but also improve the quality of life in affected patients. This work revealed that cilia-mediated pathways control MGs expansion and lipid production without affecting the lipid composition, pointing to a novel therapeutic target to combat MGD.
## Mice
Mouse strains Ift88tm1Bky (here referred to as Ift88fl/fl)68, B6N.Cg-Tg(KRT14-cre)1Amc/J (K14-Cre, Jackson Laboratory stock No 018964)69, and Gt(Rosa)26Sor(tm4(ACTB-tdTomato,-EGFP)Luo)/J (mT/mG), Jackson Laboratory stock No 007676)34 were maintained on mixed C57Bl/6, FVB and 129 genetic backgrounds. The Ift88 conditional knockouts (cKO) were generated by crossing K14-Cre;Ift88fl/+ males with Ift88fl/fl females. Other allelic combinations than K14-Cre;Ift88fl/fl (cKO) were considered as controls (Ctrl). Mouse strain Tg(CAG-Arl13b/mCherry)1 K and Tg(CAG-EGFP/CETN2)3-4Jgg/KandJ (here referred to as Arl13b-mCherry;Centrin2-GFP)37 was purchased at Jackson Laboratory (stock No 027967). All animal procedures were performed in accordance with the guidelines and approval of the Animal Care and Use Committee at Johns Hopkins University and with the ARVO Statement for the Use of Animals on Ophthalmic and Vision Research.
## Histology and immunofluorescence staining
Upper and lower eyelids from P6 and P21 mice were dissected, fixed overnight in $4\%$ paraformaldehyde (PFA) in PBS, and embedded in paraffin for histological analysis. Hematoxylin and eosin (HE) staining was performed following standard procedures. Sections were imaged with an Olympus slide scanner VS200 (Olympus, Center Valley, PA). Eyelids from P3 mice were dissected, fixed for 30 min to 2 h in $4\%$ PFA in PBS and embedded in optimal cutting temperature compound (OCT Tissue-Tek, Sakura Finetek, Torrance, CA). Cryosections were processed for primary cilium staining. After 10 min fixation with cold acetone (−20 °C) and 20 min permeabilization with $0.5\%$ Triton X-100 in PBS, sections were incubated with a mouse anti-acetylated tubulin antibody (1:1000, T6793, Sigma-Aldrich, St Louis, MO) and/or a rabbit anti-ARL13B antibody (1:800, 17711-1-AP, ProteinTech Group, Rosemont, IL) in $2\%$ BSA/$0.1\%$ Triton X-100/PBS overnight at 4 °C. Sections were then incubated in secondary fluorescent antibodies donkey anti-rabbit Alexa Fluor™ 647 (1:500, A-31573, Thermo Fisher Scientific, Waltham, MA), donkey anti-mouse fluorescein (FITC) (1:200, 715-095-150, Jackson ImmunoResearch Laboratories Inc., West Grove, PA) and donkey anti-mouse Rhodamine (TRITC) (1:200, 715-025-150, Jackson ImmunoResearch Laboratories Inc.) in $2\%$ BSA/PBS for 2 h. Sections were mounted with VECTASHIELD Antifade Mounting Medium (H-1000, Burlingame, CA) and imaged with a Zeiss LSM880 confocal microscope (Zeiss, Jena, Germany).
## Fluorescent imaging of whole mount MGs
Eyelids from P1, P3, P4 and P8 K14-Cre;Ift88fl/fl;mT/mG mice (cKO) and K14-Cre;Ift88fl/+;mT/mG (Ctrl) littermates were dissected, fixed in $4\%$ PFA in PBS for 1 h and washed in PBS. During the fixation, most of the connective tissues and muscles covering the tarsal plate were manually removed. MGs were mounted in $90\%$ glycerol and imaged with a LSM880 confocal microscope (for samples at P1) or a LSM710/NLO two-photon microscope (for samples at P3, P4 and P8). Serial optical sections were acquired in 1 or 2 µm steps through the entire MG. MG volume was quantified after 3D reconstruction of the z-stacks with Imaris (Bitplane, South Windsor, CT), and the number of acini per MG was manually counted.
## Oil Red O (ORO) staining in whole-mount MGs
Eyelids from P6, P8, and P21 mice were dissected, fixed in $4\%$ PFA in PBS for 1 h and washed in PBS. During the fixation, most of the connective tissues and muscles covering the tarsal plate were removed. MGs were stained for 1 h in ORO solution (Electron Microscopy Sciences, Hatfield, PA) at room temperature (RT), rinsed with distilled H2O, mounted in $90\%$ glycerol and imaged with an Olympus MVX10 dissecting scope (Olympus). MG size was determined by averaging the MG area of individual MGs measured with Fiji70.
## Cilia localization
Eyelids from P3, P6, P8, P12 and P25 Arl13b-mCherry;Centrin2-GFP mice were dissected and fixed for 1 h in $4\%$ PFA/$1\%$ Triton X-100 (Mallinckrodt Pharmaceuticals, Staines-upon-Thames, United Kingdom) in PBS. Eyelids were then processed for K14 staining on whole MGs or embedded in OCT. For whole MGs, prior to staining, most of the connective tissues and muscles covering the tarsal plate were removed and tarsal plates were permeabilized for 1 h with $2\%$ BSA/$1\%$ Triton X-100/PBS at RT. MGs for whole mount samples and cryosections were stained with a rabbit polyclonal antibody directed to K14 (1:1000, 905301, BioLegend, San Diego, CA). Nuclei were counterstained with DAPI on cryosections. MG whole mounts (P3, P6, and P8) and cryosections (P12 and P25) were imaged with a Zeiss LSM880 confocal microscope. Serial optical sections were acquired in 1 µm steps. After 3D reconstruction of the z-stacks with Imaris, MGs were outlined using the Surfaces tool, and primary cilia and basal bodies were counted in MGs using the Spots tool. Centrin2-GFP-labeled centrioles were considered as a single basal body when less than 2 µm apart. Since the number of basal bodies was similar to the number of nuclei as shown in Supplement Fig. 4, the number of ciliated cells in MGs was determined by normalizing the number of primary cilia to the number of basal bodies and expressed as a percentage.
## In vivo cell proliferation assay
Mice at P4, P6 and P21 received a single intraperitoneal injection of 50 mg/kg EdU (EdU-Click 594, baseclick, Germany) and were sacrificed after 6 h, as described elsewhere71. Eyelids were dissected and snap-frozen in OCT. Twenty-micron cryosections were processed following manufacturer instructions (EdU-Click 594, baseclick, Germany). Briefly, sections were fixed for 15 min with $4\%$ PFA in PBS, permeabilized for 20 min with $0.5\%$ Triton X-100 in PBS and then stained for 30 min with the reaction cocktail in the dark at RT. MGs were localized using the mT/mG fluorescent reporter or stained using a rabbit anti-K14 polyclonal antibody (1:1000, 905301, BioLegend). Nuclei were counterstained with DAPI. Sections were imaged with a Leica DMI6000 microscope equipped with a Yokogawa confocal spinning disc or a Zeiss LSM880 confocal microscope. For each cryosection, serial optical sections were acquired in 2.45 μm steps. For each mouse, serial cryosections were processed to acquire the entirety of the MGs. After 3D reconstruction of the z-stacks with Imaris, MGs were outlined using the Surfaces tool, and EdU-positive nuclei and DAPI-positive nuclei were counted in each MG using the Spots tool. Quantification was performed on the MGs located in the center of the upper eyelid. The cell proliferation rate per MG was determined by normalizing the number of EdU-positive nuclei to the number of DAPI-positive nuclei. Quantification of the EdU-positive and DAPI-positive nuclei was also performed, separating the proximal part (from the eyelid margin to the middle of the gland) and the distal part (from the middle of the gland to the tip) of the MGs.
## Cell death TUNEL assay
Eyelids from P21 mice were dissected, fixed overnight with $4\%$ PFA in PBS and embedded in paraffin. Sections were processed for apoptosis immunofluorescent staining using the In situ Cell Death Detection Kit, TMR Red (Roche Applied Science, Mannheim, Germany), as described in72. Sections were permeabilized for 15 min with 10 µg/mL proteinase K in 10 mmol/L Tris/HCl (pH 7.4) at RT and then stained for 1 h with the reaction mixture at 37 °C in the dark. Nuclei were counterstained with DAPI. Sections were imaged with a Zeiss LSM880 confocal microscope. TUNEL-positive and DAPI-positive nuclei were counted on 3 serial sections, 20 µm apart from each other. The cell death rate per MG was determined by normalizing the number of TUNEL-positive nuclei to the number of DAPI-positive nuclei.
## Quantitative RT-qPCR
Tarsal plates were isolated by dissection and were immediately submerged in RNAlater (AM7020, ThermoFisher, Waltham, MA) and stored at −20 °C for up to 1 month. RNA was extracted from eyelids using the RNeasy mini kit (Qiagen, Germantown, MD) as per the manufacturer’s instructions. One microgram of RNA was reverse transcribed using the SuperScript III reverse transcriptase kit (18080051, ThermoFisher) as per the manufacturer’s instructions. Quantitative PCR was carried out using SYBR green PCR master mix (4309155, ThermoFisher) in 20 µL in duplicate on a CFX96 qPCR (BioRad, Hercules, CA), machine using a 2-step cycle with annealing temperature of 60 °C. Quantification was carried out using the 2-ΔΔcT method73 with the geometric mean of Gapdh and Polr2a used as normalization74. Primer sequences are listed in Table 1.Table 1RT-qPCR primers. GeneForwardReverseReferenceAxin25′-CTCCCCACCTTGAATGAAGA-3′5′-ACTGGGTCGCTTCTCTTGAA-3′22CyclinD5′-AAGTGCGTGCAGAAGGAGAT5′-TTAGAGGCCACGAACATGC-3′22Dhh5′-TGGCATTGTGAGTTTCCTCCT-3′5′-AGCATGGACTTGGTTGGCTT-3′79Gapdh5′-CATCACTGCCACCCAGAAGACTG-3′5′-ATGCCAGTGAGCTTCCCGTTCAG-3′22Gli15′-TCCGGGCGGTTCCTACG-3′5′-ACCATCCCAGCGGCAGTCT-3′22Hes15′-GGAAATGACTGTGAAGCACCTCC-3′5′-GAAGCGGGTCACCTCGTTCATG-3′22Hey15′-CCAACGACATCGTCCCAGGTTT-3′5′-CTGCTTCTCAAAGGCACTGGGT-3′22Ihh5′-CTGCAAGGACCGTCTGAACT-3′5′-TGGCTTTACAGCTGACAGGG-3′22Maml15′-CCAGCTTTGATGGCATATCTTCC-3′5′-CTACAGGGACACTGGAAGGGTT-3′22Notch15′-GCTGCCTCTTTGATGGCTTCGA-3′5′-CACATTCGGCACTGTTACAGCC-3′22Polr2a5′-CGAGAAGGTCTCATTGACACGG-3′5′-ACCACCTGGTTGATGGAGTTCC-3′MP211208, Origene, Rockville, MDPtch15′-ACGGGGTCCTCGCTTACAAAC-3′5′-TCTCGTAGGCCGTTGAGGTAGAA-3′22Shh5′-TGTGTTCCGTTACCAGCGAC-3′5′-AGCGAGGAAGCAAGGATCAC-3′79
## Lipid analysis
Meibomian lipids were extracted from surgically excised mouse tarsal plates (4 from each mouse) at 4 °C using three sequential extractions with a chloroform:methanol (3:1, vol:vol) solvent mixture. The extracts (3 x 1 mL) were pooled, and the solvent was evaporated under a stream of compressed nitrogen at 37 °C. The oily residue was redissolved in 1 mL of LC/MS quality iso-propanol and stored in a nitrogen-flushed, crimper-sealed HPLC 2-mL autoinjector vial at −80 °C before the analyses.
The gradient and isocratic reverse-phase liquid chromatography/high-resolution time-of-flight atmospheric pressure chemical ionization mass spectrometry (LC/MS) analyses were conducted using, correspondingly, an Acquity UPLC C18 (1 mm × 100 mm; 1.7 µm particle size) and an Acquity UPLC C8 (2 mm × 100 mm; 1.7 µm particle size) columns (both from Waters Corp., Milford, MA, USA) as described in detail in our earlier publications for mouse and human meibum75–77. Between 0.5 and 1.0 µL of the sample solution was injected per experiment. A Waters Acquity M-Class binary ultra-high performance LC system (UPLC, Waters Corp.) was operated at a 20 µL/min flow rate. The analytes were eluted using acetonitrile/iso-propanol isocratic solvent mixtures with $5\%$ of 10 mM ammonium formate as an additive. The analytes were detected using a high-resolution Synapt G2-Si QToF mass spectrometer [equipped with a ZSpray interface, an IonSabre-II atmospheric pressure chemical ionization (APCI) ion source, and a LockSpray unit (all from Waters Corp.)]. All the experiments were conducted in the positive ion mode. Most of the lipids were detected as (M + H)+ and (M + H – H2O)+ adducts. The major lipid analytes were identified using the EleComp routine of the MassLynx v.4.1 software package (Waters Corp.). However, some of the compounds, for example, triacylglycerols and cholesteryl esters, underwent spontaneous in-source fragmentation producing (M + H − fatty acid)+ species and were additionally characterized as (M + Na)+, (M + K)+, and (M + NH4)+ adducts. Finally, chromatographic retention times and mass spectra of major analytes were compared with those of authentic lipid standards (where available). The results of a detailed analysis of the MG lipidome of mutant mice are to be reported separately.
The total lipid production by MGs was estimated on the basis of the total ion chromatograms of their lipid extracts recorded in isocratic LC/MS APCI experiments as described recently77. The unbiased, untargeted analysis of the lipidomic data was conducted using Progenesis QI and EZinfo software packages (Waters Corp.). The Principal Component Analysis (PCA) approach was used to evaluate the differences between the control and cKO samples.
## Statistics and Reproducibility
For each experiment, control and cKO mutant littermates from at least 2 different litters were compared. Data were presented as mean ± SD. Mann Whitney test was used to compare the cKO mutant to the control mice, Wilcoxon signed-rank test was used to compare different regions of the same glands (acini vs. duct and proximal vs. distal halves of MGs or central ducts), and the Kruskal-Wallis test with Dunn post-hoc test was used to compare the percentage of ciliated cells per gland throughout MG development and the MG volume throughout MG development. Statistical tests were performed with the online web statistical calculators https://astatsa.com/ or RStudio78. A P value < 0.05 was considered significant.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Peer Review File Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04632-5.
## Peer review information
Communications Biology thanks Duarte Barral and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Tiago Dantas and Eve Rogers. Peer reviewer reports are available.
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|
---
title: Lateral hypothalamic leptin receptor neurons drive hunger-gated food-seeking
and consummatory behaviours in male mice
authors:
- Young Hee Lee
- Yu-Been Kim
- Kyu Sik Kim
- Mirae Jang
- Ha Young Song
- Sang-Ho Jung
- Dong-Soo Ha
- Joon Seok Park
- Jaegeon Lee
- Kyung Min Kim
- Deok-Hyeon Cheon
- Inhyeok Baek
- Min-Gi Shin
- Eun Jeong Lee
- Sang Jeong Kim
- Hyung Jin Choi
journal: Nature Communications
year: 2023
pmcid: PMC10023672
doi: 10.1038/s41467-023-37044-4
license: CC BY 4.0
---
# Lateral hypothalamic leptin receptor neurons drive hunger-gated food-seeking and consummatory behaviours in male mice
## Abstract
For survival, it is crucial for eating behaviours to be sequenced through two distinct seeking and consummatory phases. Heterogeneous lateral hypothalamus (LH) neurons are known to regulate motivated behaviours, yet which subpopulation drives food seeking and consummatory behaviours have not been fully addressed. Here, in male mice, fibre photometry recordings demonstrated that LH leptin receptor (LepR) neurons are correlated explicitly in both voluntary seeking and consummatory behaviours. Further, micro-endoscope recording of the LHLepR neurons demonstrated that one subpopulation is time-locked to seeking behaviours and the other subpopulation time-locked to consummatory behaviours. Seeking or consummatory phase specific paradigm revealed that activation of LHLepR neurons promotes seeking or consummatory behaviours and inhibition of LHLepR neurons reduces consummatory behaviours. The activity of LHLepR neurons was increased via Neuropeptide Y (NPY) which acted as a tonic permissive gate signal. Our results identify neural populations that mediate seeking and consummatory behaviours and may lead to therapeutic targets for maladaptive food seeking and consummatory behaviours.
Eating behviours consist of seeking and consummatory phases. Here, authors show that two distinct lateral hypothalamic leptin receptor neurons orchestrate seeking and consummatory phases of eating behaviour via hunger signal, Neuropeptide Y.
## Introduction
Eating consists of various motivated behaviours. Each such behaviour is regulated by distinct drives modulated by external information and internal state information1–3. Furthermore, these eating behaviours are multi-phase behaviours which initiate with appetitive phase (seeking behaviours), that sequentially leads to a consummatory phase (when animal is proximate to the food, manipulation of food by biting, chewing then finishing with intake of food occurs)4,5. Since seeking and consummatory behaviours have distinct characteristics in aspects of motivational state and behavioural decision, it is physiologically crucial for two distinct functional populations to guide each behaviour. Similarly, regarding other context motivated behaviours (mating or aggression), previous studies have shown that seeking and consummatory behaviours are regulated by distinct neural populations6–8. Although several studies have investigated food seeking and consummatory neurons9,10, the identity of these two distinct neuronal populations is yet to be clarified.
Several studies have highlighted the lateral hypothalamus gamma aminobutyric acid (LHGABA) neurons to be heterogenous9,11–13 populations driving various motivated behaviours such as food consumption9,14, chewing objects15,16 exploring novel environments17,18, thermoregulation19 and social interaction20. In addition, even within the eating context, it has been suggested that there are two distinct LHGABA neuron populations that encode seeking and consummatory behaviours9,10. These findings suggest that there could be two distinct eating specific subpopulations of LHGABA neurons which are responsible for appetitive and consummatory behaviours. To elucidate which subpopulations in LHGABA neurons exclusively contribute to seeking and/or consummatory behaviours, several studies have been dedicated to identifying subpopulations and neural circuits10,16,17,20. LH leptin receptor expressing (LHLepR) neurons are subpopulation of LHGABA neurons and has been reported to be associated with eating10,16,21–23. However, the role of LHLepR neurons is controversial; no effect on eating10,24, decreased eating16, decreased eating after leptin treatment in the LH22, and increased eating by LHLepR–Ventrolateral Periaqueductal Grey (vlPAG) circuit23.
Employing in vivo calcium imaging and phase specific behavioural tasks, we identified two distinct LHLepR neural populations that are separately activated during seeking and consummatory behaviours, respectively. Further, neural activation results clearly demonstrated that LHLepR neurons are sufficient for driving seeking behaviours and consummatory behaviours. Also, neural inhibition results clearly showed that LHLepR neurons are necessary for driving consummatory behaviours.
Given that Agouti-related peptide/neuropeptide Y(AgRP/NPY) neurons deliver food-need information to downstream neurons (including the LH) via NPY25, we hypothesized that NPY neurotransmitters regulate LHLepR neural activity. The present study showed that NPY is sufficient to increase LHLepR neural activity through a disinhibition mechanism. Collectively, these data highlight the orchestration of eating phases within the LH circuitry and how these circuits are regulated by hunger signals.
## Overview of multiphasic experimental paradigms
To investigate seeking and consummatory behaviours, we developed phase specific tests to dissect two phases via temporal distinctions. behaviours, we developed seeking phase specific tests, which minimised consummatory behaviours (manipulating, licking, biting, chewing, and swallowing). To exclusively measure consummatory behaviours, we developed consummatory phase specific tests, which minimised seeking behaviours (searching and digging) (Supplementary Fig. 9a).
## LHLepR neurons are the food-specific subpopulation of LHGABA neurons
To investigate the heterogeneous LHGABA neurons and test if LHLepR neurons are part of food-specific LHGABA subpopulation, we first investigated the anatomical distribution of LHLepR neurons via whole-LH three-dimensional (3D) tissue clearing (Supplementary Movie 1) and 2D histological mapping using LepR-tdTomato mice (Supplementary Fig. 1a–k). As a result, LHLepR neurons were mainly distributed in the middle region (−1.5 mm from bregma).
According to our mapping results, vesicular GABA transporter (Vgat)-cre and LepR-cre mice were injected with cre-dependent adeno-associated virus (AAV) carrying GCaMP6s and implanted a gradient index (GRIN) lens in the middle LH (Fig. 1a, b). Using micro-endoscopic imaging of calcium dynamics, we analysed three eating behaviours in fasted mice; running toward expected food (seeking behaviour, Fig. 1e, f left in the food test), approach toward proximate food (consummatory behaviour, Fig. 1e, f middle in food test) and chewing the proximate food (consummatory behaviour, Fig. 1e, f right in the food test). We first measured individual LHGABA neural activity (Fig. 1c) during these tests compared to non-food behavioural test (chewing behaviour towards inedible Lego brick, Fig. 1e, f non-food test). We defined neurons as food-specific responsive (yellow) when they were activated during all three eating behavioural tests and not activated during a non-food behavioural test. Non-food-specific responsive neurons (blue), non-specific responsive neurons (grey), and no responsive neurons (white) were defined based on neural activity patterns during the tests (see ‘Methods’).Fig. 1LHLepR neurons are the food-specific subpopulation of LHGABA neurons.a, b Schematic of micro-endoscopic calcium imaging (left, middle), and image of GCaMP6s expression (right) in the LH from Vgat-cre (a) and LepR-cre mice (b). The experiment was repeated 6 times (a) or 4 times (b) independently with similar results. fx, fornix; 3 V, the 3rd ventricle. c, d Spatial map of raw data (left), accepted cells using CNMFe (middle), and cells that only respond to food-related behaviour (right) from LHGABA neurons (c) and LHLepR neurons (d). Cells are coloured according to the maximum Z-score. Scale bar: 50 μm. e, f Schematic of the multi-phase test 2, consummatory behaviour test 1, consummatory behaviour test 2 (food and non-food) (top). Heatmap depicting calcium signals aligned to the onset of feeding behaviours (running to food, rearing to food, contact with food, contact with edible object) (below). Four populations are discriminated: food-specific responsive (yellow), non-specific responsive (grey), non-food-specific responsive (blue), and non-responsive (white) cells. ( LHGABA neurons 218 cells, 6 mice (e), LHLepR neurons 48 cells, 4 mice (f)). g, m Representative traces of four populations from LHGABA neurons (g) and LHLepR neurons (m). The dotted line separates each behavioural experiment. h, k Venn diagram of food responsive and non-food responsive neurons. Percentage of food-responsive neurons are as follows (LHGABA neurons $8\%$ ($\frac{18}{218}$ cells) (h), LHLepR neurons $63\%$ ($\frac{30}{48}$ cells) (k)). i, l Proportion of food-specific responsive (yellow), non-specific responsive (grey), non-food-specific responsive (blue), and non-responsive (white) cells from LHGABA neurons (i) and LHLepR neurons (l). j Venn diagram simulating the number of LHLepR positive (yellow) and food-specific (grey) neurons when the total number of LH GABA neurons is simulated as 1000. Source data are provided as a *Source data* file. The schematics in a, b, e and f were created using BioRender.
Among LHGABA neurons, most neurons ($64\%$) were activated in non-food behavioural tests (Fig. 1e, g–i, grey and blue panels). Instead, only a small subpopulation of neurons ($8\%$) was food-specific responsive neurons (activated only in eating behaviour-related tests) (Fig. 1e, g–i, yellow panel), suggesting that only a small food-specific subpopulation exists within the vast total population of LHGABA neurons. Of note, when LHLepRcre mice conducted the same experiments (Fig. 1f), most LHLepR neurons ($63\%$) were food-specific responsive (Fig. 1f, k–m, yellow panel).
Based on the previous single-cell RNA sequencing data for the LH26, LHLepR neurons are mostly GABAergic (Supplementary Fig. 1l), consistent with previous results21,23. Further, LHLepR neurons constitute only $4\%$ of LHGABA neurons (VGAT positive cells) (Supplementary Fig. 1m). A previous study has reported that LHLepR neurons constitute less than $20\%$ of LHGABA neurons21. Although LHLepR neurons represented only a minor portion (4–$20\%$) of LHGABA neurons (Supplementary Fig. 1m), our results indicate that most ($79\%$; $\frac{63}{80}$) of food-specific responsive LHGABA neurons are LHLepR neurons (Fig. 1j, Supplementary Fig. 2k). Furthermore, LHLepR neurons were not activated to non-food investigation (Supplementary Fig. 2f–j). Compared to the robust response to food, only a minor response was observed to water (Supplementary Fig. 2a–e). These results suggest that LHLepR neurons are food-specific population among LHGABA neurons.
## LHLepR neurons are activated during seeking and consummatory behaviours
Next, to investigate temporal dynamics of LHLepR neural activity during eating behaviour, neural activity was measured using fibre photometry at the population level (Fig. 2a, b). LHLepR neural activity significantly increased at each eating bout with time-locked temporal dynamics in fasted mice (Fig. 2c–g, Supplementary Fig. 2f–j, Supplementary Movie 2). Interestingly, LHLepR neural activity increased even before physical contact with food, implying that LHLepR neurons may also be involved in seeking behaviours. Fig. 2Activity of LHLepR neurons is time-locked to seeking and consummatory behaviours.a Schematic of virus injection/fibre insertion for fibre photometry in LH from LepR-cre mice. b A representative image validates GCaMP6s expression in LepR neurons and optical fibre tract above the LH. Scale bar: 500 µm. The experiment was repeated 5 times independently with similar results. fx, fonix. c Schematic of the consummatory behaviour test 1 (obtainable). d *Representative calcium* traces from LHLepR neurons. Yellow shaded box: from the moment of food contact to the end of food consumption. e Average Z-score from LHLepR calcium response aligned to contact with food (5 mice, 22 trials). f Quantification of Z-score in calcium signal change from (e). Comparison between baseline (−8 to −7 s) and after contact (9 to 10 s). g Heatmap depicting normalised LHLepR neural activity aligned to the moment of contact with food. h Schematic of the multi-phase test 2. i, j *Representative calcium* traces (i) and average Z-score (j) from LHLepR neural calcium signal response aligned to voluntary seeking initiation (4 mice, 67 trials). k Quantification of Z-score in calcium signal change from (j). Comparison between baseline (−8 to −7 s) and after voluntary seeking behaviour (2 to 3 s). l Heatmap depicting normalised LHLepR calcium signal aligned to voluntary seeking behaviour. m Representative neural onset of LHLepR calcium signal aligned to the onset of voluntary seeking behaviour. Yellow line near –5 s is the neuronal onset timing. n, o Cumulative probability distribution (n) and histogram (o) of LHLepR neural onset (4 mice, 66 trials). Neural onset occurred at −5.623 ± 0.418 s. p Schematic and schedule of the multi-phase test 1. q, r Dynamic feeding phase before conditioning (q) and after conditioning (r). s Time from food accessibility to food contact before and after conditioning ($$n = 4$$ mice). t, u *Representative calcium* signal of LHLepR neurons aligned to food accessibility (left) and quantification of Z-score in calcium signal change (right) before (t) and after (u) conditioning. Comparison between baseline (−2 to −1 s) and after locomotion or seeking behaviour (1 to 2 s). Two-sided paired t-test; n.s., $p \leq 0.05$ (t), *$$p \leq 0.02$$ (u). Data are mean ± s.e.m. See Supplementary Table 1 for statistics. Source data are provided as a *Source data* file. The schematics in a, c, h, p, q and r were created using BioRender.
To precisely measure the temporal onset of LHLepR neural activity with regard to voluntary behavioural onset, fasted mice were conditioned with random probability of electrical shock in the maze. Therefore, the mice hesitated before initiating seeking behaviours from the shelter. When the drive for food was higher than the fear of an electric shock, mice made the voluntary decision to initiate seeking. As expected, LHLepR neural activity began to increase significantly before the mice initiated voluntary seeking (Fig. 2h–l, Supplementary Movie 2).
To precisely analyse the LHLepR neural activity onset, we calculated the derivatives from polynomial regression traces from calcium activity traces27,28. By calculating the time point when the 3rd derivative reaches its maximum value, we calculated the neuronal onset when the neuronal activity begins to increase. As a result, LHLepR neural activity onset significantly preceded the onset of seeking by an average of approximately 6 s (Fig. 2m–o). Additional tests revealed that LHLepR neural activity decreased when mice voluntarily terminated both seeking or consummatory behaviours (Supplementary Fig. 3a–j). These results provide temporal causality evidence that LHLepR neurons are the cause and drive for voluntary seeking behaviours, not the consequence of seeking behaviour (Supplementary Fig. 3a–j, Supplementary Movie 2), suggesting that LHLepR neural activity is associated with voluntary behaviours.
To dissect seeking and consummatory phase, we developed a multi-phase test to provide sufficient temporal distinction between seeking and consummatory behaviours (Fig. 2p, Supplementary Movie 2). In the L-shaped chamber, fasted mice sequentially explored an empty corridor and arrived proximate to food. Before conditioning, mice explored the whole maze since mice were not aware of the food location (non-goal-directed locomotion, Fig. 2q). LHLepR neural activity did not increase during this non-goal-directed locomotion (Fig. 2t). LHLepR neural activity significantly started to increase when mice conducted consummatory behaviours at the end of the corridor. However, after conditioning (Fig. 2r), the mice moved directly to the food at the end of the corridor (goal-directed seeking; significantly shorter time from accessibility to food contact) (Fig. 2s). When compared with the neural activity results before conditioning, LHLepR neural activity started to increase significantly when the mice initiated seeking, and there was an additional activity increase in the consummatory phase (Fig. 2u).
## Two distinct subpopulations of LHLepR neurons individually encode seeking and consummatory behaviours
Our photometry data showed that LHLepR neural population is activated sequentially at seeking and consummatory behaviours. We thought that two hypotheses could be possible; [1] one homogenous LHLepR neuronal population encodes both seeking and consummatory behaviours, or [2] two distinct LHLepR neuron populations encode seeking or consummatory behaviours, respectively. However, individual neural dynamics is not accurately reflected in the fibre photometry. To prove this hypothesis, we investigated changes in LHLepR neural activity using micro-endoscope during seeking and consummatory behaviours (Fig. 3a, Supplementary Movie 5). To distinguish between seeking and consummatory behaviours, we modified the multi-phase test described above (Fig. 2h). During food sessions, fasted mice sequentially performed seeking and consummatory behaviours (Fig. 3b left). In contrast, during no-food sessions, mice performed seeking, but not consummatory behaviours since food was not present in food zone (Fig. 3b right). We identified two distinct neural populations that specifically responded to seeking or consummatory behaviours (Fig. 3c), which was robustly consistent across numerous trials (Supplementary Fig. 4). One population of neurons were activated only during seeking and not during consummatory behaviours (seeking LHLepR neurons) (Fig. 3f–i, Supplementary Fig. 4a). Another population of neurons were activated only during consummatory and not during seeking behaviours (consummatory LHLepR neurons) (Fig. 3j–m, Supplementary Fig. 4b). The two populations were distinctively separated in a 3D scored plot which consists of phase-specific scores that describe the neuronal characteristics (Fig. 3d). Among the population of LHLepR neurons, $25\%$ were seeking neurons, and $39\%$ were consummatory neurons (Fig. 3e). Collectively, our micro-endoscope data showed that seeking LHLepR neurons and consummatory LHLepR neurons; [1], respectively, encode seeking or consummatory behaviours [2] are sequentially activated (Fig. 3c) and are exclusively activated (not simultaneously activated).Fig. 3Two distinct populations of LHLepR neurons encode seeking and consummatory behaviours.a Schematic of virus injection/GRIN lens insertion for micro-endoscopic calcium imaging in the LH from LepR-cre mice. b Schematic of the multi-phase test 2. Seeking with consummatory behaviours, in the presence of food (left). Seeking without consummatory behaviours, in the absence of food (right). c Representative single-cell traces of LHLepR neurons within several trials (left) and one trial (right). Green shaded box indicated seeking behaviours and purple shaded box indicated consummatory behaviours. d Three-dimensional scored plot of LHLepR neurons. e Proportion of cell populations. f, j Representative contour map of seeking (f, green) and consummatory (j, purple) neurons of an accepted cell (top). The degree of colour brightness represents the cell activity degree (max Z-score) (bottom). g, k Representative single-cell traces of LHLepR neurons of seeking (g) and consummatory (k) neurons during food and no-food trials. h, i, l, m Heatmap depicting the calcium signals (top) and average Z-scores (bottom) of seeking neurons (h, i) or consummatory neurons (l, m). The magnitude of the calcium signals corresponds to its colour density. ( 4 mice, 15 cells (h, i), 4 mice, 25 cells (l, m)). Data are mean ± s.e.m. See Supplementary Table 1 for statistics. Source data are provided as a *Source data* file. The schematics in a and b were created using BioRender.
## LHLepR neurons fail to evoke eating behaviours in experiments with combination of seeking and consummatory phases
Optogenetic stimulation induces simultaneous activation of both seeking and consummatory LHLepR neurons, which are unphysiological in contrast to our physiological micro-endoscope results. These results imply that optogenetic activation of both seeking and consummatory LHLepR neurons will not induce effective behavioural changes if the mice have choice of both seeking and consummatory behaviours due to competition between two distinct behavioural choices.
To examine this hypothesis, LepR cre-mice were injected with cre-dependent channelrhodopsin 2 (ChR2)/halorhodopsin (NpHR) or enhanced yellow fluorescent protein (EYFP) AAV vector, and an optic fibre was implanted in the LH (Fig. 4a, b). We conducted a multi-phase test, in which ad-libitum mice had choice of both seeking and consummatory behaviours in a large chamber (33 × 33 × 33 cm) (Fig. 4c). As expected, unphysiological simultaneous activation/inhibition of both seeking and consummatory LHLepR neurons failed to show any change in seeking (food zone duration and food zone entry number) or consummatory (food contact number and food intake) behaviours (Fig. 4d–g, Supplementary Fig. 5a–j).Fig. 4Activation of LHLepR neurons drives seeking or consummatory behaviours.a, b Schematic of optogenetic activation and image of ChR2 expression in LHLepR neurons. The experiment was repeated at least 4 times independently with similar results. fx, fornix; 3 V, the 3rd ventricle. c Schematic of the multi-phase test 3. d–g Number of food zone entries (d), duration in the food zone (e), number of food contacts (f) and food intakes (g) ($$n = 4$$ mice). Two-sided paired t-test; n.s., $p \leq 0.05.$ h, Schematic and schedule of the seeking behaviour test 2. i Raster plot during (h). j Behavioural probability from (i). k–n Quantification of distance moved (k), total digging duration (l), number of digging behaviours (m) and frequency of food zone entries (n) ($$n = 7$$ mice). Two-sided paired t-test; *$$p \leq 0.02$$ (k, pre vs laser), *$$p \leq 0.04$$ (l, pre vs laser), **$$p \leq 0.003$$ (l, laser vs post), **$$p \leq 0.002$$ (m, pre vs laser), ***$$p \leq 0.00018$$ (m, laser vs post), *$$p \leq 0.04$$ (n, pre vs laser). o Schematic of the consummatory behaviour test 3. p, Raster plot during (o). q–s Number (q) and duration (r) of consummatory behaviours, and food intake (s) ($$n = 5$$ mice). Two-sided paired t-test; **$$p \leq 0.007$$ (q, pre vs laser), *$$p \leq 0.0105$$ (q, laser vs post), *$$p \leq 0.0105$$ (r, pre vs laser), **$$p \leq 0.005$$ (r, laser vs post), *$$p \leq 0.015$$ (s, pre vs laser), *$$p \leq 0.017$$ (s, laser vs post). Data are represented as mean ± s.e.m. See Supplementary Table 1 for statistics. Source data are provided as a *Source data* file. The schematics in a, c, h and o were created using BioRender.
## LHLepR neurons evoke seeking or consummatory behaviours in phasic-specific conditions
Our micro-endoscope data distinguished two distinct subpopulation (seeking and consummatory LHLepR neurons) that drive respective behaviours, which are sequentially activated and not simultaneously activated. Therefore, we hypothesised that activation of LHLepR neurons could evoke respective seeking or consummatory behaviours, when the seeking or consummatory phase was isolated so that mice only had a choice of one specific behaviour.
To isolate the seeking phase, mice were conditioned to seek hidden foods in the four corners of an open-field chamber filled with bedding (Fig. 4h, Supplementary Movie 3). On the photostimulation day, ad libitum mice were placed in the same chamber covered with bedding without food to only evoke sustained seeking behaviour. Activation of LHLepR neurons significantly increased seeking behaviours (digging with the nose, digging with the paw, and digging after floor exposure), entry into the food zones, and seeking locomotion compared to no-stimulation (Fig. 4i–n) or control conditions (Supplementary Fig 5k–n). However, inhibition of LHLepR neurons failed to show significant differences in seeking behaviours (Supplementary Fig. 5o–r). Collectively, these results show that LHLepR neurons are sufficient to drive seeking behaviours when the seeking phase is isolated.
To isolate the consummatory phase, ad-libitum mice were placed in a chamber of minimised size (17 × 6 × 30 cm) and were provided with ad libitum food at proximate range (Fig. 4o). Of note, activation of LHLepR neurons significantly increased the number and total duration of consummatory behaviours and food intake when compared to no stimulation (Fig. 4p–s, Supplementary Movie 3). EYFP control mice did not show any significant change in consummatory behaviour (Supplementary Fig. 5s–w). We further performed two validation tests using different behaviour analysis methods to accurately analyse consummatory behaviours. First, by using a manual behaviour analysis method, we revealed that closed-loop stimulation of LHLepR neurons when mice were proximate to food significantly increased consummatory behaviours compared to no stimulation conditions (Supplementary Fig. 6a–d, Supplementary Movie 3). Second, we used a deep-learning-based animal pose estimation method (DeepLabCut)29 to computationally extract consummatory behaviours from trials (Supplementary Fig. 6e–i, Supplementary Movie 4). This computerized analysis also validated that stimulating LHLepR neurons significantly evoked consummatory behaviour.
Next, we hypothesised that inhibition of LHLepR neurons decreases consummatory behaviours. Fasted mice were tested in a small chamber where the mice could perform only consummatory behaviours rather than seeking (Figs. 5a, 5b left). To quantify the consummatory behaviour, multiple small snacks were presented during several interleaved photoinhibition blocks (Fig. 5b right, Supplementary Movie 3). During the session, the mice exhibited consummatory behaviours (sniffing, biting and chewing). NpHR mice, but not EYFP control mice, significantly decreased consummatory behaviours (total duration and bout duration) (Fig. 5c-g, Supplementary Fig.7). Collectively, these results show that LHLepR neurons are sufficient and necessary for driving consummatory behaviours when the consummatory phase is isolated. Fig. 5Inhibition of LHLepR neurons decreases consummatory behaviours.a Schematic of optogenetic inhibition (left, middle) and image of NpHR expression in LHLepR neurons (right). The experiment was repeated 8 times independently with similar results. 3V, the 3rd ventricle; STN, subthalamic nucleus; cp, cerebral peduncle. b Schematic of the consummatory behaviour test 5 and schedule of laser stimulation. c Raster plot of consummatory behaviours during (b) ($$n = 8$$ mice). d Average duration of consummatory behaviours (top). Calibrated graph (bottom) of the top. Two-sided paired t-test; **$$p \leq 0.006$$ (time bin 2–4 min vs 4–6 min), ***$$p \leq 0.0007$$ (time bin 4–6 min vs 6–8 min), ***$$p \leq 0.0009$$ (time bin 10–12 min vs 12–14 min), *$$p \leq 0.02$$ (time bin 12−14 min vs 14–16 min), *$$p \leq 0.019$$ (time bin 14–16 min vs 16–18 min). e–g Total duration (e), bout duration (f), and number (g) of consummatory behaviours. Two-sided paired t-test; ****$p \leq 0.0001$ (e), ***$$p \leq 0.0009$$ (f), n.s., $$p \leq 0.93$$ (g). Data are mean ± s.e.m. See Supplementary Table 1 for statistics. Source data are provided as a *Source data* file. The schematics in a left and b were created using BioRender.
## NPY increases LHLepR neuron activity via disinhibition of GABAergic interneuron in the LH
We hypothesised that NPY drives LHLepR neuron activity since [1] a previous study clearly demonstrated that NPY is a key neurotransmitter that drives sustained eating behaviours after the deactivation of AgRP neurons30, [2] AgRP/NPY neurons innervate LH31,32, [3] NPY receptors are distributed in the LH33–35 and [4] the administration of NPY in the LH drives eating behaviours36,37. To examine if LHLepR neurons respond to NPY, we performed slice calcium imaging where artificial cerebrospinal fluid (ACSF) with NPY was applied to brain slices containing GCaMP6s-expressing LHLepR neurons (Fig. 6a). As a result, calcium in LHLepR increased after NPY treatment (Fig. 6b, c). The effect of NPY was not observed in the presence of NPY receptor (NPYR) antagonists (Fig. 6d–f). The effect of NPY in the presence of NPYR antagonists was significantly lower than that of NPY alone (Fig. 6g–j). In addition, the effect of NPY on LHLepR neurons was significantly higher than leptin (Supplementary Fig. 8a–d). The response to leptin was heterogenous, which was similar to previous studies22,24, reporting that substantial proportion of LHLepR neurons showed decreased activity in response to leptin. Therefore, we speculate that leptin may suppress eating via inhibiting LHLepR neurons. Fig. 6NPY increases LepR neuron activity via disinhibition of GABAergic interneuron in the LH.a, d Representative image of GCaMP6s-expressing LHLepR neurons during the application of NPY (a) or NPY + Antagonist (d) using brain slice calcium imaging. The degree of colour brightness represents the degree of cell activity (max Z-score). Scale bar: 50 μm. The experiment was repeated 7 times (a) or 9 times (d) independently with similar results. b, e Representative traces of calcium activity of LHLepR neurons marked in (a or d). Calcium activity aligned to application of NPY (b) or NPY + Antagonist (e). Dotted black line is start of NPY application. Application scheme is shown on top. c, f Heatmap depicting calcium signals aligned to application of NPY (c) or NPY + Antagonist (f). g Average Z-score from the LHLepR calcium signal aligned to application of NPY (red) and NPY + Antagonist (purple). h, i Quantification of AUC (h) and max Z-score (i) before and after application of NPY or NPY + Antagonist. 68 cells from 7 slices (a), 70 cells from 9 slices (d). Two-sided paired t-test; ***$$p \leq 0.0002$$ (h, left), *$$p \leq 0.019$$ (i, left), two-sided unpaired t-test; *$$p \leq 0.014$$ (h, right), **$$p \leq 0.002$$ (i, right). j Quantification of the percentage of active cells. Two-sided unpaired t-test; *$$p \leq 0.014.$$ k Representative images of td-Tomato-expressing LHLepR neurons during brain slice whole-cell recording. 3 V, the 3rd ventricle. l Representative traces of spontaneous inhibitory postsynaptic current comparing ACSF (top) and NPY (bottom). m–p Time course of sIPSCs frequency (m), amplitude (n) and quantification of normalised sIPSCs frequency (o), amplitude (p) in the last 1 min after the ACSF or NPY application. td-Tomato-expressing LHLepR neurons; 8 cells from ACSF, 6 cells from NPY. Two-way repeated measures ANOVA followed by Bonferroni post hoc test (m, n); ***$$p \leq 0.0005$$ (m), two-sided paired t-test (o, p); *$$p \leq 0.043$$ (o, right). Data are mean ± s.e.m. See Supplementary Table 1 for statistics. Source data are provided as a *Source data* file.
Since NPY is reported to have an inhibitory effect on neural activity38,39, we sought to determine the mechanism underlying the excitatory effect of NPY on LHLepR neurons. According to our analysis of previous single-cell RNA sequencing data26, major portion of NPY receptor expressing LH neurons (LHNPYR neurons) were part of a GABAergic population distinct from that of LHLepR neurons, while minor portion co-expressed NPY receptor and leptin receptor neurons (LHNPYR/LepR neurons) (Supplementary Fig. 1l). Therefore, we assumed that the excitatory effects of NPY in LHLepR neurons arose from the major part of the two portions, by disinhibition of presynaptic GABAergic neurons. To confirm whether the activation of LHLepR neurons resulted from a decrease in presynaptic inhibitory inputs, we recorded spontaneous inhibitory postsynaptic currents (sIPSCs) in LHLepR neurons before and after the application of NPY (Fig. 6k, l). The frequency of sIPSCs was significantly decreased with application of NPY, but not ACSF alone (Fig. 6m, o). However, there were no changes in the amplitude of sIPSCs (Fig. 6n, p), suggesting an effect of NPY on presynaptic GABAergic neurons. Overall, these results suggest that NPY increases the activity of LHLepR neurons by decreasing inhibitory inputs.
## Discussion
The present study demonstrated that two distinct LHLepR neuronal populations are activated sequentially and exclusively during the seeking and consummatory eating phases. Further, activation of LHLepR neurons evoked seeking or consummatory behaviours. Neurotransmitter NPY disinhibited LHLepR neurons as a permissive gate signal. Collectively, the present study findings suggest that two distinct LHLepR neuronal populations drive seeking and consummatory behaviours gated by the NPY signal.
Using in vivo micro-endoscope imaging, the present study clearly demonstrated that LHLepR neurons comprise [1] two distinct populations (seeking and consummatory LHLepR neurons), which are sequentially activated during seeking and consummatory behaviours and [2] encode the voluntary drive for eating behaviours. The present study discovered findings compared to previous literature, as follows. A previous micro-endoscope study concluded that LHLepR neurons are one specific population that discriminates between reward cues and non-reward cues10. However, this paper did not provide a conclusion regarding different subpopulations among LHLepR neurons. Another previous micro-endoscope study on LHLepR neurons did not classify subpopulation heterogeneity of LHLepR neurons and did not differentiate the different phases of eating23. In contrast, applying our comprehensive eating behavioural paradigm, we could successfully distinguish the two distinct populations. In the present study, one population of LHLepR neurons (seeking LHLepR neurons) were only activated during seeking behaviours, not with those for consummatory behaviours. Further, the LHLepR neural activity onset precedes the voluntary seeking behaviour initiation onset, which suggests that LHLepR neurons are drivers of seeking behaviour rather than the consequence of seeking behaviour. The other population of LHLepR neurons (consummatory LHLepR neurons) is only activated during consummatory behaviours, not during seeking behaviours. This population robustly starts to be activated when animals are proximate to food and sustain its activity during consummatory behaviours. These two distinct populations are sequentially activated within the two distinct behavioural phases in eating. For survival, it is crucial for eating behaviours to be correctly sequenced and successfully executed through two distinct phases: seeking (appetitive) and consummatory phases4,5. This is equivalent to other motivated behaviours such as social or mating behaviours6,8.
Our optogenetics results clearly demonstrated that the causal role of LHLepR neurons in driving seeking and consummatory behaviours via eating phase-specific paradigms. Previous studies reported controversial results that activation of LHLepR neurons decrease eating16 or fails to drive eating10,24. Another study showed that activation of LHLepR -vlPAG neurons drive eating23. To investigate the underlying mechanism of these controversial results, we conducted the following experiments with three phase-specific designs. Since our single-cell resolution results of LHLepR neuron using micro-endoscope robustly distinguished two distinct subpopulations (seeking and consummatory behaviours), we hypothesised that optogenetic stimulation of LHLepR neurons should be conducted during each phase-specific design. As expected, in seeking phase-specific experiments (when only seeking behaviours are possible), LHLepR neurons were sufficient to drive seeking behaviours (searching and digging for expected food). This is consistent with previous results showing that activation of LHLepR neurons increase operant behaviour (lever presses) for food since the operant conditioning test is a one of the seeking-phase-specific experiments21. Regarding the consummatory phase-specific experiments (when only consummatory behaviours are possible), as expected, LHLepR neurons are sufficient and necessary for consummatory behaviours only in consummatory phase-specific experiments. On the other hand, in experiments with combination of seeking and consummatory phases (when both seeking and consummatory behaviours are possible), activation of LHLepR neurons failed to evoke seeking or consummatory behaviours (Fig. 4c–g), similar to the previous studies10. These phase context-specific optogenetics results provide the neural mechanistic explanation why previous research failed to show increase food intake with large chamber (standard rat/mouse housing cage) experiments where both seeking and consummatory behaviours are possible10. These phase context-specific optogenetics results are consistent with our micro-endoscope results regarding two distinct phase-specific activation patterns. These results provide wider understanding of how LHLepR neurons regulate seeking and consummatory behaviours.
Collectively, our data clearly showed that LHLepR neurons fulfil the major criteria necessary to identify them as eating phase specific neurons1,40; they are sufficient to drive seeking/consummatory behaviour, they are necessary for consummatory behaviours, LHLepR neurons are activated during seeking and consummatory behaviours. We suggest that the two distinct types of seeking and consummatory LHLepR neurons could have different molecular or connectivity identities. Since voluntary seeking and consummatory behaviours must precede decision-making through the integration of sensory modality information, the medial prefrontal cortex (mPFC) or insular cortex might mediate this process by communicating with LHLepR neurons41,42. Seeking and consummatory LHLepR neurons should have distinct upstream and downstream neurons to specifically drive seeking or consummatory behaviours, respectively. Since LHGABA-Ventral Tegmental Area (VTA)14,43, LHGABA-vlPAG23,44 and LHGABA-Locus Coeruleus (LC)43 have been known to mediate eat behaviour, LHLepR seeking or consummatory neurons may innervate VTA, vlPAG or LC. Further, the LH is known to receive input from (mPFC), Orbitofrontal Cortex (OFC), Nucleus Accumbens (NAc), Arcuate Nucleus (ARC) and Nucleus Tractus Solitarii (NTS)45. LHLepR neurons also received monosynaptic input from diverse regions such as intra LH, Anterior Cingulate (ACC), Diagonal Band of Broca (DBB), Tuberomammillary Nucleus (TMN) and Ventral Premammillary Nucleus (PMV)46. Neural circuit mechanisms related to LHLepR neurons could be elucidated through future studies with activity-dependent tagging, molecular subtyping and projection-specific labelling.
Previously, it was believed that AgRP/NPY neurons directly drive the whole phase of eating behaviours47,48. However, recent research has indicated that AgRP/NPY neurons deactivate even in response to a food cue49. This suggests that, after the inactivation of AgRP/NPY neurons, another set of neuron drive seeking or consummatory behaviours50,51.
In addition, a recent study demonstrated that NPY is a crucial neurotransmitter responsible for sustained hunger after AgRP inactivation30. In the present study, the ex vivo results indicated that NPY administration gates LHLepR neurons into an active state via decreasing tonic inhibition (Fig. 6). The effect of NPY was completely abolished in the presence of NPYR antagonist, which further confirms NPYR specific mechanism. This implies that in a sated state, low NPY concentration from low AgRP/NPY neural activity, produces tonic inhibition as a lock, prohibiting LHLepR neurons from responding to food-related cues (Supplementary Fig. 9b). In contrast, in fasted state, high NPY concentration from high AgRP/NPY neural activity unlocks this tonic inhibition and allows LHLepR neurons to generate appropriate eating drive in response to diverse food-related cues. Of note, our in vivo results indicated that LHLepR neuron activity begins to increase in response to seeking (e.g. availability) and consummatory-related cues (e.g. proximate to food), during fasted state (high NPY concentration) (Supplementary Fig. 9b). This internal state-dependent conditional action of eating drive increases rate of survival by restricting eating behaviours only in the fasting state and by avoiding futile and unnecessary behaviours while the animal is sated. Together, our ex vivo results imply that NPY plays a permissive gate role in the manipulation of LHLepR neurons.
Given that a small population of LHLepR neurons co-express NPYR, there may be another alternative mechanism that acts directly through NPY. Both the indirect permissive gate role and the direct role of NPY are not mutually exclusive and could have different complementary roles for the orchestration of LHLepR neuron activity, which requires additional investigation.
The present study provides insight into the role of two distinct LHLepR neurons in orchestrating seeking and consummatory behaviours in eating gated by hunger signal from AgRP/NPY neurons. Understanding the neural circuit mechanism for multi-phase eating behaviours may provide specific treatment options for patients with maladaptive food-seeking and consummatory behaviours.
## Animals
All experimental protocols were performed in compliance with the Guide for the Care and Use of Laboratory Animals from the Seoul National University, and approved by the Seoul National University Institutional Animal Care and Use Committee. Mice were housed on a 08:00 to 20:00 light cycle (temperature 22 ± 1 °C, humidity 50 ± $10\%$) with standard mouse chow (38057, Purina Rodent chow) and water provided ad libitum, unless otherwise noted. Behavioural tests were conducted during the light cycle. Adult male mice (at least 8 weeks old) of the following strains were used: LepR-Cre (JAX stock no.008320), Ai-14 Td-Tomato (JAX stock no. 007914), Vgat-Cre (JAX stock no. 028862)
## sStereotaxic virus injection
Mice were anaesthetised with xylazine (20 mg/kg) and ketamine (120 mg/kg). A pulled-glass pipette was inserted into the LH (400 nl total; AP, −1.5 mm; ML, ±0.9 mm; DV, 5.25 mm from the bregma) based on the 2D LHLepR distribution (Supplementary Fig. 1a–k). The GCaMP6 virus (AAV1.Syn. Flex. GCaMP6s. WPRE.SV40, Addgene 100843; titre: 1.45 × 1013 genome copies per ml with 1:2 dilution) was utilised for calcium imaging. The AAV5.EF1α. DIO.hChR2(H134R).EYFP (Addgene 20298; titre: 2.4 × 1013 genome copies per ml) or AAV5.EF1α. DIO.eNpHR3.0.EYFP (Addgene 26966; titre: 1.1 × 1013 genome copies per ml) or AAV5.EF1α. DIO.EYFP (Addgene 27056; titre: 2.6 × 1013 genome copies per ml) was utilised for optogenetic experiments.
## Optical fibre/GRIN lens insertion
For fibre photometry experiments, a ferrule-capped optical cannula (400 µm core, NA 0.57, Doric Lenses, MF2.5, $\frac{400}{430}$–0.57) was unilaterally placed 0–50 µm above the virus injection site and attached to the skull with Metabond cement (C&B Super Bond). For optogenetic manipulation, optic fibres (200 µm core, NA 0.37, Doric Lenses or Inper) were bilaterally implanted 100–200 µm above the LH injection site at a 10° angle from the vertical in the lateral-to-medial direction. For micro-endoscope imaging, a GRIN lens (500 µm core, 8.4 length, Inscopix #1050-004413) was inserted after 3 weeks of recovery following virus injection. Dexamethasone, ketoprofen, and cefazolin were administered for postoperative care.
## Calcium imaging using fibre photometry and micro-endoscope
For bulk calcium imaging, we used a Doric Lenses fibre photometry system. In the experiment, 465 nm and 405 nm LED light sources (Doric LED driver) were delivered continuously through a rotary joint (Doric Lenses, FRJ_1×1_PT-$\frac{400}{430}$/LWMJ-0.57_1m) connected to the patch cord (Doric Lenses, MFP_$\frac{400}{430}$/1100-0.57_1m), and the GCaMP6 signal was collected back through the same fibre into the photodetector (Doric Lenses). For single-cell calcium imaging, we used nVoke (Inscopix).
## Optogenetics
Laser stimulation (473 nm for activation and 594 nm or 532 nm for inhibition, Shanghai DPSS Laser) was delivered through an FC-FC fibre patch cord (Doric Lenses) connected to the rotary joint, following which the FC-ZF 1.25 fibre patch cord delivered stimulation to the cannula (200 µm core, NA 0.37, Doric Lenses or Inper). The laser intensity was approximately 10 mW at the tip.
## Animal condition
Prior to the experiments, all mice were habituated to the experimental cages, and fibre handling was conducted for at least 3 days. Chocolate-flavoured snack (Oreo O’s, $\frac{1}{8}$ aliquot: 0.2 g) was utilised during eating behavioural tests.
## Multi-phase test 1
The multi-phase test 1 (Fig. 2p) is a behavioural paradigm test with seeking and consummatory phases designed to provide sufficient temporal distinction between seeking and consummatory behaviours. To measure neuronal activity before and after conditioning with food, fasted (80–$90\%$ of the body weight in the ad libitum state) mice received a chocolate-flavoured snack at the edge of an L-shaped chamber (60 cm × 8.5 cm) with a shelter (6 cm × 12 cm × 18 cm triangle box). Conditioning sessions (day 1–2) were performed for 15 trials in 2 days to provide sufficient experience for the mice to learn the location of the food by providing a chocolate-flavoured snack. The test session (day 3) was also performed for 15 trials. Each trial started when a door was removed (“accessibility moment”) with scheduled timing from the experimenter. ‘ Proximate to food’ was analysed when the mouse arrived at the top of bridge. ‘ Food contact’ was defined as the moment when the mouse physically contacted the food. Mice usually entered the shelter spontaneously after each trial (end of consumption). Otherwise, the experimenter closed the door after gently pushing the mice to the shelter.
## Multi-phase test 2
The multi-phase test 2 (Fig. 1e, f food test first column, Fig. 2h, Fig. 3b) is a behavioural paradigm test which mimicked the natural environment of mice in a cave, running to seek and consume food despite the risk of outdoor threats. To measure the temporal onset of LHLepR neural activity in voluntary behaviour, we eliminated all reward-associated cues (e.g., door open, sound) in the experiment. We placed a shelter as cave and delivered an electrical shock as punishment in a square chamber (30 cm × 30 cm square chamber with narrow corridors sized 6 cm). Electrical shock was given at a mean of 0.2 mA/shock for 7 s with a 10-s interval. We adjusted the total duration of shock delivery to maximise the performance of mice. During conditioning sessions, fasted (80–$90\%$ of the body weight in the ad libitum state) mice received a chocolate-flavoured snack at the edge of chamber. During the test session, we exclude the shock and analysed the moment when the mouse’s whole body came out of the shelter (onset of seeking behaviours). Trials that were successful in consuming food were analysed. For micro-endoscope experiments, food and no-food trials were conducted randomly during the test session without shock.
## Multi-phase test 3
The multi-phase test 3 (Fig. 4c, Supplementary Fig 5a, f) is a behavioural paradigm test which was designed to provide ad libitum accessibility to both seeking and consummatory behaviours, simultaneously. To measure seeking and consummatory behaviours during photostimulation, sucrose agarose gel ($30\%$ sucrose in $3\%$ agarose gel) was placed in a food tray (3 cm height) at one side of the open-field box (33 × 33 × 33 cm). Condition of mice was as followed; ad libitum (ChR2)/fasted (NpHR). The food zone was defined as the zone that included the food tray. The size of food zone was defined as approximately 10 cm × 10 cm.
## Seeking behaviour test 1
The seeking behaviour test 1 (Supplementary Fig. 3a) is a seeking-specific behavioural paradigm test which was designed to evoke only seeking behaviours without any consummatory behaviours. To measure the neural activity during seeking termination, we randomly presented food cue (vertical stripe) and no-food cue (horizontal stripe). During conditioning, fasted (80–$90\%$ of the body weight in the ad libitum state) mice received chocolate-flavoured snacks only when the food cue was presented. The success rate ([S2/W], S1 = number of seeking termination, S2 = number of consumptions after food cue, W = S1 + S2) was recorded during training until it reached $80\%$. The duration and amplitude of shocks during training were optimised for each mouse to achieve the best success rate. During experiment, fasted mice initiated seeking after presentation of the food cue, but eventually terminated voluntarily, when the mice realised there was no food.
## Seeking behaviour test 2
The seeking behaviour test 2 (Fig. 4h, Supplementary Fig 5k, o) is a seeking specific behavioural paradigm test which was designed to evoke sustained seeking behaviours without any consummatory behaviours. To solely measure seeking behaviours during photostimulation, we conditioned mice to conduct seeking behaviours but removed food at the test day. For the conditioning sessions, chocolate-flavoured snacks or raisins were hidden under the wooden bedding at each edge of the open-field box. Twice a day for 3 consecutive days, the ad libitum mice (ChR2/Control) or fasted mice (NpHR) were allowed to seek the box for hidden food during the 10 mins of the experiment. For the test session, there was only wooden bedding without food in which ad libitum mice (ChR2/Control/NpHR) were put to test. Food zone was defined as four corners divided into 16 zones. Seeking behaviours were analysed in three behaviours manually: digging with nose, digging with paw and digging after floor exposure in food zone.
## Consummatory behaviour test 1
The consummatory behaviour test 1 is a consummatory specific behavioural paradigm test which was designed to evoke consummatory behaviours with or without swallowing. To measure neural activity during consummatory behaviours, a chocolate-flavoured snack was placed in the tray on one side of the wall. During obtainable height (8 cm) sessions (Fig. 1e, f food test, second column, Fig. 2c), the fasted (80–$90\%$ of the body weight in the ad libitum state) mice engaged in sequential consummatory behaviours such as rearing toward visible food, biting, licking, and swallowing. We analysed the moment the mice made physical contact with the hanging food. During the unobtainable height (11 cm) sessions (Supplementary Fig. 3f), the fasted mice initiated consummatory behaviour, rearing toward the visible food, but eventually terminated consummatory behaviours when the mice realised that the mice could not eat it. We analysed the moment the mice voluntarily terminated the consummatory behaviours to the hanging food.
## Consummatory behaviour test 2
The consummatory behavioural test 2 (Fig. 1e, f food test, third column, non-food test, fourth column; Supplementary Fig. 2f) is a consummatory behavioural paradigm test which was designed to determine whether the neural activity is food specific. To measure neural activity during consummatory behaviour for edible food or inedible non-food objects, fasted (80–$90\%$ of the body weight in the ad libitum state) mice performed chewing behaviour toward food (chocolate-flavoured snack) or an inedible object (a Lego brick). We analysed the moment when the mice made physical contact with the food or inedible object.
## Consummatory behaviour test 3
The consummatory behavioural test 3 (Fig. 4o, Supplementary Fig. 5s–w) is a consummatory specific behavioural paradigm test which was designed to evoke consummatory behaviours without any seeking behaviours. To solely measure consummatory behaviours during photostimulation, we minimised chamber size (17 × 6 × 30 cm). Ad libitum mice (ChR2/Control) or fasted mice (NpHR) were placed in the chamber with sucrose agarose gel ($30\%$ sucrose, $3\%$ agarose). During photostimulation, consummatory behaviours were measured; food contact, biting and chewing.
## Consummatory behaviour test 4
The consummatory behavioural test 4 (Supplementary Fig 6a, e) is a consummatory specific behavioural paradigm test which was designed to evoke consummatory behaviours without any seeking behaviours. To solely measure consummatory behaviours during photostimulation using DeepLabCut behavioural analysis, ad libitum mice were placed in a transparent chamber (10 cm × 10 cm × 15 cm) with a vivid colour food (cheese-flavoured snack). The test was recorded from below (ELP-USB4KHDR01-KV100, no-distortion camera) and from the side (Microsoft LifeCam HD-3000, no-distortion camera). During the testing session, laser stimulation (sham or real) was administered manually for 10 s when the head of the mouse directly faced the food recording the bottom and side views. Bottom and side views of the recorded movies were used for DeepLabCut analysis (24 frames per second). We labelled the snout, mouth (upper jaw, oral commissure, lower jaw), hands, paws, tail base, and food. We trained the network with 960 frames (in the bottom view) or 600 frames (in the side view) using a cut-off of 0.9 p for a total of 500,000 times. For manual behavioural analysis (Supplementary Fig. 6a–d), ad libitum mice were placed in a steel wire cup (10 cm diameter) with a cheese-flavoured snack which is a vivid colour food. The period of consummatory behaviours (licking annotated while tongue was visible, biting) was annotated.
## Consummatory behaviour test 5
The consummatory behavioural test 5 (Fig. 5b) is a consummatory specific behavioural paradigm test which was designed to evoke discrete short consummatory bouts using small food portions without any seeking behaviours. To solely measure consummatory behaviours during photostimulation, we conducted experiment in a minimised chamber size (13 × 17 × 30 cm). The mice (fasted 16–24 h) were given ad libitum chocolate-flavoured snacks. On the test day, laser stimulation was delivered for 20 min at 2-min intervals.
## Water test
Mice were dehydrated for 2 days. The water test was performed using an open-field chamber where a water bottle was placed. We analysed the moment when mice licked the spout of the water bottle.
## 3D clearing
Fixed tissue was incubated in reflective index matching solution (C Match, Cat.50-3011) at 37 °C for 2 days. Images were obtained using SPIM (LaVision Biotech, Bielefeld, Germany) and analysed using IMARIS 9.5 (Bitplane AG, Zürich, Switzerland).
## Calcium imaging of brain slices
Brain was extracted after mice were decapitated under isoflurane anaesthesia at least 3 weeks after virus injection. LH slices were dissected to a thickness of 250 μm using a vibratome (VT1200S, Leica, Nussloch, Germany) in ice-cold standard artificial cerebrospinal fluid (ACSF) containing the following (in mM): 125 NaCl, 2.5 KCl, 1 MgCl2, 2 CaCl2, 1.25 NaH2PO4, 26 NaHCO3, and 10 glucose, bubbled with $95\%$ O2 and $5\%$ CO2. For recovery, the slices were incubated at 32 °C for 15 min and then further incubated for 1 h at room temperature. The slices were then transferred to the recording chamber and perfused with ACSF at 32 °C during imaging. Calcium measurements were performed using a CMOS camera (Photometrics, Tucson, AZ) attached to an upright microscope (BX50WI, Olympus, Tokyo, Japan) with a 40X or 10X water-immersion objective (NA 0.8 or 0.3, LUMPlanFL N or UMPlanFl; Olympus) at 10 frames per second. A broad white light source (pE-340 Fura, CoolLED, Andover, UK) was passed through an excitation filter (450–480 nm) and collected through an emission filter ($\frac{525}{50}$ nm). Fluorescence images were acquired using VisiView software (Visitron Systems GmbH, Puchheim Germany).
## Whole-cell patch-clamp recording
Brain was extracted after mice were decapitated under isoflurane anesthesia from LepR-tdTomato mice. LH slices were dissected to a thickness of 250 μm using a vibratome (Leica, VT1200S) with carbogen-saturated ($95\%$ O2 and $5\%$ CO2) sucrose solution containing the following (in mM): 75 NaCl, 75 sucrose, 25 glucose, 26 NaHCO3, 7 MgCl2, 2.5 KCl, 1.25 NaH2PO4, and 0.5 CaCl2. For recovery, slices were incubated at 32 °C for 15 min in standard ACSF containing the following (in mM): 125 NaCl, 2.5 KCl, 1 MgCl2, 2 CaCl2, 1.25 NaH2PO4, 26 NaHCO3, and 10 glucose, bubbled with $95\%$ O2 and $5\%$ CO2. Following further incubation for 1 h at room temperature, the slices were transferred to the recording chamber and perfused with ACSF at 32 °C during recording. Whole-cell patch-clamp recordings were performed in LH neurons expressing tdTomato using EPC9 (HEKA, Ludwigshafen am Rhein, Germany). The resistance of the pipette was 2–5 MΩ when filled with an intracellular solution containing the following (in mM): 135 CsMS, 10 CsCl, 10 HEPES, 0.2 EGTA, 4 Na2-ATP, and 0.4 Na3-GTP (pH 7.2–7.3). All electrophysiological recordings were started at least 4 mins after the whole-cell configuration had been established.
Spontaneous inhibitory postsynaptic currents (sIPSCs) were analysed using the Minhee Analysis Package55. The effects of NPY on sIPSCs were analysed by measuring the percentage change, compared to baseline for each neuron. The neurons in which the change was higher than $20\%$ from the baseline were discarded, and those that exhibited sIPSCs over 5 Hz were used.
## Drugs
Neuropeptide Y (NPY), BIBO 3304 trifluoroacetate (NPY Y1 receptor antagonist) and CGP 71683 hydrochloride (NPY Y5 receptor antagonist) were purchased from Tocris (Bristol, UK). Recombinant mouse Leptin Protein was purchased from R&D systems (Minneapolis, MN). These were dissolved in ACSF for slice application. The drug concentrations used in slice Ca2+ imaging experiments were as follows: 1 µM NPY, 1 µM BIBO 3304 trifluoroacetate, 10 µM CGP 71683 hydrochloride and 100 nM Recombinant mouse Leptin Protein. The drug concentration of NPY used in whole-cell recording with pico-pump was 2 µM.
## Histology, immunohistochemistry and imaging
Animals were deeply anesthetized by a mixture of ketamine and xylazine. Transcranial perfusion was performed using phosphate-buffered saline, followed by $4\%$ neutral-buffered paraformaldehyde (T&I, BPP-9004). The brains were extracted, post-fixed in $4\%$ paraformaldehyde at 4 °C, and transferred to $10\%$ sucrose, followed by $30\%$ sucrose for cryoprotection. Cryoprotected brains were sectioned coronally on a cryostat (Leica Biosystems, CM3050) at 50 µm, and their sections were stained with 4’,6-diamidino-2-phenylindole (DAPI) to visualise the nuclei. To verify the scientific exactitude, images of viral fluorescence and fibre/cannula placement were captured using a confocal microscope (Olympus, FV3000).
## Single-cell RNA-sequence analysis
scRNA-sequence data with the LH (GSE125065) were analysed26. Of the initial 7232 cells (3439 male and 3793 female), 598 cells with less than 500 unique molecular identifiers (UMIs) or >$40\%$ of mitochondrial reads were discarded. The R package Monocle3 was used to classify the cells52. Using Monocle 3, we subjected single-cell gene expression profiles to uniform manifold approximation and projection (UMAP) visualisation. Altogether, we identified 4091 cells as neural clusters on the basis of cell type-specific marker gene expression26,52,53. These neural clusters containing 4091 cells were extracted for further clustering using Monocle 3 as above, which yielded 37 clusters. Clusters were classified as GABAergic when the median expression of Slc32a1 was greater than that of Slc17a6 in each cluster and glutamatergic when the median expression of Slc17a6 was greater than that of Slc32a1. Consistent with previous result ($92\%$21, $80\%$23), most of LHLepR neurons were GABAergic ($70\%$; $\frac{100}{141}$).
## Simulated distribution of food-specific LHLepR neurons among LHGABA neurons
Simulated results of 1000 LHGABA neurons. We assumed that $10\%$ of LHGABA neurons are LHLepR neurons, given that our result (Supplementary Fig. 1m) and a previous result21 indicated that LHLepR neurons constitute 4–$20\%$ of LHGABA neurons. In our result, among LHGABA neurons, $8\%$ of LHGABA neurons were food specific (80 neurons) (Fig. 1i). Among LHLepR neurons ($10\%$ of 1000 LHGABA neurons, 100 neurons), $63\%$ of LHLepR neurons were food specific (63 neurons) (Fig. 1l). Therefore, LHLepR neurons comprise the majority of food-specific LHGABA neurons ($79\%$; $\frac{63}{80}$) in this simulation results.
## Behavioural tests
All data analyses were performed using custom-written MATLAB (MathWords, Natick, MA) and Python codes. Behavioural experiments were analysed using Observer XT 13 or EthoVision 14 or DeepLabCut.
## Computational extraction of consummatory behaviours
To distinguish consummatory behaviours from other behaviours, we defined three criteria (Supplementary Movie 4). In the bottom view, mice consuming the cheese-flavoured snacks stood up slightly, and the distance between the front paws decreased because they had been brought together to catch the food. Therefore, the first criterion was met when the distance between the left and right front paws was less than that between the left and right hind paws in the bottom view. The second criterion was met when the y-coordinates decreased sequentially for the tail base, midpoint of the two hind paws, and midpoint of the two front paws in the bottom view. The third criterion was met when the snout coordinates were in the food zone in both the bottom and side views.
## Quantification of neural onset
The neural onset can be determined by differentiating the neural activity recorded from calcium signals and analysing the maximum value of the third derivative of the neural activity27,28. For each trial, neural activity that was computed into the Z-score with a baseline (–10 s to –5 s from seeking), was fitted to the optimal polynomial degree for differentiation. We calculated 1st derivative of neural activity, which is the velocity of neural activity. From the velocity, we calculated the 2nd derivative, which is the acceleration of neuronal activity. From the acceleration, we calculated the 3rd derivative, which is the jolt of neural activity. By calculating the time point when jolt (3rd derivative) reaches its maximum value, we could calculate the mathematical signal onset when the signal begins to increase. The optimal polynomial degree was determined manually according to traces that best fit the computed trace. Then, the third derivative of the fitted neural activity was calculated. Afterwards, the optimal time of the maximum value of the third derivative around the neural onset was determined manually and quantified into the latency to the seeking behavioural onset (Fig. 2m–o).
## Fibre photometry imaging
Fibre photometry signal data were acquired using the Doric Studio software. Two signals from fibre photometry, 465 nm calcium and 405 nm isosbestic signals (for artefact correction), were obtained for correction before performing any analysis. Signals from fibre photometry were corrected as follows to minimise artefact recordings: corrected 465 nm signal = (465 nm signal − 405 nm signal)/405 nm signal54. Signals were decimated to obtain approximately 25 data points in 1 s. For photometry experiments, all corrected signals shown were initially computed to Z-scores before further normalisation. The baseline was designated as –10 s to –5 s before recording the initiation of behaviour ($t = 0$). The mean of the baseline (m) and standard deviation (σ) of the baseline were computed to normalise the corrected signals into Z-scores (Z = (corrected 465 nm − m)/σ). The behaviour time point for each test was manually annotated. For the heatmap, each trial was normalised before visualisation (normalised Z = (Z − minimum Z)/(max Z − min Z)). Trials were excluded if the trial length exceeded the optimal trial length (15 s for the multi-phase, 10 s for the rest).
## Micro-endoscopic imaging
All data from the micro-endoscope experiments were recorded using nVoke (Inscopix). The raw signal output from CNMF-E (Craw) was converted into Z-scores (Z = (Craw − m)/σ), according to the mean (m) and standard deviation (σ) of the baseline (−10 s to −5 s before behavioural initiation).
To discriminate food-specific neurons in Fig. 1, we applied the following criteria. We defined neurons as food-specific responsive(yellow) when they were activated during all three eating behavioural tests (>4σ) and not activated during a non-food behavioural test (<4σ). We defined neurons as non-specific-responsive (grey) when they were both activated during three eating behavioural tests (>4σ) and a non-food behavioural test (>4σ). We defined neurons as non-food-specific responsive (blue) when they were not activated during all three eating behavioural tests (<4σ) but, activated during a non-food behavioural test (>4σ). We defined neurons as no responsive (white) when they were neither activated during three eating tests nor a non-food behavioural test (<4σ).
To distinguish the distinct populations of LHLepR neurons in Fig. 3, the neural activity of LHLepR neurons was recorded in multi-phase test 2 and processed as described above. Trials that exceeded 25 s of total trial length were excluded from the test (seeking moment – food consumption end [food trial] or food zone exit [no food trial]). Activated neurons were defined as cells with Z-scores of >4σ. Otherwise, we defined non-responsive neurons if neural activity was Z-scores of <4σ. Neural activity was then normalised as follows: (NF0) = (Craw − minimum Craw)/(max Craw − minimum Craw). Further analysis was performed with the average normalised activity of the group of trials that had sufficient length. Seeking-score-1 was defined as NF0 at the food contact moment in the seeking with consummatory behaviour session. Seeking-score-2 was defined as NF0 at the food contact moment in the seeking without consummatory session. Consummatory score was defined as the difference in the value of NF0 at the last moment of the trial and seeking-score-1. Neurons that had higher seeking-score-1 than consummatory score and a seeking-score-2 higher than NF0 0.4 were defined as seeking neurons. Neurons that had higher consummatory score than seeking-score-1, or if seeking-score-2 was lower than NF0 0.4 were defined as consummatory neurons. Other neurons were defined as ambiguous neurons.
## Brain slice calcium imaging
For brain slice data, first 2 min of slice data were excluded from analysis to exclude the photobleaching effect of the camera during the first minutes of the recording. Z-score was computed as stated above (baseline: −480 s ~ 0 s from drug delivery), then, linear regression of the mean of baseline of each brain slice activity was computed (mean trace from all cell traces from the brain slice) and subtracted from the cell activity trace to account for the photobleaching or brain slice movement effect during the experiment. Afterwards, cells were defined excitatory if neural activity exceeded the adjusted Z-score of 4σ. Due to the lack of spontaneous activity of LHLepR neurons, decreased neural activity was not analysed in slice Ca2+ imaging.
## Statistical analysis
All statistical data were analysed using MATLAB or IBM SPSS 25.0 (IBM Corp., Armonk, NY). Data in the figures are reported as the mean ± standard error of the mean. Paired t-tests were used to compare data between two groups. Two-way repeated-measures analyses of variance (ANOVA) were used for multiple comparisons. P-values for comparisons across multiple groups were corrected using the Greenhouse–Geisser method in IBM SPSS 25.0. Levels of significance were as follows: *$p \leq 0.05.$ ** $p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Movie 1 Supplementary Movie 2 Supplementary Movie 3 Supplementary Movie 4 Supplementary Movie 5 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37044-4.
## Peer review information
Nature Communications thanks Clemence Blouet and the other, anonymous, reviewers for their contribution to the peer review of this work.
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|
---
title: An epidemiological study on skin tumors of the elderly in a community in Shanghai,
China
authors:
- Jianhua Huang
- Linglin Zhang
- Lei Shi
- Minfeng Wu
- Ting Lv
- Yunfeng Zhang
- Yongxian Lai
- Qingfeng Tu
- Xiuli Wang
- Hongwei Wang
journal: Scientific Reports
year: 2023
pmcid: PMC10023674
doi: 10.1038/s41598-023-29012-1
license: CC BY 4.0
---
# An epidemiological study on skin tumors of the elderly in a community in Shanghai, China
## Abstract
The morbidity of skin tumors (ST) in *China is* a great concern as the population ages. No epidemiological survey on ST in elderly communities in China has been reported. A questionnaire survey was conducted among the residents over 60 years old in a community in Shanghai, China from May 1, 2011 to November 30, 2011. The prevalence of cutaneous tumors and associated factors were analyzed. Among 2038 valid cases, a total of 78 ($3.8\%$, $95\%$ CI 3.0–4.7) skin cancers (SC) were confirmed. According to the final multivariate regression analysis, age, gender and previous occupation were the significantly influential factors for SC. Actinic keratosis (AK) accounted for the largest proportion (63, $3.1\%$) in SC. The head and neck was the physiological site with the highest incidence of SC (64, $82.1\%$), and AK was the most common (55, $87.3\%$) in head and neck SC. The common concomitant diseases of SC were hypertension (26, $33.3\%$) and diabetes mellitus (9, $11.5\%$). Seborrheic keratosis (SK) was the most common benign skin tumor with a prevalence of $100\%$. Men and women developed SK in significantly different parts of the body ($P \leq 0.0001$). The incidence of ST in the elderly population in Shanghai community increased with age. ST preferred to occur in the head and neck, which might be attributed to excessive ultraviolet (UV) exposure in these areas. Therefore, early diagnosis and sun-protection education are essential interventions for ST in the elderly.
## Introduction
SC is diagnosed more commonly than other malignancies and is one of the most distressing fatal skin diseases, especially in the elderly1–4. It poses an enormous global public health burden on socioeconomic and medical costs5. During 2007–2011, approximately 5 million adults received SC treatment in the U.S. each year, resulting in an average annual cost of $8.1 billion6. An estimation of the total direct, indirect and intangible costs per basal cell carcinoma (BCC) in Canada in 2011 was $4312.97. In addition to the medical costs, melanoma was responsible for the majority of SC deaths, accounting for approximately 9000 annual deaths in the U.S.8. These findings underlined that the health and economic burden of SC was substantial and its incidence continued to be high over the past decades9. Therefore, an epidemiological investigation on the prevalence of ST and related high-risk factors is considered to be significant for ST control and prevention measures.
Besides the above worrying figures from Caucasians, the condition of ST in *China is* also a great concern with changes in lifestyle, ozone layer destruction and aging10. However, it may exhibit diverse features and prognosis from western countries. Previous epidemiological studies on tumors in China was limited to some highly lethal malignancies, such as lung cancer or gastrointestinal cancer11–13. To date, there is a lack of comprehensive survey on the prevalence and risk factors of SC in elderly communities in China. The aim of the this study is to elucidate the epidemiology of SC in urban elderly communities by exploring the regularity of SC in an elderly community in Shanghai, China.
## Study settings
An observational, cross-sectional prevalence study was conducted with an epidemiological cluster sampling questionnaire among elderly residents in a community in Shanghai, China from May 1, 2011 to November 30, 2011. This study was approved by the ethics committee of Shanghai skin disease hospital affiliated to Tongji University. All methods were performed in accordance with the relevant guidelines and regulations of Declaration of Helsinki.
## Sampling and sample size
We selected a mature community with typical aging characteristics in Shanghai as the research object, which was fully able to reflect the status of ST among the elderly in the city and was highly representative. The sampling framework was based on the elderly population for Shanghai in 2011. The projected sample size was expected to be 385 based on a margin of error of $5\%$ and $95\%$ confidence interval with an estimated $50\%$ response rate14. Actually, almost the entire community of seniors was studied, so the sample size exceeded the calculated size. Such an expansion of sample size did not diminish the accuracy of our final statistical results.
## Inclusion and exclusion criteria
All permanent residents over the age of 60 who had resided in the area for more than 6 months were eligible, with the exception of elderly residents in nursing homes and hospitals. Those that refused to supply true information, were unwilling to fill in informed consent, and had poor compliance should be excluded. Written informed consent was obtained from the participants.
## Data extraction and quality assessment
Our survey involved basic information, categories of SC, risk factors and concomitant diseases, etc. To minimize bias, each eligible participant was randomly selected and underwent dermatological examinations by three board-certified dermatologists according to standard procedures. The dermatologists underwent special training and technical assessment, and all of them were qualified as experienced specialists in the field of skin cancer before this project. Data were extracted through questionnaires and dermatological examinations. Unified forms, methods, form filling instructions and diagnostic criteria were adopted. The elderly were summoned to the residential committee for examination. Door-to-door inspection was provided for those with mobility difficulties. It usually took 10–20 min to complete the medical history inquiry and dermoscopy of each subject’s skin lesions. All the completed forms were checked and verified by the quality control personnel. The data were recorded and checked twice by different personnel. Most lesions could be clinically diagnosed, while for highly suspicious lesions, pathological diagnosis was performed prior to statistical analysis. Diagnosis was based on the International Classification of Diseases (ICD-10). We collectively referred to malignant melanoma (MM), squamous cell carcinoma (SCC), BCC, AK, keratoacanthoma (KA) and cutaneous horn (CH) with malignant cells as SC, and the rest as benign ST including SK and skin tags. All the screened lesions were diagnosed for the first time.
## Statistics
All categorical variables were summarized as percentages, and continuous variables were represented as mean ± standard deviation (SD). Inter-group comparisons were performed using the chi-square or Fisher’s exact test. Univariate regression analysis preliminarily screened the influence of various independent factors on SC prevalence, and multivariate regression analysis was used to further eliminate the interference of confounding factors, making the statistical results more reliable. SPSS (Version 24, IBM Corporation, New York, NY) was employed for all statistical work. Odds ratios (OR) and $95\%$ CIs were calculated, with a CI excluding 1.00 considered statistically significant. Graphics were produced with GraphPad Prism 8.0 (GraphPad Software Inc., San Diego, CA, USA). $P \leq 0.05$ was considered statistically significant.
## General characteristics
A total of 2082 questionnaires were distributed to the elderly aged over 60 years old and 2049 questionnaires were returned with a response rate of $98.41\%$. Finally, 2038 questionnaires were valid after eliminating 11 incomplete data of refusing physical examination. The average age was 72.05 ± 8.17 years old.
## Population composition ratio
The age group of ≥ 85 has the smallest population (106, $5.2\%$), while the 60–64 age group accounted for the largest population (524, $25.7\%$). The population of the other age groups fell between the above two groups. The number of female participants (1271, $62.4\%$) was nearly twice that of male participants (767, $37.6\%$). The overall education level was relatively high, with 203 ($10.0\%$) having a college degree or above and 1030 ($50.5\%$) having a secondary school degree. Type IV phototype represented the largest proportion (1395, $68.5\%$) of the elderly population, followed by type III (527, $25.9\%$) and type II (38, $1.9\%$). The populations of I, V and VI phototypes were quite scarce. The majority manifested type 3 (1623, $79.6\%$) photoaging, followed by type 4 (283, $13.9\%$), type 2 (124, $6.1\%$) and type 1 (8, $0.4\%$). $47.1\%$ [959] residents worked indoors and only a minority (243, $11.9\%$) worked outdoors. Mixed jobs (836, $41.0\%$) meant working both indoors and outdoors. More than half of residents (1143, $56.1\%$) never adopted sun protection, while approximately a quarter (496, $24.3\%$) adopted frequently and even fewer adopted occasionally (399, $19.6\%$). The population proportion distribution of other factors can be referred to Table 1.Table 1Demographic characteristics of the elderly in the community, the prevalence of cutaneous malignancies. FactorsTotal, n (%)Prevalence of Skin cancer, n (%)Model 1Model 2N203878 (3.8, $95\%$ CI 3.0–4.7)Age (years) (Model 1***, Model 2**) 60–64524 (25.7)8 (1.5, $95\%$ CI 0.5–2.6)ReferenceReference 65–69291 (14.3)7 (2.4, $95\%$ CI 0.6–4.2)1.590 (0.571–4.429)1.325 (0.462–3.806) 70–74309 (15.2)7 (2.3, $95\%$ CI 0.6–3.9)1.495 (0.537–4.164)1.251 (0.424–3.694) 75–79511 (25.1)30 (5.9, $95\%$ CI 3.8–7.9)4.023 (1.826–8.862)***3.945 (1.575–9.880)** 80–84297 (14.6)15 (5.1, $95\%$ CI 2.5–7.6)3.431 (1.437–8.191)**3.125 (1.109—8.807)* ≥ 85106 (5.2)11 (10.4, $95\%$ CI 4.5–16.3)7.468 (2.927–19.055)***7.749 (2.478–24.232)***Sex (Model 1*, Model 2**) Male767 (37.6)20 (2.6, $95\%$ CI 1.5–3.7)ReferenceReference Female1271 (62.4)58 (4.6, $95\%$ CI 3.4–5.7)1.786 (1.066–2.993)*2.936 (1.490–5.787)**Educational qualification (Model 1**) College degree or above203 (10.0)6 (3.0, $95\%$ CI 0.6–5.3)ReferenceReference Secondary school1030 (50.5)25 (2.4, $95\%$ CI 1.5–3.4)0.871 (0.331–2.017)0.771 (0.297–2.000) Primary school378 (18.6)21 (5.6, $95\%$ CI 3.2–7.9)1.931 (0.767–4.865)1.101 (0.394–3.076) No school qualification427 (21.0)26 (6.1, $95\%$ CI 3.8–8.4)2.129 (0.862–5.257)0.964 (0.329–2.822)Marriage Married1629 (79.9)57 (3.5, $95\%$ CI 2.6–4.4)ReferenceReference Unmarried22 (1.1)1 (4.5, $95\%$ CI − 4.9–14.0)1.313 (0.174–9.934)0.916 (0.111–7.586) Divorced14 (0.7)1 (7.1, $95\%$ CI − 8.3–22.6)2.121 (0.273–16.497)1.878 (0.217–16.273) Loss of spouse373 (18.3)19 (5.1, $95\%$ CI 2.9–7.3)1.480 (0.870–2.519)0.625 (0.338–1.154)Fitzpatrick skin type Light complexion48 (2.4)1 (2.1, $95\%$ CI − 2.1–6.3)ReferenceReference I10 (0.5)0 (0.0, $95\%$ CI 0.0–0.0) II38 (1.9)1 (2.6, $95\%$ CI − 2.7–8.0) Medium Beige1922 (94.3)76 (4.0, $95\%$ CI 3.1–4.8)1.935 (0.263—14.211)2.359 (0.303–18.386) III527 (25.9)20 (3.8, $95\%$ CI 2.2–5.4) IV1395 (68.5)56 (4.0, $95\%$ CI 3.0–5.0) Dark complexion68 (3.3)1 (1.5, $95\%$ CI − 1.5–4.4)0.701 (0.043—11.499)0.713 (0.041–12.445) V57 (2.8)1 (1.8, $95\%$ CI − 1.8–5.3) VI11 (0.5)0 (0.0, $95\%$ CI 0.0–0.0)Degree of photoaging (Model 1**) Type 18 (0.4)1 (12.5, $95\%$ CI − 17.1–42.1)ReferenceReference Type 2124 (6.1)4 (3.2, $95\%$ CI 0.1–6.4)0.233 (0.023–2.374)0.151 (0.012–1.910) Type 31623 (79.6)51 (3.1, $95\%$ CI 2.3–4.0)0.227 (0.027–1.880)0.067 (0.006–0.726)* Type 4283 (13.9)22 (7.8, $95\%$ CI 4.6–10.9)0.590 (0.069–5.015)0.104 (0.009–1.183)Hair Smooth and shiny496 (24.3)14 (2.8, $95\%$ CI 1.4–4.3)ReferenceReference Thin533 (26.2)27 (5.1, $95\%$ CI 3.2–6.9)1.837 (0.952–3.545)1.866 (0.888–3.920) Yellow and forked1009 (49.5)37 (3.7, $95\%$ CI 2.5–4.8)1.311 (0.702–2.447)1.152 (0.592–2.243)Eye wrinkles Superficial wrinkles172 (8.4)2 (1.2, $95\%$ CI − 0.5–2.8)ReferenceReference Medium depth wrinkles1004 (49.3)37 (3.7, $95\%$ CI 2.5–4.9)3.252 (0.777–13.620)4.001 (0.827–19.346) Deep wrinkles with clear edges862 (42.3)39 (4.5, $95\%$ CI 3.1–5.9)4.028 (0.963–16.841)3.003 (0.602–14.979)Hereditary skin history No1985 (97.4)76 (3.8, $95\%$ CI 3.0–4.7)ReferenceReference Yes53 (2.6)2 (3.8, $95\%$ CI − 1.5–9.1)0.985 (0.235–4.122)1.392 (0.318–6.092) Epidermolysis bullosa35 (66.0)1 (2.9, $95\%$ CI − 2.9–8.7) Vitiligo6 (11.3)0 (0.0, $95\%$ CI 0.0–0.0) Psoriasis5 (9.4)0 (0.0, $95\%$ CI 0.0–0.0) Xeroderma pigmentosum1 (1.9)0 (0.0, $95\%$ CI 0.0–0.0) Lupus erythematosus1 (1.9)0 (0.0, $95\%$ CI 0.0–0.0) Ichthyosis1 (1.9)0 (0.0, $95\%$ CI 0.0–0.0) Seasonal dermatitis1 (1.9)1 (100.00) Others4 (7.6)0 (0.0, $95\%$ CI 0.0–0.0)History of cataract (Model 1*) No1255 (61.6)39 (3.1, $95\%$ CI 2.1–4.1)ReferenceReference Yes783 (38.3)39 (5.0, $95\%$ CI 3.5–6.5)1.634 (1.039–2.571)*1.190 (0.725–1.951)History of macular degeneration No1986 (97.5)75 (3.8, $95\%$ CI 2.9–4.6)ReferenceReference Yes52 (2.6)3 (5.8, $95\%$ CI − 0.8–12.3)1.560 (0.475–5.119)1.456 (0.422–5.023)Previous profession (Model 2*) Outdoor work243 (11.9)15 (6.2, $95\%$ CI 3.1–9.2)ReferenceReference Mixed work836 (41.0)31 (3.7, $95\%$ CI 2.4–5.0)0.585 (0.311–1.103)0.544 (0.268–1.105) Indoor work959 (47.1)32 (3.3, $95\%$ CI 2.2–4.5)0.525 (0.279–0.985)*0.371 (0.188–0.731)**Use of physical sun protection Never1143 (56.1)44 (3.8, $95\%$ CI 2.7–5.0)ReferenceReference Frequently496 (24.3)17 (3.4, $95\%$ CI 1.8–5.0)0.886 (0.501–1.567)0.998 (0.539- 1.848) Occasionally399 (19.6)17 (4.3, $95\%$ CI 2.3–6.3)0.112 (0.628–1.969)1.056 (0.574–1.943)Smoke Never1743 (85.5)67 (3.8, $95\%$ CI 2.9–4.7)ReferenceReference Frequently218 (10.7)7 (3.2, $95\%$ CI 0.9–5.6)0.830 (0.376–1.831)1.763 (0.735–4.229) Occasionally77 (3.8)4 (5.2, $95\%$ CI 0.1–10.3)1.371 (0.487–3.861)2.179 (0.710–6.689)Photosensitive food consumption history No1629 (79.9)64 (3.9, $95\%$ CI 3.0–4.9)ReferenceReference Yes409 (20.1)14 (3.4, $95\%$ CI 1.7–5.2)0.867 (0.481–1.562)0.932 (0.497–1.747) Spinach203 (49.6)7 (3.4, $95\%$ CI 0.9–6.0) Carrot312 (76.3)11 (3.5, $95\%$ CI 1.5–5.6) Celery339 (82.9)10 (2.9, $95\%$ CI 1.1–4.8) Marinated mud snail16 (3.9)0 (0.0, $95\%$ CI 0.0–0.0) Mango30 (7.3)1 (3.3, $95\%$ CI − 3.5–10.2)Chemical exposure history No1792 (87.9)70 (3.9, $95\%$ CI 3.0–4.8)ReferenceReference Yes246 (12.1)8 (3.3, $95\%$ CI 1.0–5.5)0.827 (0.393–1.740)1.159 (0.532–2.524)Sunburn history Yes60 (2.9)2 (3.3, $95\%$ CI − 1.3–8.0)ReferenceReference No1978 (97.1)76 (3.8, $95\%$ CI 3.0–4.7)1.159 (0.278–4.833)1.299 (0.293–5.748)Radiation or chemotherapy Yes23 (1.1)1 (4.3, $95\%$ CI − 4.7–13.4)ReferenceReference No2015 (98.9)77 (3.8, $95\%$ CI 3.0–4.7)0.874 (0.116–6.569)0.993 (0.115–8.573)Odds ratios for associated potential risk factors were determined by univariable and multivariable logistic regression analyses. Model 1 represented univariate regression analysis, and Model 2 represented multivariate regression analysis. $95\%$ CI = $95\%$ confidence interval. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001.$
## SC prevalence
A total of 78 cases of SC were confirmed, and the overall prevalence was $3.8\%$ ($95\%$ CI 3.0–$4.7\%$). The composition ratio displayed the prevalence of cutaneous malignancies dominated the higher age range. The highest SC prevalence rate (11, $10.4\%$, $95\%$ CI 4.5–$16.3\%$) emerged in the ≥ 85 age group, while the lowest was in the 60–64 age group (8, $1.5\%$, $95\%$ CI 0.5–$2.6\%$). The standardized prevalence rates of SC in 60–70, 70–80 and ≥ 80 age group was 954.8 (CI 944.5–965.0) per 100,000, 1355.1 (CI 1343.0–1367.3) per 100,000 and 1167.3 (CI 1156.0–1178.6) per 100,000, respectively, using the Shanghai elderly population as standard. Men (20, $2.6\%$, $95\%$ CI 1.5–$3.7\%$) appeared to be less likely to develop SC than women (58, $4.6\%$, $95\%$ CI 3.4–$5.7\%$). The standardized prevalence rates of SC for females and males were 2394.3 (CI 2378.3–1178.6) per 100,000, and 1239.6 (CI 1227.9–1251.2) per 100,000, respectively, using the Shanghai elderly population as standard. The percentages implied that those with primary (21, $5.6\%$, $95\%$ CI 3.2–$7.9\%$) and illiterate (26, $6.1\%$, $95\%$ CI 3.8–8.4) education seemed to be more susceptible to skin neoplasms than those with secondary (25, $2.4\%$, $95\%$ CI 1.5–$3.4\%$) and college degrees (6, $3.0\%$, $95\%$ CI 0.6–$5.3\%$). In our investigation, the overall trend appeared that type IV (56, $4.0\%$, $95\%$ CI 3.0–$5.0\%$) was more susceptible to cutaneous tumors than type III (20, $3.8\%$, $95\%$ CI 2.2–$5.4\%$). The SC incidence was the lowest in those who worked indoors (32, $3.3\%$, $95\%$ CI 2.2–$4.5\%$), compared to those with mixed occupations (31, $3.7\%$, $95\%$ CI 2.4–$5.0\%$) and outdoor jobs (15, $6.2\%$, $95\%$ CI 3.1–$9.2\%$). Other percentages of SC prevalence were detailed in Table 1.
## Associated factors of skin malignancies
The preliminary screening of the risk factors of skin malignancies was conducted by univariate regression analysis in our survey. As can be seen in Table 1 (Model 1), univariate analysis showed that age (60–64 group: Reference; 65–69 group: OR 1.590, $95\%$ CI 0.571–4.429; 70–74 group: OR 1.495, $95\%$ CI 0.537–4.164; 75–79 group: OR 4.023, $95\%$ CI 1.826–8.862; 80–84 group: OR 3.431, $95\%$ CI 1.437–8.191; ≥ 85 group: OR 7.468, $95\%$ CI 2.927–19.055), sex (Male: Reference; Female: OR 1.786, $95\%$ CI 1.066–2.993), education (College degree or above: reference; Secondary school: OR 0.871, $95\%$ CI 0.331–2.017; Primary school: OR 1.931, $95\%$ CI 0.767–4.865; No school qualification: OR 2.129, $95\%$ CI 0.862–5.257), photoaging (Type 1: Reference; Type 2: OR 0.233, $95\%$ CI 0.023–2.374; Type 3: OR 0.227, $95\%$ CI 0.027–1.880; Type 4: OR 0.590, $95\%$ CI 0.069–5.015), cataract history (No: Reference; Yes: OR 1.634, $95\%$ CI 1.039–2.571), and previous occupation (Outdoor work: Reference; Mixed work: OR 0.585, $95\%$ CI 0.311–1.103; Indoor work: OR 0.525, $95\%$ CI 0.279–0.985) exerted significant influence on the incidence of SC. As judged by the univariate analysis, none of the other factors we investigated exhibited any aggravating or protective effect on SC. After preliminary univariate analysis, the identification of confounders required further incorporation of the above factors into the multivariate regression model (Model 2). Age remained to be the most influential factor for SC (60–64 group: Reference; 65–69 group: OR 1.325, $95\%$ CI 0.462–3.806; 70–74 group: OR 1.251, $95\%$ CI 0.424–3.694; 75–79 group: OR 3.945, $95\%$ CI 1.575–9.880; 80–84 group: OR 3.125, $95\%$ CI 1.109–8.807; ≥ 85 group: OR 7.749, $95\%$ CI 2.478–24.232). SC was found to be more prevalent in women than in men (Male: Reference; Female: OR 2.936, $95\%$ CI 1.490–5.787). Although univariate analysis revealed the incidence of SC was inversely proportional to education, multivariate logistic data indicated that educational qualification was only a confounding factor in our study (College degree or above: reference; Secondary school: OR 0.771, $95\%$ CI 0.297–2.000; Primary school: OR 1.101, $95\%$ CI 0.394–3.076; No school qualification: OR 0.964, $95\%$ CI 0.329–2.822). Similarly, the contribution of photoaging and cataract history to SC was also rendered negligible by multivariate analysis (Table 1). Multivariate logistic result turned out that indoor work was a protective factor and outdoor work was a risk for SC (Outdoor work: Reference; Mixed work: OR 0.544, $95\%$ CI 0.268–1.105; Indoor work: OR 0.371, $95\%$ CI 0.188–0.731). Apart from these factors, none of the other factors such as smoke, sunburn history and photosensitive food history showed any significant impact on SC (Table 1).
## Elaboration of the percentages of various SC
AK, SCC, BCC, Bowen's disease (BD), KA and CH were the six major categories of skin malignancies. Table 2 revealed that AK was the most common SC with a total of 63 ($3.1\%$) cases, while the other epithelial neoplasms were relatively seldom. There were 3 ($0.1\%$) cases of SCC, 9 ($0.4\%$) cases of BCC, and 1 ($0.0\%$) case of BD, KA and CH, respectively. No malignant tumors such as melanoma, mycosis fungoides, Paget’s diseases were detected. The prevalence rates of AK in 75–79 (26, $5.1\%$), 80–84 (13, $4.4\%$) and ≥ 85 (8, $7.5\%$) age groups were significantly higher than that in 60–64 (5, $0.9\%$) age group ($P \leq 0.05$).Table 2Prevalence of various cutaneous malignancies in different age groups. Age groupSurveyed populationAKSCCBCCBDKACH60–645245 (0.9)0000065–692915 (1.7)01 (0.3)00070–743096 (1.9)1 (0.3)2 (0.6)00075–7951126 (5.1)1 (0.2)4 (0.8)00080–8429713 (4.4)1 (0.3)2 (0.7)1 (0.3)1 (0.3)0 ≥ 851068 (7.5)00001 (0.9)Sum203863 (3.1)3 (0.1)9 (0.4)1 (0.0)1 (0.0)1 (0.0)AK Actinic keratosis, SCC Squamous cell carcinoma, BCC Basal cell carcinoma, BD Bowen's disease, KA Keratoacanthoma, CH Cutaneous horn.
The incidence of cutaneous neoplasms varied widely across body sites. Statistics revealed that ST occurred most frequently in the head and neck, up to $82.1\%$ [64], followed by hands and feet $6.4\%$ [5], limbs $6.4\%$ [5], trunk $5.1\%$ [4], and perineum and mucosa almost no ST. Compared with other parts, AK was prone to distribute in the head and neck ($87.3\%$), much higher than that in the limb ($3.2\%$) and trunk ($1.6\%$), and its distribution percentage was comparable to that of BCC ($66.7\%$ in the head, $11.1\%$ in the trunk and $22.2\%$ in the body). Perhaps due to the limited sample size, only 1 case of BD was detected in the trunk in our survey. Intriguingly, SCC distribution rate was more uniform ($33.3\%$ for the limb, trunk and head) (Table 3).Table 3Analysis of the location of various skin cancers. Skin cancerPerineum and mucosaHand and footLimbTrunkHead and neckAK05 (7.9)2 (3.2)1 (1.6)55 (87.3)SCC001 (33.3)1 (33.3)1 (33.3)BCC002 (22.2)1 (11.1)6 (66.7)BD0001 (100.00)0KA00001 (100.0)CH00001 (100.0)Sum05 (6.4)5 (6.4)4 (5.1)64 (82.1)AK Actinic keratosis, SCC Squamous cell carcinoma, BCC Basal cell carcinoma, BD Bowen's disease, KA Keratoacanthoma, CH Cutaneous horn.
The large proportions of AK were found in type III (16, $3.0\%$) and IV (46, $3.3\%$), and BCC was second to AK, accounting for $0.4\%$ and $0.5\%$ in type III and IV respectively. Confusingly, SC seemed not to occur in type I and VI. However, these two skin phototypes should not be included in the statistics due to too small population with these complexions in China (Table 4).Table 4The distribution of various skin malignancies in skin phototypes of the elderly. Skin cancerIIIIIIIVVVIAK01 (2.6)16 (3.0)46 (3.3)00SCC001 (0.2)2 (0.1)00BCC002 (0.4)7 (0.5)00BD0001 (0.0)00KA001 (0.2)000CH00001 (1.8)0Sum01 (2.6)20 (3.8)56 (4.0)1 (1.8)0AK Actinic keratosis, SCC Squamous cell carcinoma, BCC Basal cell carcinoma, BD Bowen's disease, KA Keratoacanthoma, CH Cutaneous horn.
## Comorbidities
The common systemic comorbidities of SC and their percentages were elaborated in Supplementary Table S1. Among the 78 SC cases, 47 ($60.3\%$) cases were accompanied by comorbid diseases: hypertension (26, $33.3\%$), diabetes mellitus (9, $11.5\%$), rheumatoid arthritis (4, $5.1\%$), psoriasis (4, $5.1\%$), chronic obstructive pulmonary disease (3, $3.8\%$) and urticaria (1, $1.3\%$) (Supplementary Table S1).
## Benign ST
SK (2038, $100.0\%$) was identified as the most common benign skin tumor. Body sites under chronic UV exposure, such as hand back ($85.3\%$), temporal ($80.4\%$), cheek ($71.0\%$), were found to be prone to SK. Significant gender difference in the distribution of SK body parts was observed by chi-square test ($P \leq 0.0001$) (Supplementary Table S2 and Supplementary Fig. S1). Supplementary Fig. S2 depicted an ascending tendency that the average SK number was roughly proportional to the rise of age. Except SK, there were 67 ($3.3\%$, $95\%$ CI 2.5–$4.1\%$) cases of other benign ST, including 21 ($2.7\%$, $95\%$ CI 1.6–$3.9\%$) males and 46 ($3.6\%$, $95\%$ CI 2.6–$4.6\%$) females, with no difference in gender distribution ($$P \leq 0.3407$$). No statistical difference was found in age group stratification of benign tumors except SK ($$P \leq 0.6673$$) (Supplementary Table S3).
## Discussion
SC is the most frequently diagnosed cancer in white populations and numerous studies have demonstrated that incidence of SC is ascending worldwide15. The highest prevalence of SC globally has been reported to be in New Zealand and Australia16. Nearly 50-fold and 100-fold differences in the frequency of BCC and SCC respectively occurred between Caucasian populations in northern Europe and Australia17,18. So far, the SC epidemiology in the elderly has not been well reported in Shanghai, China. After our representative survey, we concluded that the prevalence of elderly SC was $3.8\%$, which was significantly lower than that recorded in western countries. This discrepancy might lie not only in ethnic differences, but also in the fact that we mainly investigate the elderly population. As skin malignancies would result in a poor prognosis once they progressed, the necessity of SC cognition rendered this study the focus. This pioneer study was the first to describe the epidemiology of ST in an elderly community in Shanghai.
Since the morbidity of ST was considered to be in association with senescence, it was summarized from the perspective of age stratification in first. Our results found that cutaneous malignancies manifested an growth trend with age, with a substantial increment in the groups over 75 years old. The propellant role that age played in the morbidity of SC had also been supported by other studies19–21. Previous studies in western countries documented that more than $80\%$ of skin neoplasms occurred in the elderly over 60 years old22,23. Approximately $53\%$ of SC-related deaths occurred in persons over 65 years old24. The susceptibility of the elderly to epithelial neoplasms might be due to cumulative exposure to UV, decreased melanocyte density and immune senescence20,25.
Gender is also a key factor in the development of SC. Men's characteristics of physical work, outdoor lifestyle and neglect of sun protection all support the higher prevalence of epidermal tumors in men26,27. On the contrary, here we discovered females were more likely to develop cutaneous malignancies than males. An important explanation might be that in the last century, Shanghai’s labor force was mainly engaged in industrial production. A large number of young people might work in factories with occupational hazards, such as ultraviolet radiation. Also, this phenomenon might be attributed to women’s longer longevity compared with men and larger constituent of women in elderly community, as evidenced by the negative correlation between aging and the proportion of the male population (not shown in the table). The deviation in the distribution of SC between the sexes in our findings seemed to be inconsistent with the previous conclusions, which was recognized as a reflection of the particularity of SC in the elderly rather than a contradiction. What's more, further analysis of the interaction of various factors demonstrated no interaction between gender and occupations or between gender and sun protection habits (not shown in table). This proved that men was not bound to work outdoors and adopt sun protection measures infrequently. Similar confounding effects also happened to photoaging and history of cataract.
Education plays a pivotal role in the occurrence of SC. Admittedly, a good education qualification ensures us the enlightenment of regular physical examinations, evocation of self-skin examination and emphasis on early detection of SC28. Our outcome of multivariate analysis validated no significant decrease in the probability of SC among those with higher education. As we stated above, a proportion of subject in our survey were the bulk of the industrial workforce in the last century and were exposed to ultraviolet regardless of gender. Another clue could be that despite the fact that the majority were well educated and nearly all acknowledged the harm from prolonged sun exposure, sun protection habits during outdoor activities were still largely neglected. They were more concerned with dark spots, but less attentive to sunburn, scaly erythema and even SC. Therefore, strengthening and publicizing sunscreen consciousness is also the profound essence of this survey.
Skin phototypes were defined based on complexion, the degree of post-sun erythema, and sunburn by reference to Fitzpatrick system29. Available epidemiological data documented a preferential morbidity of SC in populations of skin phototypes I–II30,31. The pigmentary system generated by light-absorbing melanin biopolymers in melanocytes of epidermis serves as a visible marker of the skin’s defense against solar radiation32. Disappointingly, our data showed no significant distinction across all the phototypes, failing to support the previous documents. This might be due to the fact that Shanghai communities were generally homogeneous in terms of race and phototypes. $94.3\%$ of the population had medium complexion, with very small percentages of light ($2.4\%$) and dark ($3.3\%$) skin tones.
Admittedly, multiple predisposing risks are involved in the etiology of skin malignancies33, and therefore SC vary vastly by geographical areas4,34. Outdoor workers are often exposed to high levels of UV radiation35. New Zealand36, France37, and Austria38 all reported high occupational UV exposure of farmers. The adverse effects of working outdoors is particularly relevant to possible photochemical damage to the skin and eyes. Examples are actinic keratosis, non-melanoma and malignant melanoma of the skin, and pterygium, cataract and macular degeneration of the eye. A meta-analysis by Bauer et al. revealed that outdoor workers had a $40\%$ increased risk of BCC compared with indoor workers39. Our survey indicated a greater incidence of cutaneous tumors in the elderly who had experienced excessive outdoor work. Strangely, no beneficial effect from sun protection was supported by our findings. We believed this might be attributed to the interference of other factors, such as age and education. In addition, it also reflected the very weak differences in sun protection behaviors among Shanghai community residents, and only indoor and outdoor work could better elucidate the effects of UV rays on their skin. Other seemingly harmful factors such as eye wrinkles and inherited skin diseases did not exert any influence in this study. Further exploration will be conducted in a larger population sample in the future.
The exposed area of head and neck was the prone area of SC. In this study, AK and BC occupied $87.3\%$ and $66.7\%$ of head and neck SC respectively, which was close to previous studies40,41. These implied that AK and BCC might be more vulnerable to chronic UV exposure34. AK is acknowledged as a premalignant skin lesion that can evolve towards intraepidermal SCC, requiring prompt treatment42,43. A systematic review reported that the progression rate of AK to SCC was 0–$0.075\%$44. In our investigation, AK exhibited a dominant position in all SC ($80.8\%$).
A previous investigation asserted that AK affected nearly half of the global population (beyond $40\%$)15, far higher than our percentage. This was perhaps attributed to the majority of previous studies derived from clinical and pathological records rather than from large-scale surveys of healthy residents in a community. Another reason was thought to be that SC prevalence also varied by geographic locations, skin types, ethnicity or lifestyle.
We screened out 47 cases of SC with main comorbidities (For those complications overlapping in the same individual, the categorization was based on the most severe disease). Hypertension and diabetes constituted the largest proportion, representing $33.3\%$ and $11.5\%$ respectively. One meta-analysis once presented that calcium channel blockers and β-blockers users were at increased risk of developing SC and melanoma, respectively, owing to photosensitizing properties of these anti-hypertensive drugs45. The second dominant complication of SC was diabetes (9 cases). A retrospective cohort study in Taiwan reported that SC incidence was $\frac{3.2}{10}$,000 person-years in the diabetes cohort, 1.18 times higher than that in the non-diabetic cohort46. The sustained hyperglycemia, high-level serum insulin and insulin-like growth factor were regarded as possible mechanisms for carcinogenesis in diabetic patients47,48. Additional investigations are needed for correlation mechanism between SC and its complications.
Almost every elderly person surveyed suffered from SK, which was in accordance with previous reports49,50. SK is usually considered as a sign of skin senility and the preferred areas where it appears are the exposed body-sites such as cheek and forehead51 (Supplementary Table S2 and Supplementary Fig. S1). Empirical evidence demonstrated that aging and long-term UV radiation were believed to be the dominant etiology for SK52. We calculated SK number at each age and discovered that senescence was positively correlated with SK number (Supplementary Fig. S2). These benign tumors were deemed to ruin aesthetics and be not harmful to health.
With regard to limitations to this study, one was the absence of an insight into the dynamic trend of ST incidence over successive years. As a matter of fact, we once attempted a follow-up to these objects in subsequent years. As we expected, there were many lost visits due to various reasons (such as visceral disease or death, especially those over the age of 80), resulting in an invalid statistics. Additionally, the accuracy and severity of these comorbidities could not be objectively assessed because they were self-reported based on the memory of the elderly. A further limitation was that although we surveyed a highly representative community, our study was restricted to one community. Therefore, this deficiency needs to be further improved in our future large-scale investigation of ST.
Despite above limitations, we performed the first epidemiological survey of epithelial tumors of the elderly in a community in Shanghai. Our constructive findings emphasized intensive propaganda work for early prevention and provided valuable reference for clinicians and public health authorities to guild early diagnosis and timely treatment.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-29012-1.
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|
---
title: Evaluation of pre-processing methods for tear fluid proteomics using proximity
extension assays
authors:
- Daphne P. C. Vergouwen
- Amber J. Schotting
- Tanja Endermann
- Harmen J. G. van de Werken
- Dwin G. B. Grashof
- Sinthuja Arumugam
- Rudy M. M. A. Nuijts
- Josianne C. ten Berge
- Aniki Rothova
- Marco W. J. Schreurs
- Marlies Gijs
journal: Scientific Reports
year: 2023
pmcid: PMC10023677
doi: 10.1038/s41598-023-31227-1
license: CC BY 4.0
---
# Evaluation of pre-processing methods for tear fluid proteomics using proximity extension assays
## Abstract
Tear fluid forms a potential source for biomarker identification, and can be minimal invasively collected via Schirmer strips. The lack of knowledge on the processing of Schirmer strips however complicates the analysis and between-study comparisons. We studied two different pre-processing methods, specifically the use of punches of the strip versus elution of the strip in a buffer. Tear fluid filled Schirmer strips were collected from 5 healthy participants, and divided into two halves over the length of the strip. In either part, punches or eluates were obtained from 4 different locations, from the first part touching the eye (head) to the end, to assess the protein distribution along the strips. The levels of 92 inflammatory proteins were measured in the punches/eluates using proximity extension assays. The punch method yielded higher protein detectability compared to the elution method ($76\%$ vs $66\%$; p ≤ 0.001). Protein expression level was found to be slightly higher in the head of the strip, however, 3 out of 5 punches from the head failed quality control. Protein expression levels over the remaining parts of the strips were similar. Our study showed beneficial use of punches of any part of the strip except the head in future biomarker research.
## Introduction
Tear fluid is a crucial component of the human eye, even though it represents a layer of only a few microns thick. It has an important refractive function, provides oxygen and electrolytes to the cornea, offers smooth eye lid movement and protects the ocular surface from environmental factors. The latter is accomplished through regulated secretion of protective factors, including hydrating glycoproteins, antimicrobials, wound healing factors, and anti-inflammatory proteins1,2.
Dysfunction of tear fluid production or stability can lead to decreased visual acuity and complications3. Various (inflammatory) eye conditions or systemic disorders may lead to altered tear fluid composition. Given the close contact to the eye, and its highly abundant and concentrated proteome, changes in tear fluid composition may reflect pathogenic mechanisms4–6. At present there is an increasing interest in using tear fluid samples to investigate ocular and systemic diseases2,4,7–13. Multiplex protein technologies are preferred for its analyses, due to the low sample volume required and high sensitivity. For example by addressable laser bead immunoassay (Luminex), electrochemiluminescence (Meso Scale Discovery), and proximity extension assays (PEA). PEA enables analysis of hundreds of analytes simultaneously with high specificity and relative protein quantification.
Tear fluid can be collected using various methods, of which Schirmer strips and capillaries are the most common4. Tear fluid collection by Schirmer strips is easy to perform, well-accepted by patients, and yields the highest protein content5,14,15. As a consequence, the majority of studies investigating tear proteins use these strips16–18. However, the handling of Schirmer strips for subsequent protein analysis varies greatly, and knowledge on the protein behaviour on Schirmer strips is lacking2,19,20. Standardization is essential to compare studies and allow further research into potential biomarkers for personalized medicine.
In this study we aim to optimize the use of Schirmer strips for tear fluid protein analysis using PEA technology. We will compare two different pre-processing methods of tear fluid Schirmer strips (punch versus elution). Furthermore, we examine the protein composition and migration along the strip area.
## Protein detectability using two pre-processing methods (punch versus elution)
The experimental set-up is shown in Fig. 1. In the analysis 92 inflammatory proteins were tested, that might be of importance in future biomarker research. The tear fluid expression of included proteins is yet partly unknown. From the total of 40 samples, three did not pass the Olink quality control, indicating possible interference with the internal control. These samples were all punches from the head of the strip, and they were excluded in the results shown in Fig. 2A–C. The protein-wise heatmap shows overall higher values using the punch method versus the tear fluid eluates (Fig. 2A). Protein detectability was significantly higher for the punch method ($76\%$ ± $6\%$) compared to the elution method ($66\%$ ± $5\%$; Fig. 2B; p ≤ 0.001). When analysing the different locations of the Schirmer strip, the superiority of the punch method remained consistent. ( A: p = not applicable; B: $$p \leq 0.028$$; C: $$p \leq 0.006$$; D: $$p \leq 0.002$$; Fig. 2C).Figure 1Experimental set-up for comparison of tear fluid pre-processing methods, and protein distribution on a Schirmer strip using PEA technology by Olink Proteomics. Tear fluid from 5 healthy volunteers was collected with Schirmer strips. Strips were cut vertically into two equal portions. Out of one part of the strip, four 1.2 mm punches were taken from various locations of the strip (A: head; B: 0–10 mm; C: 10–20 mm; D: 20–30 mm). The other part of the strip was cut into horizontal locations (A: head; B: 0–10 mm; C: 10–20 mm; D: 20–30 mm) and subsequently the locations were eluted in 60 µL buffer. The punches as well as 1 out of 40 µl of the eluates were supplied to Olink Proteomics in a 96 wells plate. Figure 2Protein detectability using the punch method versus elution of tear fluid Schirmer strips. ( A) Heatmap of protein expression level (normalized protein expression (NPX)) of 92 proteins tested with the Olink Target 96 panel. Individual proteins are depicted on the X-axis, while samples of the different pre-processing methods and locations are shown on the Y-axis. ( B) The boxplots depict the protein detectability (% of proteins of a total number of 92 proteins above the lower limit of detection) between the punch and elution method. Overall, protein detectability is significantly higher in the punch method compared to the elution method ($$p \leq 0.002$$). ( C) Protein detectability of different locations of the strip using the punch method (A: 82 ± $0.8\%$; B: 79 ± $7.6\%$; C: 75 ± $6.3\%$; D: 73 ± $4.2\%$) and the elution method (A: 70 ± $7.2\%$; B: 68 ± $5.5\%$; C: $64\%$ ± $1.9\%$; D: 63 ± $1.8\%$) showed significant differences (A: p = –; B: $$p \leq 0.028$$; C: $$p \leq 0.006$$; D: $$p \leq 0.002$$). Protein detectability was not significantly different between the locations of the strip for the elute method ($$p \leq 0.122$$) as well as for the punch method ($$p \leq 0.231$$). Three out of twenty Schirmer strip pieces did not pass the quality control and were excluded in 2A, 2B, and 2C. These three Schirmer strip pieces were all from location A of the punch method. ( D) The boxplot depicts the total protein concentration found in the different Schirmer strip locations. Total protein concentration, measured by the bicinchoninic acid assay (BCA), was similar between all locations of the Schirmer’s strip. Surface area difference of location A vs the other locations of the strip was corrected for with the correction factor = 4/π.
A Student’s t-test test was used in (B) and (C), while a one-way ANOVA with Tukey's post hoc analysis was used to calculate the p values in (C) and (D). $$n = 20$$ for Fig. 2B, $$n = 5$$ for Fig. 2C,D.
## Protein composition of different locations of the Schirmer strip
The total protein concentration of the tear fluid eluates of different locations is shown in Fig. 2D. Similar total protein concentrations were seen between the Schirmer strip locations ($A = 300$ ± 105 µg/mL, $B = 299$ ± 123 µg/mL, $C = 279$ ± 97 µg/mL, $D = 283$ ± 71 µg/mL: $$p \leq 0.981$$). Location A of the strips showed overall slightly higher protein expression levels of the 92 proteins tested compared to the remaining locations of the strip for the punches (A: 5.82 ± 0.04 normalized protein expression (NPX); B: 4.91 ± 0.24 NPX; C: 4.65 ± 0.25 NPX; D: 4.58 ± 0.21 NPX: $p \leq 0.001$). In the eluates the difference was not statistically significant (A: 4.13 ± 0.33 NPX; B: 3.96 ± 0.41 NPX; C: 3.80 ± 0.20 NPX; D: 3.66 ± 0.27 NPX: $$p \leq 0.134$$).
A principal component analysis was performed of the protein composition in the two methods and the different locations of the Schirmer strip (Fig. 3). Here, the samples that did not pass the quality control are also shown. Location A clusters differently than the remaining locations of the strip. Moreover, the punch method samples group slightly different than the elution method samples. Figure 3Principal component analysis of the protein composition (92 proteins) of different locations of the strips using the punch versus elution method. Scatterplot of the first two principal components based on Z-score scaled protein composition of all samples ($$n = 20$$*2) measured by the Olink Target 96 Inflammation panel. Location A (head of the strip) groups differently than Schirmer strip locations B, C and D, most noticeably the punch method location A groups separately. The samples with bold symbols provided a warning on the Olink proteomics quality control, which reflects the possible interference of proteins in these samples with the internal control.
## Influence of molecular weight on protein migration
The influence of molecular weight on the protein migration is shown in Fig. 4. Proteins with higher molecular weight generally migrated less over the Schirmer strip, since the protein expression levels of the head of the strip (location A) were higher compared to the end of the strip (location D). This phenomenon of migration was significant, however not strong ($r = 0.227$, $$p \leq 0.009$$; Fig. 4A). The three heaviest proteins in the analysis were latency-associated peptide transforming growth factor beta (LAP TGFß; 129 kDa), cluster of differentiation 6 (CD6;105 kDa), and Axin-1 (96 kDa). No difference in protein expression level of LAP TGF-Beta of the different locations of the strip was detected ($$P \leq 0.926$$) (Fig. 4B), which was shown by both methods. However, a significant difference was observed in the protein migration of CD6 and Axin-1. The protein expression level of CD6 was significantly higher in the head compared to the other locations of the Schirmer strip (location A vs B: $$p \leq 0.001$$; A vs C: $$p \leq 4.72$$ ∙ 10–4; A vs D: $$p \leq 1.27$$ ∙ 10–4). Protein expression level in Axin-1 runs down from location A until location D (location A vs C: $$p \leq 0.006$$; A vs D: $$p \leq 0.001$$, B vs D: $$p \leq 0.034$$) (Fig. 4B). The three lightest proteins examined were interleukin-8 (IL8; 8 kDa), chemokine ligand 3 (CCL3; 7.8 kDa), and chemokine ligand 4 (CCL4; 7.8 kDa). No significant differences were found in the protein migration of these proteins. ( Fig. 4C). To evaluate the influence of molecular weight on protein migration using a different approach, two proteins with various molecular weight, lysozyme (14.4 kDa) and glutathione (0.31 kDa), were spiked on independent Schirmer strips. The total protein concentration differed significantly between the first 5 mm and the following strip lengths up to 30 mm for both the proteins, while the protein concentration was similar throughout the remaining Schirmer strip (Fig. 4D).Figure 4The influence of the molecular weight on protein migration over tear fluid Schirmer strips. ( A) Scatterplot of the difference in protein expression level (NPX) of the head, location A, compared to location D, against the protein molecular weight. ( Pearson rho = 0.227; $$p \leq 0.009$$). ( B) Significant differences were found in the protein migration of cluster of differentiation 6 (CD6) (location A vs B: $$p \leq 0.001$$; A vs C: $$p \leq 4.72$$ ∙ 10–4; A vs D: $$p \leq 1.27$$ ∙ 10–4), and Axin-1 (location A vs C: $$p \leq 0.006$$; A vs D: $$p \leq 0.001$$; B vs D: $$p \leq 0.034$$). ( C) No significant differences were found in the protein migration over the strip of interleukin-8 (IL8), chemokine ligand 3 (CCL3), and chemokine ligand 4 (CCL4). ( D) The protein migration of spiked lysozyme (14.4 kDa) and glutathione (0.307 kDa) is displayed in the boxplots. A significant difference in protein concentration was found with respect to the migration of both lysozyme (head–5 mm vs 5–10 mm: $$p \leq 2.29$$∙10–7; head–5 mm vs 10–15 mm: $$p \leq 2.19$$∙10–7; head–5 mm vs 15–20 mm: $$p \leq 6.63$$∙10–7; head–5 mm vs 20–25 mm: $$p \leq 0.1$$∙10–5; head–5 mm vs 25–30 mm: $$p \leq 3.21$$∙10 -7; head–5 mm vs 30–35 mm: $$p \leq 6.91$$∙10–8) and glutathione (head–5 mm vs 5–10 mm: $$p \leq 3.12$$∙10–8; head–5 mm vs 10–15 mm: $$p \leq 3.83$$∙10–8; head–5 mm vs 15–20 mm: $$p \leq 8.99$$∙10–8; head–5 mm vs 20–25 mm: $$p \leq 1.45$$∙10–7; head–5 mm vs 25–30 mm: $$p \leq 1.11$$∙10–7; head–5 mm vs 30–35 mm: $$p \leq 1.78$$∙10–7).
Protein expression levels were measured by the Target 96 Inflammation panel in 4A, 4B, and 4C, while protein concentrations of spiked proteins were measured by the BCA assay in Fig. 4D. One-way ANOVA with Tukey’s post-hoc test was applied for differences in protein concentrations between the locations. Differences between the proteins were analysed using an independent samples t-test without variances using Welch correction. Error bars represent $95\%$ confidence intervals. $$n = 5$$ for Fig. 4A–C and $$n = 3$$ for Fig. 4D.
## Intracellular proteins
We hypothesized that location A of the strip might contain cellular remnants and therefore increased intracellular proteins. In the analyzed panel of 92 proteins, 10 proteins were intracellular proteins, which indeed, in comparison to extracellular proteins, showed a significantly higher difference in protein NPX level in the head of the strip compared to the end. ( $$p \leq 0.005$$; Student’s t-test). However, they also had a slightly higher average molecular weight compared to extracellular proteins (48 kDa vs 30 kDa; $$p \leq 0.03$$; Student’s t-test), which might be a confounding factor.
## Proteins with altered migration
The top 10 proteins with the highest difference in protein expression level between the head and the end of the strip were FMS-like tyrosine kinase 3 ligand (Flt3L), interleukin-18, adenosine deaminase (ADA), T-cell surface glycoprotein CD8 alpha chain (CD8A), NAD-dependent protein deacetylase sirtuin-2 (SIRT2), hepatocyte growth factor (HGF), caspase 8 (CASP8), STAM-binding protein (STAMBP), Sulfotransferase 1A1 (ST1A1), and tumor necrosis factor ligand superfamily member 14 (TNFSF14). The difference occurred mainly between the head of the strip and location B. Data on the migration capability of a specific protein is available in the supplementary material.
## Discussion
Tear fluid is a potential treasure for biomarker analysis in various eye and systemic conditions. However, the best method of pre-processing tear samples (before the specific protein determination) is not known. We show that Schirmer strips can be pre-processed using the simple punch method, which was superior to the elution method, before analyzing the proteome. The head of the strip, which is in contact with the conjunctiva during sampling, should be studied with caution. Protein expression levels over the remaining strip locations are similar.
Proteomics studies using tear fluid are highly expanding, as several analysis methods can nowadays deal with small samples’ volumes4,10,21,22. Next to the widely used mass spectrometry (MS), PEA technology is an attractive option. Using MS, a high number of proteins could be detected in tear fluid5,13,16,23,24, however, the pre-analysis remains time-consuming, deep profiling requires a depletion step, and interesting chemokines/cytokines that are less abundant remain hard to detect. Also, exact quantification remains an issue23–25. Using an antibody-based assay, the panel of biomarkers of interest can be chosen, and only a few microliters of sample are required22. PEA technology can evaluate a much greater number of proteins, compared to other multiplex techniques, but so far, very few studies have used this method studying tear fluid18,22,26,27, and little is known about the optimal pre-processing method, which is crucial for between-study comparisons.
Most often, tear proteins are eluted from the Schirmer strip before analyses. The idea of directly using punches for proteomic analysis came from the experience with dry blood spots28. To the best of our knowledge, the punch method has been used once with Schirmer strips to study metabolomics of tear fluid by Dammeier et al.29 In our experience, taking 1.2 mm diameter punches from the Schirmer strips was a highly simple and rapid procedure. Also, this enables the usage of the rest of the strip for other analyses or validation experiments. Our study showed higher NPX values and protein detectability using the punch method compared to the elution method for all locations of the strip, which might be explained by the absence of extraction loss, a dilution step, and the multiple sample transfer steps required in the elution method14,19,20,30,31. A mean protein detectability of around $70\%$ was fairly high. Csosz et al. found that $45\%$ of proteins of the Olink inflammation panel were present in > $75\%$ of their samples. In that study, 11 out of 184 proteins (of multiple panels) could not be detected in tears, of which interleukin (IL)-2, IL-2RB, IL-20, IL-22RA1, IL-24, fibroblast growth factor 5, interferon gamma, signalling lymphocytic activation molecule, tumour necrosis factor and thymic stromal lymphopoietin were also not detected in our series32.
The proteome of the Schirmer strip head differed from the rest of the strip in our study. The study by Arslan et al. also identified a slightly higher number of proteins in the head of the strip (1153 proteins) compared to the rest of the strip (1107 proteins) using MS, while some proteins were solely identified in the head of the strip (246 proteins)23. We hypothesize this might be due to the presence of cellular remnants of the conjunctival epithelium, which could interfere with the internal control in the Olink analysis. However, this is subject for further studies.
When applying the punch method, it is crucial to know which location of the strip is representative. We found that protein expression level is higher in the head of the strip, which is correlated to the molecular weight of the proteins. The correlation coefficient however was small, and other factors, such as hydrophobicity, hydrophilicity, protein charge, and the formation of aggregates of proteins, might also be of influence. A study by Denisin et al. showed a significant, but small correlation between molecular weight, as well as hydrophobicity and in-strip retention of proteins during the elution step, and no significant correlation with protein charge20,33. In contrast, protein composition were similar over the other locations of the strip. No restriction by cellulose fibers of the Schirmer strip or a chromatography effect was seen in our study.
Our study covered a specific set of 92 inflammatory proteins, and extrapolation to not tested proteins remains uncertain. Regarding the influence of molecular weight, it is important to note that one of the heaviest proteins in our study is 105 kDa, which showed some restriction of migration from head to the remainder of the strip. In a study by Arslan et al. a 641 kDa protein was solely found in the head of the strip. It is possible that the effect of molecular weight might be slightly underestimated in our study23. We used phosphate buffered saline (PBS) as an extraction buffer in the elution method, while elution in ammonium bicarbonate containing 50 mM NaCl might gain a higher extraction rate and identification of proteins by MS, as shown in a study by Aass et al.31. However, no comparison with PBS was made in this study, and to what extent the ammonium buffer could interfere with the PEA analysis is yet unknown.
To conclude, we highlight the use of tear fluid samples in biomarker research and show that tear fluid samples could be studied with PEA technology, using the simple punch method of the Schirmer strip for its pre-processing. We encourage using one method and one location consistently and use internal control samples to allow comparisons of results in future studies.
## Sample collection
Schirmer strips (True Blue Optics) containing tear fluid from 5 healthy participants without any history of eye diseases or dryness were collected from both eyes under comparable conditions. The samples were collected by the Maastricht Tear Fluid Biobank, University Eye Clinic Maastricht, Maastricht University Medical Centre (MUMC+), and Erasmus MC, University Medical Centre Rotterdam, the Netherlands. The head of the Schirmer strip was placed behind the lower eyelid at approximately $\frac{2}{3}$th from the medial cantus. After 5 min, the strip was removed using disposable tweezers, and placed in an Eppendorf or Cryovial tube. The migration length in millimetres was reported, and the samples were stored at – 80 °C until further processing. The local Ethics Committee of both University medical centres (Medical Ethics Review Committee Erasmus MC and Medical Ethics Review Committee azM/UM) approved the study protocols and all methods were performed in accordance with the Tenets of the Declaration of Helsinki and its later amendments. All subjects gave written informed consent before tear fluid collection.
## Tear fluid pre-processing methods
Schirmer strips from 5 healthy participants were selected (one eye randomly selected per participant, all with tear migration length > 30 mm). The strips were cut longitudinally into two equal portions, one part was used for the punch method, and the other part for the elution method. The experimental set-up is shown in Fig. 1.
In the punch method, out of one half of the Schirmer strip, punches of 1.2 mm diameter using a biopsy punch tool (Tisch Scientific, Cleves, OH, VS) were taken from 4 locations of the strip (location A: head; location B: 0–10 mm; location C: 10–20 mm; location D: 20–30 mm), with sterilization of the biopsy punch tool between sampling. The punches were directly transferred to the Olink incubation buffer for analysis.
In the elution method, one half of the Schirmer strips was first cut into 4 locations (location A: head; location B: 0–10 mm; location C: 10–20 mm; location D: 20–30 mm), and subsequently cut into small pieces (approximately 1 mm) with sterilized scissors. The strips were eluted using a piggy-bag method described previously9. In short, the pieces were submerged in 60 µl extraction buffer PBS pH 7.4 with Complete mini protease inhibitor (Sigma Aldrich, Burlington, USA). The samples were incubated on a thermomixer for 1.5 h at 4 °C and 900 rpm. Thereafter, the strip pieces were transferred to a 0.5 mL Eppendorf tube with a syringe needle-punctured hole at the tip. The 0.5 mL Eppendorf tube was placed in the original Eppendorf tube, and centrifuged at 13,000 rpm for 1 min at 4 °C to obtain the eluates. 1 out of 40 µl of all eluates was supplied for PEA analysis.
## Total protein measurement (BCA)
The total protein content of tear fluid eluates was determined using the bicinchoninic acid (BCA) Protein Assay Kit (Pierce™, Thermo Fisher Scientific, Waltham, USA) according to the manufacturer’s instructions. Absorbance was measured at 562 nm on a microplate reader (CLARIOstar PLUS, BMG Labtech, Germany). Total protein concentration measurements of location A of the strips (head) were corrected for surface area, wherein the BCA result of locations A of the strip were multiplied with a correction factor. The correction factor was calculated with the following formula (surface area half circle/ surface are half square = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2{r}^{2}$$\end{document}2r2/\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{2}\pi {r}^{2}$$\end{document}12πr2 = 4/π).
## Targeted proteomics using proximity extension assay (PEA)
Punches of the Schirmer strip as well as 1 µl of tear fluid eluates were measured with the Olink Target 96 inflammation panel (product number: 95302) using the PEA technology (Olink, Uppsala, Sweden). A full list of 92 included inflammatory proteins is available in the supplementary material. In PEA, matched pairs of antibodies carry a unique DNA tag that will bind to the respective target protein in the tear sample. The matched pair of antibodies with their DNA are able to hybridize when brought in proximity. The hybridized tags are extended to an amplicon, and subsequently detected and quantified using quantitative PCR (qPCR). The number of qPCR cycles is related to the expression of the protein in the tear sample, shown in log base-2 NPX values. The technology uses 4 internal controls, incubation, extension, and detection control, that are spiked into each sample and well. They are used for normalization and evaluation of sample and run quality. Samples pass quality control if internal incubation and detection control are within ± 0.3NPX of the plate median. Protein detectability (%) was defined as the number of detectable proteins (having values above the lower limit of detection) from the total number of proteins tested [92].
## Protein distribution of spiked proteins within a Schirmer strip
On the head of the Schirmer strip 35μL of either 2 mg/mL lysozyme (Roche Diagnostics GmbH, Mannheim, Germany), or glutathione (VWR international BV, Amsterdam, The Netherlands) in 1× PBS was pipetted and then stored at − 80 °C. After storage, the entire strip was horizontally cut into 5 mm pieces which were separately eluted following previously noted protocol. The protein content of each 5 mm strip location was quantified using the BCA assay. Each condition was repeated in triplicate.
## Data analysis
Statistical analysis was performed using IBM SPSS Statistics 27. Descriptive statistics are shown in boxplots, as mean ± standard deviation (SD), or as mean and $95\%$ confidence interval (CI). To assess statistical significance between variables, a Student’s t-test, or one-way ANOVA were applied. Olink NPX values were loaded into R (> = 4.2.2) and a Principal Component Analysis (PCA) was performed using “prcomp” on z-score scaled NPX values. A p-value below 0.05 was considered statistically significant, and is marked with an asterisk in the graphs. A double asterisk indicates a p-value below 0.01.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2. The online version contains supplementary material available at 10.1038/s41598-023-31227-1.
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|
---
title: Modeling of three-dimensional innervated epidermal like-layer in a microfluidic
chip-based coculture system
authors:
- Jinchul Ahn
- Kyungeun Ohk
- Jihee Won
- Dong-Hee Choi
- Yong Hun Jung
- Ji Hun Yang
- Yesl Jun
- Jin-A Kim
- Seok Chung
- Sang-Hoon Lee
journal: Nature Communications
year: 2023
pmcid: PMC10023681
doi: 10.1038/s41467-023-37187-4
license: CC BY 4.0
---
# Modeling of three-dimensional innervated epidermal like-layer in a microfluidic chip-based coculture system
## Abstract
Reconstruction of skin equivalents with physiologically relevant cellular and matrix architecture is indispensable for basic research and industrial applications. As skin-nerve crosstalk is increasingly recognized as a major element of skin physiological pathology, the development of reliable in vitro models to evaluate the selective communication between epidermal keratinocytes and sensory neurons is being demanded. In this study, we present a three-dimensional innervated epidermal keratinocyte layer as a sensory neuron-epidermal keratinocyte co-culture model on a microfluidic chip using the slope-based air-liquid interfacing culture and spatial compartmentalization. Our co-culture model recapitulates a more organized basal-suprabasal stratification, enhanced barrier function, and physiologically relevant anatomical innervation and demonstrated the feasibility of in situ imaging and functional analysis in a cell-type-specific manner, thereby improving the structural and functional limitations of previous coculture models. This system has the potential as an improved surrogate model and platform for biomedical and pharmaceutical research.
Skin-nerve crosstalk is a major element of skin physiological pathology. Here the authors report a 3D innervated epidermal keratinocyte layer as a sensory neuron-epidermal keratinocyte coculture model on a microfluidic chip using the slope-based air-liquid interfacing culture and spatial compartmentalization.
## Introduction
The skin contains a complex network of sensory nerve fibers as a highly sensitive organ; mechanoreceptors, thermoreceptors, and nociceptors. A variety of neuronal subtypes whose cell bodies reside in the dorsal root ganglia (DRG) are densely and distinctly innervated into cutaneous layers1–4. These neurons exhibit distinct anatomical localization according to their morphological, neurochemical, and sensory functions. Specifically, large diameter and thickly myelinated Aβ-fibers which detect mechanical stimuli are innervated in the dermis, whereas unmyelinated C-fibers and thinly myelinated Aδ-fibers detect thermal and nociceptive stimuli are innervated both in the epidermis and dermis (Fig. 1a)3,5–8. Free nerve endings of peptidergic or non-peptidergic C-fibers are mainly located close to keratinocytes in the spinous layer or granular layer of the epidermis, providing the structural basis for functional interaction such as synaptic-like contacts9–12. Consistently with this physical contact, recent studies have shown that sensory nerve fibers in the skin can express and release nerve mediators such as neuropeptides that signal the skin, including calcitonin gene-related peptide (CGRP), substance P (SP), and neurokinin A13,14. In addition, it has been shown that skin cells themselves, such as keratinocytes, can also release neurotrophic factors that determine nerve fiber density, morphology, axon growth, and neuropeptide levels14–16. These reports have extended the biological significance of nerves to sensation as well as other biological skin functions, suggesting their physical and pathological correlations with several skin diseases. They naturally led to the development of several in vitro models to understand skin-nerve interactions 15–20.Fig. 1Microfluidic platform and culture system for sensory neurons-keratinocytes co-culture.a Schematic illustration and design of human skin anatomy (left) and the innervated epidermal chip to coculture sensory neurons and keratinocytes (right). Schematic design of the innervated epidermal chip compartments (right lower). HEK; human keratinocyte, SN; sensory neuron, COL 3; collagen I at 3 mg/ml concentration, COL 1.5 L; collagen I at 1.5 mg/mL with $10\%$ laminin, Scale unit; μm. b Top view of the microfluidic chip (left) and experimental concept of slope-based air-liquid interface (ALI) method for epidermal development (right, longitudinal vertical section view). Each cell channel was marked with a different color dye. c Cell-type-specific assays for the innervated epidermal chip. d Experimental workflow of cell seeding and culture for generating the innervated epidermal chip.
However, the traditional 2D coculture systems have failed to spatially locate a cell or cell portion (e.g., the axon and cell body of a neuron) and to selectively analyze and probe specific cells. Cultured keratinocytes also suffer from morphological and functional limitations15,17,18,21. The keratinocytes in vivo have existed in proliferating states at the basal layer of the epidermis, and they undergo differentiation to form a spinous, granular, and cornified layer (Fig. 1a)22. 3D transwell culture platforms and microfluidic chips have been developed and further technologically improved by designing 3D culture conditions for epidermal morphogenesis and cell-customized compartmentalization for co-culture13,19,20,23–26. In the 3D transwell insert culture system, a full-thickness human skin model with histological and functional properties that exhibit physiological similarity to in vivo skin was developed, but a reliable innervated skin model has yet to be reported20,23–25,27–31. A recently reported sponge-based co-culture model, like the transwell insert culture, also failed to mimic the anatomical distribution of intra-epidermal free nerve ending and axon patterning, notwithstanding the well-differentiated epidermal layer (Supplementary Table 1)20. The advantages of microfluidic chips, commonly referred to as lab-on-a-chip or cell chips19,32, have made them attractive candidates to replace traditional experiments, by reducing the sample volume and the cost of reagents, and providing investigators with substantially precise control and predictability of the spatiotemporal dynamics of the cell microenvironments and fluids19,32. In particular, the advantages of the spatiotemporal control allow researchers to closely recapitulate in vivo functions (both normal and disease states) by integrating several well-understood components into a single in vitro chip. However, reliable skin-nerve interactions and communication in the anatomically innervated epidermis have not yet taken advantage of microfluidics because they are based on the structure of vertically stacked systems, such as transwell insert cultures 16,19,33,34.
This work presents a microfluidic model for coculture and analyzes 3D interactions of keratinocytes and sensory neurons (SN) in vitro. Technically, a slope-air liquid interface (slope-ALI) culture was applied to provide an air contact necessary for epidermal differentiation without additional devices, demonstrating advancements in keratinocyte development in terms of epidermal differentiation, cell layering, and barrier function compared to conventional microfluidic chip systems using planar liquid culture. It was also shown that the hydrogel-based multi-channel system recapitulated the cellular/subcellular compartmentalization and cell-cell/cell-matrix interactions, leading to the physiologically relevant organization of the innervated epidermal-like layer and enabling functional analysis in a cell-type-specific manner, such as the in-situ permeability assay of the epidermis and sensory transmission assay initiated by topical stimulation to epidermal keratinocytes. Finally, we modeled epidermal keratinocyte-sensory neuron crosstalk in our platform under hyperglycemic conditions mimicking acute diabetes and demonstrated its feasibility as a model for investigating the underlying mechanisms of the pathological condition.
## Microfluidic chip for keratinocyte-sensory neuron co-culture
In order to mimic the physiologically innervated epidermal anatomy (Fig. 1a), we designed and fabricated a hydrogel-incorporated microfluidic chip (Fig. 1a, modified from the previous design32). The chip contains physically comparted four cell culture and analysis units (channels), including one soma channel for neurons and another epidermal channel for keratinocytes. The soma and epidermal compartments are connected by two 500 μm width axon-guiding microchannels that function as a physical barrier to confine neuronal soma in the soma channel, allowing axons to grow toward the epidermal channel by neuronal sub‐compartmentalization. Posts arranged between channels help the spatial distribution of multiple hydrogels and/or cells. Keratinocytes loaded into the epidermal channel grow on one side of extracellular matrix (ECM) hydrogel and interact only with axons but not with neuronal soma, enabling localized axon-keratinocyte interaction studies like in vivo physiology (Fig. 1a and c). This cellular compartmentalization allows two independent cells to be conducted on a single device maintaining cellular identity and function, and also allows to selectively analyze and/or probe specific cells and cell portions (e.g., the axon and cell body in a neuron) that cannot be done in 2D and transwell insert co-culture system (Fig. 1c). Each axon-guiding microchannel is individually filled by physiologically relevant ECM hydrogel, i.e., type 1 collagen, acting as a layer of acellular dermal ECM, yet exclusively without fibroblasts22,35,36. After seeding DRG neuron cells (in the soma channel) and human epidermal keratinocytes (HEK, in the epidermal channel) sequentially, the medium in the keratinocyte channels was emptied and the cell-filled chip was tilted to maintain above 30 degrees tilt to mimic the air-liquid interface (slope-ALI culture), a common and critical microenvironment for the skin cell differentiation (Fig. 1b, 1d, and Supplementary Fig. 1 and 4a–c)31,37,38. The developed microfluidic chip enables various imaging, biochemical and functional analyses such as axonal response testing and integrity/permeability tests, which can be conducted directly on the innervated epidermis-on-chips, thus improving the limitations of conventional transwell insert culture or microfluidic culture system (Fig. 1c).
## Fine-tuning of axonal patterns in the multi-compartment microfluidic chip
To pattern nerve fibers from the soma channel through hydrogel into the keratinocyte layer, we first optimized the composition and concentration of connected ECM hydrogel components, depending on the context of the cells it comes into contact with, respectively DRG SN and keratinocytes (HEK) (Fig. 1a). Three combinations of hydrogel conditions were examined for SNs culture on the microfluidic chip: type 1 collagen at a concentration of 2 mg/ml (COL2), type 1 collagen at a concentration of 2 mg/ml with $10\%$ laminin (COL2L), and type 1 collagen at a concentration of 1.5 mg/mL with $10\%$ laminin (COL1.5 L) (Fig. 2). Primary DRG SNs from E15 rats were loaded to the soma channel and cultured for 1 week. Whereas neurites of SNs were dispersed irregularly (without a constant axonal pattern) on conventional PDL/laminin-coated 2D plate, axons in our microfluidic chip crossed ECM channels and reached epidermal channels forming axon-only network layers. Soma of the SNs was aggregated in the ECM hydrogel and the axons were 3D aligned with directional elongation through the hydrogel in the opposite direction (axon/epidermal compartment) over the incubation time (Fig. 2a). The width of the 3D neurites in the hydrogels of the microfluidic chips was significantly thicker than that of the 2D plate (Fig. 2f). In 3D, the number of neurites per soma was also higher, forming bundle-like structures (Fig. 2a). Length and outgrowth of neurites were inversely proportional to hydrogel concentration, regardless of the presence of laminin (Figs. 2b, c and 2d, e and Supplementary Fig. 3)39,40. Interestingly, laminin was found to remold the width and angle of neurites; their initial angle in the ECM hydrogel was widely aligned under all ECM conditions at DIV 2, but that in the ECM presenting laminin was gradually narrowed and straightened at DIV 6 (not significant but trending, Fig. 2a and g). Taken together, COL1.5 L is an optimal condition for guiding axonal elongation, preserving only soluble factor-mediated communication (minimal cell migration) and resulting in dense axonal network formation. Fig. 2Optimization of 3D extracellular matrix (ECM) hydrogels for axon patterning of sensory neurons in a microfluidic chip.a Representative fluorescence images of elongated nerve fibers of sensory neurons in microchannels for each ECM condition. NF-M; neurofilament M, green, DAPI; nuclei, blue. COL 2; collagen I at 2 mg/ml concentration, COL 2 L; collagen I at 2 mg/mL with $10\%$ laminin, COL 1.5 L; collagen I at 1.5 mg/mL with $10\%$ laminin. 2D; conventional monolayer culture method. Scale bars; 100 μm. b–g *Quantitative analysis* of axonal changes according to ECM conditions of the chip. Maximum (b, d) and total neurite length (c, e) of sensory neurons at each time point after culture ($$n = 5$$–8 ROIs, at least 10 neurites were measured in each ROI, COL1.5 L(d4) vs COL2L(d4) **$$p \leq 0.0014$$, COL1.5 L(d6) vs COL2L(d6) $$p \leq 0.1211$$ for maximum neurite length, COL1.5 L(d4) vs COL2L(d4) *$$p \leq 0.0126$$, COL1.5 L(d6) vs COL2L(d6) ***$$p \leq 0.0006$$ for total neurite length, 2 independent replicates). Box plot of the neurite width (f) of a sensory neuron 6 days after culture ($$n = 19$$ ROIs, 2D vs COL2, COL2L, COL1.5 L ****$p \leq 0.0001$, COL2 vs COL2L **$$p \leq 0.0041$$, COL2 vs COL1.5 L *$$p \leq 0.0119$$, 2 independent replicates). Box plot of neurite angles (g) of sensory neurons 2 days and 6 days after culture ($$n = 36$$–40 ROIs, 2 independent replicates). One-way ANOVA, Bonferroni’s multiple comparisons test. Data are mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$ Box plot shows median and 75th and 25th percentiles, and whiskers show minimum and maximum values.
## Epidermal development in air-liquid interfacing microfluidic chip
Formation of epidermal layer structure and its barrier function are mainly accomplished by proliferation and differentiation of keratinocytes22. Basal keratinocytes adjoin the underlying ECM which forming the dermal-epidermal junction (DEJ). The ECM provides a specific niche that mediates mechanical and chemical signals to keratinocytes through cell-ECM interactions22,41. In the developed microfluidic chip, the dermal ECM layer was formed by two neighboring (double-layered) ECM hydrogels, one type 1 collagen at a concentration of 3 mg/mL to support seeded keratinocytes to form the stable epidermal-like layer, and the other COL1.5 L to support neuron’s easier adhesion and migration, similarity to the DEJ microstructure (Fig. 1a). We monitored morphologies of the mono-cultured keratinocyte layer under HEK medium in the epidermal channel and varied the medium in the soma channel (HEK medium, SN medium, and their 1:1 mixture). Interestingly, the SN medium (other than the medium in the epidermal channel) applied to the soma channel made the keratinocyte layer stable and thick (Supplementary Fig. 4).
To model the sensory innervation to the epidermis11, we first adapted the slope-ALI method to induce epidermal differentiation (Fig. 1b)31,37,38,42. Our slope-ALI method rapidly initiates ERK activation and the proliferation of keratinocytes than the planar-liquid method, resulting in thicker epidermal-like layers (Figs. 3a–c, 3k, l, and Supplementary Fig. 6). This method developed multicellular epidermal differentiation such as the basal (cytokeratin 14+, K 14), suprabasal (cytokeratin 10+, K 10), and granular (loricrin+, for late-stage differentiation) cells compared to the planar-liquid method: which consists mainly of K14+ keratinocytes but few K10+ and loricrin+ cells (Fig. 3d–f). The K14+ and K10+ keratinocytes of the slope-ALI method formed the suprabasal layer just above the basal layer like human epidermal tissue, showing a structurally more organized cell layer than the planar-liquid method (Fig. 3g). Under slope-ALI conditions, undulating micropatterned structures were noticed in the keratinocytes layer, like Rete ridge (RR) in natural human skin which has never been noticed in current tissue-engineered or 3D skin equivalents (Supplementary Fig. 7)41. The keratinocyte layer in slope-ALI condition was tortuous but tightly interconnected showing a strong barrier function to 3.984 kDa FITC-conjugated dextran, consistent with more intense and continuous distribution results (Fig. 3g). It also showed enhanced blocking for the diffusive transport from the epidermal channel to the soma channel, consequently facilitating cell-type-specific functional analysis (Fig. 1c). Taken together, these results indicate that our slope-ALI culture can accelerate the proliferation of keratinocytes and their aligned layering during differentiation, reconstituting the tortuous layered epidermal keratinocyte layer. Fig. 3Advanced epidermal development on a slope-ALI microfluidic chip.a Representative bright-field images of the epidermal layer 1 and 4 d after human keratinocytes culture using conventional planar liquid (planar-liquid) or slope-based ALI (slope-ALI) methods on a microfluidic chip (3 independent replicates). Scale bars; 100 μm. b Immunofluorescence images of the developed epidermal layers stained with F-ACTIN (red) 5 d after culture on a microfluidic chip. DAPI (blue). Scale bars; 100 μm. c Quantification of the epidermal thickness ($$n = 12$$ ROIs, 3 ROIs per device *$$p \leq 0.0105$$, 2 independent replicates). d Representative immunofluorescence images for keratin 14 (K14, red), keratin 10 (K10, green), and loricrin (green) in planar-liquid or slope-ALI cultured epidermal layer. DAPI (blue). Scale bars; 50 μm. e, f Quantification of fluorescence intensity ($$n = 4$$–7 devices, planar-liquid vs slope-ALI *$$p \leq 0.0229$$ for K14, **$$p \leq 0.0012$$ for K10, **$$p \leq 0.0032$$ for loricrin, 2 independent replicates) (e) and RNA level ($$n = 5$$ devices, planar-liquid vs slope-ALI *$$p \leq 0.0391$$ for K14, *$$p \leq 0.0494$$ for K10, **$$p \leq 0.0038$$ for loricrin, 2 independent replicates) (f) in the epidermal layers cultured with planar-liquid or slope-ALI on a microfluidic chip. g 3D confocal images of K14/K10 layer development of the keratinocyte layer (3 independent replicates). Scale bars; 50 μm. h–j Permeability of planar-liquid and slope-ALI culture epidermal layers. The distribution images (h), time-lapse intensity plot (j), and its normalized fluorescent intensity (i, at 120 min) of 3.984 kDa FITC–dextran at the interface region of the white dashed line between the ECM hydrogel and epidermal keratinocyte layer in the chip ($$n = 3$$ devices, **$$p \leq 0.0041$$, 2 independent replicates). Scale bars; 200 μm. k Immunoblotting of ERK phosphorylation. ERK$\frac{1}{2}$; anti-total ERK$\frac{1}{2}$, pERK; anti-phospho ERK$\frac{1}{2.}$ l qPCR analysis of ki67 and MMP1 expression in epidermal keratinocytes 24 h after each culture ($$n = 5$$ devices, ****$p \leq 0.0001$ for Ki67, *$$p \leq 0.0181$$ for MMP1, 2 independent replicates). Data are mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$ Two-tailed t-test.
## Histological features of the innervated epidermal-like layer in the microfluidic chip
To recapitulate the physical contact between epidermal keratinocytes and SN, we co-cultured keratinocytes and SN in a microfluidic chip and evaluated the structural and functional characteristics in a cell-type-specific manner as described in Fig. 1c. First, morphological features of co-cultured SNs were characterized by immunostaining. PGP 9.5 + sensory neurites arise from the soma channel, penetrate the double-layered ECM hydrogels, move toward the tortuous epidermis, and finally terminate around the epidermal-keratinocyte interface similar to intraepidermal nerve endings (Fig. 4a–c). Fibers from mono-cultured SNs were smoother and less branched, while the outgrowth of nerve fibers from co-cultured SNs relaxed near the keratinocytes, producing thinly branched ends; divided into multiple strands of the free nerve endings (Fig. 4b and 4d, e). Please note serpentine morphology of co-cultured SN’s free nerve endings in epidermal keratinocyte layer correlating interfaces of keratinocytes, ending at variable heights with slightly varicose and branched patterns in the basal and spinous layers of the epidermis (Fig. 4b–e and 4g–i).Fig. 4The structural complexity of the innervated epidermal-like layer in the microfluidic chip.a, c 3D confocal images of innervated epidermal-like layer for K10, K14 (green) and TUJ1, NF-M (red) (2 independent replicates). Scale bars; 100 μm. b Immunofluorescence images for PGP 9.5 (green) and F-ACTIN (red) in SN only or in the SN + HEK group. Magnifications (bottom) of the region highlighted in the white dashed box (top) (2 independent replicates). Scale bars; 100 μm. d, e Morphological quantification of sensory neurons along the regions. The number of sensory neurites ($$n = 3$$ independent replicates, SN + HEK vs SN only *$$p \leq 0.0437$$ for A3, A2 vs A3 *$$p \leq 0.0301$$ and A3 vs A4 **$$p \leq 0.0097$$ for SN + HEK) (d) and the width of sensory neurite bundles ($$n = 8$$–39 ROIs, SN + HEK vs SN only *$$p \leq 0.0109$$ for A2, ****$p \leq 0.0001$ for A3 and A4, 3 independent replicates) (e) in SN only or in the SN + HEK group. f–i Comparison of sensory neuron types by quantifying the fluorescence intensity of NF200+, CGRP+, or IB4+ cells between SN only and SN + HEK groups. Quantitative analysis of the total amount ($$n = 5$$–10 ROIs, 2 ROIs per device, SN + HEK vs SN only *$$p \leq 0.0121$$ for total CGRP, *$$p \leq 0.0323$$ for total IB4, 2 independent replicates) (f) and spatial distribution (h, i) of neuron types along the regions (g) ($$n = 5$$–10 ROIs, CGRP ratio of SN + HEK vs SN only ***$$p \leq 0.0004$$ for A1, **$$p \leq 0.0018$$ for A3, IB4 ratio of SN + HEK vs SN only ****$p \leq 0.0001$ for A1 and A2, ***$$p \leq 0.0007$$ for A3, 2 independent replicates). A1 and A2; areas of the dermal ECM, A3; areas under and inside the epidermal layer, A4; area of the deep epidermal layer. j, k Image-based quantification of the epidermal layer differentiation in HEK only or in the HEK + SN group. Representative immunofluorescence images (j) of K14/K10 layer development. $Y = 0$ (μm): Interface of collagen gel channel and HEK channel, Y > 0: apical (ALI), and Y < 0: basal (gel) directions (k) (4 independent replicates). Scale bars; 50 μm. l, m Epidermal layer permeability of 376.27 Da FITC-sodium at 120 min (m) and a 3D confocal image (l) of K14 (red) and K10 (green) in the epidermal layer. Scale bars; 50 μm ($$n = 3$$ devices, 1 independent replicate). Data are mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$ Two-tailed t-test, two-tailed Mann–Whitney test or one-way ANOVA, Tukey’s multiple comparisons test.
In the experiments, we found that NF200+ A-fibers (myelinated A-fibers) were more predominant than CGRP (peptidergic unmyelinated C-fibers) or IB4 (non-peptidergic unmyelinated C-fibers) positive neurons (Fig. 4f and 4h, i). NF200+ A-fibers from co-cultured SNs have morphologically thinner and longer than those from mono-cultured SNs and usually terminate in dermal ECMs falling short of the epidermal layer (Fig. 4f and 4h, i). CGRP+ peptidergic neurons were significantly more in co-cultured SNs and were mainly confined in the region under the epidermal layer, some terminated within the epidermis as free nerve endings. Whereas IB4+ non-peptidergic fibers from mono-cultured SNs had more quantity in ECM hydrogel. Although IB4+ neurons from co-cultured SNs migrated through ECM hydrogel relatively longer than those from mono-cultured SNs, they did not innervate into the deep epidermis (granular layer) and failed to recapitulate the complete anatomy of the native skin (Fig. 4f and 4h, i)8–10. Co-culture of SNs influence the development of epidermal keratinocytes in terms of morphogenesis and differentiation (Fig. 4j, k). When innervated, the epidermal-like layer grew on, not invading into the hydrogel, and presented enhanced alignment of K14, K10 and Loricrin (Supplementary Figs. 7a, 8a and 9a). In addition, the co-cultured epidermal keratinocyte layer showed a slight improvement in barrier function against 376.27 Da FITC-sodium (Fig. 4l, m). The co-culture of keratinocytes and SN in our slope-ALI microfluidic chip was proved to recapitulate cellular and histological structures of the innervated epidermis more successfully than conventional 3D transwell insert culture or microfluidic culture system.
## Functional integration of the innervated epidermal-like layer in the microfluidic chip
Innervation of the epidermal layer in the developed microfluidic model noticed that sensory neuron innervation influenced the epidermal development by increasing epidermal thickness and differentiation (Fig. 4). Structural and functional similarities acquired by the model enabled the study of 3D interactions of skin cell components, including functional cross-talk between keratinocytes and neurons during innervating epidermis. Consistent with the previous reports13–15,43, cell-cell contacts between keratinocytes and neurons were observed in developing epidermal-like layers (Figs. 4b and 5a). Sensory nerve endings sprouting into the epidermal layer were associated with growth-associated protein 43 (GAP43), indicating that the interaction permissive to the outgrowth of neurons occurred spatiotemporally (Fig. 5a)44. Levels of CGRP and SP were found to be increased when SN cultured with keratinocytes (Fig. 5b, c), implying paracrine communication by neuron-derived soluble mediators contributing to epidermal integrity33. Sensory neurites seemed to form intimate physical interactions with keratinocytes during innervated epidermal development in our microfluidic model. Fig. 5Functional integrity of the innervated epidermal-like layer in the microfluidic chip.a Representative immunofluorescence images of TRPV1 (green) and GAP-43 (red) expression in sensory neurons co-cultured with keratinocytes on a chip. Arrowheads indicate TRPV1+ cells co-stained with GAP-43 in either the outer epidermal and ECM layers (yellow) or the intraepidermal layer (white). White dashed line; the outer epidermal layer. Magnifications (bottom) of the region are highlighted in the yellow dashed box (top). Scale bars; 100 μm, 25 μm, respectively (1 independent replicate). b, c Quantification of neuropeptides released from HEK only, SN only, and SN + HEK group under unstimulated conditions. The concentration of substance P ($$n = 4$$ devices, SN + HEK vs HEK *$$p \leq 0.036$$, SN + HEK vs SN *$$p \leq 0.0248$$, 2 independent replicates) (b) or CGRP ($$n = 3$$ independent replicates, mean ± SEM) (c) is determined in culture supernatants. d–f TRPV1 and TRPV4 expression in the innervated epidermal chip. Representative immunofluorescence images (top of d) of epidermal keratinocytes TRPV1 or TRPV4 (green) and F-ACTIN (red) expression. TRPV1 expression (bottom of d) was confirmed with a human-specific antibody (TRPV1-H, red) or with a rat-specific antibody (TRPV1-R, green). Scale bars; 100 μm, 50 μm, respectively. Quantification of total TRPV1+ neurons (e) and spatial distribution (f) of TRPV1+ neurons along the regions (presented in Fig. 4g) ($$n = 10$$ ROIs, 2 ROIs per device, SN + HEK vs SN ****$p \leq 0.0001$ for A1 and A3, 2 independent replicates). g, h Capsaicin-evoked Ca2+ transients of innervating sensory neurons. Intracellular Ca2+ images (g) of neurons responding to topical application of capsaicin (0.1 mM) and the fluorescence intensity time course (h) of peak Ca2+ transients (calcium fluorescence intensities along the axon was indicated mean ± SD, 2 independent replicates). i, j The CGRP release from sensory neurons co-cultured with keratinocytes following topical application of capsaicin (i, agonist for TRPV1) ($$n = 4$$ devices, cap(0.7) vs cap[0] *$$p \leq 0.0286$$ for SN + HEK, 2 independent replicates) or 4α-PDD (j, agonist for TRPV4) ($$n = 7$$–11 devices, cap(0.1) vs cap[0] *$$p \leq 0.028$$, cap(0.2) vs cap[0] *$$p \leq 0.0192$$ for SN + HEK, 2 independent replicates) at indicated concentrations (unit: mM). Data are mean ± SD, *$p \leq 0.05$, ***$p \leq 0.001$, ****$p \leq 0.0001.$ Two-tailed t-test or two-tailed Mann–Whitney test.
The model could present a cue for the crosstalk between keratinocytes and SN by cutaneous nociception in normal (healthy) conditions. Sensory-free nerve endings, nerve fibers of innervated sensory neuron, has been known to be a major cutaneous nociceptor. Epidermal keratinocyte also acts as a primary nociceptive transducer, expressing functional sensory receptors and releasing neuroactive substances which specifically activate nociceptive SN to ultimately elicit pain12,26,45–47. To assert the nociceptive transduction (Fig. 1a), we treated sensory receptor-specific agonists in the epidermal channel based on the expression of TRPV1 (transient receptor potential vanilloid 1) and TRPV4 (transient receptor potential vanilloid 4) in the epidermal-like layer (Fig. 5d). Topical applications of capsaicin (for TRPV1) or 4α-PDD (for TRPV4) to epidermal keratinocytes sequentially activated innervated neurons and caused the calcium-dependent release of CGRP from CGRP+ neurons (Fig. 5g–j). Single-cell calcium imaging verified that the sensitivity and activity of co-cultured neurons in the epidermal-like layer were enhanced (Fig. 6n and 6p). The SN in the epidermal-like layer seemed to be more sensitive and active than mono-cultured ones. The enhanced sensitivity of SN by epidermal integration can explain the increased CGRP by topical treatment of capsaicin. The increased number of CGRP+ TRPV1+ fibers and TRPV1+ fibers from co-cultured SN could also be the reason for the increased CGRP. However, the topically applied capsaicin had a low chance of directly initiating the nociceptive response of TRPV1+ SNs, due to the strong and intact barrier function of the innervated epidermal-like layer to FITC-sodium comparable to capsaicin (Fig. 4l and 5j, and Supplementary Fig. 8). The developed microfluidic model can demonstrate structural and functional integration of SN in the keratinocyte layer for transmitting afferent information, and protecting capacity for the integrated neurons from the direct impact of topically applied stimuli. The SN also appear to be more active and sensitive when integrated with the keratinocyte layer. Fig. 6Acute hyperglycemia-induced pathological modeling using innervated epidermal-like layer chips.a Modeling of hyperglycemia (HG)-induced innervated epidermis on a microfluidic chip, and analyzing in a cell-type-specific manner (b). c Quantification of fluorescence intensity of the cleaved caspase 3+ population in sensory neurons ($$n = 8$$ ROIs, 2 ROIs per device, Ctrl vs HG $$p \leq 0.8536$$ for SN-HEK and $$p \leq 0.2947$$ for SN + HEK, SN + HEK vs SN-HEK $$p \leq 0.0694$$ for Ctrl, 2 independent replicates). d Intracellular reactive oxygen species (ROS) levels in the innervating neurons ($$n = 7$$ ROIs, 2 ROIs per device **$$p \leq 0.0027$$, 1 independent replicates). Scale bars; 50 μm. e Immunofluorescence images of innervated epidermis for K14 or K10 (green) and TRPV1 or TUJ1 (red) after 3 d of high glucose exposure (2 independent replicates). Scale bars; 200 μm. f,g Hyperglycemia-induced changes in TRPV1+ neurons are determined by quantification of neurite length (f) of TRPV1+ neurons ($$n = 19$$–37 ROIs, SN + HEK (Ctrl) vs SN-HEK (Ctrl, HG) ****$p \leq 0.0001$, SN + HEK (Ctrl) vs SN + HEK (HG) **$$p \leq 0.0062$$, SN + HEK (HG) vs SN-HEK (HG) **$$p \leq 0.0018$$, 2 independent replicates, Kruskal–Wallis test) and free nerve endings (FNEs, g) of TRPV1+ neurons innervating the epidermal keratinocyte layer ($$n = 4$$–5 devices, *$$p \leq 0.0317$$, 2 independent replicates). h–l Hyperglycemia-induced changes of epidermal layer development. Quantification of the epidermal thickness ($$n = 4$$–8 devices, HEK-SN (Ctrl) vs HEK-SN (HG) *$$p \leq 0.0207$$, HEK + SN (Ctrl) vs HEK-SN (HG) *$$p \leq 0.0336$$, 2 independent replicates) (h) and K14+ and K10+ layers (j) between controls and HG groups. Immunofluorescence images (i) of K14, K10, and ki67-positive cells (yellow arrowheads) and fluorescence intensity plots (k) of K14 and K10 in epidermal layers. Scale bars; 200 μm. The relative ratio of K10 over the K14 layer along the Y-axis showing layer organization (l) ($$n = 2$$–4 devices, 2 independent replicates). m Hyperglycemia-induced changes in epidermal permeability of 376.27 Da FITC-sodium. n–q Capsaicin(0.1 mM)-evoked Ca2+ transients between controls and HG groups. Amplitude (SN + HEK (Ctrl) vs SN-HEK (Ctrl) **$$p \leq 0.0072$$, SN + HEK (Ctrl) vs SN + HEK (HG) *$$p \leq 0.0117$$) (n), peak time (o), peak width (p), and rise time (SN-HEK (Ctrl) vs SN-HEK (HG) **$$p \leq 0.0067$$) (q) ($$n = 5$$–6 ROIs for SN-HEK, 12 ROIs for SN + HEK, 2 ROIs per device, 2 independent replicates). Data are mean ± SD, *$p \leq 0.05$, **$p \leq 0.01$, ***$p \leq 0.001$, ****$p \leq 0.0001.$ Two-tailed t-test, two-tailed Mann–Whitney test or one-way ANOVA, Tukey’s multiple comparisons test.
## Mimicking hyperglycemia-induced diabetic neuropathy
Diabetic neuropathy occurs in patients with impaired glucose regulation and is typically characterized by sensory symptoms including pain48,49. Though the etiology of diabetic neuropathy is complicated and not fully understood, it is proposed that dysfunctions of intraepidermal nerve fibers under their cutaneous microenvironmental change by hyperglycemia in diabetes may play a significant role43,48–50. The developed innervated epidermal-like layer in a microfluidic chip was applied to evaluate the pathophysiological mechanisms of intraepidermal nerve fibers and epidermal keratinocytes in the development and progress of hyperglycemia-induced cutaneous neuropathy by simulating hyperglycemia (Fig. 6a, b).
The effect of a high concentration of glucose (100 mM) on the survival, apoptosis, and oxidative stress of SN was first investigated by staining with a marker for caspase 3 activation or cellular reactive oxygen species (ROS) level. A high glucose environment had no statistically significant effect on neurons’ survival and apoptosis regardless of the epidermal-like layer’s presence (Fig. 6c). However, it increased cellular ROS accumulation in SN, implying the induction of oxidative stress in the neurons similar to the previous reports (Fig. 6b and d)49,51. To determine whether this oxidative stress is structurally and functionally linked to SN, TRPV1+ intraepidermal nerve fibers anatomically distributed in the epidermis were analyzed. The fibers are robust nociceptive SN in the skin having a strong association with clinical disease-associated pain conditions52–55. The total number of TRPV1+ neurons had no significant difference under high glucose conditions (Supplementary Fig. 9c), but the length and the number of intraepidermal nerve fibers were significantly decreased. Axonal outgrowth and epidermal innervation of SN seemed to be particularly inhibited (Fig. 6e–g)54,55. The high glucose environment did not significantly affect apoptosis, proliferation, and the total thickness of the epidermal-like layer (Fig. 6h, i, and supplementary Fig. 9a, b). However histological analyses revealed that basal and spinous cells were regularly ordered and closely aligned in the epidermal-like layer and became irregular and loose in the high glucose condition (Fig. 6i–l). Keratinocytes in the co-cultured epidermal-like layer showed an altered morphology, being larger and swollen when treated with glucose (Fig. 6i). The expression patterns of K 14 and K 10 showed hyperglycemia perturbed the physiological epidermal differentiation. Added glucose markedly decreased the K10 expression ratio over K14 expression (Fig. 6k, l), and even impair RR morphogenesis and epidermal barrier permeability (Fig. 6i and m).
When capsaicin was topically treated to innervated epidermal-like layer, Ca2+ influx responses occurred in epidermal neurons at a single-cell level (Fig. 6n–q). Hyperglycemia reduced intraepidermal SN, but conversely, afferent transmission from epidermal keratinocytes to SN was slightly increased (Fig. 6n). The impaired barrier function of the epidermal-like layer under high glucose conditions increased penetration of topical substance and might induce the direct response of TRPV1+ fibers beneath the epidermal-like layer. The result can simulate the susceptible skin of diabetic patients due to the barrier defects, and provide a possible mechanism for early features of acute hyperglycemia/prediabetes without electrophysiological evidence of nerve damage or sensory dysfunction such as neuropathic pain behavior in diabetic patients despite the loss of intraepidermal nerve fibers. Hyperglycemia is responsible for the aberrant structural development of innervated epidermal-like layers by changing cellular communications, implying the possibility of abnormal functional integration.
## Discussion
Understanding the complex communications and interactions among various cells and neighboring microenvironmental components in the skin is essential for R&D and industrial applications, but challenges have existed in reconstituting 3D structures of cutaneous innervation in vitro and developing analysis tools in a cell-type-specific manner. The in vitro model can be not only an alternative to animals but also an approximation for various human skin diseases and side effects of other diseases on the skin. This paper describes a microfluidic co-culture system to form 3D innervated epidermal-like layers and its qualitative improvements in applicability, reliability, and complexity compared to previous microfluidic co-cultures. Precisely regulated spatial features and co-culture parameters allow compartmental patterning of neurons and epidermal keratinocytes, forming an organized innervated epidermal keratinocyte layer, being clearly visualized in microfluidic format. The microfluidic protocols allow, first, fluidically isolated culture for the two cell populations in distinct patterns or indirect juxtaposition on the same plane of medium and ECM hydrogel (Fig. 1a). Second, the protocols allow temporal and selective analysis and/or probing of specific cells, for example in situ Ca2+ response monitoring and epidermal permeability measurement for the topically applied substances. Finally, we hope to mention the benefit of the sloped ALI culture on the epidermal-like layer with low hydrostatic pressure, similar to but slightly different from the conventional insert culture assay. The sloped ALI protocol has the capacity for inducing a mechanical niche for the epidermal-like layer, which is certainly mechanosensitive and may sense compressive stress caused by tilting, and transduce it into physiological biochemical signals such as ERK$\frac{1}{2}$ cascade and MMPs as previously reported41,56. ECM remodeling and internal force made by the proliferation of keratinocytes can help keratinocytes migrate and form bifurcated RR structures, similar to in vivo skin41. The RR has physiological significance in strengthening dermal-epidermal connectivity and improving keratinocyte differentiation by increasing the surface area of the DEJ. Pillars designed in the microfluidic chip to compartmentalize ECM hydrogels might also contribute to the formation of the RR 57.
Another interesting achievement of the developed chip is the spatially distributed various types of SN in the acellular dermal ECM layer and intraepidermal free nerve endings in the epidermal-like layer, recapitulating the physiological histology of human skin. Nerve and skin cells formed spatiotemporal and physical contacts, representing their functional crosstalk and naive alignments. The chip could successfully model the interactions reciprocally contributed to the development and maturation of innervated epidermis-like layer and afferent transmission of topical stimuli from epidermal keratinocytes to SN. The complex organization of the epidermal-like layer intimately associated with keratinocytes and free nerve endings makes it hard to selectively stimulate keratinocytes while ignoring SN in conventional models. However, the developed innervated epidermal-like layer in a microfluidic chip allows us to overcome the pitfalls and keratinocytes to initiate nociceptive transduction, thanks to the spatial compartmentalization of the cells and ECM hydrogels with apparent controllability and perfect barrier function. The developed chip can experimentally model a hyperglycemic environment to understand pathological roles and changes of innervated skin components in the development of diabetic neuropathy. Consistent with known pathogenesis of diabetes and diabetic complications, acute hyperglycemia-induced loss of TRPV1+ intraepidermal nerve fibers and disrupted development of epidermal layer by ROS accumulation rather than apoptosis. Hyperglycemia-induced impaired barrier function of epidermal-like layer penetrating topical substances suggests the reason why the skin of diabetic patients is more susceptible to barrier defects caused by external stimuli. The afferent transmission provides a possible mechanism for early features of acute hyperglycemia/prediabetes without electrophysiological evidence of nerve damage. It also could explain sensory dysfunction such as neuropathic pain behavior in diabetic patients despite the loss of intraepidermal nerve fibers.
However major limitations still remain and impede the completeness of the developed chip and protocol. Due to restricted access to primary adult human SN, the developed model utilized rodent sensory neurons (DRGs), disregarding the possible existence of interspecies differences. Recent hiPSC or hiNSC-derived SN are new translational alternatives but still remain with inconsistencies in cellular function and population compared to native human or rodent cells33,58. The developed protocol could be a good guide to exploit a chip with an innervated epidermal-like layer with full human origin cells for future study, because it already shows its capability for studying neurocutaneous diseases and drug screening especially for topical applications with a level of complexity not found in conventional skin models and close prediction of in vivo results by the barrier function and transmission of sensory stimuli. Our data shows a structurally and functionally integrated innervated epidermal-like layer, recapitulating abnormal cellular interactions in a pathophysiologically relevant human setting. We hope to provide insights on the future integration of these skin models with other cell components onto microfluidic platforms as well as potential readout technologies for high-throughput drug screening. We also hope to prolong the culture period to allow keratinocytes to reach a higher level of maturity, reaching the epidermis layer with IB4+ nerve fibers deeply localized in the granular layer.
## Ethical statement
The animal experiments that isolating DRG of embryonic day 15 Sprague-Dawley rat embryos were approved by the Korea University Institutional Animal Care and Use Committee (KUIACUC-2017-138).
## Microfluidic design and fabrication
The design of the chip was modified and fabricated from the previous report (Fig. 1a)32,42. Briefly, the device was produced from a SU-8 patterned wafer via soft lithography using polydimethylsiloxane (PDMS, SYLGARD 184; Dow Chemical Company). A PDMS replica was punched with a dermal biopsy punch (diameters of 4 mm or 1 mm, for the medium or gel channels, respectively). It was then sterilized twice at 120 °C for 20 min, followed by drying at 80 °C in an oven for 6 h. The device and a glass coverslip (18 × 18 mm2, Paul Marienfeld GmbH & Co.) were bonded using oxygen plasma (CUTE; Femto Science Inc.). Then, the microchannels were filled with a 2-mg/mL polydopamine solution (PDA, Sigma-Aldrich) and maintained at room temperature for 2 h with light protection. After washing with sterilized deionized water, the devices were dried at 80 °C in an oven for 24 h and stored at room temperature for use (Supplementary Fig. 2a).
## Gel-filling procedure
Two gel channels in the microfluidic device were filled with two types of hydrogels (Fig. 1a and Supplementary Fig. 2b). First, type I collagen gel solution (Corning Inc.) was prepared by mixing 10× phosphate-buffered saline (PBS) with phenol red, 0.5 N NaOH, and deionized water to a concentration of 3 mg/mL and a pH of 7.4. This collagen gel solution was injected into the HEK-side gel channel, then the device was placed upside down in a pre-warmed humid chamber and incubated for 30 min at 37 °C in a $5\%$ CO2 atmosphere for gelation. Next, a 1.5 mg/mL collagen solution containing $10\%$ laminin (Sigma-Aldrich) was prepared according to the previously described method. Then, it was filled in the SN-side gel channel to be connected to the pre-formed collagen channel. After gelation, the device’s medium reservoir was filled with SN medium and stored in an incubator for cell seeding (Supplementary Fig. 2a).
## Cell culture
Primary SN were isolated from DRG of embryonic day 15 Sprague-Dawley rat embryos (KOATECH, Gyeonggi, South Korea) and cultured in neurobasal media (Gibco) supplemented with 250 ng/mL recombinant nerve growth factor 7 S (Sigma-Aldrich), $10\%$ fetal bovine serum, $2\%$ B-27 supplement, 2 mM L-glutamine, and $1\%$ antibiotic solution (all from Gibco) for growth in a humidified $5\%$ CO2 incubator as previously described29,30. For keratinocytes, Adult normal HEKs were commercially obtained from Lonza Group AG (Basel, Switzerland) [00192627] and were cultured in KGM-*Gold medium* (Lonza) supplemented with the KGM-Gold Bullet Kit (Lonza) according to the manufacturer’s instructions.
## Co-culture of Keratinocytes and DRG neurons in the microfluidic chip
After adding 60 µL of a rat SN suspension with a density of 1.8 × 106 cells/mL to the SN channel, the device was placed vertically in an incubator for 3 h to attach the cells to the hydrogel, followed by culture for 5 days, (Fig. 1d). For co-culture of seeded SNs and keratinocytes, 60 µL of HEK cell suspension (1.2 × 106 cells/mL) was added to the opposite channel for seeding and the device was tilted in the opposite direction for 2 h. Two days after HEK inoculation, the HEK medium was removed from the reservoir to manipulate the ALI culture conditions by placing the device at a 30° angle. The culture medium of each cell was changed according to the culture stage; FBS-depleted neurobasal medium for SNs, ascorbic acid (50-μg/mL, Sigma-Aldrich)-added differentiation medium for HEKs as optimized and described in Fig. 3. For hyperglycemic conditions to mimic the diabetic environment, SN channels were maintained with 100 mM D-glucose (Sigma, G7021) without insulin during the slope-based culture (3 days) 43,54,55.
## Immunofluorescence microscopy analysis
Chips were fixed with $4\%$ paraformaldehyde in PBS for 30 min and then washed with PBS. Immunostaining was performed after permeabilization in PBS with $0.1\%$ Triton X-100 and blocking in $3\%$ BSA (Thermo Fisher Scientific). Antibodies used in this study were listed in Supplemental Table 2, which were incubated overnight on chips at 4 °C. Fluorescent conjugated secondary antibodies were then used and Nuclei were counterstained with 4,6-diamidino-2-phenylindole dihydrochloride (DAPI, Invitrogen). Confocal imaging was obtained using Confocal Microscope (Olympus, Japan) (LSM 700, Carl Zeiss, Germany) and analyzed using ImageJ software (https://imagej.nih.gov/ij/index.html), FluoVIEW (Olympus, Japan) or ZEN 2.3 software (Carl Zeiss, Germany).
## Quantitative real-time polymerase chain reaction (qRT-PCR) analysis
RNA was extracted from normal HEK in the microfluidic chip using TRIzol reagent (Invitrogen). Complementary DNA (cDNA) was synthesized by reverse transcription with a high-capacity RNA-to-cDNA kit (Applied Biosystems). qRT-PCR was performed using Power SYBR Green PCR Master Mix (Applied Biosystems) in the StepOne Real-time PCR equipment (Applied Biosystems). The primer sequences are listed in table S2. *The* gene expression levels were normalized to the housekeeping gene Gapdh and quantified with the comparative Ct method.
## Calcium imaging
Calcium imaging was performed using the Fluo-4 Direct Calcium Assay Kit according to the manufacturer’s instructions (Invitrogen). Briefly, chips were incubated with a 1:1 mixture of 2× reagent and SN serum-free medium in a humidified $5\%$ CO2 incubator for 60 min and then washed with serum-free medium. Images were captured every 1500 ms for 2 min with or without 0.1 mM capsaicin treatment in the HEK channel. For determination of threshold for activation in calcium imaging, the magnitude of the ratio change during exposure to agonist (ΔF) was normalized to the baseline ratio for each imaged neuron (ΔF/F0 ratio, fold-change). A histogram of the fold-change of each individual neuron in a particular experiment was constructed for each stimulus to determine the threshold for activation. The multimodal histogram contained one large peak around 1 (defined as the background response, F0) and neurons with fold-changes greater than the first minimum (defined as threshold) were considered responsive 50,59.
## ELISA
HEK channels were pre-stimulated with capsaicin (Sigma-Aldrich), 4α-PDD (Sigma-Aldrich), or DMSO and then supernatants were harvested from the SNs channels after 2 h of agonist treatment. The concentration of CGRP in the culture supernatant was measured using a rat CGRP Enzyme Immunoassay kit (SPI-Bio) and VICTOR X3 (PerkinElmer, USA) at a wavelength of 405 nm following the manufacturer’s recommended procedures.
## Reactive oxygen species (ROS) measurements
ROS was quantitatively analyzed by a fluorescent probe (Cell ROX Green Reagent, Invitrogen) for measuring oxidative stress in living cells. 5 μM Cell ROX green reagent was added to the culture medium at 37 °C for 30 min, washed with PBS and fixed with $4\%$ PFA. After counterstaining with DAPI, fluorescence images were acquired using a confocal laser scanning microscope (LSM 700, Carl Zeiss, Germany) and analyzed with ImageJ software 49,51.
## Epidermal permeability assay
3.839 kDa FITC-dextran (Sigma, FD4) or 376.27 Da FITC-sodium (Sigma, F6377) were added to keratinocyte channels. Time-lapse epidermal permeability is determined as the flux of fluorescent tracers across the epidermal layer to ECM layer by the concentration difference for 120 min. The fluorescence intensity was analyzed and calculated using ImageJ according to the previously described method 42.
## Statistics and reproducibility
The statistical calculations of the results were carried out by Prism software (GraphPad Software, San Jose, CA, USA), and data were expressed as mean ± standard derivations (SD) for n ≥ 3 or as mean ± SEM with at least three independent replicates. The unpaired, two-tailed Student’s t tests or two-tailed Mann–Whitney test was used to determine the significance of the data between the two groups. Multigroup analyses were made by one-way analysis of variance (ANOVA) followed by a Tukey’s multiple comparisons test or a Bonferroni’s multiple comparisons test and p values below 0.05 were deemed statistically important: *$p \leq 0.05$, **$p \leq 0.01$, and ***$p \leq 0.001$, ****$p \leq 0.0001.$ No statistical method was used to predetermine the sample size. Throughout the study, the sample size was determined based on our preliminary studies and on the criteria in the field.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary information Reporting Summary Peer Review File The online version contains supplementary material available at 10.1038/s41467-023-37187-4.
## Source data
Source Data
## Peer review information
Nature Communications thanks Susan Gibbs and Ramin Raouf for their contribution to the peer review of this work. Peer reviewer reports are available.
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|
---
title: DIAPH1 mediates progression of atherosclerosis and regulates hepatic lipid
metabolism in mice
authors:
- Laura Senatus
- Lander Egaña-Gorroño
- Raquel López-Díez
- Sonia Bergaya
- Juan Francisco Aranda
- Jaume Amengual
- Lakshmi Arivazhagan
- Michaele B. Manigrasso
- Gautham Yepuri
- Ramesh Nimma
- Kaamashri N. Mangar
- Rollanda Bernadin
- Boyan Zhou
- Paul F. Gugger
- Huilin Li
- Richard A. Friedman
- Neil D. Theise
- Alexander Shekhtman
- Edward A. Fisher
- Ravichandran Ramasamy
- Ann Marie Schmidt
journal: Communications Biology
year: 2023
pmcid: PMC10023694
doi: 10.1038/s42003-023-04643-2
license: CC BY 4.0
---
# DIAPH1 mediates progression of atherosclerosis and regulates hepatic lipid metabolism in mice
## Abstract
Atherosclerosis evolves through dysregulated lipid metabolism interwoven with exaggerated inflammation. Previous work implicating the receptor for advanced glycation end products (RAGE) in atherosclerosis prompted us to explore if Diaphanous 1 (DIAPH1), which binds to the RAGE cytoplasmic domain and is important for RAGE signaling, contributes to these processes. We intercrossed atherosclerosis-prone Ldlr−/− mice with mice devoid of Diaph1 and fed them Western diet for 16 weeks. Compared to male Ldlr−/− mice, male Ldlr−/− Diaph1−/− mice displayed significantly less atherosclerosis, in parallel with lower plasma concentrations of cholesterol and triglycerides. Female Ldlr−/− Diaph1−/− mice displayed significantly less atherosclerosis compared to Ldlr−/− mice and demonstrated lower plasma concentrations of cholesterol, but not plasma triglycerides. Deletion of Diaph1 attenuated expression of genes regulating hepatic lipid metabolism, Acaca, Acacb, Gpat2, Lpin1, Lpin2 and Fasn, without effect on mRNA expression of upstream transcription factors Srebf1, Srebf2 or Mxlipl in male mice. We traced DIAPH1-dependent mechanisms to nuclear translocation of SREBP1 in a manner independent of carbohydrate- or insulin-regulated cues but, at least in part, through the actin cytoskeleton. This work unveils new regulators of atherosclerosis and lipid metabolism through DIAPH1.
Male and female mice deficient in DIAPH1 are protected from the development of experimental atherosclerosis and display reduced cholesterol levels; these changes are linked to the regulation of SREBP1 subcellular localization.
## Introduction
Despite the manifold advances in therapeutic regimens, cardiovascular disease (CVD) remains the leading cause of death in the United States1,2. Beyond the panoply of lipid-lowering therapies, seminal benefits for lipid-independent anti-inflammatory treatments have been demonstrated. In the CANTOS trial, treatment with canakinumab, which targets the interleukin-1β pathway, resulted in a significantly lower rate of recurrent cardiovascular events than placebo3. A consequence of targeting this immune pathway was the increased risk for significant infection, thereby indicating the overall importance of developing effective and safe adjunctive therapies targeting atherosclerosis.
Previous work implicating the receptor for advanced glycation end products (RAGE) in the progression4–6 and regression of atherosclerosis7 spurred the current investigation. The cytoplasmic domain of RAGE binds to the formin Diaphanous 1 (DIAPH1), through DIAPH1’s formin homology 1 (FH1) domain8. This interaction is important for RAGE ligand-stimulated signal transduction9,10. Formins such as DIAPH1 possess diverse functions relevant to the biology of RAGE, such as F-actin polymerization; the organization and regulation of the actin cytoskeleton; cellular migration; signal transduction through the Rho GTPases;11,12 and the regulation of RAGE ligand-mediated upregulation of Egr1 (Early Growth Response 1) via serum response factor (SRF) in hypoxia, factors which induce expression of proinflammatory and prothrombotic factors in oxygen deprivation13,14.
Recently, we showed that transplantation of aortic arches from diabetic Western diet (WD)-fed mice devoid of the low-density lipoprotein receptor (Ldlr) into diabetic wild-type C57BL/6 J chow-fed mice devoid of Ager (the gene encoding RAGE) or Diaph1 accelerated regression of diabetic atherosclerosis; in parallel, we observed reduced donor atherosclerotic lesion content of neutral lipids, macrophages, oxidative stress and RAGE ligand AGEs, and increased lesional collagen content7. Importantly, the aforementioned studies solely probed the effects of transplantation of atherosclerosis-laden aortic arches into an environment of diabetes in normolipidemic mice devoid of Diaph1; hence, DIAPH1-dependent mechanisms in the progression of atherosclerosis have never been explored7. For this reason, the current investigation was designed to probe if DIAPH1 contributes to progression of atherosclerosis in Ldlr−/− mice. Here we show that deletion of Diaph1 protected from progression of atherosclerosis in male and female Ldlr−/− mice and we demonstrate an unforeseen role for DIAPH1 in the regulation of cholesterol and triglyceride metabolism.
## DIAPH1 is expressed in human and mouse atherosclerotic lesions
To explore potential roles for DIAPH1 in the progression of atherosclerosis, we began by probing expression of DIAPH1 in atherosclerosis. DIAPH1 was expressed in human atherosclerotic plaques, at least in part in macrophages and smooth muscle cells (SMCs), as illustrated by co-localization of DIAPH1 with CD68 and Smooth Muscle Actin (SMA) epitopes (Supplementary Fig. 1a, b, respectively). Analogous to these findings in human atherosclerosis, DIAPH1 was expressed in macrophages and SMCs of atherosclerotic lesions of Ldlr−/− mice fed a WD for 16 weeks (Supplementary Fig. 1c, d, respectively). These results set the stage for testing potential roles for DIAPH1 in atherosclerosis through utilization of a mouse model.
## Effect of deletion of Diaph1 in Ldlr−/− mice on atherosclerosis and lesion characteristics
Male mice devoid of the Ldlr and Ldlr−/− mice intercrossed with Diaph1−/− mice (C57BL/6 J background) were fed WD from age 6 to 22 weeks. After 16 weeks feeding, atherosclerosis was assessed. By en face analysis, Ldlr−/− Diaph1−/− mice displayed lower neutral lipid content in the aorta, $$p \leq 0.0002$$ (Fig. 1a). Next, sections were prepared through the aortic arch. Significantly less atherosclerosis was observed in Ldlr−/− Diaph1−/− compared to Ldlr−/− mice, $p \leq 0.0001$ (Fig. 1b). Neutral lipid content was significantly lower in the aortic arch atherosclerotic lesions of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice; $p \leq 0.0001$ (Fig. 1c). Lesional macrophage content was significantly lower in the atherosclerotic plaques of Ldlr−/− Diaph1−/− mice vs. Ldlr−/− mice, $p \leq 0.0001$ (Fig. 1d). Deletion of Diaph1 resulted in significantly higher collagen content in the atherosclerotic lesions vs. that observed in Ldlr−/− mice, $p \leq 0.0001$ (Fig. 1e). As RAGE and DIAPH1 mediate oxidative and inflammatory stress15, which regulate expression of RAGE and RAGE ligands such as AGEs16, we determined if deletion of Diaph1 affected the expression of the ligand-RAGE axis. These investigations revealed that both AGEs and RAGE expression were significantly lower in the lesions of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $p \leq 0.0001$ and $$p \leq 0.0289$$, respectively (Supplementary Fig. 1e, f).Fig. 1Deletion of Diaph1 in male Ldlr−/− mice attenuates the progression of atherosclerosis. Ldlr −/− and Ldlr−/− Diaph1−/− male mice were fed Western Diet (WD) for 16 weeks. a Representative images of en face Oil Red O staining of aortas. Quantification of plaque area as percentage of Oil Red O-stained area over total aortic surface area is shown. b–e Representative images of aortic arch sections are shown and quantified for the following: b H&E; c Oil Red O; d CD68; and e, Picrosirius Red. In d, the secondary antibody-alone control is shown. Scale bar: 250 µm. The mean ± SEM is reported. The number of independent mice/group is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P values were determined by unpaired T-test.
We studied a distinct cohort of male mice to assess for the effects of Diaph1 deletion on atherosclerosis in the Ldlr−/− background at a different anatomical location, the aortic sinus. As illustrated in Supplementary Fig. 2a, male Ldlr−/− Diaph1−/− mice fed WD for 16 weeks displayed significantly less atherosclerosis compared to Ldlr−/− mice; $$p \leq 0.0144.$$ In addition to examination of male mice, we performed studies in female mice and found that female Ldlr−/− Diaph1−/− mice fed WD for 16 weeks displayed significantly less atherosclerosis at the aortic sinus compared to female Ldlr−/− mice; $$p \leq 0.0454$$ (Supplementary Fig. 2b).
## Effect of deletion of Diaph1 in Ldlr−/− mice on inflammation
To begin to account for the mechanisms underlying these differences in atherosclerosis upon global deletion of Diaph1, we performed studies to determine the state of inflammation in these mice. First, we performed flow cytometry on the aortic arches of male Ldlr−/− vs. Ldlr−/− Diaph1−/− mice fed WD for 16–18 weeks to characterize macrophage phenotypes. These studies revealed that there were no significant genotype-dependent differences in the percentage of “pro-inflammatory” markers in macrophages, Ly6C and CD14. Similarly, there were no significant genotype-dependent differences in the percentage of “anti-inflammatory” markers in macrophages, CD206 and CD163 (Supplementary Fig. 3a, b and Supplementary Table 1).
Second, we probed for mRNA expression of genes related to inflammation in the aortas of male mice. mRNA transcripts encoding pro-inflammatory Nos2 and Tnfa were significantly lower in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice ($p \leq 0.0001$ and $$p \leq 0.0006$$, respectively) and transcripts encoding anti-inflammatory Il10 and Arg1 were significantly higher in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice ($$p \leq 0.0167$$ and $$p \leq 0.0006$$, respectively) (Supplementary Fig. 4a–d). However, no differences were observed in the mRNA expression of Ccl2 ($$p \leq 0.2171$$) (Supplementary Fig. 4e).
Third, we explored if systemic inflammation was affected by DIAPH1 expression in these mice. There were no significant differences in plasma concentrations of TNF-alpha or IL6 in Ldlr−/− vs. Ldlr−/− Diaph1−/− male mice, $$p \leq 0.2314$$ and $$p \leq 0.8643$$, respectively. ( Supplementary Fig. 4f, g).
Taken together, these data highlight complex effects of Diaph1 deletion in Ldlr−/− mice on measures of inflammation; whereas there were no differences in the relative content of “pro-” vs. “anti-“ inflammatory macrophages within the aortic arch or in plasma concentrations of TNF-alpha or IL6, altered mRNA transcript expression of markers of inflammation was noted in the aortas compared to Ldlr−/− Diaph1−/− mice. However, mRNA expression of Ccl2 did not differ by genotype in the aortas. Hence, we next sought to test if DIAPH1 affected concentrations of lipids in Ldlr−/− mice to uncover mechanisms related to atherosclerosis.
## Effect of deletion of Diaph1 in Ldlr−/− mice on plasma concentration of cholesterol and triglyceride
We examined the concentrations of cholesterol and triglyceride in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice to determine if modulation of lipid profile contributes to the observed differences in atherosclerosis. We retrieved plasma after 16 weeks WD feeding and five hours (h) fasting and observed that plasma cholesterol concentrations were significantly lower in male Ldlr−/− Diaph1−/− mice (983 ± 66.6 mg/dl) vs. Ldlr−/− mice (1,390.8 ± 35.4 mg/dl, respectively), $p \leq 0.0001$ (Fig. 2a). Similarly, the concentrations of plasma triglyceride were significantly lower in male Ldlr−/− Diaph1−/− mice (77.4 ± 13.8 mg/dl) vs. Ldlr−/− mice (102.2 ± 20.7 mg/dl), $$p \leq 0.0057$$ (Fig. 2b). In contrast, there were no differences in plasma concentrations of high-density lipoprotein cholesterol (HDL-C) between genotypes, $$p \leq 0.8759$$ (Fig. 2c). We performed fast performance liquid chromatography (FPLC) to establish the nature of the cholesterol particles that accounted for these differences and found that the majority of the reduction in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice was observed in the very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) fractions, whereas no differences were observed in the HDL fractions (Fig. 2d).Fig. 2Effect of DIAPH1 on lipid parameters. Ldlr −/− and Ldlr−/− Diaph1−/− male mice were fed WD for 16 weeks. a Concentrations of total plasma cholesterol; b concentrations of total plasma triglycerides; c concentrations of plasma high-density lipoprotein cholesterol (HDL-C). In a–c the mean ± SEM is reported. The number of independent mice/group is indicated in the figure as individual data points. d Plasma lipoprotein fraction concentrations were measured by Fast Performance Liquid Chromatography (FPLC). CM/VLDL Chylomicron and very-low-density lipoprotein, IDL/LDL Intermediate-density and low-density lipoproteins, HDL High-density lipoprotein. The mean ± SEM is reported from $$n = 6$$ independent mice/group. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P values were determined by unpaired T-test.
In light of the observation that female Ldlr−/− Diaph1−/− mice also displayed significantly less atherosclerosis than female Ldlr−/− mice, we examined the plasma concentrations of cholesterol and triglyceride. We found that plasma concentrations of cholesterol were significantly lower in female Ldlr−/− Diaph1−/− vs. Ldlr−/− mice (604. 3 ± 21.7 vs. 1094.0 ± 84.1 mg/dl;$p \leq 0.0001$) (Supplementary Table 2). However, in contrast to the findings in male mice, there were no significant differences in plasma concentrations of triglyceride in female Ldlr−/− Diaph1−/− vs. Ldlr−/− mice (99.9 ± 10.6 vs. 116.9 ± 17.0 mg/dl; $$p \leq 0.3690$$) (Supplementary Table 2). Furthermore, there were no genotype-dependent differences in body weight or plasma concentrations of glucose in the female mice, $p \leq 0.05$ (Supplementary Table 2).
## Deletion of Diaph1 in male Ldlr−/− mice reduces hepatic lipid concentrations
To probe DIAPH1-dependent mechanisms in lipid metabolism, we turned our focus to the liver, as it is a major organ responsible for the regulation of lipid metabolism. DIAPH1 is expressed in the liver of Ldlr−/− mice but not Ldlr−/− Diaph1−/− mice fed WD; $$p \leq 0.0024$$ (Fig. 3a). In normal mouse liver, hepatocytes demonstrated expression of DIAPH1 (arrowheads, Supplementary Fig. 5a, left panel); cholangiocytes of the bile ducts (BD), sinusoidal mononuclear cells and endothelial cells of the hepatic artery (HA) also expressed DIAPH1 (arrows, Supplementary Fig. 5a, left panel). Control sections with omission of the primary anti-DIAPH1 antibody revealed the absence of staining (Supplementary Fig. 5a, right panel).Fig. 3Deletion of Diaph1 in Ldlr−/− mice reduces hepatic lipid content and liver fibrosis. Ldlr−/− and Ldlr−/− Diaph1−/− male mice were fed WD for 16 weeks. a Representative immunofluorescence staining and quantification of DIAPH1 in the liver of Ldlr −/− and Ldlr−/− Diaph1−/− male mice. b Representative images of H&E and Oil Red O staining in liver and quantification is shown. c Quantification of free cholesterol content in liver. d Quantification of total cholesterol content in liver. e Quantification of total liver triglycerides. f Representative images of Picrosirius Red staining in liver and quantification is shown. g Quantification of whole liver weight. The mean ± SEM is reported. The number of independent mice/group is indicated in the figure as individual data points. In a the secondary antibody–alone control is shown. Scale bars: 250 µm, and inset boxes: 50 µm. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values were determined by unpaired T-test or Wilcoxon rank-sum test depending on if the data passed the Shapiro-Wilk normality test.
In normal human liver, hepatocytes also demonstrated cytoplasmic staining for DIAPH1 and linear dark staining of hepatocyte apical (canalicular) membranes. Dot-like cross-sections of canaliculi were noted (long arrows, Supplementary Fig. 5b, left panel). Some hepatocytes also showed typical features such as intracytoplasmic lipid and binucleation (* and arrowheads, respectively, Supplementary Fig. 5b, left panel). Some mononuclear cells in the sinusoids also showed strong cytoplasmic DIAPH1 staining, but sinusoidal endothelial staining was not noted (Supplementary Fig. 5b, left panel). Furthermore, human bile ducts (BD) showed cytoplasmic staining for DIAPH1, as did nearby hepatocytes. Endothelial cells of portal veins (PV) and hepatic artery (HA) and mononuclear cells within the portal tract also revealed staining for DIAPH1 (Supplementary Fig. 5b, middle panel). Control sections with omission of the primary anti-DIAPH1 antibody revealed the absence of staining (Supplementary Fig. 5b, right panel).
Having established that DIAPH1 was expressed in the liver, at least in part in hepatocytes, we probed for the effects of Diaph1 deletion in this organ. In the livers of WD-fed male Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, histological analysis revealed a significantly lower neutral lipid content, $p \leq 0.0001$ (Fig. 3b). The concentrations of free and total cholesterol in the liver were also significantly lower in Ldlr−/−Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0002$$ and $$p \leq 0.0056$$, respectively (Fig. 3c, d). The concentrations of hepatic triglyceride were also significantly lower in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0259$$ (Fig. 3e). Hepatic collagen content was significantly lower in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0035$$ (Fig. 3f) and liver weight was significantly lower in Ldlr−/−Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0003$$ (Fig. 3g). There were no significant differences in plasma concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), albumin/globulin ratio (A/G) or total bilirubin (TBIL) (Supplementary Fig. 5 c–g, i, j). Only plasma globulin (GLOB) concentrations were significantly lower in Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0247$$ (Supplementary Fig. 5h). Collectively, these data demonstrate that deletion of Diaph1 in male Ldlr−/− mice resulted in significantly lower concentrations of hepatic cholesterol and triglyceride and lower hepatic mass, fibrosis and lipid deposition compared to that observed in Ldlr−/− mice expressing Diaph1.
## RNA-Sequencing revealed that DIAPH1 plays roles in hepatic lipid metabolism
To identify the basis of the effect of Diaph1 deletion on cholesterol and triglyceride metabolism, we performed bulk RNA-sequencing (RNAseq) on the liver tissue of male Ldlr−/− and Ldlr−/− Diaph1−/− mice. Mice were fed WD for 16 weeks and livers were removed after a 5 h fast. A total of 468 genes was differentially expressed with p-value ≤ 0.05 and false discovery rate (FDR) ≤ 0.05. A dendrogram and heatmap of these genes is shown in Fig. 4a and the list of the differentially expressed genes is shown in *Supplementary data* 6. Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated “Glycerophospholipid Metabolism” as among the top differentially regulated pathways (Supplementary Table 3). Reactome pathway analysis (FDR < 0.05) revealed that the top candidate pathway was “Metabolism;” this pathway included multiple differentially expressed genes comparing Ldlr−/− Diaph1−/− vs. Ldlr−/− mice (Supplementary Table 4). Notably, a number of genes within the “Metabolism” pathway were linked to lipid metabolism. An additional 49 Reactome pathways met statistical significance in this data set (Supplementary Table 5). Of these additional pathways, at least 17 were related to lipid metabolism. All of the genes highlighted from the Reactome pathway met the criterion of $p \leq 0.05.$ In agreement with the above results, the Gene Ontology (GO) pathway analysis linked DIAPH1 with lipid metabolism as well, identifying pathways such as “Fatty Acid catabolic process,” “Lysophospholipid transport,” and “Vesicle-mediated cholesterol transport” (Supplementary Table 6).Fig. 4RNA-Sequencing reveals roles for DIAPH1 in regulation of hepatic lipid metabolism. Ldlr −/− and Ldlr−/− Diaph1−/− male mice were fed WD for 16 weeks. a Hierarchical clustering of differentially expressed genes in liver tissue from the indicated $$n = 4$$ independent mice/group. b The expression of the indicated genes identified as differentially expressed in the RNAseq data between Ldlr −/− mice vs. Ldlr−/− Diaph1−/− mice was determined by RT-qPCR. c The expression of the indicated genes identified as not differentially expressed in the RNAseq between Ldlr −/− mice vs. Ldlr−/− Diaph1−/− mice was confirmed by RT-qPCR. The number of independent mice/group is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values (in b, c) were determined by unpaired T test or Wilcoxon rank-sum test depending on if the data passed the Shapiro-Wilk normality test.
We next undertook an in-depth analysis of these data to identify putative mechanisms underlying the effects of Diaph1 on cholesterol and lipid metabolism. With respect to lipid uptake, RNAseq data revealed no significant differences in the mRNA transcripts encoding the fatty acid transporter Cd36 (*Supplementary data* 6); although fatty acid binding protein 7, Fabp7, was identified as one of the differentially-expressed genes, its expression was higher in the livers of Ldlr−/−Diaph1−/− vs. Ldlr−/− mice fed WD (*Supplementary data* 6), thus not likely accounting for the observed differences in plasma and hepatic cholesterol and triglyceride concentrations between genotypes. There were no differences in expression of genes regulating hepatic fatty acid oxidation, such as Ppara, Cpt1, Cpt2, Ucp2, Acadl, or Acadm (*Supplementary data* 6). RNAseq revealed that there were no differences in expression of genes regulating Importins and Scap, which have been ascribed roles in the processing and cellular transport of Sterol Regulatory Element Binding Proteins (SREBPs)17 (*Supplementary data* 6). Despite significant differences in plasma and hepatic concentrations of cholesterol in Ldlr−/−Diaph1−/− vs. Ldlr−/− mice, RNAseq revealed no significant differences in the expression of the genes linked to cholesterol biosynthesis18.
We performed reverse transcription (RT) quantitative PCR (RT-qPCR) analysis on select genes implicated in multiple facets of lipid metabolism using liver tissue retrieved from a distinct cohort of Ldlr−/− Diaph1−/− and Ldlr−/− mice fed WD for 16 weeks. First, as shown in Fig. 4b, consistent with the RNAseq data, mRNA transcripts encoding Lpin1 and Lpin2 were significantly lower in the livers of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0015$$ and $$p \leq 0.0285$$, respectively. Genes related to “fatty acid biosynthesis,” including acetyl co-A carboxylase enzymes, Acaca and Acacb; glycerol-3-phosphate acyltransferase, Gpat2, and fatty acid synthase, Fasn, were significantly lower in Ldlr−/−Diaph1−/− vs. Ldlr−/− livers, $$p \leq 0.0013$$, $$p \leq 0.0029$$, $$p \leq 0.0179$$ and $$p \leq 0.0127$$, respectively (Fig. 4b). Second, as shown in Fig. 4c, we verified the RNAseq findings and found no significant differences in genes regulating “cholesterol and triglyceride metabolism”, including the transcription factors Srebf1, Srebf2, Rxra, Nrlh2 (encodes LXRβ), and Nrlh3 (encodes LXRα). With respect to genes in the Peroxisome Proliferator-Activated Receptor (PPAR) transcription factor family, RT-qPCR experiments confirmed that there were no differences in Ppargc1a and Ppard in Ldlr−/−Diaph1−/− vs. Ldlr−/− livers (Fig. 4c). In the context of lipid secretion and transport, there were no significant differences in triglyceride and cholesterol transporters such as Mttp, Abca1, or Abcg1, which confirmed the RNAseq findings (Fig. 4c).
Further, we assessed triglyceride secretion in vivo in chow-fed Ldlr−/− and Ldlr−/− Diaph1−/− mice. After an overnight fast, mice were given an intraperitoneal injection of [35S] methionine/cysteine labeling mixture combined with pluronic F127 poloxamer-407, the latter to inhibit lipoprotein clearance from plasma. When comparing Ldlr−/− vs. Ldlr−/− Diaph1−/− mice, there were no significant differences in triglyceride secretion or secretion of apolipoprotein B100 (apoB100) or apolipoprotein B48 (apoB 48) into the plasma (Supplementary Fig. 6a–c).
Collectively, these data indicate that deletion of Diaph1 in Ldlr−/− mice downregulated key genes implicated in hepatic lipid metabolism, without affecting the mRNA expression of upstream regulatory transcription factors such as Srebf1, Srebf2 or Mxlipl, which encode sterol and carbohydrate regulatory element binding proteins, SREBP1, SREBP2 and Carbohydrate Response Element Binding Protein (ChREBP), respectively.
## Nuclear content of SREBP1, SREBP2 and ChREBP is reduced upon deletion of Diaph1 in Ldlr−/− mice
SREBP1, SREBP2, and ChREBP are central regulators of cholesterol, triglyceride and fatty acid biosynthetic pathways19. As RNAseq data illustrated that numerous Srebf1/Srebf2 or Mlxipl-dependent genes were downregulated in the livers of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, we hypothesized that DIAPH1 affects the activity of these transcription factors independently of changes in their mRNA expression. To test this premise, we prepared cytoplasmic and nuclear extracts from the livers of Ldlr−/− mice expressing or devoid of Diaph1. There were no significant differences in cytoplasmic SREBP1, SREBP2 or ChREBP (normalized to GAPDH) (Fig. 5a, b). In contrast, nuclear SREBP1, SREBP2 and ChREBP (normalized to Lamin A/C) were significantly lower in Ldlr−/− Diaph1−/− mice vs. Ldlr−/− mice livers, $$p \leq 0.0173$$, $$p \leq 0.0221$$ and $$p \leq 0.0260$$, respectively (Fig. 5a, c). Importantly, total SREBP1 protein expression, normalized to tubulin, did not differ in the livers of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice; $$p \leq 0.4979$$ (Fig. 5d). To determine if deletion of Diaph1 in mice devoid of Ldlr affected other transcription factors related to metabolism, we probed for CEBPα content and found that cytoplasmic or nuclear amounts of this factor did not significantly differ between Ldlr−/− vs. Ldlr−/− Diaph1−/− livers, $$p \leq 0.5848$$ and $$p \leq 0.6683$$, respectively (Supplementary Fig. 7a–c). Collectively, these data suggested that DIAPH1 had no effect on total levels of SREBP1 in the liver but that DIAPH1-dependent effects on nuclear content of SREBP1, SREBP2 and ChREBP appeared to be independent of the mRNA transcripts encoding these factors. Fig. 5Deletion of Diaph1 in Ldlr−/− mice reduces nuclear content of SREBP1, SREBP2 and ChREBP in liver. Ldlr −/− and Ldlr−/− Diaph1−/− male mice were fed WD for 16 weeks. a Representative Western Blots for the detection of cytosolic and nuclear DIAPH1, SREBP1, SREBP2 and ChREBP performed on liver fractions isolated from the indicated mice. b Quantification of cytosolic DIAPH1, SREBP1, SREBP2 and CHREBP, relative to GAPDH. c Quantification of nuclear DIAPH1, SREBP1, SREBP2 and ChREBP, relative to Lamin A/C. d Representative Western blot and quantification of total SREBP1 normalized to tubulin in total liver of the indicated mice. e–g DEXA scans were performed for determination of body mass (e), lean mass (f), and fat mass (g). h Caloric intake was determined over 3 consecutive days. i mRNA expression of the gene encoding RAGE (Ager) was determined in the livers of the indicated male mice after 16 weeks WD. The mean ± SEM is reported. The number of independent mice/group is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values were determined by unpaired T-test or Wilcoxon rank-sum test depending on if the data passed the Shapiro-Wilk normality test.
## Effect of Diaph1 deletion in Ldlr−/− mice on metabolic factors
As nuclear translocation of the transcription factors SREBP1, SREBP2 and ChREBP may be regulated by metabolic pathways, we investigated multiple factors related to body mass and composition, and glucose/carbohydrate and insulin metabolism.
With respect to body mass and composition, Ldlr−/− mice devoid of Diaph1 weighed modestly but significantly less than Ldlr−/− mice (26.2 ± 0.2 vs. 27.7 ± 0.2 g, respectively), $p \leq 0.0001$ (Supplementary Table 7). We examined body composition by Dual-Energy X-ray Absorptiometry (DEXA) scanning. Total body mass by DEXA was significantly lower in the Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0086$$; lean mass was significantly lower in the Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0156$$; there were no significant differences in fat mass between genotypes, $$p \leq 0.0778$$ (Fig. 5e–g). Caloric intake measured on three consecutive days revealed no significant differences between Ldlr−/− Diaph1−/− vs. Ldlr−/− mice on any of these three days, $p \leq 0.05$ (Fig. 5h).
We next examined metabolic factors and the potential effects of DIAPH1. We found that there were no statistically significant differences in the concentrations of plasma glucose, serum insulin, serum glucagon, insulin/glucagon ratio or the Homeostatic Model of Insulin Resistance (HOMA-IR) when comparing Ldlr−/− vs. Ldlr−/−Diaph1−/− mice, $p \leq 0.05$ (Supplementary Table 7). Collectively, these data suggested that DIAPH1-dependent effects on nuclear content of SREBP1, SREBP2 and ChREBP appeared to be independent of classical insulin- and glucose/carbohydrate-related metabolic factors.
## DIAPH1 contributes to atherosclerosis at least in part through effects on lipid not carbohydrate metabolism in Ldlr−/− mice
To provide further insight into the question of whether DIAPH1-dependent roles in atherosclerosis were mediated through regulation of lipid vs. glucose/carbohydrate or insulin metabolism, we performed a series of correlation analyses. In male Ldlr−/− and Ldlr−/− Diaph1−/− mice, atherosclerotic lesion area was significantly correlated with lesion neutral lipid content, 0.80 ($$p \leq 0.0057$$) and 0.84 ($$p \leq 0.0026$$), respectively (Supplementary Table 8a, left) and atherosclerotic lesion area was significantly correlated with lesion macrophage content in both Ldlr−/− and Ldlr−/− Diaph1−/− mice, 0.93 ($$p \leq 0.00028$$) and 0.78 ($$p \leq 0.0134$$), respectively (Supplementary Table 8b, left). Further, atherosclerotic lesion neutral lipid content was significantly associated with macrophage content in Ldlr−/− and Ldlr−/− Diaph1−/− mice, 0.93 ($$p \leq 0.0003$$) and 0.82 ($$p \leq 0.0068$$), respectively (Supplementary Table 8c, left).
We tested the associations between the plasma concentrations of cholesterol and triglyceride with atherosclerosis and lesion characteristics. In both Ldlr−/− and Ldlr−/− Diaph1−/− mice, the concentrations of plasma cholesterol significantly correlated with atherosclerotic lesion area (0.83 and 0.86), lesion neutral lipid content (0.93 and 0.97) and lesion macrophage content (0.84 and 0.92), respectively, $p \leq 0.01$ (Supplementary Table 8d–f, left). In contrast, in Ldlr−/− and Ldlr−/− Diaph1−/− mice, the concentrations of plasma triglyceride did not significantly correlate with atherosclerotic lesion area (0.53 and 0.0183), lesion neutral lipid content (0.51 and 0.22) and lesion macrophage content (0.36 and 0.10), respectively, $p \leq 0.05$ (Supplementary Table 8g–i, left). Furthermore, in Ldlr−/− and Ldlr−/− Diaph1−/− mice, there were no significant associations between atherosclerotic lesion area and concentrations of glucose (−0.09 and 0.37) or insulin (−0.43 and −0.35), respectively, $p \leq 0.05$ (Supplementary Table 8j, k, left).
We performed similar analyses in female mice, as illustrated in Supplementary Table 9. The concentrations of plasma cholesterol did not significantly correlate with atherosclerotic lesion area in female Ldlr−/− mice (−0.12; $$p \leq 0.7755$$), but did significantly correlate with atherosclerotic lesion area in the Ldlr−/− Diaph1−/− mice (0.70; $$p \leq 0.0364$$). As in male mice, the concentrations of plasma triglyceride did not correlate with atherosclerotic lesion area in either Ldlr−/− or Ldlr−/− Diaph1−/− mice (0.003; $$p \leq 0.9948$$) and (0.20; $$p \leq 0.6105$$), respectively. Similarly, analogous to male mice, in female mice, plasma concentrations of glucose did not correlate with atherosclerotic lesion area in Ldlr−/− or Ldlr−/− Diaph1−/− mice (−0.05; $$p \leq 0.8955$$) and (0.43; $$p \leq 0.2522$$), respectively (Supplementary Table 9). Collectively, these data suggested that lipid (cholesterol)-driven and not glycemia- or insulin-related mechanisms appeared more likely to contribute to atherosclerosis in these mice. However, these correlation analyses did not discern if there were specific roles for DIAPH1 in these processes.
Hence, we specifically queried if the deletion of Diaph1 in Ldlr−/− male mice exerted more prominent effects on the associations with atherosclerosis through lipid or glucose/insulin-related factors when compared to Ldlr−/− mice. To address this point, we tested if the change in the slope of the lines generated from each set of compared parameters in Supplementary Table 8 was more relevant when comparing male Ldlr−/− Diaph1−/− vs. Ldlr−/− mice. We found that the only genotypic dependence which was significant, both statistically and in terms of effect size, was the dependence of atherosclerotic lesion area on the concentration of plasma cholesterol, $$p \leq 0.0082$$ (Supplementary Table 8d, right and Supplementary Fig. 8). The increase of atherosclerotic lesion area per unit plasma cholesterol in Ldlr−/− Diaph1−/− mice was approximately one-third of its value in Ldlr−/− mice. This result implies that about two-thirds of the effect of cholesterol on atherosclerotic lesion formation appears to occur through DIAPH1.
Analogous findings were demonstrated in female mice; as shown in Supplementary Table 9 (right), the only significant genotype-dependent factor (Ldlr−/− Diaph1−/− vs. Ldlr−/−) in female mice was the dependence of atherosclerotic lesion area on the concentration of plasma cholesterol ($$p \leq 0.0224$$), but not on the plasma concentrations of triglyceride or glucose.
## Effects of deletion of Diaph1 in Ldlr−/− mice on signal transduction regulatory factors
On account of the evidence linking DIAPH1 to atherosclerosis through lipid metabolism, we examined potential signaling pathways by which DIAPH1 might regulate nuclear translocation of SREBP1, SREBP2 and ChREBP in the livers of the mice under study. First, phosphorylation of AKT has been shown to stimulate nuclear translocation of SREBP1 and SREBP220. We found that the phosphorylated (Ser473) AKT/total AKT was significantly higher in the livers of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0341$$ (Supplementary Fig. 9a, c), which is in disagreement with our findings that the nuclear SREBP1, SREBP2 and ChREBP were lower in the livers of the Ldlr−/−Diaph1−/− vs. Ldlr−/− mice. AKT-related regulation of SREBPs and lipogenesis has been shown to be potentially regulated by mTORC1 pathways21. However, there were no significant differences in phosphorylated (Ser2448) mTOR/total mTOR or its downstream target, phosphorylated (Ser$\frac{240}{244}$) S6/total S6, respectively, when comparing Ldlr−/− Diaph1−/− and Ldlr−/− livers, $$p \leq 0.2857$$ and $$p \leq 0.5905$$, respectively (Supplementary Fig. 9a, d, e). Furthermore, phosphorylated AMPKα has been shown to affect SREBP nuclear translocation;22 we found that there were no differences in phosphorylated (Thr172) AMPK/total AMPK in Ldlr−/− Diaph1−/− vs. Ldlr−/− livers, $$p \leq 0.8948$$ (Supplementary Fig. 9a, b). Overall, these findings indicated that signal transduction pathways responsive to metabolic cues were not significantly different when comparing Ldlr−/− Diaph1−/− vs. Ldlr−/− livers in a manner that would support differences in nuclear translocation of SREBPs or ChREBP.
## Reduced nuclear content of SREBP1, SREBP2 and ChREBP upon deletion of Diaph1 in Ldlr−/− mice and relationship to ROCK, LIMK1, Cofilin pathway
Until this point, multiple data described above do not implicate DIAPH1 directly in the classical metabolic factors or signaling pathways that regulate nuclear translocation of SREBP1, SREBP2 and ChREBP, that is, AKT, mTOR/S6 and AMPK pathways. These considerations suggested that distinct DIAPH1-dependent pathways likely regulate the nuclear translocation of these key transcription factors. Accordingly, review of the GO and Reactome pathways highlighted multiple DIAPH1-dependent pathways linked to protein localization, actin cytoskeleton and overall protein transport (Supplementary Tables 5, 6). Previous work highlighted roles for the ROCK-LIMK-Cofilin pathway in nuclear translocation of the SREBPs induced by shear stress23. Hence, we probed for these factors in the livers of the mice under study and found that there were no significant differences in ROCK1 or phosphorylated (Thr508) LIMK1/total LIMK1 in Ldlr−/− vs. Ldlr−/− Diaph1−/− liver tissue, $$p \leq 0.0800$$ and $$p \leq 0.6789$$, respectively. However, phosphorylated (Ser3) Cofilin/total Cofilin was significantly higher in the livers of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0013$$ (Fig. 6a–e). Note that our studies examine Cofilin1, and not Cofilin2, as the expression of the latter is restricted to striated muscle24 and that our RNAseq studies did not reveal differences in expression of the genes encoding Cofilin 1 or 2 (Cfl1 or Cfl2) (*Supplementary data* 6). To determine if Slingshot1 (SSH1), a phosphatase linked to regulation of phosphorylation of Cofilin25 might account for these differences, we probed for this factor in the livers of the mice under study; no differences in phosphorylated (Ser978) SSH1/total SSH1 were observed between genotypes, $$p \leq 0.2270$$ (Fig. 6a, f). In addition, RNAseq results from the livers of Ldlr−/− vs. Ldlr−/−Diaph1−/− mice revealed that expression of Ctsd which encodes Cathepsin D and is linked to regulation of phosphorylation of Cofilin26, also did not differ (*Supplementary data* 6). To determine if DIAPH1-dependent phosphorylation of Cofilin was unique to the liver, we assessed the phosphorylation status in the whole aorta and found that phosphorylated Cofilin (Ser3)/total Cofilin was also significantly higher in the aortas of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, $$p \leq 0.0399.$$ ( Supplementary Fig. 10a, c). Collectively, these data suggested that in two different organs, liver and aorta, the expression of DIAPH1 was associated with the phosphorylation state of Cofilin 1. As roles for phosphorylation of Cofilin have been ascribed to the regulation of actin polymerization27–29, these findings pointed us to new directions for probing DIAPH1-dependent mechanisms in the regulation of the nuclear translocation of the transcription factors under study. Fig. 6Deletion of Diaph1 in Ldlr−/− mice increases phosphorylated (Ser3)/Cofilin/total Cofilin in liver. Ldlr −/− and Ldlr−/− Diaph1−/− male mice were fed WD for 16 weeks. a Representative Western Blots for the detection of DIAPH1, phosphorylated (Ser3) Cofilin and total Cofilin, ROCK1, phosphorylated (Thr508) LIMK1 and total LIMK1, phosphorylated (Ser978) SSH1 and total SSH1 on total liver lysates isolated from the indicated mice. b Quantification of DIAPH1 relative to GAPDH. c Quantification of phosphorylated Cofilin (Ser3) relative to total Cofilin. d Quantification of ROCK1 relative to GAPDH. e Quantification of phosphorylated LIMK1 (Thr508) relative to total LIMK1. f Quantification of phosphorylated SSH1 (Ser978) relative to total SSH1. In b–f, the mean ± SEM is reported. The number of independent mice/group is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values were determined by unpaired T-test.
## Silencing of Diaph1 in Hepa 1-6 cells increases Cofilin phosphorylation, reduces F-actin intensity at baseline and after treatment with RAGE ligands, and affects nuclear translocation of SREBP1
We next focused our experiments in hepatocyte-like cells and utilized mouse Hepa 1-6 cells with siRNA approaches to silence Diaph1 vs. controls (Fig. 7a, b). Analogous to the effect of deletion of Diaph1 in livers, silencing of Diaph1 did not affect expression of ROCK1 ($$p \leq 0.2226$$), phosphorylated (Thr508) LIMK1/LIMK1 ($$p \leq 0.3336$$), or phosphorylated SSH1 (Ser978)/SSH1 ($$p \leq 0.4066$$) (Fig. 7a, d–f). In contrast, phosphorylated (Ser3) Cofilin/total Cofilin was significantly higher in Diaph1-silenced Hepa 1-6 cells vs. scramble controls, $p \leq 0.0001$ (Fig. 7a, c). As the phosphorylation state of *Cofilin is* linked to actin polymerization, we hypothesized that silencing Diaph1 in Hepa 1-6 cells might modulate F-actin intensity. We silenced Diaph1 as illustrated in Hepa 1-6 cells (Supplementary Fig. 11). Compared to scr control, silencing of Diaph1 significantly reduced F-actin intensity by Phalloidin staining, which reflects the extent of F-actin polymerization, $$p \leq 0.0267$$ (Fig. 7g).Fig. 7Silencing of Diaph1 increases phosphorylated (Ser3) Cofilin/total Cofilin and DIAPH1 and RAGE ligands contribute to F-actin polymerization in Hepa 1-6 cells.a Representative Western blots for the detection of DIAPH1, phosphorylated (Ser3) Cofilin, total Cofilin, phosphorylated (Ser978) SSH1, total SSH1, ROCK1, phosphorylated (Thr508) LIMK1, total LIMK1 and GAPDH performed on mouse Hepa 1-6 cells after Diaph1 or scramble control siRNA knockdown. b Quantification of DIAPH1 relative to GAPDH. c Quantification of phosphorylated (Ser3) Cofilin relative to total Cofilin. d Quantification of phosphorylated (Ser978) SSH1 relative to total SSH1. e Quantification of ROCK1 relative to GAPDH. f Quantification of phosphorylated (Thr508) LIMK1 relative to total LIMK1. g Immunofluorescence staining and quantification of the mean intensity of F-actin (phalloidin) in mouse Hepa1-6 cells after Diaph1 or scramble control siRNA knockdown. Scale bar: 250 µm. The mean ± SEM is reported. The number of independent biological/independent replicates is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values were determined by unpaired T-test or Wilcoxon rank-sum test depending if data passed the Shapiro-Wilk normality test.
As these studies in Fig. 7g indicated that DIAPH1 contributes to F-actin polymerization in Hepa 1-6 cells, we next probed if silencing of Diaph1 in Hepa 1-6 cells directly affected nuclear translocation of the transcription factors under study. We employed 2-hydroxypropyl-β-cyclodextrin (HPCD) with its sterol chelating properties to elicit nuclear translocation of these factors in the cell-based model30. Of note, in Hepa 1-6 cells we were unable to detect SREBP2. Silencing of Diaph1 significantly reduced nuclear translocation of SREBP1 induced by HPCD compared to scramble control, $$p \leq 0.0443$$, but had no effect on nuclear translocation of ChREBP, $$p \leq 0.9659$$ (Fig. 8a, b).Fig. 8RAGE, DIAPH1, actin organization and SREBP1.a Western blots for the detection of nuclear DIAPH1, ChREBP and SREBP1 performed on mouse Hepa 1-6 cells after Diaph1 or scramble control siRNA knockdown and 30-min sterol depletion with $1\%$ 2-hydroxypropyl-β-cyclodextrin (HPCD). b Quantification of nuclear DIAPH1, SREBP1, and ChREBP, relative to Lamin A/C. c Western blots for the detection of nuclear SREBP1 performed on mouse Hepa 1-6 cells after 30 min pre-treatment with latrunculin B (LatB; 1 µm) followed by the addition for 30 min of sterol depletion with $1\%$ HPCD. d Quantification of nuclear SREBP1 relative to Lamin A/C. e mRNA expression of the gene encoding RAGE (Ager) was determined in the livers of the indicated male mice after 16 weeks WD. f Hepa 1-6 cells bearing scramble control or Diaph1 siRNA silencing were treated with RAGE ligand CML-AGE (100 µg/ml) or vehicle for 6 h followed by quantification of the mean intensity of F-actin (phalloidin). Scale bar: 250 µm. The mean ± SEM is reported. The number of independent biological/independent replicates is indicated in the figure as individual data points. Statistical analyses regarding testing for the normality of data followed by appropriate statistical analyses were described in Materials and Methods. P-values were determined by unpaired T-test or Wilcoxon rank-sum test depending if data passed the Shapiro-Wilk normality test.
Hence, we focused our next experiments on SREBP1. As actin organization has been demonstrated to regulate gene transcription31, we tested if nuclear translocation of SREBP1 was linked to the modulation of actin organization in Hepa 1-6 cells. To test this premise, we treated DIAPH1-expressing Hepa 1-6 cells with HPCD in the presence of latrunculin-B, which reduces F-actin polymerization32 and found that compared to treatment of Hepa 1-6 cells with HPCD and vehicle, treatment of these cells with HPCD and latrunculin-D significantly attenuated nuclear content of SREBP1; $$p \leq 0.0039$$ (Fig. 8c, d).
To address potential mechanisms by which DIAPH1 contributes to actin polymerization in the livers of mice with atherosclerosis, we probed for involvement of the RAGE pathway in Ldlr−/− mice fed WD for 16 weeks. First, consistent with roles for RAGE in DIAPH1-dependent mechanisms, we found that mRNA expression of Ager was significantly lower in the livers of the Ldlr−/−Diaph1−/− mice vs. the Ldlr−/− mice fed WD for 16 weeks, $$p \leq 0.0101$$ (Fig. 8e). Second, we note that previous studies demonstrated that the ligands of RAGE accumulate in atherosclerosis7 and that RAGE ligands induce the formation of RAGE homodimers on the cell surface, which results in the recruitment of DIAPH133. On account of these considerations, we tested if RAGE ligands induced F-actin polymerization and if DIAPH1 was required. As shown in Fig. 8f, treatment of Hepa 1-6 cells bearing scr si with RAGE ligand carboxymethyllysine (CML)-advanced glycation end product (AGE) for 6 h resulted in a significant increase in F-actin polymerization compared to vehicle (Veh) treatment, $$p \leq 0.0001.$$ However, in Diaph1-silenced Hepa 1-6 cells treated with CML-AGE, significantly less F-actin polymerization was noted compared with scr si cells treated with CML-AGE, $$p \leq 0.0187$$ (Fig. 8f). Collectively, these data link RAGE ligands to induction of actin polymerization, at least in part through DIAPH1.
## Discussion
DIAPH1 is a large complex molecule whose multiple functions are mediated through its distinct domains34. Among these multiple functions, the formin homology 1 (FH1) domain of DIAPH19 binds to the cytoplasmic domain of RAGE. As RAGE has been extensively studied in atherosclerosis, here we sought to test potential roles for DIAPH1 in vascular pathology. Through multiple experimental strategies, this work identified that deletion of Diaph1 in atherosclerosis-prone mice protected from progression of atherosclerosis and uncovered unanticipated roles for DIAPH1 in the regulation of lipid metabolism. Although this work identified that deletion of Diaph1 reduced hepatic expression of genes such as Acaca, Acacb, Gpat2, Lpin1, Lpin2 and Fasn in Ldlr−/− mice, it was surprising that the mRNA transcripts encoding the master transcriptional regulators of cholesterol and triglyceride metabolism, such as Srebf1, Srebf2, and Mlxipl, as well as genes regulating expression of the PPAR family (Ppargc1a and Ppard) and the LXR family (Nrlh2 and Nrlh3), did not differ in the livers of Ldlr−/− mice expressing or devoid of Diaph1. In contrast, although there were no differences detected at the mRNA level, studies in the mouse liver underscored that nuclear content of SREBP1, SREBP2 and ChREBP proteins was reduced upon deletion of Diaph1. Yet, our data suggested that these processes did not appear to be predominantly regulated by classical metabolic factors that may affect the activities of these transcription factors, such as glucose, carbohydrate or insulin pathways. Indeed, the plasma concentrations of glucose, glucagon and insulin, and their correspondingly regulated signaling pathways, such as AKT, mTOR and AMPK20–22, which may govern the activities of the SREBPs and ChREBP, did not differ by DIAPH1 expression status. Furthermore, although body mass was modestly but significantly lower in male Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, in female Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, there were no differences in body mass; in both cases, however, the mice devoid of both the Ldlr and Diaph1 displayed significantly less atherosclerosis and lower plasma concentrations of cholesterol vs. the Ldlr−/− mice. Nevertheless, we acknowledge that subtle differences in body mass and composition may have contributed to the observed differences in atherosclerosis and lipid metabolism and that studies using distinct Diaph1-tissue targeted deleted mice will be required to fully dissect these relative contributions.
Previous studies in endothelial cells linked the RHO-ROCK-LIMK-Cofilin pathway to nuclear translocation of SREBPs and ChREBP; however, the proximate mechanisms were not identified23. Here, in both liver tissue and in mouse Hepa 1-6 cells, the present work revealed that genetic deletion or silencing of Diaph1, respectively, resulted in significantly higher phosphorylated (Ser3) Cofilin/total Cofilin compared to the respective controls. Cofilin, an actin binding molecule, plays key roles in actin cytoskeleton organization; yet, its biology is complex. Cofilin may bind to F-actin and G-actin and has been implicated in both elongation vs. severing of actin filaments27–29. *In* general, the phosphorylation of *Cofilin is* linked to its inactivation35. As F-actin binding suppresses phosphorylation of Cofilin Ser336,37, reduced F-actin, which was demonstrated by deletion/silencing of Diaph1, should cause increased phosphorylation of Cofilin. This is exactly what was observed in our studies in Hepa 1-6 cells. Hence, although we acknowledge that next studies must determine the precise biophysical relationships between DIAPH1, Cofilin and actin, and between DIAPH1, phosphorylated (Ser3) Cofilin and actin, the work presented in this manuscript, nevertheless, suggests new insights into contributory roles for RAGE ligands, DIAPH1 and actin cytoskeleton organization in mechanisms linked to nuclear translocation of SREBP1.
Indeed, precedent for roles for actin organization on nuclear translocation of transcription factors has been demonstrated but the full scope of the upstream regulators of these processes has yet to be identified. Specifically, phosphorylated (Ser3) Cofilin mediates Angiotensin II-stimulated nuclear translocation of NF-kB (p65) in HK2 cells38. In other studies, actin organization and Cofilin phosphorylation were linked to nuclear translocation of transcription factors beyond SREBPs and ChREBPs, such as NF-kB and Serum Response Factors complexes39,40. The complexity of Cofilin phosphorylation and nuclear translocation of transcription factors was underscored by studies in T cells in which phosphorylated (Ser3) Cofilin was associated with suppression of nuclear translocation of NF-kB components41. These findings in T cells are directly analogous to those identified in the present work, suggesting that deletion of Diaph1 in liver or in Hepa1-6 cells was associated with increased phosphorylation of Cofilin and suppression of nuclear translocation of SREBPs. Certainly, these intricacies of the phosphorylation state of Cofilin and nuclear translocation (or not) of various transcription factors suggests finely-tuned cell type- and stress-dependent regulation of these processes. As noted above, in-depth biophysical studies will be required to further probe these complex mechanisms.
With respect to implications for the RAGE pathway in DIAPH1-dependent regulation of F-actin polymerization, it is important to note that previous studies linked S100A8/A9 (ligands of RAGE) to phosphorylation of Cofilin in MDA-MB-231 breast cancers through RAGE, as revealed in experiments with silencing of AGER42. In that study, although the content of phospho-Cofilin was not normalized to total Cofilin, the authors did suggest that RAGE ligands contributed to activation of NF-kB and epithelial-mesenchymal transition through actin polymerization pathways42. Hence, it will be important to address if/how RAGE ligands/RAGE, DIAPH1 and Cofilin act in concert or independently to regulate nuclear translocation of transcription factors. It is, however, acknowledged that factors beyond modulation of the actin cytoskeleton may underlie the effects of Diaph1 silencing/deletion on regulation of SREBP1 nuclear translocation in the present work, especially as these first studies were performed in mice globally devoid of Diaph1. Hence, future studies in tissue-specific Diaph1-deleted mice, such as in hepatocytes, will be required in order to probe the full range of potential DIAPH1-dependent mechanisms.
In the present studies, we found that despite significant reductions in plasma and hepatic concentrations of cholesterol, no significant differences in the expression of genes directly regulating cholesterol biosynthesis were identified. It is possible that DIAPH1-dependent regulation of cholesterol metabolism may be mediated through modulation of the activities of the protein products of the genes regulating cholesterol biosynthesis. It is also possible that DIAPH1-dependent regulation of Fasn in the livers of WD-fed Ldlr−/− mice, as illustrated by our RNAseq data and confirmatory RT-qPCR studies (Fig. 4), may account for these findings. Specifically, through the actions of FASN, acetoacetyl CoA, critical to cholesterol biosynthesis, is formed through the condensation of acetyl CoA and malonyl CoA; processes leading to generation of the fatty acid palmitate. Indeed, it was recently shown that in macrophages, FASN-mediated generation of acetoacetyl CoA contributes to cholesterol synthesis43. Hence, it is plausible that DIAPH1-dependent effects on plasma and hepatic cholesterol were mediated, at least in part, through FASN.
Of note, we demonstrated that the aortic arches of male Ldlr−/− Diaph1−/− mice displayed significantly lower macrophage content compared to Ldlr−/− mice. Although lesional macrophage content in both Ldlr−/− Diaph1−/− and Ldlr−/− mice was significantly correlated with atherosclerosis and plasma cholesterol and triglyceride concentrations, our analyses (Supplementary Table 8) probing specific roles for the Diaph1 genotype in Ldlr−/− mice suggested that DIAPH1 appears to exert negligible effects on the dependence of atherosclerotic lesion area on macrophage content. These findings were supported by the results of additional experiments reported herein regarding DIAPH1 and its apparently complex effects on inflammation. Specifically, although expression of mRNA transcripts encoding Nos2 and Tnfa was significantly lower, and expression of mRNA transcripts encoding Il10 and Arg1 was significantly higher in the aortas of Ldlr−/− Diaph1−/− vs. Ldlr−/− mice, by flow cytometry, there were no genotype-dependent differences in the percentage of pro- vs. anti-inflammatory macrophage markers in the aortic arches and there were no differences in plasma protein concentrations of TNF-alpha or IL6 between the two genotypes. In previous work, pharmacological antagonism of RAGE/DIAPH1 in mice with type 1 and type 2 diabetes resulted in significant reductions in plasma concentrations of TNF-alpha and IL644, thereby emphasizing that the effects of DIAPH1 are dependent on the specific immuno-metabolic characteristics in the discrete milieus. Therefore, although the results of the present work do not support a clear compelling role for modulation of inflammation underlying DIAPH-dependent effects on atherosclerosis, it is important to note the following: First, there is precedent for intrinsic roles for DIAPH1 expression in immune cells and inflammation. For example, our previous investigations demonstrated regulation of hypoxia-stimulated expression of Egr1 and downstream pro-inflammatory and pro-thrombotic genes in macrophages via serum SRF pathways through DIAPH114. In other work, DIAPH1 was implicated in activation and migration of neutrophils45. Second, the present studies were performed in mice globally devoid of Diaph1, thereby potentially masking discrete effects of DIAPH1 in other cell types, such as T lymphocytes, in the overall inflammatory milieu. For these reasons, we note that future studies in mice bearing distinct immune cell-specific deletion of Diaph1 in the absence of Ldlr, such as in myeloid cells or T lymphocytes, should directly uncover if myeloid Diaph1 contributes to atherosclerosis and if such findings are dependent- or independent of differences in plasma/liver content of cholesterol and triglyceride. Such experiments, coupled with single cell/single nucleus transcriptomic studies, may distinguish the effects of DIAPH1 in vascular cells, immune cells or hepatocytes and their cross-talk on lipid metabolism, inflammation and atherosclerosis.
In the present study, we demonstrated that both male and female Ldlr−/− Diaph1−/− mice displayed significantly less atherosclerosis at the aortic sinus compared with Ldlr−/− mice; however, in both sexes, although the mice devoid of both Ldlr−/− and Diaph1−/− displayed significantly lower plasma concentrations of cholesterol compared to that observed in Ldlr−/− mice; only in male but not female Ldlr−/− Diaph1−/− mice, were significant reductions in plasma concentrations of triglyceride also observed. These considerations notwithstanding, our correlation analyses revealed that the plasma concentrations of cholesterol, but not triglycerides, significantly correlated with atherosclerotic lesion area in male Ldlr−/− Diaph1−/− mice and Ldlr−/− mice. In female mice, the concentrations of plasma cholesterol correlated with atherosclerotic lesion area only in Ldlr−/− Diaph1−/− mice but not Ldlr−/− mice. It is notable that multiple reports in the literature have identified sex differences in plasma concentrations of cholesterol and/or triglyceride in mice devoid of Apoe or the Ldlr in various settings, such as during pharmacological interventions or upon superimposed genetic modifications46–52.
Finally, a question of great interest has been the dissection of overlapping vs. unique roles for RAGE and DIAPH1 in regulation of signal transduction and gene expression stimulated by RAGE ligands. Multiple studies have demonstrated that deletion of Ager reduces atherosclerosis and attenuates vascular inflammation4–7. Interestingly, although previous studies did not ascribe lipid regulation functions to RAGE, in fact, closer inspection suggested potential roles for modest RAGE-dependent reductions in cholesterol and triglyceride concentrations in Ager-deficient mice53,54. Indeed, in a recent study using a model of diabetic donor Ldlr−/− atherosclerotic plaques transplanted into recipient diabetic wild-type (C57BL/6 J), Ager−/− mice fed standard chow, concentrations of serum cholesterol were modestly but significantly lower in the Ager−/− vs. wild-type recipient mice, although there were no differences in concentrations of serum triglyceride7. In that study, in male diabetic mice globally devoid of Diaph1, modest but significantly lower plasma concentrations of cholesterol and triglyceride were observed compared to wild-type diabetic male mice in the same C57BL/6 J background7. Although these mice were diabetic and did not have atherosclerosis, these findings nevertheless suggested that DIAPH1 might contribute to regulation of lipid metabolism; the underlying mechanisms or direct effects on atherosclerosis were not explored in that study. Collectively, the results of the present studies buttress the connections between RAGE and DIAPH1 and suggest that blockade of RAGE/DIAPH1 may be a key adjunctive strategy in therapeutic approaches to atherosclerosis, at least in part through regulation of lipid metabolism.
In summary, our findings unveil new roles for the formin DIAPH1 in the regulation of cholesterol and triglyceride metabolism in a manner independent of direct transcriptional regulation of Srebf1, Srebf2, and Mxlipl. This work presents a new lens into DIAPH1 functions in the regulation of hepatic lipid metabolism, actin organization, nuclear translocation of SREBP1 and their collective impact on atherosclerosis.
## Animal studies and induction of atherosclerosis
All experiments were performed according to the National Institutes of Health Guide for the Care of Laboratory Animals and the protocols were approved by the Institutional Animal Care and Use Committee at NYU Grossman School of Medicine. Mice (C57BL/6 J background) were deficient for the low-density lipoprotein receptor (Ldlr−/−) (The Jackson Laboratories, Stock No 002207, Bar Harbor ME) or for Diaphanous 1 (DIAPH1) (Diaph1−/−)55 backcrossed > 10 generations into Ldlr−/− (Ldlr−/− Diaph1−/−). Male and female mice were used in this study. The mice were housed under a 12 h (h) light/dark cycle in a specific pathogen-free facility and had free access to food and water. Mice were fed a Western diet (Research Diets, Inc., D01061401Ci; $0.15\%$ cholesterol) for 16 weeks, starting at 6 weeks of age, unless otherwise stated. At sacrifice, mice were deeply anesthetized with ketamine/xylazine injection. Whole blood was collected from the aorta after a 6 h fast, unless otherwise indicated. For serum isolation, whole blood was allowed to clot in BD Microtainer SST [365967] and collected by centrifugation. For plasma isolation, whole blood was collected with EDTA and then subjected to centrifugation. For tissue collection, mice were perfused through a butterfly needle heart puncture with 1x phosphate-buffered saline (PBS). Mouse aortic arches and roots and livers were removed after perfusion with cold PBS, embedded in optimal cutting temperature (OCT) compound and frozen until analyses (see below).
## Dual Energy X-Ray (DEXA) absorptiometry
DEXA scans were performed using the Lunar PIXImus DEXA instrument (PIXImus, WI). Before each scan session, the instrument was calibrated, mice were weighed, briefly anesthetized via isoflurane inhalation and placed on the scanner. The mean lean mass and fat mass were recorded.
## Serum insulin and glucagon
Serum concentrations of insulin or glucagon were determined using the Insulin ELISA kit (Mercodia Mouse Insulin ELISA #10-1247-10), and the Glucagon Quantikine ELISA kit (R&D Systems #DGCG0), respectively, according to the manufacturer’s instructions. HOMA I-R was calculated as glucose (mMol/L) X Insulin (mIU/L) /22.5.
## Plasma liver function analyses
Plasma was tested for concentrations of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALP), total protein (TP), albumin (ALB), globulins (GLOB), albumin/globulins ratio (A/G), and total bilirubin (TBIL). The studies were performed at Charles River Labs following their protocols.
## Plasma lipid concentrations
Plasma total cholesterol (Infinity, Thermo Fisher Scientific, 948541) and triglyceride (Infinity, Thermo Fisher Scientific, TR22421) concentrations were measured according to manufacturers’ directions. HDL-C was measured using a kit from Wako [997-01301].
## Fast-performance liquid chromatography (FPLC)
Plasma samples were filtered using column-filters units of 0.22 µm pores (Millipore, UFC30GV00) and lipoproteins were separated in SuperoseTM6 $\frac{10}{300}$GL column (GE Healthcare, GE29-0915-96) on a Shimadzu FPLC system using FPLC Buffer (150 mM NaCl, 1 mM EDTA). Equal volumes of filtered plasma were injected into the FPLC system for each mouse. Following separation, 72 fractions were collected for each mouse plasma sample and total cholesterol content was quantified in each single fraction using an enzymatic assay (Wako Diagnostics, 439-17501).
## Plasma ELISAs for detection of inflammation
Plasma was assessed for concentrations of TNF-alpha and IL6 using commercially-available ELISA kits from R&D Systems according to the manufacturer’s instructions: TNF-alpha (MTA00B, Lot # P338520) and IL6 (M6000B, Lot # P342576).
## Liver Triglyceride measurements
Measurement of liver triglyceride content was performed as follows: 100 mg of liver tissue was homogenized in chloroform:methanol using zirconia beads. Lower organic phase containing triglycerides was isolated adding 0.1 M NaCl and 3 M KOH in $65\%$ ethanol. Colorimetric triglyceride assay (Thermo Fisher Scientific, TR22421) was performed in the samples following manufacturer´s recommendations.
## Amplex red assay
Single cell suspensions from mouse livers were prepared by incubating liver tissue with Hyaluronidase I (Sigma, H3506), Collagenase type XI (Sigma, C7657), Collagenase type I (Sigma, C1639) and DNase I (Qiagen, 79254) in PBS with $0.2\%$ BSA and 2 mM EDTA for 30 min at 37 °C56. Cells were passed through a 100 µm cell strainer in 1x PBS containing $0.2\%$ BSA and 2 mM EDTA. Mouse liver single cell suspension intracellular cholesterol content was quantified using the Amplex Red Cholesterol Assay Kit (Invitrogen, A12216) following the manufacturer’s recommendations. Total cholesterol was measured using the Amplex Red Assay reagent containing cholesterol esterase. Free cholesterol was measured using the Amplex Red Assay reagent lacking cholesterol esterase. Samples were normalized to total protein levels measured with the Pierce BCA Protein Assay kit (Thermo Scientific, 23225). Plates were read using the Spectra Max Reader57.
## Determination of triglyceride, apolipoprotein B100 (apoB100) and apolipoprotein B48 (apoB48) secretion
After an overnight fast, mice were given an intraperitoneal injection of 200 µCi of [35S] methionine/cysteine protein labeling mix (NEG772002MC, Perkin Elmer) combined with 1,000 mg/kg pluronic F127 poloxamer-407 (BASF, P2443, Sigma-Aldrich) to inhibit lipoprotein clearance from plasma as previously described58. Triglyceride secretion was calculated using plasma collected at 2 h post-injection. Total plasma apoB100 and apoB48 secretion was determined by taking 2 µl of plasma from the 2 h time point and separating the proteins by SDS-PAGE gel electrophoresis followed by densitometric quantification using ImageJ. Plasma from Apobec1−/− mice (which express only apo B100) was used as control to identify apoB100 and apoB48 bands59,60.
## Histological measurement of atherosclerosis
Atherosclerosis was measured by the following three methods: [1] at the aortic arch; [2] at the aortic sinus; and [3] by en face analysis of the aorta according to established methods per published recommendations:61 Aortic arch and brachiocephalic artery analyses was performed as follows, the arch of the aorta was dissected under a microscope and frozen in OCT embedding medium for serial cryosectioning. To quantify cross-sectional lesion area in the brachiocephalic artery, the Y-shaped piece of brachiocephalic artery was sectioned distally to proximally at 6 μm thickness, starting from the subclavian and carotid arteries. Atherosclerotic lesions lumenal to the internal elastic lamina were quantified in 6 equidistant (100 μm) H&E-stained sections 300–600 μm from the branching point of the brachiocephalic into the carotid and subclavian arteries were collected. The processing of the tissues was handled identically in all of the mice to ensure uniformity. H&E staining is described below. Atherosclerosis at the aortic sinus was assessed as follows, mouse hearts were removed, oriented in a supine position and cut above the midline using a standard blade. The apex of the heart was discarded. Plastic molds were filled with OCT and the heart was placed midline down. The molds were placed on pre-cooled metal racks in dry ice and allowed to cool until they were completely set. The frozen roots were then placed into a −80 °C freezer for storage until sectioning. OCT-embedded hearts were sectioned through the aortic root (6 µm). Four cross sections (50 µm apart) of the aortic sinus were cut per mouse per slide and 5 slides per mouse were prepared. The first section was harvested when the first cusp became visible in the lumen of the aorta. Tissue blocks were cut into sections (10 µm thick). The processing of the tissues was handled identically in all of the mice to ensure uniformity. Sections were fixed with $10\%$ neutral buffered formalin for 7–10 min at room temperature. H&E staining is described in the section to follow.
## H&E Staining of histological sections
Slides were stained with Gill’s Hematoxylin (SigmaAldrich, SLCH6216) by dipping three times into a staining dish. The slides were then rinsed thoroughly in deionized water until run-off was clear. The slides were then stained with a bluing solution for 30 seconds and rinsed with deionizzed water until clear run off. The slides were dehydrated with $70\%$ ethanol solution for 5 min and then dipped once into Eosin to counterstain. Dehydration in $100\%$ ethanol was continued three times for five min each, and in Xylene three times for five min each. Slides were cover-slipped with Eukitt Mounting Medium (Sigma-Aldrich, SKU03989-100ML), covered with a glass coverslip, and left to dry for 24 h. A Keyence imaging microscope (BZ X-800) was used to capture high resolution images of each section. Images were obtained from each slide and the images were quantified using Fiji (Image J) and mean atherosclerotic lesion area of the H&E stained images was calculated per mouse and reported in µm2.
## En face lesion area of the mouse aorta
The full descending aorta was dissected, excised, and pinned (Thermo Fisher Scientific, NC9681411); fixed in $4\%$ paraformaldehyde for 10 min. Aorta tissue was stained with Oil Red O.
## Oil Red O staining
Sections were fixed with $10\%$ neutral buffered formalin for 10 min at room temperature. Sections were incubated with propylene glycol for 2 min, then incubated with pre-heated Oil Red O staining solution (American MasterTech, STOROPT) at 60 °C for an additional 8 min. Sections were placed in $85\%$ propylene glycol for 1 min and counterstained with modified Mayer’s hematoxylin (American MasterTech, HXMMHLT) for 1 min, rinsed with water and then mounted with glycerine jelly (Fisher scientific, NC0301797).
## Picrosirius Red (PSR) staining
Sections were fixed with $10\%$ neutral buffered formalin for 10 min at room temperature. Using Picrosirius red stain kit (Polyscience, 24901-500), sections were incubated with Weigert Hematoxylin (Sigma, HT1079) for 10 min, then washed in running tap water for 10 min. Sections were incubated with Solution A $0.2\%$ Phosphomolybdic Acid (Polysciences) for 2 min followed by rinsing in distilled water and then incubated with Solution B (2,4,6 Trinitrophenol + Direct Red 80) (Polysciences) for 60 min. Sections were placed in Solution C (0.1 N Hydrochloric Acid) (Polysciences) for 2 min and dehydrated in ethanol followed by cleaning in xylene and then mounting with xylene-based permount (Fisher, 15820100). Images were taken using the Zeiss Axioplan Wide-field microscope.
## Detection of Aorta AGEs in mouse tissue
Immunohistochemistry was performed on 6 µm OCT-embedded frozen mouse aortic sections using polyclonal rabbit anti-Advanced Glycation End Products (AGE, Abcam, ab23722). Frozen sections were allowed to come to room temperature (from −20 °C). Sections were fixed in $10\%$ Neutral Buffered Formalin for 15 min and then rinsed in distilled water. Antibody incubation and detection was performed on a Ventana Discovery XT (Ventana Medical Systems 750-701) using Ventana reagent buffer and detection kits. Anti-AGE was diluted 1:200 in PBS (Life Technologies Grand Island, New York USA) and incubated for 30 min at 37 °C. AGE was detected with biotinylated horse anti-rabbit (Vector Laboratories, BA-1000), diluted 1:1000 and incubated for 30 min. This was followed by the application of streptavidin-horse radish peroxidase conjugate. The complex was visualized with 3,3 Diaminobenzidine and enhanced with copper sulfate. Slides were washed in distilled water, counterstained with Hematoxylin and mounted with permanent media. Table 1 lists the specific reagents used in this experiment. Table 1Antibodies for Immunofluorescence and Immunohistochemistry Studies. AntibodyCompanyCatalog NumberClone#DilutionSpeciesClonalityPrimary antibodiesHuman tissueDIAPH1AbcamAb111731:200RabbitPolyclonalCD68DakoM08141:200MouseMonoclonalα-SMAMillipore SigmaA25471:500MouseMonoclonalDIAPH1Abcamab129167EPR79481:200RabbitMonoclonalMouse tissueAGEAbcamab237221:200RabbitPolyclonalCD68Bio-RadMCA1957FA-111:1000RatMonoclonalα-SMAThermoFisher ScientificPA5-182921:200GoatPolyclonalRAGEGenetexGTX277641:150GoatPolyclonalDIAPH1Abcamab129167EPR79481:200RabbitMonoclonalDIAPH1AbcamAb111731:200RabbitPolyclonalSecondary AntibodiesHuman tissueGoat Anti-Rabbit IgG Alexa Fluor 488, Thermofisher Scientific, A11034, 1:200 DilutionDonkey Anti-Mouse IgG Alexa Fluor 594, Thermofisher Scientific, A21203, 1:200 DilutionAnti-Rabbit conjugated polymer detection system, Leica BOND Polymer Refine Detection System, DS9800Mouse tissueBiotinylated horse anti-rabbit IgG, Vector Laboratories, BA-1000, 1:100 DilutionBiotinylated rabbit anti-rat IgG, Vector Laboratories, BA-4001, 1:1000 DilutionDonkey anti-goat Alexa Fluor 555, ThermoFisher Scientific, A-32816, 1:200 DilutionDonkey anti-goat Alexa Fluor 594, ThermoFisher Scientific, A-11058, 1:200 DilutionDonkey anti-rat Alexa Fluor 555, ThermoFisher Scientific, A-48270, 1:200 DilutionGoat anti-rabbit Alexa Fluor 488, ThermoFisher Scientific, A11008, 1:200 Dilution
## Detection of aorta CD68
CD68 staining was performed on 10 µm OCT embedded frozen mouse aortic sections using rat anti-mouse CD68 clone FA-11 (AbD Serotech, MCA1957). Frozen sections were brought to room temperature and were fixed in acetone for 15 min, then air dried for 15 min. Antibody incubation and detection was performed on a Ventana Discovery XT (Ventana Medical Systems, 750-701) using Ventana’s reagent buffer and detection kits. Anti-CD68 was diluted 1:1000 in Dulbecco’s PBS from (Life Technologies Grand Island, New York USA) and incubated for 1 h. Sections were incubated for 30 min with mouse-adsorbed, biotinylated rabbit anti-rat IgG (BA-4001, 1:1000, Vector Laboratories) for CD68 detection. This was followed by the application of alkaline phosphatase-streptavidin conjugate (Ventana Medical Systems). The complex was visualized with Naphthol-AS-MX phosphatase (Ventana Medical Systems) and Fast Red complex (Ventana Medical Systems). Slides were washed in distilled water, counterstained with hematoxylin, air dried and then heated for 15 min at 60 °C prior to mounting with permanent media. Table 1 lists the specific reagents used in this experiment.
## Detection of aorta RAGE
Immunohistochemistry was performed on OCT embedded frozen mouse aortic sections. Frozen sections were allowed to come to room temperature. Sections were fixed in acetone for 15 min and then allowed to air dry for 30 min. Sections were blocked for 1 h at room temperature with Protein block serum free ready to use (Dako, X0909). Antibody incubation and detection was performed manually. Goat anti-mouse RAGE (Genetex, GTX27764) was diluted 1:150 in diluent and incubated for 24 h at 4 °C. The following day, the primary antibody was removed and the slides were washed three times with 1x PBS. RAGE was detected with secondary donkey anti-goat Alexa Fluor 594 (ThermoFisher Scientific A-11058) diluted 1:200 and incubated 1 h at room temperature. The secondary antibody was then removed and sections were stained with DAPI, 5 mg/ml, diluted to 1:5000. Slides were washed 3 times with 1x PBS and then washed once with distilled H2O. Slides were cover-slip mounted with Prolong gold antifade permanent media (Fisher Scientific, P10144). Appropriate secondary antibody only controls were done in parallel and included with the study sections. The slides were allowed to air dry for 30 min–1 h and stored at 4 °C. Table 1 lists the specific reagents used in this experiment.
## Analysis of human atherosclerosis
Deidentified human coronary artery atherosclerotic specimens with advanced lesions were obtained from CVPath Institute Sudden Death Registry. The study was approved by the CVPath Institutional Review Board (IRB) as an exempt study (#RP0063). The artery segments were fixed in formalin, and 2- to 3 mm segments were embedded in paraffin and cut (5 μm thick). The diagnosis of coronary artery disease and histopathological determination of coronary artery disease were performed by an experienced cardiac pathologist at CVPath Institute. Human sections were deparaffinized using Richard-Allan Scientific 40 Clear-Rite 3 (Thermo Fisher Scientific, 6905) through a series of $100\%$, $90\%$, and $70\%$ ethanol for 5 min each, followed by 3 washes. Samples were permeabilized for 10 min in $0.2\%$ Triton X-100 in PBS. Sections were blocked for 1 h in Dako Protein Block, Serum-Free (Agilent, X0909). All primary antibodies were diluted in Antibody diluent (Dako, S3022) and sections were incubated overnight at 4 °C with a combination of antibodies. After three washes, slides were incubated with secondary antibodies diluted in Antibody Diluent for 1 h at 37 °C. Subsequently, slides were washed 3x and stained with 1 μg/mL DAPI (Invitrogen, D3571). Slides were cover-slipped mounted with Prolong gold antifade permanent media (Thermo Fisher Scientific, P10144). Primary antibodies used: rabbit anti-DIAPH1 (Abcam, Ab11173, 1:200 dilution), mouse anti-CD68 (Dako, M0814, 1:200 dilution), and Mouse anti-αSMA (Millipore Sigma, A2547, 1:500 dilution). Different combinations of secondary antibodies utilized: goat anti-rabbit Alexa Fluor 488 (Thermo Fisher Scientific, A-11034, 1:200 dilution), and donkey anti-mouse Alexa Fluor 594 (Thermo Fisher Scientific, A-21203, 1:200 dilution). Appropriate secondary antibody only controls were done in parallel and included with the study sections. Table 1 lists the specific reagents used in this experiment.
## Immunohistochemistry of human and mouse liver for detection of DIAPH1
Deidentified normal human liver specimen (52 year-old male subject) was obtained from the Center for Biospecimen and Research Development at the NYU Grossman School of Medicine. The study was approved by the NYU Grossman School of Medicine Institutional Review Board (IRB), study number s16-00122. The sample was fixed in $10\%$ neutral buffered formalin (Fisher Chemical, SF100-4) for 48 h at room temperature. The sample was then dehydrated through graded ethanols and xylene and infiltrated with paraffin in a Leica ASP300S automated tissue processor. Mouse samples were fixed and processed on a Leica Peloris automated processor. Five µm thick sections were cut onto superfrost slides and deparaffinized for immunostaining on a Leica BondRX automated stainer, following the manufacturer’s instructions. In brief, sections underwent epitope retrieval for 20 min at 100 °C with Leica Biosystems ER2 solution (pH9, AR9640). Slides were incubated with rabbit monoclonal anti-DIAPH1 antibody (Abcam, ab129167, clone EPR7948), diluted 1:200 for 30 min at room temperature or with antibody diluent alone (no primary control) and followed by anti-rabbit HRP-conjugated polymer and the substrate diaminobenzidine (Leica BOND Polymer Refine Detection System, DS9800). Sections were counter-stained with hematoxylin and scanned at a 40x magnification (pixel size 0.22 μm) on a Nanozoomer (Model C9600-12, NDP.scan v3.1.9) whole slide scanner (Hamamatsu) and the image files uploaded to the NYU Grossman School of Medicine’s OMERO Plus image data management system (Glencoe Software). Table 1 lists the specific reagents used in this experiment.
## Image quantification
ImagePro Plus 7.0 software was used to determine CD68 +, Oil Red O +, H&E +, AGEs +, Picrosirius red +, RAGE +, En face Oil Red O + areas and calculated as percent of total plaque area. Quantification of mean intensity of phalloidin was completed by measuring and then averaging the mean intensity of 6–8 images per animal using ImageJ. Quantification of intensity within specific positive area was completed using ImageJ by restricting the intensity measurement to only areas within an automated pre-set threshold mask of another signal channel. Averages for each sample were calculated and was used for statistical analysis.
## Flow cytometry
To phenotype aortic macrophages, atherosclerosis was induced by feeding Western diet (Research Diets, Inc., D01061401Ci $0.15\%$ cholesterol) for 18 weeks. After a 5 h fast, the mice were subjected to deep anesthesia with ketamine and xylazine; the aortas were perfused with PBS and the aortic arches were collected from experimental mice and whole aortas were retrieved from control mice; the latter control mice were used for the flow cytometry standardization studies. Aortas were digested using a cocktail of liberase TH (0.88 mg/ml, Roche, 5401151001), deoxyribonuclease (DNase) I (58 µg/ml, Sigma, DN25), and hyaluronidase (99 µg/ml, Sigma, H3506) in HBSS with $0.5\%$ BSA and 1 mM of calcium solution. Digestion was performed for 15 min at 37 °C using the program m_37SDK1 in the Gentle Macs dissociator (Miltenyi). Single cell suspensions were filtered through 100 µM filters (Fisher Scientific, #22363549) and pelleted by centrifugation (400 x g for 5 min at 4 °C). Aortic single cell suspensions were live/dead stained with Fixable Blue Dead Cell Stain Kit (Invitrogen L34961) and blocked with CD$\frac{16}{32}$ (See Table below for the full list / details of all antibodies used in the flow cytometry studies) for 30 min at 4 °C in the dark. To identify aortic macrophages and monocytes, cells were further incubated with antibodies recognizing CD45R, CD3ε, CD170/Siglec-F and Ly-6G (FITC), as well as CD45 (AF700), CD11b (APC-Cy7), CD206 (BV650), CD163 (PE), CD14 (BV421) and Ly-6C (BV510) (See Table below) for 30 min at 4 °C in the dark. Aortic macrophages were identified as UV-, Lin-, CD45 + and CD11b +, and further characterized as CD206high, CD163high, CD14high and Ly-6Chigh subsets. Cells were acquired on a LSRII UV (BD Biosciences) and analyzed with FlowJo 10.8.1 (BD Biosciences). For all flow cytometry experiments, UltraComp eBeads compensation beads (Invitrogen, 01-2222-42) were used to set single stain compensation, and FMO controls were used to set all gates. Table 2 lists the specific reagents used in this experiment. Table 2Antibodies for Flow Cytometry. AntibodyCompanyCatalog NumberClone#DilutionFluoro-phoresSpeciesClonalityCD$\frac{16}{32}$Biolegend101302931:25RatMonoclonalCD45Biolegend10312830-F111:100Alexa Fluor 700RatMonoclonalCD11bBD Biosciences557657M$\frac{1}{701}$:100APC-Cy7RatMonoclonalCD45R/B220Biolegend103206RA3-6B21:100FITCRatMonoclonalCD3εBiolegend100306145-2C111:100FITCArmenian HamsterMonoclonalCD170 (Siglec-F)Biolegend155504S17007L1:100FITCRatMonoclonalLY-6GBiolegend1276061A81:100FITCRatMonoclonalCD206Biolegend141723C068C21:100Brilliant Violet 650RatMonoclonalLY-6CBiolegend128033HK1.41:100Brilliant Violet 510RatMonoclonalCD14Biolegend123329Sa14-21:100Brilliant Violet 421RatMonoclonalCD163Invitrogen12-1631-82TNKUPJ1:100PERatMonoclonal
## Cultured mouse hepatocellular carcinoma cells (Hepa 1-6)
The mouse hepatocellular carcinoma cell line Hepa 1-6 was purchased from American Type Culture Collection (ATCC® CRL-1830™). Cells were propagated in Dulbecco’s Modified Eagle Medium (DMEM), supplemented with $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin/streptomycin in a humidified atmosphere of $5\%$ CO2 at 37 °C.
## Detection of Phalloidin and treatment of Hepa 1-6 cells with Carboxymethyllysine (CML)-AGE
Hepa1-6 cells were plated in a 6 well plate the day before silencing/knockdown approaches. Cells were serum-starved and placed in Opti-MEM Reduced Serum Medium (Gibco, Thermo Fisher Scientific, 31985062) for 2 h. Cells were transfected using MISSION siRNA Transfection Reagent (MilliporeSigma, S1452) and 75 nM of scrambled siRNA (QIAGEN, 1022076) or Diaph1 #8 Flexitube siRNA (QIAGEN, SI02732289) for 48 h in $10\%$ FBS DMEM. Cells were treated with CML-AGE (100 µg/ml) or vehicle for 6 h. Cells were fixed in $4\%$ PFA at 4 °C for 10 min and washed 3x TBST. Cells were then blocked for 1 h in Licor Odyssey Blocking Buffer (Li-cor, P/N 927-40100). Phalloidin 400x solution was diluted to 1x (ThermoFisher Scientific, A12379) and placed on cells at room temperature for 1 h. Cells were then washed with TBST 3 times and then stained with DAPI (1:5000) and coverslipped with fluorescence mounting medium (Dako, S3023).
## Silencing of Diaph1 and pharmacological treatments
Hepa 1-6 cells were plated into 6 well tissue culture plates the day before application of silencing and scramble reagents. Cells were serum-starved and placed in Opti-MEM Reduced Serum Medium (Gibco, Thermo Fisher Scientific, 31985062) for 2 h. Cells were transfected using MISSION siRNA Transfection Reagent (Millipore Sigma, S1452) and 75 nM of scrambled siRNA (Qiagen, 1022076) or Diaph1 #8 Flexitube siRNA (Qiagen, SI02732289) for 48 h in $10\%$ FBS DMEM. For sterol depletion, cells were transfected as previously described in $1\%$ FBS DMEM and then incubated in DMEM (no FBS), supplemented with $1\%$ 2-hydroxypropyl-β-cyclodextrin (HPCD) (Sigma, C0926-5G) for 30 min30. Where indicated, cells were pre-treated for 30 min with Latrunculin B (LatB) (Sigma, L5288), 1 µM, vs. equal volumes of vehicle (DMSO), prior to the subsequent addition of HPCD for 30 min. Hence, the total incubation time for LatB was 60 min.
## Subcellular fractionation of livers and Hepa 1-6 cells
Cytosolic and nuclear fractions from snap-frozen livers were obtained using the Qproteome Nuclear Protein Kit (Qiagen, 37582), following the manufacturer´s recommendations. Nuclear fractions from Hepa 1-6 cells were obtained using the NE-PER™ Nuclear and Cytoplasmic Extraction Reagents (Thermo Fisher Scientific, 78833), following the manufacturer´s recommendations. The enriched cytosolic and nuclear fractions were confirmed by immunoblotting for GAPDH and Lamin A/C, respectively.
## RNA sequencing and bioinformatics
RNAseq was performed on the top left lobe of livers from a. WT, fed chow; b. Ldlr−/− fed Western diet; and c, Ldlr−/− Diaph1−/−, fed Western Diet ($$n = 4$$ independent mice/group). RNA was isolated with RNeasy® Plus Micro Kit (Qiagen, #74004). RNA integrity numbers (RIN) were measured using an RNA 6000 Pico Kit in 2100 Bioanalyzer (Agilent). Samples with a minimum RIN of 9.0 were prepared for sequencing using the NuGEN Ovation RNA-Seq system v2 reagents for cDNA preparation and the Ovation Ultralow DR Multiplex system for the adapter ligation step. 30 M 50-nucleotide, single-end reads were sequenced on an Illumina 2500 HiSeq using v4 chemistry (Illumina) at the NYU Langone Health Genome Technology Center. Fastq files were aligned to the mm10 assembly of the human genome with Rsubread62 and gene expression was quantified with featureCounts63. Data were deposited in the Gene Expression Omnibus64, with accession number GSE156403. Differential expression was analyzed using weighted Limma-voom65 with a significance cutoff of the Benjamini-Hochberg FDR ≤ 0.05. Genes with FDR ≤ 0.05 were analyzed further: iPathwayGuide66 was used to analyze differential expression in terms of the KEGG67 database using Signaling Pathway Impact Analysis68 and the Biological Process Gene Ontology69. WebGestalt70 was used to analyze differential expression in terms of Reactome pathways71. Hierarchical clustering was performed on genes with FDR ≤ 0.05 using Cluster 3.072. Dendrograms and heatmaps were displayed using JavaTreeview73.
## Quantitative reverse transcription PCR experiments
Total RNA from livers or aortas was extracted using the RNeasy Plus Mini kit (Qiagen, 74136). cDNA was synthesized using iScript cDNA Synthesis Kit (Bio-Rad, 1708891) and amplified using TaqMan assays using a 7300 Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific). Table 3 lists the specific reagents used in this experiment. Table 3Reagents for quantitative reverse transcription PCR studies. ProbeThermo Fisher Catalog #Abca1Mm00442646_m1Abcg1Mm00437390_m1AcacaMm01304257_m1AcacbMm01204691_m1AgerMm00545815_m1Arg1Mm00475988_m1Ccl2Mm00441242_m1Diap1Mm00492170_m1FasnMm00662319_m1Gpat2Mm01335101_m1HprtMm03024075_m1Il10Mm01288386_m1Lpin1Mm00550511_m1Lpin2Mm00522390_m1MlxiplMm02342723_m1MttpMm00435015_m1Nos2Mm00440502_m1Nr1h2Mm00437265_m1Nr1h3Mm00443451_m1PpiaMm02342430_g1PpardMm00803184_m1Ppargc1aMm01208835_m1RxraMm00441185_m1Srebf1Mm00550338_m1Srebf2Mm01306292_m1TnfMm00443258_m118 sHs99999901_s1
## Western blotting
Protein concentrations from previously described subcellular fractions or total protein lysates extracted with RIPA buffer (Cell Signaling, 9806 S) supplemented with protease inhibitor (Thermo Fisher Scientific, A32953) and phosphatase inhibitor (Thermo Fisher Scientific, A32957) cocktails, were quantified using a Pierce BSA Protein Assay kit (Thermo Fisher Scientific, 23225). A total of 30 μg of protein were separated by $7.5\%$ or 4–$20\%$ polyacrylamide gel (Bio-Rad, 456-1026 or 456-8096) electrophoresis, transferred to 0.2 μm pore size nitrocellulose membranes (Bio-Rad, 170-4270), blocked for 1 h at room temperature with blocking buffer (LI-COR, 927-60001), and incubated overnight at 4 °C with primary antibody (See Below). After washing, membranes were incubated with secondary antibody (See Below) for 1 h at room temperature. Protein signals were visualized with the Odyssey Imaging System (LI-COR) detection system. Densitometric analysis was performed using Image Studio software (LI-COR). Cytosolic and total protein amounts were calculated relative to GAPDH or Tubulin, nuclear protein amounts were calculated relative to Lamin A/C. Phosphorylated protein was normalized to the respective total protein. Table 4 lists the specific reagents used in this experiment. Table 4Antibodies for Western Blotting and ELISAs for Detection of TNF-alpha and IL6.AntibodyCompanyCatalog NumberDilutionSpeciesClonalityDIAPH1BD Biosciences6108491:1000MouseMonoclonalSREBP-1Santa Cruzsc-135511:2000MouseMonoclonalSREBP-1Novus BiologicalsNB100-22151:1000RabbitPolyclonalSREBP-2Santa Cruzsc-135521:2000MouseMonoclonalChREBPNovus BiologicalsNB400-1351:2000RabbitPolyclonalChREBPCell Signaling58069 S1:1000RabbitPolyclonalLamin A/CCell Signaling2032 S1:2000RabbitPolyclonalAKT Ser473Cell Signaling9271 S1:2000RabbitPolyclonalAKTCell Signaling9272 S1:2000RabbitPolyclonalAMPKα Thr172Cell Signaling2535 T1:1000RabbitPolyclonalAMPKαCell Signaling5831 T1:2000RabbitPolyclonalmTOR Ser2448Cell Signaling5536 T1:2000RabbitPolyclonalmTORCell Signaling2972 S1:2000RabbitPolyclonalS6 Ser$\frac{240}{244}$Cell Signaling2215 S1:2000RabbitPolyclonalS6Cell Signaling2317 S1:2000RabbitPolyclonalROCK1Cell Signaling4035 T1:2000RabbitPolyclonalLIMK1 Thr508AbcamAb1947981:1000RabbitPolyclonalLIMK1Cell Signaling3842 S1:2000RabbitPolyclonalSSH1 Ser978ECM BiosciencesSP39011:1000RabbitPolyclonalSSH1ECM BiosciencesSP17111:1000RabbitPolyclonalCOFILIN Ser3Cell Signaling3313 T1:1000RabbitPolyclonalCOFILINCell Signaling5175 T1:2000RabbitPolyclonalC/EBPαCell Signaling8178 S1:2000RabbitPolyclonalGAPDHSanta Cruzsc-322331:5000MouseMonoclonalTubulinMillipore SigmaT51681:25000MouseMonoclonalIRDye 680RD Goat anti-mouseLI-COR925-680701:5000GoatPolyclonalIRDye 800RD Goat anti-rabbitLI-COR925-322111:5000GoatPolyclonalIL6 ELISAR&D SystemsM6000BTNF-alpha ELISAR&D SystemsMTA00B
## Statistics and reproducibility
Sample sizes were based on our previous studies in which similar experimental endpoints were tested. Analyses were performed using GraphPad Prism 8.2.0. Data are presented as mean ± SEM. Normality of the data was assessed using the Shapiro-Wilk normality test. A nonparametric test was performed when data did not follow a normal distribution. Independent 2-sample t-tests (2 sided) were used to assess the difference between 2 groups of samples (Mann-Whitney U tests were used instead if normality was violated). For over 2 groups, One-way ANOVA was used, and Tukey’s or Holm-Šídák post hoc test for pairwise comparisons or comparisons of selected groups was performed, respectively. Kruskal-Wallis test with post-hoc Dunn’s test was performed instead if the normality test was not passed. Pearson’s correlation coefficient was assessed to evaluate the associations between 2 variables. The dependence of atherosclerotic lesion area, and related quantities such as macrophage content, on molecular concentrations such as cholesterol and glucose, as a function of Diaph1 genotype, was estimated by ANCOVA (ANalysis of COVAriance)74 (Supplementary Tables 8, 9). Normality was tested using the Shapiro-Wilk test75, and qqplots76. All of the analysis presented in Supplementary Tables 8, 9 were performed in R77,78. $P \leq 0.05$ was denoted statistically significant.
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Data 1 Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Data 6 Reporting Summary The online version contains supplementary material available at 10.1038/s42003-023-04643-2.
## Peer review information
Communications Biology thanks Yang Kai-Chien and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Jesmond Dalli and Luke R. Grinham.
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---
title: 'Factors associated with incidence of acute kidney injury: a Japanese regional
population-based cohort study, the Shizuoka study'
authors:
- Hisashi Dote
- Eiji Nakatani
- Kiyoshi Mori
- Akira Sugawara
journal: Clinical and Experimental Nephrology
year: 2022
pmcid: PMC10023756
doi: 10.1007/s10157-022-02310-0
license: CC BY 4.0
---
# Factors associated with incidence of acute kidney injury: a Japanese regional population-based cohort study, the Shizuoka study
## Abstract
### Background
Acute kidney injury (AKI) is a globally critical issue. Most studies about AKI have been conducted in limited settings on perioperative or critically ill patients. As a result, there is little information about the epidemiology and risk factors of AKI in the general population.
### Methods
We conducted a population-based cohort study using the Shizuoka Kokuho Database. We included subjects with records of health checkup results. The observation period for each participant was defined as from the date of insurance enrollment or April 2012, whichever occurred later, until the date of insurance withdrawal or September 2020, whichever was later. Primary outcome was AKI associated with admission based on the ICD-10 code. We described the incidence of AKI and performed a multivariate analysis using potential risk factors selected from comorbidities, medications, and health checkup results.
### Results
Of 627,814 subjects, 8044 were diagnosed with AKI (incidence 251 per 100,000 person-years). The AKI group was older, with more males. Most comorbidities and prescribed medications were more common in the AKI group. As novel factors, statins (hazard ratio (HR) 0.84, $95\%$ confidence interval (CI) 0.80–0.89) and physical activity habits (HR 0.79, $95\%$ CI 0.75–0.83) were associated with reduced incidence of AKI. Other variables associated with AKI were approximately consistent with those from previous studies.
### Conclusions
The factors associated with AKI and the incidence of AKI in the general Japanese population are indicated. This study generates the hypothesis that statins and physical activity habits are novel protective factors for AKI.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10157-022-02310-0.
## Introduction
Acute kidney injury (AKI) is defined by a rapid rise in serum creatinine, a decrease in urine output, or both. The “Kidney Disease: Improving Global Outcomes (KDIGO)” diagnostic criteria and severity classification are used widely [1]. AKI is not only a poor prognostic factor in critically ill patients [2] but is also associated with worse long-term survival and renal outcome [3]. AKI is recognized as a critical issue globally, and the International Society of Nephrology has launched the 0by25 initiative, an international cross-sectional study to eliminate preventable deaths from AKI [4]. Patient-related risk factors for AKI have been identified, including nephrotoxic drugs (including drugs aimed to treat other conditions) and various comorbidities [5].
Although many cohort studies have been conducted to date, epidemiological information, such as the true incidence of AKI, is still unclear [6]. Although many studies have investigated the risk factors and prognostic implications of AKI, most have focused on a limited setting, such as critically ill patients, specific comorbidities, or the perioperative period [2, 7, 8]. Few studies conducted in the general population have investigated the effects of modifiable lifestyles.
Identifying the risk factors of AKI (especially modifiable risks) and informing citizens and healthcare providers may reduce the occurrence of AKI. Therefore, this study aims to describe the incidence of AKI associated with hospital admission in the general population and to explore the risk factors.
## Study design and data source
This population-based cohort study analyzed the data obtained from the Shizuoka Kokuho Database (SKDB) [9], an administrative claims database of health insurance subscribers in the municipalities of Shizuoka Prefecture, Japan. This database includes both National Health Insurance (those aged < 75 years) and Latter-Stage Elderly Medical Care System (those aged ≥ 75 years) subscribers. SKDB covers 8.5 years (April 1, 2012 to September 30, 2020) and includes 2,230,848 individuals, and contains basic information (sex, age, observation period, and reason for disenrollment, including death) about individuals, records of health checkup results, and dates of diagnosis and treatment based on the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), with medications prescribed.
## Participant population
The subjects were those registered in SKDB with records of health checkup results. The observation period for each participant was defined as from the date of insurance enrollment or April 2012, whichever occurred later, until the date of insurance withdrawal or September 2020, whichever was later.
We extracted the records of the first health checkup during the observation period. One year before the date of the first health checkup was defined as the baseline period. We extracted information about comorbidities and prescribed medications during the baseline period. We excluded participants who were on maintenance dialysis. Participants who had not received health checkups or who did not realize 1-year baseline periods were also excluded.
## Outcome and variables
The primary outcome was AKI associated with admission. We identified this outcome using the ICD-10 codes for AKI (N17, N19) [10–12] that are used for diagnosis during hospitalization. Our definition included community-acquired and hospital-acquired AKI. Comorbidities were extracted using ICD-10 codes for diagnoses equivalent to the Charlson Comorbidity Index [13]. Prescribed medications were extracted using the Anatomical Therapeutic Chemical Classification System. In selecting medications, we referred to previous studies and guidelines [14, 15]. We selected medications commonly used to treat chronic disease in the general population among those that might be associated with AKI as covariates. In addition, we extracted the following results from the health checkups: age, sex, body mass index (BMI), blood pressure, questionnaire responses about smoking habits and physical activity (“In your daily life, do you walk or do any equivalent amount of physical activity more than one hour a day?”), and results from laboratory examinations.
## Statistical analysis
Continuous and categorical variables were described as mean ± standard deviation and number (%), respectively. We used Fisher’s exact test to compare the proportions of binary variables and Student’s t-test to compare the continuous variables, and log-rank tests to evaluate differences in the cumulative incidence of AKI. We used a cause-specific proportional hazards model to adjust for confounders in the primary outcome. Death was considered as a competing event for AKI. Variables that were significant in the univariate model were included in the multivariate model. Complete case analysis was conducted to construct the multivariate model. Variables with Spearman’s correlation coefficients greater than 0.4 were used as covariates, whichever was considered more clinically important. Factors that were statistically significant in the multivariate model were defined as risk factors. We also performed a sensitivity analysis including interaction terms. Survival time variables were drawn as Kaplan–Meier curves, and log-rank tests were performed for comparisons between groups. The missing covariates used in the analysis did not occur completely randomly among all participants; therefore, we did not impute them. Statistical significance was defined as $P \leq 0.05.$ We used JMP 16.2 (SAS Institute, Cary, NC, USA) and EZR version 1.55 (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [16] for all statistical analyses.
## Characteristics of the patients
Among all individuals in the SKDB, 627,814 were included in the analysis. Four hundred and nine maintenance dialysis patients were excluded. During the mean 5.1 years of observation, 8044 cases were diagnosed with AKI associated with hospital admission (Fig. 1).Fig. 1Flow diagram of our study. SKDB Shizuoka Kokuho Database, AKI acute kidney injury Patients’ characteristics, divided into two groups according to AKI associated with admission (AKI group) or not (No AKI group), are shown in Table 1. The AKI group was older and comprised more males. BMI was higher in the AKI group, but the difference was minimal. Most comorbidities and prescribed medications were more common in the AKI group. The results of health checkups showed that base renal function (serum creatinine and estimated glomerular filtration rate (GFR)) was slightly worse in the AKI group. The AKI group also had a lower hemoglobin level. The number of habits with no physical activity was higher in the AKI group. Table 1Characteristics of the patientsVariableCategory or unitAKI groupNo AKI groupP value($$n = 8044$$)($$n = 619$$,361)Age, mean (SD)1 year77.8 (9.0)68.1 (11.2) < 0.001Age, n (%)0 to < 40 years3 (0.0)4762 (0.8) < 0.00140 to < 50 years71 (0.9)45,676 (7.4) < 0.00150 to < 60 years166 (2.1)54,298 (8.8) < 0.00160 to < 70 years1231 (15.3)233,171 (37.6) < 0.00170 to < 80 years2738 (34.0)188,901 (30.5) < 0.001 ≥ 80 years3835 (47.7)92,553 (14.9) < 0.001SexMale4842 (60.2)265,917 (42.9) < 0.001Comorbidities Congestive heart failure, n (%)Presence2615 (32.5)55,072 (8.9) < 0.001 Myocardial infarction, n (%)Presence449 (5.6)9724 (1.6) < 0.001 Peripheral vascular disease, n (%)Presence1834 (22.8)64,733 (10.5) < 0.001 Cerebrovascular disease, n (%)Presence2531 (31.5)91,213 (14.7) < 0.001 Dementia, n (%)Presence558 (6.9)16,096 (2.6) < 0.001 Chronic pulmonary disease, n (%)Presence2602 (32.3)149,794 (24.2) < 0.001 Rheumatic disease, n (%)Presence407 (4.9)17,734 (2.9) < 0.001 Peptic ulcer disease, n (%)Presence2600 (32.3)118,709 (19.2) < 0.001 Liver disease, n (%)Presence1919 (23.9)101,026 (16.3) < 0.001 Diabetes, n (%)Presence1440 (17.9)34,374 (5.5) < 0.001 Hypertension, n (%)Presence6674 (83.0)295,166 (47.7) < 0.001 Hemiplegia or paraplegia, n (%)Presence153 (1.9)4821 (0.8) < 0.001 Renal disease, n (%)Presence2510 (31.2)12,225 (2.0) < 0.001 Any malignancy, n (%)Presence1319 (16.4)51,129 (8.3) < 0.001Medications ACE inhibitor, n (%)Prescribed601 (7.5)16,631 (2.7) < 0.001 ARB, n (%)Prescribed4334 (53.9)150,430 (24.3) < 0.001 MRA, n (%)Prescribed545 (6.8)8335 (1.3) < 0.001 CCB, n (%)Prescribed4885 (60.7)191,099 (30.9) < 0.001 β-Blocker, n (%)Prescribed1081 (13.4)28,682 (4.6) < 0.001 Diuretics, n (%)Prescribed2105 (26.2)35,291 (5.7) < 0.001 SGLT2 inhibitor, n (%)Prescribed15 (0.2)2160 (0.3)0.02 NSAIDs, n (%)Prescribed4261 (51.4)228,337 (36.9) < 0.001 Statin, n (%)Prescribed2656 (33.0)157,807 (25.5) < 0.001 Fibrate, n (%)Prescribed281 (3.5)12,137 (2.0) < 0.001 Gout suppressant, n (%)Prescribed2342 (29.1)37,724 (6.1) < 0.001Result of health checkup Systolic blood pressure, mean (SD)1 mmHg134.27 (17.88)129.40 (17.28) < 0.001 AST, mean (SD)1 U/L24.36 (12.06)24.16 (11.01)0.12 ALT, mean (SD)1 U/L17.59 (11.90)20.30 (13.64) < 0.001 γ-GTP, mean (SD)1 U/L35.72 (56.70)32.90 (46.78) < 0.001 HDL-Chol, mean (SD)1 mg/dL55.38 (16.06)62.38 (16.64) < 0.001 LDL-Chol, mean (SD)1 mg/dL110.41 (31.48)123.90 (31.24) < 0.001 Serum creatinine, mean (SD)1 mg/dL1.30 (0.80)0.76 (0.21) < 0.001 Estimated GFR, mean (SD)1 mL/min/1.73 m245.99 (19.74)69.58 (15.45) < 0.001 Hemoglobin, mean (SD)1 g/dL12.24 (1.87)13.60 (1.47) < 0.001Questionnaire response Physical activity habits, n (%)No3813 (55.5)275,905 (49.5) < 0.001The eGFR was calculated as follows: 194 × creatinine−1.094 × age−0.287 (× 0.739 [if women])AKI acute kidney injury, SD standard deviation, ACE angiotensin-converting enzyme, ARB angiotensin receptor blocker, MRA mineralocorticoid receptor antagonist, CCB calcium channel blocker, SGLT2 sodium-glucose transporter 2, NSAIDs nonsteroidal anti-inflammatory drugs, AST aspartate transaminase, ALT alanine aminotransferase, GTP glutamyl transpeptidase, HDL high-density lipoprotein, LDL low-density lipoprotein, Chol cholesterol, GFR glomerular filtration rate.
## Incidence of AKI associated with admission
The incidence of AKI was 251 per 100,000 person-years (mean 5.1 years of observation). The cumulative incidence of AKI is shown in Figs. 2, 3. In the stratified analysis, the incidence was higher in latter-stage older adults (Fig. 2) and males (Fig. 3).Fig. 2Cumulative incidence of acute kidney injury stratified by latter-stage elderly (LSE) or notFig. 3Cumulative incidence of acute kidney injury stratified by sex
## Risk factors for AKI associated with admission
Correlation coefficients between all variables included in the multivariable model were less than 0.4. Multivariate cause-specific proportional hazards models showed that several variables were risk factors (Table 2). The variables with a multivariate hazard ratio (HR) > 1.2 were dementia, chronic pulmonary disease, rheumatic disease, liver disease, diabetes, hemiplegia or paraplegia, renal disease, any malignancy, mineralocorticoid receptor antagonists, calcium channel blockers, and diuretics. The multivariate variables with HR < 0.9 were female sex, statins, fibrates, high estimated GFR, and physical activity habits. Table 2Results of cause-specific proportional hazards modelVariable (reference)Category or unitUnivariateMultivariateHazard ratio ($95\%$ CI)P valueHazard ratio ($95\%$ CI)P valueAge (0 to < 40 years)1 year1.11 (1.11–1.11) < 0.001NANAAge40 to < 50 years1.80 (0.57–5.72)0.31.67 (0.41–6.82)0.550 to < 60 years2,54 (0.81–7.97)0.11.41 (0.35–5.70)0.660 to < 70 years3.82 (1.23–11.9)0.021.34 (0.34–5.37)0.770 to < 80 years9.07 (2.92–28.2) < 0.0011.90 (0.47–7.61)0.4 ≥ 80 years28.2 (9.10–87.6) < 0.0013.20 (0.80–12.8)0.1Sex (male)Female0.45 (0.43–0.47) < 0.0010.52 (0.49–0.55) < 0.001Body mass index1 kg/m21.02 (1.01–1.03) < 0.0010.97 (0.96–0.98) < 0.001Comorbidities Peripheral vascular disease (absence)Presence2.57 (2.44–2.71) < 0.0011.13 (1.06–1.20) < 0.001 Cerebrovascular disease (absence)Presence2.69 (2.56–2.82) < 0.0011.10 (1.03–1.16) < 0.001 Dementia (absence)Presence3.95 (3.62–4.30) < 0.0011.58 (1.43–1.75) < 0.001 Chronic pulmonary disease (absence)Presence1.74 (1.66–1.83) < 0.0011.24 (1.18–1.31) < 0.001 Rheumatic disease (absence)Presence1.91 (1.73–2.11) < 0.0011.38 (1.23–1.54) < 0.001 Peptic ulcer disease (absence)Presence2.06 (1.97–2.16) < 0.0011.12 (1.06–1.18) < 0.001 Liver disease (absence)Presence1.72 (1.63–1.81) < 0.0011.22 (1.15–1.30) < 0.001 Diabetes (absence)Presence4.26 (4.03–4.51) < 0.0011.88 (1.76–2.00) < 0.001 Hemiplegia or paraplegia (absence)Presence2.97 (2.53–3.48) < 0.0011.25 (1.03–1.52) < 0.001 Renal disease (absence)Presence24.8 (23.7–26.0) < 0.0013.36 (3.14–3.59) < 0.001 Any malignancy (absence)Presence2.45 (2.31–2.60) < 0.0011.27 (1.19–1.36) < 0.001Medications ACE inhibitor (no use)Prescribed2.68 (2.47–2.92) < 0.0011.08 (0.98–1.19)0.1 ARB (no use)Prescribed3.30 (3.15–3.44) < 0.0011.14 (1.08–1.21) < 0.001 MRA (no use)Prescribed5.94 (5.45–6.48) < 0.0011.26 (1.14–1.40) < 0.001 CCB (no use)Prescribed3.20 (3.06–3.35) < 0.0011.36 (1.29–1.44) < 0.001 β-Blocker (no use)Prescribed3.31 (3.10–3.52) < 0.0011.11 (1.03–1.20)0.006 Diuretics (no use)Prescribed5.57 (5.30–5.86) < 0.0011.31 (1.23–1.40) < 0.001 SGLT2 inhibitor (no use)Prescribed3.41 (2.06–5.67) < 0.0011.72 (0.95–3.12)0.07 NSAIDs (no use)Prescribed1.58 (1.51–1.65) < 0.0011.02 (0.97–1.08)0.4 Statin (no use)Prescribed1.27 (1.21–1.33) < 0.0010.84 (0.80–0.89) < 0.001 Fibrate (no use)Prescribed1.72 (1.55–1.92) < 0.0010.83 (0.73–0.95)0.008 Gout suppressant (no use)Prescribed6.64 (6.33–6.97) < 0.0011.18 (1.11–1.25) < 0.001Result of health checkup AST10 U/L1.04 (1.02–1.06) < 0.0010.98 (0.96–1.00)0.12 Estimated GFR5 mL/min/1.73 m20.62 (0.61–0.62) < 0.0010.73 (0.73–0.74) < 0.001Questionnaire response Physical activity habits (no)Yes0.69 (0.66–0.73) < 0.0010.79 (0.75–0.83) < 0.001The eGFR was calculated as follows: 194 × creatinine−1.094 × age−0.287 (× 0.739 [if women]). The number of samples used for the complete case analysis was 541,014, and the number of events (AKI) was 6560 (86,391 samples were removed owing to missing data)NA not applicable, ACE angiotensin-converting enzyme, ARB angiotensin receptor blocker, MRA mineralocorticoid receptor antagonist, CCB calcium channel blocker, SGLT2 sodium-glucose transporter 2, NSAIDs nonsteroidal anti-inflammatory drugs, AST aspartate transaminase, GFR glomerular filtration rate The sensitivity analysis taking into account the interaction terms for statins (cerebrovascular disease and peripheral vascular disease) showed a similar result to that for just statins (HR 0.85, $95\%$ confidence interval 0.79–0.91).
## Discussion
We found several factors related to AKI associated with admission. In addition, the incidence of AKI in the general population was estimated. Our study is the first investigation to examine these issues using claims data from an Asian population.
Multivariate analysis results suggested that the administration of statins and physical activity habits are novel protective factors against AKI.
Robust evidence supports the preventive effect of statins on cardiovascular and cerebrovascular disease [17]. Statins are also suggested to benefit kidney disease via mechanisms such as anti-inflammatory, anti-oxidative, and endothelial protective effects [18]. Regarding the association between kidney disease and statins, statins showed beneficial effects in lipid management in patients with chronic kidney disease (CKD) [17, 19]. There is insufficient evidence of an association between statins and AKI. Existing studies suggest that statins both increase the risk [20, 21] and do not affect the risk [22, 23] of AKI. In previous studies unable to indicate the effectiveness of statins, the severity of the underlying disease might have hindered the efficacy of statins [17]. Given that our study was conducted in a healthy general population, we would expect to detect the therapeutic effectiveness of statins.
Historically, healthy lifestyles, including physical activity habits, have prevented several diseases. Studies using data from the Japanese Specified Health Checkups, similar to ours, have shown the association between healthy lifestyles, including physical activity habits, and CKD [24], diabetes, and hypertension [25]. In addition, large prospective studies have shown that lifestyle modifications, including physical activity habits, can prevent cardiovascular disease [26]. Although no previous studies have examined the association between AKI and physical activity habits, a meta-analysis suggested that frailty is a risk factor for AKI [27]. Considering the results of previous studies and our current data, it is suggested that improving physical activity habits may contribute to the prevention of AKI.
Other variables associated with AKI in our study were approximately consistent with those in previous studies [5, 28–31]. In contrast with previous reports [30], nonsteroidal anti-inflammatory drugs and angiotensin-converting enzyme inhibitors were not a significant risk factor. Although fibrates are generally known to affect kidney function [33], we obtained the contrary result. We assume that these discrepancies are due to the avoidance of prescribing these drugs to patients at high risk for kidney injury.
The incidence of AKI estimated in this study (251 per 100,000 person-years) differs somewhat in comparison with previous studies. Several prospective observational studies in the general population have reported the incidence of severe AKI requiring dialysis as 13–14 cases per 100,000 person-years [33, 34], one of which was conducted in the same region as the present study (Shizuoka, Japan) [34]. Another cohort study in a population with normal renal function reported the incidence of AKI requiring hospitalization as 100 per 100,000 person-years, of which 10 cases in 100,000 were severe AKI requiring dialysis [35]. These results estimate that severe AKI accounts for less than $10\%$ of all AKI. Our study includes patients with impaired baseline renal function and estimated AKI incidence for all severities. Therefore, the higher incidence of AKI than previously reported elsewhere is reasonable. Additionally, we consider that the present results provide useful epidemiological information about AKI in the general population, especially among older people in Japan. Risk factors for AKI are generally similar between our cohort and others, assuming that the populations of these cohorts are comparable. Based on the aforestated, we consider that we extracted AKI from the administrative claims database with a reasonable level of accuracy. Therefore, we were able to explore factors associated with AKI and estimate its incidence in this reliable population.
Although we constructed a linear risk prediction model for the overall population, recent reports have shown that there is heterogeneity in predictive outcomes between individual subgroups in the prediction model and that interactions between risk factors should also be taken into account [36]. The present study attempted to identify risk factors that are independent in nature from linear regression analysis and to suggest the existence of intervening factors that contain bias but have large effects. Moreover, no causal effect estimates for individual factors were made. Therefore, we did not assess predictive performance or examine interactions as a potential risk and confounding factors. Despite these limitations, we believe that the presentation of modifiable factors from simple studies such as ours can help prevent disease.
There are some other limitations to this study. First, only ICD-10 codes were used to define AKI because the data concerning urinary output and changes in renal function around the hospitalization period were unavailable. To preserve the validity of the study, we used ICD-10 codes in accordance with previous studies [10–12]. Nevertheless, as a limitation of the coding-based definition of AKI, AKI extraction in our cohort had high specificity, but low sensitivity, and could have been biased to severe cases [37]. Second, we could not use variables not included in the user database; thus, potential risk factors may not have been investigated. We included the major known risk factors for AKI (comorbidities and medications) in multivariate analysis to explore the circumstances as much as possible and conducted a sensitivity analysis with consideration of interaction terms. Third, because the SKDB contains only a Japanese population, caution should be exercised in extrapolating this study’s results to other ethnic groups. Fourth, our dataset may have a time discrepancy between the prescription of medication as risk factors and the onset of AKI. Most medications are regularly prescribed for chronic disease. Therefore, the effect of this limitation is considered to be relatively small. In contrast to previous studies [28], non-steroidal anti-inflammatory drugs were not significant in the multivariate analysis. These types of medications with a shorter prescription duration may have been more affected by this limitation.
## Conclusions
The factors associated with AKI and the incidence of AKI associated with hospital admission in the general Japanese population are highlighted. This study generates the hypothesis that statins and physical activity habits are novel protective factors for AKI, although these exploratory results need to be validated in prospective trials. This study of AKI promises to provide essential insights into its etiology and prevention strategy.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 17 KB)
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|
---
title: Changes in general and abdominal obesity in children at 4, 6 and 9 years of
age and their association with other cardiometabolic risk factors
authors:
- Honorato Ortiz-Marrón
- Gloria Cabañas Pujadas
- Maira Alejandra Ortiz-Pinto
- Aránzazu Martín García
- Carolina Matesanz Martínez
- María del Castillo Antonaya Martín
- Olga Cortés Rico
- Iñaki Galán
journal: European Journal of Pediatrics
year: 2023
pmcid: PMC10023764
doi: 10.1007/s00431-022-04802-3
license: CC BY 4.0
---
# Changes in general and abdominal obesity in children at 4, 6 and 9 years of age and their association with other cardiometabolic risk factors
## Abstract
Temporary changes in childhood obesity and their association with cardiometabolic risk factors have been receiving increased attention. The objective of this study was to evaluate changes in general (GO) and abdominal (AO) obesity in children from 4 to 9 years of age and their associations with cardiometabolic risk factors at 9 years of age. This study includes 1344 children from the Longitudinal Childhood Obesity Study (ELOIN). Physical examinations performed at 4, 6 and 9 years of age and a blood sample was only taken at 9 years of age. Changes in obesity from 4 to 9 years of age were estimated using Body Mass Index and waist circumference. Participants were classified into four groups according to GO and AO: [1] stable without obesity (no obesity at all three measurements); [2] remitting obesity at 9 years (obesity at 4 and/or 6 years but not at 9 years); [3] incident or recurrent obesity at 9 years (obesity only at 9 years, at 4 and 9 years or at 6 and 9 years); and [4] stable or persistent with obesity (obesity at 4, 6 and 9 years). Dyslipidemia and dysglycemia were defined by the presence of at least one altered parameter of the lipid or glycemic profile. Odds ratios (OR) were estimated using logistic regression. Compared with children without GO at all ages, those with persistent GO had an OR of 3.66 ($95\%$ CI: 2.06–6.51) for dyslipidemia, 10.61 ($95\%$ CI: 5.69–19.79) for dysglycemia and 8.35 ($95\%$ CI: 4.55–15.30) for high blood pressure. The associations were fairly similar in the case of AO, with ORs of 3.52 ($95\%$ CI: 1.96–6.34), 17.15 ($95\%$ CI: 9.09–32.34) and 8.22 ($95\%$ CI: 4.46–15.15), respectively, when comparing persistent versus stable without AO. Children with incident obesity at 9 years presented a moderate cardiometabolic risk that was nevertheless higher compared to those stable without obesity, whereas those with remitting obesity did not show any significant associations.
Conclusion: Incident, and especially, persistent obesity, is associated with an increased cardiometabolic risk. The very early prevention of obesity, with a focus on nutrition, physical activity and sedentary behaviour, as well as tracking growth from birth to age 5, should be a priority to prevent the burden of cardiometabolic disease with consequences for adulthood. What is Known:• General and abdominal obesity has been shown to be associated with other cardiometabolic risk factors such as dyslipidemia, insulin resistance and hypertension.• Temporary changes in obesity and their associations with cardiometabolic risk factors have not been sufficiently explored in childhood. What is New:• Children with incident, and especially persistent, general and/or abdominal obesity, had an increased risk of dyslipidemia, dysglycemia and high blood pressure.•Remitting obesity was not associated with an increased cardiometabolic risk.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00431-022-04802-3.
## Introduction
Obesity is a major public health problem that affects more than 300 million children worldwide [1]. Childhood obesity has negative effects on physical and mental health [2, 3] and tends to persist in adulthood, carrying an increased risk of morbidity and mortality [4, 5]. In Europe, the prevalence of overweight and obesity during childhood is high, with large variations between regions and a higher prevalence in the Mediterranean basin [6]. In Spain, the prevalence of obesity in the population aged 6 to 9 years, using World Health Organization (WHO) criteria, was $17.3\%$ in 2019 [7].
Moreover, cardiovascular diseases are the main cause of death in Western countries [8]; in 2018, they were the leading cause of death in Spain, accounting for $28.3\%$ of all deaths [9]. Obesity and other cardiometabolic risk factors (CMRFs), such as high triglyceride levels, low high-density lipoprotein cholesterol (Col-HDL) levels, high blood pressure (BP) and insulin resistance, initiate atherosclerosis in childhood, which subsequently produces subclinical cardiovascular disease in adulthood [10]. Recently, a large prospective study showed that the presence of these factors in childhood is associated with fatal and nonfatal cardiovascular events in young adults [11].
General (GO) and abdominal obesity (AO) are associated with dyslipidemia, diabetes, insulin resistance, hypertension and a state of general inflammation that increase cardiovascular risk in childhood and throughout life; these relationships are well-documented in adolescents but underexplored in children, especially preschool-age children [12–15].
CMRFs typically co-occur and the presence of three or more of these risk factors is defined as metabolic syndrome [16]. However, given the intrinsic variations in age and growth during this childhood period, in children under 10 years of age, analysis of CMRFs should be calculated individually [17]. A longer duration and persistence of obesity are associated with a worse prognosis on all CMFRs [18], and the study of BMI trajectories during childhood can serve to predict future cardiometabolic risk [19, 20].
The objective of the study was to determine the association between changes in GO and AO at 4, 6 and 9 years of age and the risk of developing cardiometabolic alterations, such as dyslipidemia, dysglycemia and high blood pressure at 9 years in a prospective cohort of children representative of the population in Community of Madrid, Spain. Our hypothesis was that exposure to persistent or recurrent obesity during childhood would significantly impact on the subsequent development of cardiometabolic alterations, even during childhood and preadolescence.
## Study population
This study has a cross-sectional design in 9-year-old children. The data were extracted from the Longitudinal Childhood Obesity Study (ELOIN), a population-based prospective cohort study consisting of a baseline cohort representative of the 4-year-old population of the Community of Madrid. These data consisted of standardised anthropometric measurements, BP data and a telephone interview with parents performed at 4, 6 and 9 years as well as a blood test performed at 9 years of age. The ELOIN methodology has been previously published [21]. This study included 1344 children who had completed all three physical examinations at 4, 6 and 9 years of age and provided a blood sample at the age of 9 years. Figure S1 (Supplementary Information) shows the flowchart of participants included throughout the study period.
## Anthropometric measurements
The physical examinations were performed by paediatricians and nurses in the Sentinel Network of the 31 primary care centres that participated in this study [22]. Standardised measurements of weight, height, WC and BP were collected.
Weight measurements were performed using a digital scale, height measurements were collected with a telescopic stadiometer and the abdominal circumference was measured above the iliac crests with an approved inextensible measuring tape with a buckle. Two measurements were performed and the mean was used. Using weight and height, BMI (kg/m2) was estimated according to age (months) and sex. Weight status was categorised with the WHO-2007 reference tables [23] and GO was defined if the z score of BMI (z-BMI) exceeded 2 × standard deviations [24]. AO was defined according to the WC, by age and sex the using criteria of the International Diabetes Federation at the ≥ 90th percentile cut-off points proposed by Fernández et al. [ 25]. To classify temporary changes in GO and AO at 4, 6 and 9 years, participants were classified into four groups: [1] stable without obesity (no obesity at all three measurements); [2] remitting obesity at 9 years (obesity at 4 and/or 6 years but not at 9 years); [3] incident or recurrent obesity at 9 years (obesity only at 9 years, at 4 and 9 years or at 6 and 9 years); and [4] stable or persistent with obesity (obesity at 4, 6 and 9 years).
## Blood pressure
BP was measured at the paediatric examination using the auscultatory method from the right arm. The participants remained seated for 5 min before BP was measured. Two measurements were performed on the same day at least 2 min apart, and a third was performed if the difference between the first two measurements exceeded 4 mmHg. The mean of the measurements was used for the analyses. The BP values were standardised according to age, sex and height using the reference tables of the Fourth Report of Diagnosis, Evaluation, and Treatment of High Blood Pressure in Children and Adolescents (NHBPEP) [26].
## Biochemical parameters
The biochemical parameters were obtained from a blood sample extracted by venipuncture using Vacutainer© at the health centre after fasting for 8 h.
Total cholesterol (TC) and high-density lipoprotein cholesterol (Col-HDL) were calculated by the enzymatic method of cholesterol oxidase, esterase and peroxidase; low-density lipoprotein cholesterol (Col-LDL) was calculated using the *Friedewald formula* [Col-LDL = (TC-Col-HDL) − (TG/5)]. Triglycerides (TG) were calculated by the lipase/glycerol kinase colorimetric method, C-reactive protein by immunoturbidimetry and glucose by the enzymatic method of glucose hexokinase coupled to glucose 6-P dehydrogenase. All determinations were performed with equipment from Siemens Healthineers.
Insulin was measured by chemiluminescence and glycated haemoglobin (HbA1c) by high-performance liquid chromatography in a TOSOH G8 analyzer. Insulin resistance (IR) was estimated using the homeostatic model assessment of insulin resistance (HOMA-IR), calculated as glucose (mg/dL) × insulin (µU/mL)/405.
## Definition of metabolic abnormalities
To define abnormal values of lipid and glycemic profiles, the criteria of the National Heart, Lung and Blood Institute [27] and the European Guide for Cardiovascular Prevention [28] were applied as follows: Dyslipidemia and dysglycemia were defined by the presence of at least one altered parameter of the lipid (TC ≥ 200 mg/dL, TG ≥ 100 mg/dL, Col-HDL < 40 mg/dL or Col-LDL ≥ 130 mg/dL) or glycemic (glucose ≥ 100 mg/dL, HOMA-IR ≥ 3.16 or glycated haemoglobin ≥ $5.7\%$) profile, respectively.
High BP was defined by age and sex using the 90th percentile of the systolic and/or diastolic BP for children and adolescents, according to recommendations of the European Society of Hypertension [29].
## Covariates
The sociodemographic variables were sex, age and family purchasing power; family purchasing power was estimated by the score on the Family Affluence Scale (FAS-II), which is a global indicator of family socioeconomic status in which family affluence is classified as low (0–3 points), medium (4–5 points) or high (6–9 points) [30].
Other covariates included as follows: dietary quality, which was evaluated according to the Mediterranean Diet Quality Index (Med-DQI) [31], using a semiquantitative food consumption frequency questionnaire that recorded the frequency of consumption (daily, weekly, monthly or annual) of 145 food items in the past year; and physical activity, which was determined based on the validated Physical Activity Questionnaire-Children (PAQ-C), on which scores range from 1 (little physical activity) to 5 (high physical activity) [32]. The information collected at 9 years of age was used for this study.
The study protocol was approved by the Ethics Committee of the Ramón y Cajal University Hospital in Madrid (CIHURC- $\frac{122}{11}$). Written consent was obtained from the parents and/or guardians of the participants, and the data were anonymised to ensure confidentiality. The present study was conducted according to the Declaration of Helsinki and all methods were performed.
## Statistical analysis
The descriptive statistics are expressed in percentages and means with their corresponding $95\%$ confidence intervals ($95\%$ CIs). Analyses of variance (ANOVAs) were used to estimate the differences in means between groups, and Pearson’s chi-squared test was used to determine differences in categorical variables.
To study the association between changes in obesity and biochemical parameters, multiple linear regression models were constructed adjusted for sex, age, family purchasing power, diet quality and physical activity.
Using logistic regression, the association between changes in obesity and metabolic alterations was estimated by calculating prevalence odds ratios (ORs), adjusting for the possible confounding factors described above. Finally, through multinomial logistic regression, the association of obesity changes with combined metabolic alterations (0, 1 or 2–3 alterations of the lipid profile, glycemic profile or BP) was determined in terms of relative risk ratios (RRRs).
The threshold for statistical significance was established at $p \leq 0.05.$
The analyses were performed with the STATA 16.1 software (StataCorp, College Station, TX, USA).
## Results
A total of 1344 participants were included in the GO analysis, and 1324 were included in the AO analysis. Of these participants, $49\%$ were boys, with a mean age of 9.2 years. Table 1 presents the sociodemographic characteristics of the sample. The group with stable GO was slightly older, and there were more boys than girls in the group with incident GO. Regarding GO and AO, the groups with stable and incident obesity had a lower FAS-II score (lower socioeconomic position). Table 1Characteristics of the sample according to changes in general and abdominal obesity at 4, 6 and 9 years of ageGeneral obesitybTotalStable without obesityaRemitting obesityIncident obesityStable with obesityp value($$n = 1344$$)($$n = 1094$$)($$n = 24$$)($$n = 171$$)($$n = 55$$)Age (months), mean (SD)110.3 (3.9)110.3 (3.8)110.3 (4.0)110.5 (4.0)111.8 (5.0)0.037Sex (%)0.003 Male665 (49.5)517 (47.3)12 (50.0)107 (62.6)29 (52.7) Female679 (50.5)577 (52.7)12 (50.0)64 (37.4)26 (47.3)Family purchasing powerd* (%)0.002 Low223 (17.0)164 (15.3)3 (13.0)39 (23.6)17 (33.3) Medium417 (31.8)342 (31.9)5 (21.7)53 (32.1)17 (33.3) High671 (51.2)566 (52.8)15 (65.2)73 (44.2)17 (33.3)Physical activitye*, mean (SD)3.1 (0.6)3.1 (0.6)3.2 (0.6)3.0 (0.6)3.0 (0.6)0.136Diet quality scoref*, mean (SD)6.4 (1.6)6.4 (1.6)5.9 (1.9)6.5 (1.7)6.1 (1.6)0.169Abdominal obesitycTotalStable without obesityRemitting obesityIncident obesityStable with obesityp value($$n = 1324$$)($$n = 1080$$)($$n = 46$$)($$n = 147$$)($$n = 51$$)Age (months), mean (SD)110.3 (3.9)110.3 (3.8)110.7 (4.4)110.5 (4.1)111.4 (5.3)0.182Sex (%)0.488 Male655 (49.5)535 (49.5)21 (45.7)78 (53.1)21 (41.2) Female669 (50.5)545 (50.5)25 (54.3)69 (46.9)30 (58.8)Family purchasing powerd* (%)0.005 Low222 (17.2)164 (15.5)10 (23.8)32 (22.5)16 (32.7) Medium412 (31.8)341 (32.1)8 (19.1)48 (33.8)15 (30.6) High660 (51.0)556 (52.4)24 (57.1)62 (43.7)18 (36.7)Physical activitye*, mean (SD)3.1 (0.6)3.1 (0.7)3.1 (0.7)3.0 (0.5)3.0 (0.6)0.114Diet quality scoref*, mean (SD)6.4 (1.6)6.4 (1.6)6.3 (1.8)6.5 (1.8)6.1 (1.7)0.478SD standard deviation*Contains missing valuesaStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 yearsbGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF)dMeasured through the Family Affluence Scale (FAS-II)ePhysical Activity Questionnaire-Children (PAQ-C), scores of 1–5fMediterranean-Diet Quality Index (Med-DQI), scores from 1 to 14 Table 2 shows the means of the biochemical parameters according to the changes in obesity. In terms of the lipid profile, all GO and AO groups exhibited lower values of Col-HDL and higher values of TG and triglycerides/HDL ratio (TG/HDL ratio), than those observed in children stable without GO and stable without AO. Regarding the glycemic profile, notable differences were observed in insulin and HOMA-IR levels, which gradually widened between the groups with remitting, incident and stable obesity, reaching a maximum in the latter. Additionally, systolic and diastolic BP increased gradually in the obesity groups, reaching the highest values in the GO and stable AO groups. Table 2Cardiometabolic parameters according to changes in general and abdominal obesity at 4, 6 and 9 years of ageGeneral obesitybTotalStable without obesityaRemitting obesityIncident obesityStable with obesityp valueMean (SD)Lipid profile ($$n = 1344$$) Total cholesterol (mg/dL)165.0 (26.4)165.4 (26.1)174.1 (25.8)162.7 (29.6)158.8 (21.5)0.060 HDL cholesterol (mg/dL)60.3 (13.4)62.0 (13.3)57.3 (11.5)53.5 (11.4)50.0 (10.8) < 0.001 Non-HDL cholesterol (mg/dL)104.6 (24.5)103.4 (23.9)116.8 (23.4)109.2 (28.4)108.8 (19.6) < 0.001 LDL cholesterol (mg/dL)91.5 (23.1)91.2 (22.7)102.8 (21.5)93.1 (25.7)88.6 (19.9) < 0.001 Triglycerides (mg/dL)65.5 (33.4)61.2 (27.4)70.1 (26.6)80.6 (43.2)100.6 (63.2) < 0.001 Triglycerides/HDL ratio1.2 (0.9)1.1 (0.6)1.3 (0.7)1.6 (1.1)2.3 (2.0) < 0.001Glycemic profile Glycemia (mg/dL) ($$n = 1343$$)83.9 (7.5)83.8 (7.6)82.2 (6.8)85.5 (7.1)83.0 (7.1)0.013 Glycated haemoglobin (%) ($$n = 1337$$)5.3 (0.3)5.3 (0.3)5.3 (0.3)5.3 (0.3)5.4 (0.3)0.014 Insulin (µU/mL) ($$n = 1314$$)7.6 (8.1)6.3 (5.2)7.8 (4.1)12.2 (9.8)19.0 (23.3) < 0.001 HOMA-IRc ($$n = 1314$$)1.6 (2.0)1.4 (1.5)1.6 (0.9)2.6 (2.3)4.0 (5.3) < 0.001Blood pressure ($$n = 1344$$) Systolic pressure (mmHg)97.9 (11.4)96.1 (10.4)97.0 (10.0)105.2 (12.2)110.4 (11.1) < 0.001 Diastolic pressure (mmHg)59.0 (9.1)57.9 (8.4)58.9 (10.3)63.5 (10.5)66.6 (8.8) < 0.001Abdominal obesitydTotalStable without obesityaRemitting obesityIncident obesityStable with obesityp valueMean (SD)Lipid profile ($$n = 1324$$) Total cholesterol (mg/dL)165.0 (26.5)165.6 (26.4)161.1 (25.4)164.5 (28.2)156.4 (22.9)0.073 HDL cholesterol (mg/dL)60.5 (13.4)62.1 (13.4)57.2 (12.3)53.9 (10.6)47.9 (9.6) < 0.001 Non-HDL cholesterol (mg/dL)104.5 (24.6)103.6 (24.4)104.0 (20.4)110.6 (26.9)108.5 (21.4)0.008 LDL cholesterol (mg/dL)91.5 (23.1)91.3 (23.2)91.0 (18.7)94.1 (24.6)89.1 (20.0) < 0.001 Triglycerides (mg/dL)65.2 (32.0)61.3 (27.3)64.7 (23.1)82.3 (43.7)97.0 (52.5) < 0.001 Triglycerides/HDL ratio1.2 (0.8)1.1 (0.6)1.2 (0.6)1.6 (1.1)2.2 (1.6) < 0.001Glycemic profile Glycemia (mg/dL) ($$n = 1323$$)83.9 (7.5)83.8 (7.5)83.5 (6.1)85.4 (7.6)83.5 (7.1)0.105 Glycated haemoglobin (%) ($$n = 1317$$)5.3 (0.3)5.3 (0.3)5.3 (0.2)5.3 (0.3)5.4 (0.3)0.009 Insulin (µU/mL) ($$n = 1295$$)7.6 (8.1)6.3 (5.2)8.6 (5.9)12.2 (8.3)21.1 (24.8) < 0.001 HOMA-IR ($$n = 1295$$)1.6 (2.0)1.3 (1.5)1.8 (1.3)2.6 (2.0)4.5 (5.6) < 0.001Blood pressure ($$n = 1324$$) Systolic pressure (mmHg)97.8 (11.3)95.9 (10.3)101.3 (10.2)106.8 (11.2)110.8 (11.6) < 0.001 Diastolic pressure (mmHg)59.0 (9.1)57.8 (8.4)60.7 (9.1)64.6 (9.7)66.6 (10.5) < 0.001SD standard deviationaStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 yearsbGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cHomeostatic Model Assessment-Insulin ResistancedAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF) Table 3 shows the association between biochemical parameters and changes in obesity, adjusting for the main covariates. Compared with children without obesity from 4 to 9 years, the incident- and stable-with-GO participants at 9 years presented lower values of Col-HDL and higher values of TG, TG/HDL ratio, insulin, HOMA-IR and BP. Similar patterns were observed with the AO coefficients. However, children with remitting obesity showed very similar values to those without obesity. Table 3Association of changes in general and abdominal obesity at 4, 6 and 9 years of age with cardiometabolic parameters at 9 years of ageGeneral obesitybStable without obesityaRemitting obesityIncident obesityStable with obesityβe ($95\%$ CI)Lipid profile ($$n = 1344$$) Total cholesterol (mg/dL)(ref)9.65 (− 1.01; 20.31) − 1.81 (− 6.08; 2.46) − 3.93 (− 11.12; 3.27) HDL cholesterol (mg/dL)(ref) − 4.07 (− 9.25; 1.11) − 8.38 (− 10.45; − 6.30)** − 10.90 (− 14.39; − 7.41)** Non-HDL cholesterol (mg/dL)(ref)13.72 (3.87; 23.56)*6.57 (2.63; 10.51)**6.97 (0.34; 13.61)* LDL cholesterol (mg/dL)(ref)11.73 (2.45; 21.02)*2.71 (− 1.01; 6.43) − 0.66 (− 6.93; 5.60) Triglycerides (mg/dL)(ref)10.18 (− 2.65; 23.02)19.35 (14.21; 24.49)**37.92 (29.26; 46.57)** Triglycerides/HDL ratio(ref)0.27 (− 0.06; 0.58)0.57 (0.44; 0.69)**1.16 (0.95; 1.38)**Glycemic profile Glycemia (mg/dL) ($$n = 1343$$)(ref) − 1.46 (− 4.48; 1.56)1.36 (0.15; 2.57)* − 1.13 (− 3.17; 0.91) Glycated haemoglobin (%) ($$n = 1337$$)(ref)0.01 (− 0.10; 0.12)0.03 (− 0.01; 0.07)0.11 (0.03; 0.18)* Insulin (µU/mL) ($$n = 1$$S314)(ref)1.73 (− 1.29; 4.76)5.78 (4.56; 7.01)**12.46 (10.38; 14.54)** HOMA-IRc ($$n = 1314$$)(ref)0.34 (− 0.43; 1.11)1.26 (0.95; 1.57)**2.61 (2.08; 3.14)**Blood pressure ($$n = 1344$$) Systolic pressure (mmHg)(ref)0.62 (− 3.71; 4.95)8.67 (6.94; 10.40)**13.51 (10.59; 16.43)** Diastolic pressure (mmHg)(ref)1.05 (− 2.53; 4.62)5.46 (4.03; 6.89)**8.61 (6.20; 11.03)**Abdominal obesitydStable without obesityRemitting obesityIncident obesityStable with obesityβ ($95\%$ CI)Lipid profile ($$n = 1324$$) Total cholesterol (mg/dL)(ref) − 3.56 (− 11.35; 4.23) − 0.33 (− 4.89; 4.22) − 7.59 (− 15.02; − 0.16)* HDL cholesterol (mg/dL)(ref) − 3.84 (− 7.62; − 0.06)* − 7.70 (− 9.91; − 5.49)** − 13.12 (− 16.73; − 9.51)** Non-HDL cholesterol (mg/dL)(ref)0.28 (− 6.92; 7.48)7.37 (3.16; 11.58)*5.53 (− 1.34; 12.40) LDL cholesterol (mg/dL)(ref) − 0.19 (− 6.98; 6.59)3.33 (− 0.64; 7.30) − 1.24 (− 7.72; 5.23) Triglycerides (mg/dL)(ref)2.22 (− 6.82; 11.25)20.12 (14.84; 25.40)**33.75 (25.13; 42.37)** Triglycerides/HDL ratio(ref)0.10 (− 0.12; 0.32)0.56 (0.43; 0.69)**1.09 (0.88; 1.30)**Glycemic profile Glycemia (mg/dL) ($$n = 1323$$)(ref) − 0.36 (− 2.57; 1.84)1.37 (0.07; 2.66)* − 0.25 (− 2.35; 1.85) Glycated haemoglobin (%) ($$n = 1317$$)(ref)0.02 (− 0.06; 0.96)0.04 (− 0.01;0.08)0.12 (0.04; 0.19)* Insulin (µU/mL) ($$n = 1295$$)(ref)2.21 (0.01; 4.41)5.59 (4.30; 6.88)**14.45 (12.38; 16.53)** HOMA-IR ($$n = 1295$$)(ref)0.45 (− 0.11; 1.02)1.22 (0.89; 1.55)**3.07 (2.54; 3.60)**Blood pressure ($$n = 1324$$) Systolic pressure (mmHg)(ref)5.16 (2.08; 8.23)*10.72 (8.92; 12.52)**14.58 (11.64; 17.51)** Diastolic pressure (mmHg)(ref)3.00 (0.43; 5.58)*6.79 (5.29; 8.30)**8.86 (6.41; 11.32)***p value < 0.05; **p value < 0.001aStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 yearsbGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cHomeostatic Model Assessment-Insulin ResistancedAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF)eβ: coefficient estimated by linear regression adjusted for sex, age, family purchasing power, diet quality index (Med-DQI) and physical activity (PAQ-C) Tables 4 and 5 present the prevalence rates and ORs of cardiometabolic alterations according to changes in obesity. For both types of obesity, the prevalence and likelihood (OR) of metabolic alterations were higher in the groups with incident or persistent obesity; no significant changes were observed in children with remitting obesity. Compared with children stable without obesity from 4 to 9 years, the groups with persistent GO and AO had a greater risk of dyslipidemia (OR: 3.66, $95\%$ CI: 2.06–6.51 and 3.52, $95\%$ CI: 1.96–6.34, respectively), dysglycemia (OR: 10.61, $95\%$ CI: 5.69 to 19.79 and 17.15, $95\%$ CI: 9.09 to 32.34, respectively), HOMA-IR (OR: 24.30, $95\%$ CI: 11.93 to 49.49 and 29.85, $95\%$ CI: 14.71 to 60.54, respectively) and higher BP (OR: 8.35, $95\%$ CI: 4.55 to 15.30 and 8.22, $95\%$ CI: 4.46 to 15.15, respectively). In Table S2 (Supplementary Information), the prevalence for each of the CMRFs is presented. Table 4Prevalence of cardiometabolic risk factors according to changes in general and abdominal obesity at 4, 6 and 9 years of ageGeneral obesitybTotalStable without obesityaRemitting obesityIncident obesityStable with obesityPrevalence ($95\%$ CI)Dyslipidemiad ($$n = 1344$$)21.6 (19.5–23.9)17.7 (15.6–20.1)29.2 (14.0–51.0)38.0 (31.0–45.6)43.6 (31.0–57.2)Dysglycemiae ($$n = 1344$$)10.6 (9.1–12.4)6.3 (5.0–7.9)8.3 (1.9–29.5)28.7 (22.3–35.9)41.8 (29.4–55.4) Prediabetesf ($$n = 1312$$)4.4 (3.4–5.7)3.5 (2.6–4.9)4.1 (0.5–26.5)8.9 (5.4–14.3)7.5 (2.8–18.8) HOMA–IRg ($$n = 1312$$)7.5 (6.2–9.1)3.5 (2.6–4.9)4.1 (0.5–26.4)22.6 (16.9–29.6)41.5 (28.9–55.4)High blood pressureh ($$n = 1344$$)12.7 (11.0–14.6)8.7 (7.2–10.5)16.7 (6.1–38.3)28.7 (22.3–35.9)41.8 (29.4–55.4)Abdominal obesitycTotalStable without obesityRemitting obesityIncident obesityStable with obesityPrevalence ($95\%$ CI)Dyslipidemia ($$n = 1324$$)21.3 (19.2–23.6)18.0 (15.8–20.4)23.9 (13.5–38.7)36.7 (29.3–44.9)45.1 (31.8–59.1)Dysglycemia ($$n = 1324$$)10.6 (9.1–12.4)6.3 (5.0–7.9)13.0 (5.8–26.6)27.2 (20.6–35.0)52.9 (39.0–66.4) Prediabetes ($$n = 1293$$)4.4 (3.4–5.7)3.5 (2.5–4.8)4.4 (1.1–16.6)7.7 (4.3–13.5)13.7 (6.5–26.5) HOMA–IR ($$n = 1293$$)7.6 (6.2–9.1)3.4 (2.5–4.7)11.1 (4.6–24.5)23.2 (17.0–31.1)47.1 (33.6–61.0)High blood pressure ($$n = 1324$$)12.7 (11.0–14.6)9.1 (7.5–10.9)13.0 (5.8–26.6)28.6 (21.8–36.5)43.1 (3.0–57.2)aStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 yearsbGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF)dAt least one parameter of the following altered lipid profile: cholesterol – total cholesterol (≥ 200 mg/dL), HDL cholesterol (< 40 mg/dL), LDL cholesterol (≥ 130 mg/dL) or triglycerides (≥ 100 mg/dL)eAt least one parameter of the following altered glycemic profile: glycemia (≥ 100 mg/dL), HOMA-IR (Homeostatic Model Assessment-Insulin Resistance) (≥ 3.16) or glycated haemoglobin (≥ $5.7\%$)fPrediabetes: subcategory of dysglycemia. Glycemia (≥ 100 mg/dL) and glycated haemoglobin (≥ $5.7\%$)gHomeostatic Model Assessment-Insulin Resistance (≥ 3.16)hSystolic or diastolic blood pressure above the 90th percentileTable 5Association of changes in general and abdominal obesity at 4, 6 and 9 years of age and cardiometabolic risk factors at 9 years of ageGeneral obesitybStable without obesityaRemitting obesityIncident obesityStable with obesityORc ($95\%$ CI)Dyslipidemiae ($$n = 1344$$)1 (ref)2.22 (0.88–5.57)2.98 (2.09–4.26)**3.66 (2.06–6.51)**Dysglycemiaf ($$n = 1344$$)1 (ref)1.65 (0.37–7.41)5.88 (3.83–9.02)**10.61 (5.69–19.79)** Prediabetesg* ($$n = 1312$$)1 (ref)1.26 (0.16–9.81)2.32 (1.23–4.39)*1.86 (0.62–5.59) HOMA–IRg ($$n = 1312$$)1 (ref)1.59 (0.20–12.93)8.88 (5.26–14.97)**24.30 (11.93–49.49)**High blood pressurei ($$n = 1344$$)1 (ref)2.11 (0.69–6.44)4.32 (2.89–6.47)**8.35 (4.55–15.30)**Abdominal obesitydStable without obesityRemitting obesityIncident obesityStable with obesityOR ($95\%$ CI)Dyslipidemiae ($$n = 1324$$)1 (ref)1.33 (0.65–2.71)2.63 (1.80–3.83)**3.52 (1.96–6.34)**Dysglycemiaf ($$n = 1324$$)1 (ref)2.23 (0.89–5.58)5.35 (3.40–8.41)**17.15 (9.09–32.34)** Prediabetesg ($$n = 1293$$)1 (ref)1.18 (0.27–5.17)2.12 (1.05–4.32)*3.94 (1.61–9.60)* HOMA–IRh ($$n = 1293$$)1 (ref)3.85 (1.36–10.88)8.92 (5.20–15.30)**29.85 (14.71–60.54)**High blood pressurei ($$n = 1324$$)1 (ref)1.39 (0.57–3.44)4.01 (2.63–6.12)**8.22 (4.46–15.15)***p value < 0.05; **p value < 0.001aStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 years bGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cOdds ratios estimated by binomial logistic regression adjusted for sex, age, family purchasing power, diet quality index (Med-DQI) and Physical Activity Questionnaire-Children (PAQ-C)dAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF)eAt least one parameter of the following altered lipid profile: cholesterol – total cholesterol (≥ 200 mg/dL), HDL cholesterol (< 40 mg/dL), LDL cholesterol (≥ 130 mg/dL) or triglycerides (≥ 100 mg/dL)gAt least one parameter of the following altered glycemic profile: glycemia (≥ 100 mg/dL), HOMA-IR (Homeostatic Model Assessment-Insulin Resistance) (≥ 3.16) or glycated haemoglobin (≥ $5.7\%$)gPrediabetes: subcategory of dysglycemia. Glycemia (≥ 100 mg/dL) and glycated haemoglobin (≥ $5.7\%$)hHomeostatic Model Assessment-Insulin Resistance: subcategory of dysglycemia (HOMA-IR ≥ 3.16)iSystolic or diastolic blood pressure above the 90th percentile
Table 6 shows the relationship between obesity transitions and the combined alterations in the lipid profile, glycemic profile and BP. Compared with children stable without obesity from 4 to 9 years, children with persistent and incident GO at 9 years showed RRRs of having one cardiometabolic alteration of 2.58 and 2.13, respectively, as well as RRRs of presenting two or three cardiometabolic alterations of 33.80 and 16.11, respectively. In terms of AO, the groups stable with obesity and with incident obesity at 9 years presented RRRs of having one cardiometabolic alteration of 2.73 and 2.12, respectively, as well as RRRs of showing two or three cardiometabolic alterations of 39.36 and 11.78, respectively. Table 6Association of changes in general and abdominal obesity at 4, 6 and 9 years of age and the combined cardiometabolic alterations at 9 years of ageGeneral obesityb ($$n = 1344$$)Stable without obesityaRemitting obesityIncident obesityStable with obesityRRRc ($95\%$ CI)No cardiometabolic alterationd----One cardiometabolic alteration1 (ref)2.16 (0.90–5.20)*2.13 (1.44–3.15)**2.58 (1.26–5.29)*Two or three cardiometabolic alterations1 (ref)4.19 (0.85–20.73)16.11 (9.56–27.16)**33.80 (15.71–72.74)**Abdominal obesitye ($$n = 1324$$)Stable without obesityRemitting obesityIncident obesityStable with obesityRRR ($95\%$ CI)No cardiometabolic alteration----One cardiometabolic alteration1 (ref)1.22 (0.62–2.41)2.12 (1.41–3.19)**2.73 (1.24–6.00)*Two or three cardiometabolic alterations1 (ref)2.14 (0.68–6.70)11.78 (6.94–20.01)**39.36 (17.94–86.36)***p value < 0.05; **p value < 0.001aStable without obesity: without obesity on all three measurements; remitting obesity: in obesity at 4 and/or 6 years old, but not at 9 years; incident obesity: in obesity only at 9 or at 4 or 6 years of age as well as 9 years of age; and stable with obesity: in obesity at 4, 6 and 9 years bGeneral obesity: body mass index (BMI) > + 2 (SD) according to the standardised tables of the WHO 2007cRRR: relative risk ratios estimated using multinomial logistic regression models adjusted for sex, age, family purchasing power and diet quality index (Med-DQI) and physical activity (PAQ-C), for all fitness variables. Reference category: no alterationdCardiometabolic alteration: alteration of the lipid profile, glycemic profile or blood pressureeAbdominal obesity: ≥ 90th percentile of waist circumference according to consensus of the International Diabetes Federation (IDF) Tables S3 and S4 present the association of GO and AO with CMRFs at 9 years of age. The likelihood of CMRFs, in terms of ORs, was greater in children with both GO and AO than those with GO or AO only.
## Discussion
This study evaluated the association between changes in GO and AO from 4 to 9 years of age and CMRFs at 9 years of age in a population-based child cohort. The results demonstrate changes in cardiometabolic parameters in children with incident obesity and especially those with persistent obesity; these changes were characterised by an increased risk of dyslipidemia, insulin resistance and altered BP, highlighting the strength of the association with the combined CMRFs. In contrast, children with remitting obesity at some point between the ages of 4 and 6 years, but not at 9 years old did not have an excess risk on these factors.
Our results are in line with most previous studies [3, 13–15]. Childhood obesity is related to a moderate increase in TC and Col-LDL, a moderate-to-severe increase in TG and TG/HDL ratio, and a reduction in Col-HDL [33, 34]. Our study observed an increase in TG and TG/HDL ratio and a reduction in Col-HDL in both GO and AO, especially in those participants stable with obesity.
Childhood obesity does not always influence blood glucose levels. The initial alterations of blood glucose homeostasis are observed in insulin levels and IR, which is consistent with the findings of the current study. Such outcomes are of greater magnitude in the stable-with-obesity group. In addition, as noted in our study, higher levels of diastolic BP and especially systolic BP have been consistently reported to accompany excess weight and AO [35–37].
As early as 2004, Weiss et al. [ 16] found that children and adolescents with obesity had a greater number of altered CMRFs. Our data shows that children with GO or AO had a very high risk of having two or three cardiometabolic alterations. This early aggregation of altered CMRFs exerts a combined effect of large magnitude [38] on health in adulthood. Thus, early detection is critical for the prevention of future cardiovascular diseases and diabetes [11].
Our work utilised a classification that allows the identification of natural changes in the state of childhood obesity, indicating a strong association between GO and stable or persistent AO in children. Similarly to other studies, these results show that a longer duration of exposure to obesity in childhood, regardless of its severity, has a greater effect on CMRFs [18, 39]. To verify these results, we analysed obesity at 9 years of age with a cross-sectional design, taking into account the exposure (Tables S3 and S4). Our results confirm that the risk of having CMRF alterations is greater among children stable with obesity between ages of 4 and 9 years than among those with obesity only at 9 years of age. These findings have important implications and consequences for public health given that obesity tends to persist once is established [40, 41]. Therefore, establishing interventions for the prevention and management of obesity in very young children is crucial to reduce the cardiometabolic risk to minimum levels, with an emphasis on the benefits of maintaining normal weight from early ages. We also emphasise that the cardiometabolic parameters of children with reduced obesity were similar to those who had never been at obesity, although the small sample size of this category limits the accuracy of these results. However, this finding is consistent with studies that showed that the cardiometabolic alterations are reversed within a short period in children with obesity who return to normal weight [42, 43].
A recent study similar to ours was conducted with children and 2 years of follow-up. These authors observed an increase in metabolic alterations in children with stable and incident obesity compared with those of children stable without obesity, although these alterations were of a lower magnitude than those estimated in our study [44]. Children with remitting obesity had a comparable number of CMRFs as stable children without obesity, except for alterations in BP. Recently, Ortiz-Pinto et al. [ 36] reported that children with obesity at 4 years of age and high BP exhibited normalised BP when they returned to normal weight at 6 years of age. In a recent article, Norris et al. found that a longer duration of obesity was associated with worse profiles for all cardiometabolic disease risk factors, and the strength of evidence persisted even after adjusting for obesity severity. The obesity epidemic is characterised by a trend towards an earlier onset and consequently greater lifetime exposure. Therefore, health policy recommendations must aim at preventing the early onset obesity, therefore reducing lifetime exposure to obesity [18].
Another aspect to highlight is the difference in the associations of GO and AO with metabolic alterations. In our study, these differences were very small regarding lipid metabolism and BP. However, children with stable AO had a higher risk of dysglycemia than children with stable GO (OR: 17.15 vs. 10.61), especially due to increased IR. Recently, Song et al. [ 45] reported that children with AO exhibited greater glycemic alterations than those with GO, and Niu et al. [ 46] showed that children with obesity, especially AO, have altered IR markers.
Finally, we found that children with both GO and AO have a higher risk of dyslipidemia, dysglycemia and altered BP. Although BMI is viewed as the most appropriate measure of obesity due to the ease of obtaining its values and its high correlation with WC [47], we believe that the joint use of BMI and WC facilitates the identification of children with higher baseline cardiometabolic risk.
The limitations and strengths of our study should be considered for the accurate interpretation of the results. First, losses to follow-up were high because many parents did not authorise blood extraction. However, children who participated in the three follow-up measurements had sociodemographic characteristics similar to those of the baseline cohort (as seen in Table S1 in the Supplementary Information). Second, this cohort lacked data on indicators for changes in obesity between birth and 4 years; therefore, the trajectories of GO and AO are incomplete. Third, the data used to define high BP consisted of one measurement instead of, on three measurements on different days as recommended by the European Society of Hypertension [29]. In addition, BP measurement in the paediatric population is not exempt from being affected by white coat hypertension, a bias that must be taken into consideration [48]. However, there is limited evidence that this bias is differential in individuals with or without obesity. Fourth, although our regression models included the main covariates, residual confounders cannot be ruled out. Finally, the BMI and WC used to define GO and AO are only indirect measures of body fat and can lead to erroneous classifications, especially in children with a BMI close to the cut-off point for obesity [49]. Fifth, the lack of vitamin D measurement could be acknowledged among limitations due to the positive association between vitamin D deficiency, childhood obesity and other CMRFs [50]. Additionally, the results of the present study can only be extrapolated to the population of Western countries, as the target population of this study was from a European Mediterranean country. Finally, blood sampling was only performed at 9 years of age, which must be pointed out since the lack of blood samples at the ages of 4 and 6 years did not allow the analysis of CMRFs changes over time.
Regarding the strengths of this study, three measures of obesity indicators were included, facilitating detailed longitudinal interpretation of exposure to obesity. In addition, the main confounding variables were considered, including diet quality and physical activity. Finally, the anthropometric measurements were based on objective and standardised criteria and entail lower validity errors than those self-reported or reported by the parents [51].
## Conclusions
Obesity, especially persistent obesity during childhood, shows a strong positive correlation with the presence of dyslipidemia, IR, altered BP and numerous metabolic alterations. From a public health perspective, the prevention and control of obesity in early childhood is a priority and can be accomplished by screening for threshold values of BMI and WC. Additionally, for children found to be at risk of obesity, biochemical parameters of cardiometabolic risk should be evaluated.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 27 KB)Supplementary file2 (DOCX 15 KB)Supplementary file3 (DOCX 17 KB)Supplementary file4 (DOCX 15 KB)Supplementary file5 (DOCX 15 KB)
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|
---
title: Insights into membrane association of the SMP domain of extended synaptotagmin
authors:
- Yunyun Wang
- Zhenni Li
- Xinyu Wang
- Ziyuan Zhao
- Li Jiao
- Ruming Liu
- Keying Wang
- Rui Ma
- Yang Yang
- Guo Chen
- Yong Wang
- Xin Bian
journal: Nature Communications
year: 2023
pmcid: PMC10023780
doi: 10.1038/s41467-023-37202-8
license: CC BY 4.0
---
# Insights into membrane association of the SMP domain of extended synaptotagmin
## Abstract
The Synaptotagmin-like Mitochondrial-lipid-binding Protein (SMP) domain is a newly identified lipid transfer module present in proteins that regulate lipid homeostasis at membrane contact sites (MCSs). However, how the SMP domain associates with the membrane to extract and unload lipids is unclear. Here, we performed in vitro DNA brick-assisted lipid transfer assays and in silico molecular dynamics simulations to investigate the molecular basis of the membrane association by the SMP domain of extended synaptotagmin (E-Syt), which tethers the tubular endoplasmic reticulum (ER) to the plasma membrane (PM). We demonstrate that the SMP domain uses its tip region to recognize the extremely curved subdomain of tubular ER and the acidic-lipid-enriched PM for highly efficient lipid transfer. Supporting these findings, disruption of these mechanisms results in a defect in autophagosome biogenesis contributed by E-Syt. Our results suggest a model that provides a coherent picture of the action of the SMP domain at MCSs.
The SMP domain of E-*Syts is* a newly identified lipid transfer module with unclear mechanisms. Here, authors show that it uses its tip region to associate with the extremely curved or negatively charged membranes to extract and unload lipids.
## Introduction
Protein-mediated non-vesicular lipid transfer at membrane contact sites (MCSs), where the membranes of two different organelles are closely apposed (10–30 nm)1,2, plays an important role in regulating lipid homeostasis in eukaryotic cells3,4. One such lipid transfer module identified in recent years is the Synaptotagmin-like Mitochondrial-lipid-binding Protein (SMP) domain, which belongs to the tubular lipid-binding protein (TULIP) domain superfamily5–8. The SMP domain-containing proteins typically act as tethers at MCSs8–19. Examples of these proteins are three extended synaptotagmins (E-Syts) in mammals and their homologs in yeast, tricalbins8,10,16,20,21. E-Syts and tricalbins have been shown to be anchored to the tubular endoplasmic reticulum (ER) via their N-terminal hydrophobic hairpins and use their C2 domains to bind to PI[4,5]P2 in the plasma membrane (PM)10,22–27.
A crystallographic study of human E-Syt2 demonstrated that its SMP domain dimerizes in an anti-parallel fashion to form a 9-nm-long cylinder and each protomer consists of a groove lined with hydrophobic residues harboring glycerophospholipid molecules without selectivity for a specific head group28. However, whether this hydrophobic groove extends throughout the length of the entire SMP dimer is unknown. The bidirectional lipid transfer capacity of the SMP domain was confirmed by in vitro fluorescence resonance energy transfer (FRET)- and liposome-based lipid transfer assays12,14,17,19,29–36. Consistently, SMP domain-containing proteins have been reported to participate in controlling lipid homeostasis, including lipid signaling and membrane expansion, in response to acute stimuli9,12–15,17,19,26,30,35,37–42. For example, cells lacking E-Syts have delayed clearance of acutely accumulated diacylglycerol (DAG)35, sustained glucose-stimulated insulin secretion41, and impaired Ca2+-induced phosphatidylserine (PS) exposure in the PM30. Loss of E-Syt in Drosophila reduces PM PI[4,5]P2 resynthesis in photoreceptors40, and deletion of E-Syt3 protects against diet-induced obesity in mice42. Moreover, overexpression of E-Syt enhances the PM expansion driving axonal growth37,38, and yeast cells lacking tricalbins show defects in PM integrity upon heat shock26. However, compared to other well-characterized lipid transfer modules, such as oxysterol-binding protein (OSBP)-related ligand-binding domain (ORD)43,44, the mechanisms underlying the actions of the SMP domain are poorly understood.
We previously used DNA nanotechnology (DNA origami) to generate a DNA nanostructure comprising two DNA-ring-templated liposomes connected by a tunable DNA rod that controls the distance between the two liposomes in lipid transfer assays31. On the basis of this system, we suggested that the SMP domain delivers lipids as a shuttle over the typical ER-PM distance, which is approximately 15 nm on average in E-Syt1-overexpressing cells under high cytosolic Ca2+ levels45. Although the structures of the SMP domains have been determined14,28,32,33,46, it remains mysterious how it associates with the ER membrane and the PM to extract and unload lipids (e.g., whether the SMP domain is parallel or perpendicular to the membrane, corresponding to the lying-down or standing-up conformation). In addition, it is unclear how the 9-nm-long SMP dimer transfers lipids at occasionally observed tight ER-PM contact sites (<10 nm in distance) in cells overexpressing E-Syts or tricalbins26,27,45.
The SMP domain has been reported to be dispensable for the tethering function of E-Syts and its membrane association is too weak to be detected by protein-membrane interaction methods, including liposome sedimentation, liposome turbidity, and optical tweezer10,20,23,24,30,31,35. Here, we performed DNA brick-aided47 lipid transfer assays and molecular dynamics (MD) simulations to elucidate the molecular basis of membrane recognition by the SMP domain of E-Syt. Our data provide evidence that the SMP dimer uses its tip region to recognize the extremely curved subdomain of tubular ER and the acidic-lipid-enriched PM for highly efficient lipid transfer, leading to a comprehensive action model for SMP domain that takes into account its membrane association. The proposed mechanism explains the roles of E-Syts in the regulation of lipid homeostasis.
## Membrane curvature-sensing information on the SMP domain of E-Syt revealed by DNA brick-aided lipid transfer assays
The N-terminal hydrophobic hairpin of E-Syt has been reported to anchor the protein to the tubular ER10,25–27, but it was absent in previous FRET- and liposome-based lipid transfer assays30,31,34–36. To better mimic the conditions in living cells, we first purified the full-length human E-Syt1 (a.a. 1-1104) including the hydrophobic hairpin and reconstituted it into ER-like liposomes at a protein to lipid ratio of 1:500 (Fig. 1a and Supplementary Fig. 1a and b). The ER-like donor proteoliposomes containing E-Syt1, phosphatidylcholine (PC), phosphatidylethanolamine (PE), and a FRET pair (NBD-PE and Rhodamine-PE) were mixed with the PM-like acceptor liposomes composed of PC, phosphatidylserine (PS) and PI[4,5]P2 (Fig. 1a). In the absence of Ca2+, NBD-PE was efficiently quenched by Rhodamine-PE in the ER-like proteoliposomes and a modest dequenching of NBD-PE was observed (Supplementary Fig. 1c). Consistent with previous reports using the cytosolic region of E-Syt1 (E-Syt1cyto)30,31,35,36,48, in which the N-terminal region of E-Syt1 including the hydrophobic hairpin was replaced by a His tag for binding DGS-NTA(Ni) lipid in ER-like donor liposomes, the addition of Ca2+ strongly increased NBD fluorescence due to dilution of NBD-PE and Rhodamine-PE, which reflects their transfer from the outer leaflet of ER-like proteoliposomes to the outer leaflet of PM-like liposomes (Supplementary Fig. 1c). Basal membrane tethering by E-Syt1 revealed by the optical density at 405 nm was also enhanced by Ca2+ binding to the protein (Supplementary Fig. 1d). Upon adding proteinase K to the mixture, the lipid transfer and membrane tethering were completely abolished suggesting a direct role of full-length E-Syt1 in these processes in vitro (Supplementary Fig. 1).Fig. 1Extreme membrane curvature facilitates full-length E-Syt1-dependent lipid transfer.a, Schematic representation of full-length E-Syt1-dependent lipid transfer and liposome tethering in the presence of Ca2+. b, Time courses of lipid transfer between distinctly sized ER-like donor proteoliposomes containing E-Syt1 and PM-like acceptor liposomes in the presence of Ca2+ at room temperature as assessed by dequenching of NBD-PE fluorescence (mean ± SD, $$n = 3$$ independent experiments). c, Negative-staining TEM images showing E-Syt1-containing ER-like donor proteoliposomes prepared from sonication and extrusion through filters with 30 nm pores. Scale bar, 100 nm. d, Size distribution of proteoliposomes measured from negative-staining TEM images in (c). Average diameters are presented as mean ± SD ($$n = 286$$ and 203 proteoliposomes from left to right). e, Schematic of assembled DNA bricks and steps for DNA brick-assisted liposome sorting. f, Negative-staining TEM images showing distinctly sized E-Syt1-containing ER-like donor proteoliposomes prepared from extrusion through filters with 30 nm pores followed by DNA-brick-assisted sorting. Scale bar, 100 nm. g, Size distribution of proteoliposomes measured from negative-staining TEM images in F. The histograms are fitted by Gaussian functions. Average diameters are presented as mean ± SD ($$n = 152$$, 306, 361 and 1165 proteoliposomes from left to right). Source data are provided as a Source Data file.
The localization of E-Syt to the tubular ER, which varies in diameter (25–90 nm) in non-neuronal cells49, raises the possibility that the SMP domain is a curvature-sensing module. To test this hypothesis, we produced ER-like donor liposomes by extrusion through filters with 30 nm, 100 nm, or 400 nm pores or by sonication before reconstituting E-Syt1 into them (named 30-nm, 100-nm, or 400-nm extruded ER-like proteoliposomes or sonicated ER-like proteoliposomes, respectively). The PM-like acceptor liposomes were extruded through filters with a pore size of 800 nm to mimic the PM with low curvature. Surprisingly, detection of lipid transfer between PM-like liposomes and distinctly sized ER-like proteoliposomes did not reveal specific differences (Fig. 1a and b). However, negative-staining transmission electron microscopy (TEM) showed that the smallest ER-like proteoliposomes we used, which were prepared from sonicated or 30-nm extruded liposomes, still had heterogeneous populations with mean diameters >60 nm (60.70 ± 28.61 nm for sonicated proteoliposomes and 64.89 ± 45.55 nm for 30-nm extruded proteoliposomes, Fig. 1c, d). Therefore, it was difficult to collect curvature-sensing information on the SMP domain across the physiologically related diameter range of 25–90 nm using common lipid transfer assays.
To overcome this problem, we capitalized on a recently reported liposome-sorting strategy47 to obtain homogeneous sub-60-nm liposomes and applied this method to lipid transfer assays. We first constructed a three-point-star DNA nanostructure (DNA brick) consist of seven oligonucleotides (Fig. 1e and Supplementary Table 1), including a core strand (C, colored in purple, Fig. 1e), three sleeve strands (S, colored in yellow, Fig. 1e) and three edge strands (E, colored in green, Fig. 1e). One of the E strand was modified with cholesterol moiety to serve as a membrane anchor (E-chol, Fig. 1e). The DNA bricks (Fig. 1e) were assembled by thermal annealing (Supplementary Fig. 2a) and further purified by rate-zonal centrifugation (Supplementary Fig. 2b). The SDS-agarose gel analysis showed that, after coating the liposomes, the DNA bricks can be efficiently digested by DNase I treatment (Fig. 1e and Supplementary Fig. 2c).
In preparation for the lipid transfer assays with DNA brick-sorted proteoliposomes, the DNA bricks were incubated with 30-nm extruded ER-like donor proteoliposomes containing E-Syt1 at a brick to lipid ratio of 1:375 (Fig. 1e). As the spherical vesicles of different sizes had similar buoyant densities but differed in their surface-area-to-volume ratios, the coating of DNA bricks, which were highly dense, gave more density to the smaller vesicles. Accordingly, we separated the DNA brick-coated ER-like proteoliposomes by isopycnic centrifugation (Fig. 1e), and the gradient fractions (F1 to F24 from top to bottom, Supplementary Fig. 2d and e) were collected, concentrated and digested by DNase I (Fig. 1e). In agreement with a previous report using protein-free liposomes47, negative-staining TEM confirmed that each fraction contained uniformly sized ER-like proteoliposomes (Fig. 1f and g), which were 81.24 ± 23.11 nm diameter in F14 (named 80-nm sorted ER-like proteoliposomes), 56.25 ± 14.68 nm diameter in F16 (named 60-nm sorted ER-like proteoliposomes), 50.71 ± 10.88 nm diameter in F18 (named 50-nm sorted ER-like proteoliposomes) and 40.84 ± 12.38 nm diameter in F20 (named 40-nm sorted ER-like proteoliposomes).
To address the potential role of membrane curvature in E-Syt1 SMP-mediated lipid transfer, we mixed the sorted ER-like donor proteoliposomes containing E-Syt1 with the 800-nm extruded PM-like acceptor liposomes (Fig. 1a). In the presence of Ca2+, the addition of 60-nm sorted ER-like proteoliposomes resulted in an increase in NBD-PE fluorescence with a similar efficiency as the extruded or sonicated ER-like proteoliposomes, whereas the 40-nm sorted ER-like proteoliposomes dramatically accelerated the lipid transfer by E-Syt1 (Fig. 1b). These data validate the application of the DNA brick-assisted sorting technique to separate vesicles reconstituted with high-molecular-weight proteins and the use of the DNA brick-aided lipid transfer assays developed in this study for uncovering information on membrane curvature-sensing by the SMP domain of E-Syt1.
We next assessed whether the membrane curvature plays a direct role in SMP-mediated lipid transfer (Fig. 2a). The ER-like donor liposomes (PC, PE, NBD-PE and Rhodamine-PE) were obtained by extrusion through filters with a pore size of 30 nm. Similar to the 30-nm extruded E-Syt1-containing proteoliposomes, these 30-nm extruded protein-free liposomes also had a broad size distribution with a mean diameter of 59.98 ± 28.73 nm (Fig. 2b). Subsequently, we performed DNA brick-assisted sorting on 30-nm extruded ER-like liposomes. Sorted liposomes with three different diameters (55.48 ± 13.73, 46.52 ± 14.99, and 38.63 ± 10.19 nm) were collected and digested by DNase I. These liposomes were homogeneous and checked by negative-staining TEM (named 60-nm sorted, 50-nm sorted, and 40-nm sorted ER-like liposomes, Fig. 2b). After mixing the extruded or sorted ER-like liposomes with 800-nm extruded PM-like acceptor liposomes and the purified SMP domain of E-Syt1 (SMP, a.a. 134-327, Supplementary Fig. 3), we monitored the NBD-PE fluorescence and optical density at 405 nm (Fig. 2b). Consistent with previously reported results30,31, no lipid transfer by the SMP domain alone was observed when extruded liposomes were not tethered (Fig. 2c and d). Interestingly, the NBD-PE dequenching due to its transfer by the SMP domain slightly increased with 50-nm sorted, but not 60-nm sorted, ER-like liposomes, and 40-nm sorted ER-like liposomes produced a robust lipid transfer level (Fig. 2c). The rapid NBD dequenching signal at the beginning of the experiment was caused by the initial uptake of NBD-PE and/or Rhodamine-PE into the SMP domain. Furthermore, Ca2+ was not needed for SMP-dependent lipid transfer (Supplementary Fig. 4), and incorporating NBD-PE and Rhodamine-PE in either the ER-like or the PM-like liposomes resulted in a similar NBD-PE dequenching efficiency, proving that the lipid transfer is bidirectional (Supplementary Fig. 4). Notably, the SMP domain alone did not tether these liposomes (Fig. 2d) even though the protein was anchored to the 40-nm sorted DGS-NTA(Ni)-containing ER-like liposomes via its N-terminal His tag before incubating with PM-like liposomes (Supplementary Fig. 5a). In agreement with these data, the SMP domain did not sediment with 40-nm sorted ER-like liposomes or PM-like liposomes with increasing amounts of PS and PI[4,5]P2 (Supplementary Fig. 5b).Fig. 2Extreme membrane curvature is essential for SMP-mediated lipid transfer.a, Schematic representation of SMP-mediated lipid transfer between curved ER-like and flat PM-like liposomes. b, Size distributions of distinctly sized ER-like donor liposomes prepared from extrusion through filters with 30 nm pores followed by DNA-brick-assisted sorting or not. Diameters of liposomes were measured from negative-staining TEM images. Average diameters are presented as mean ± SD ($$n = 548$$, 1959, 916, and 329 liposomes from left to right). c, Top, time courses of lipid transfer between distinctly sized ER-like donor liposomes and PM-like acceptor liposomes in the presence of SMP at 37 °C as assessed by dequenching of NBD-PE fluorescence. Bottom left, quantifications of NBD fluorescence after incubation for 15 min or 30 min. Bottom right, initial transfer rates. Data are presented as mean ± SD ($$n = 3$$ independent experiments). ns, not significant; * $P \leq 0.05$, **$P \leq 0.01$, ****$P \leq 0.0001$ by two-way ANOVA with Bonferroni’s multiple comparisons test (bottom left) or by one-way ANOVA with Bonferroni’s multiple comparisons test (bottom right). P values: 6.4 × 10 −3 (bottom left, 15 min), 5.3 × 10−5 (bottom left, 30 min), and 0.030 (bottom right) for [(SMP + 30-nm extruded ER-like liposomes) vs (SMP + 50-nm sorted ER-like liposomes)]; 2.6 × 10−10 (bottom left, 15 min), 1.5 × 10−12 (bottom left, 30 min), and 1.3 × 10−5 (bottom right) for [(SMP + 30-nm extruded ER-like liposomes) vs (SMP + 40-nm sorted ER-like liposomes)]; 2.0 × 10−8 (bottom left, 15 min), 3.0 × 10−10 (bottom left, 30 min), and 2.3 × 10−4 (bottom right) for [(SMP + 50-nm sorted ER-like liposomes) vs (SMP + 40-nm sorted ER-like liposomes)]. d, Time courses of the tethering of distinctly sized ER-like donor liposomes and PM-like acceptor liposomes in the presence of SMP at 37 °C as assessed by an increase in turbidity (OD at 405 nm). Data are presented as mean ± SD ($$n = 3$$ independent experiments). Source data are provided as a Source Data file.
Taken together, these results illustrate that the membrane with extreme curvature (<50 nm in diameter) facilitates the SMP-mediated lipid transfer, and confirm the shuttle mechanism of the SMP domain over long membrane distance (> 10 nm), as SMP alone can transfer lipids between untethered liposomes.
## Charge-dependent lipid transfer by the SMP domain of E-Syt
In view of the low curvature of the PM, we explored whether the acidic lipids [e.g. PS and PI[4,5]P2], which are enriched in the PM but not the ER, participate in associating with the SMP domain for lipid transfer. The DNA brick-aided lipid transfer assays with SMP developed here (Fig. 2a) allowed us to test this plausible scenario because this system did not require PI[4,5]P2-dependent liposome tethering, which was a premise for observing lipid transfer by E-Syt1 in previous studies30,31,35,36. The assays involved the SMP, 40-nm sorted ER-like donor liposomes, and 800-nm extruded PM-like acceptor liposomes [Fig. 2a, with or without removal of PS and PI[4,5]P2 from PM-like liposomes]. A much lower degree of NBD-PE and Rhodamine-PE transfer by SMP between liposomes occurred with PM-like liposomes devoid of PS and PI[4,5]P2 (Fig. 3a and b). These results reflect that the lipids with negatively charged headgroups also function as binding sites for the SMP domain to extract and unload lipids. Fig. 3Acidic lipids in the membrane facilitate SMP-mediated lipid transfer and SMP uses its tip region to associate with the membrane.a, Left, time courses of lipid transfer between ER-like donor liposomes and PM-like acceptor liposomes with or without PI[4,5]P2 and PS in the presence of SMP at 37 °C as assessed by dequenching of NBD-PE fluorescence. Middle, quantifications of NBD fluorescence after incubation for 15 min or 30 min. Right, initial transfer rates. Data are presented as mean ± SD ($$n = 3$$ independent experiments). * $P \leq 0.05$; ** $P \leq 0.01$ by two-way ANOVA with Sidak’s multiple comparisons test (middle) or by two-tailed Student’s t-tests (right). P values: 0.032 (middle, 15 min), 5.0 × 10−3 (middle, 30 min), and 1.5 × 10−3 (right) for [(SMP + 80-nm extruded PM-like liposomes) vs (SMP + 80-nm extruded PM-like liposomes no PS and PIP2)]. b, Size distribution of ER-like donor liposomes prepared from extrusion through filters with 30 nm pores followed by DNA-brick-assisted sorting. Diameters of liposomes were measured from negative-staining TEM images. Average diameters is presented as mean ± SD ($$n = 789$$ liposomes). c, Ribbon representation (top left) and surface representations illustrating the electrostatic potentials (bottom left) of the crystal structure of the SMP dimer of human E-Syt2 (PDB code 4P42) rendered in PyMOL. One SMP monomer is shown in yellow and the other in pale yellow. Lipid molecules are represented as green sticks. R256 (K227 in E-Syt1 and K207 in E-Syt3) is represented as sphere in blue. R295 and K296 (R266 and R267 in E-Syt1) are represented as cyan spheres. Y257and F258 (Y228 and F229 in E-Syt1 and I208 in E-Syt3) are represented as magenta spheres. V197 and I337 (V169 and L308 in E-Syt1 and V148 and I286 in E-Syt3) are represented as orange spheres. The basic patch at the tip region is indicated by a blue dashed circle. The basic patch at the side region is indicated by a cyan dashed circle. Right, a different view of the SMP dimer. d and e, Left, time courses of lipid transfer between ER-like donor liposomes and PM-like acceptor liposomes in the presence of WT or mutated SMP at 37 °C as assessed by dequenching of NBD-PE fluorescence. Middle, quantifications of NBD fluorescence after incubation for 15 min or 30 min. Right, initial transfer rates. Data are presented as mean ± SD ($$n = 3$$ independent experiments). ns, not significant; *** $P \leq 0.001$, **** $P \leq 0.0001$ by two-way ANOVA with Bonferroni’s multiple comparisons test (middle) or by one-way ANOVA with Bonferroni’s multiple comparisons test (right). P values: 1.8 × 10−5 (middle, 15 min), 4.2 × 10−8 (middle, 30 min), and 2.1 × 10−4 (right) for [(SMP) vs (SMP K227E)]; 1.1 × 10−5 (middle, 15 min), 8.8 × 10−8 (middle, 30 min), and 7.5 × 10−4 (right) for [(SMP) vs (SMP Y228A)]; 1.0 × 10−6 (middle, 15 min), 2.8 × 10−9 (middle, 30 min), and 2.2 × 10−4 (right) for [(SMP) vs (SMP F229A)]. f, Schematic representation of lipid harboring by the SMP domain. g, Purified WT and mutated SMP-C2AB of E-Syt1 were incubated with solubilized NBD-PE or NBD-PE and POPC, run on native-PAGE, and analyzed by fluorescence (top) and Coomassie blue staining (bottom). This experiment was repeated three times with similar results. Source data are provided as a Source Data file.
Consistent with the high similarity among the corresponding domains of all three E-Syts (Supplementary Fig. 6), the SMP domains of E-Syt2 and E-Syt3 showed similar membrane curvature- and charge-facilitated lipid transfer activities (Supplementary Fig. 7).
## Association of the SMP domain with the membrane via its tip region
To gain insights into the mechanisms underlying the membrane association of the SMP domain, we first investigated the interplay between the SMP domain and the PM. According to the crystal structure of the SMP-C2AB domains of human E-Syt228, there are two small basic patches in the SMP domain that have the potential to interact with the acidic lipids in the PM (tip region patch and side region patch, Fig. 3c). We mutated the key conserved positively charged residues (Fig. 3c and Supplementary Fig. 6) in these patches to negatively charged residues to assess their importance in SMP-mediated lipid transfer. The R266E/R267E double mutant in the side region did not inhibit lipid transfer when mixing mutated SMP, 40-nm sorted ER-like donor liposomes, and 800-nm extruded PM-like acceptor liposomes (Figs. 2a, 3c and d). In contrast, the lipid transfer ability of E-Syt1 SMP was blocked when K227 in the tip region was mutated to Glu (Figs. 2a, 3c-d, and Supplementary Fig. 3). Similar lipid transfer defects were also observed for E-Syt2 SMP bearing R256E and E-Syt3 SMP bearing K207E (Supplementary Fig. 7). The K226 in E-Syt1, which is less conserved among three E-Syts (K255 in E-Syt2 and Q206 in E-Syt3, Supplementary Fig. 6), was dispensable for membrane association, as the lipid transfer by SMP K226Q was similar to wild-type (WT) SMP (Fig. 3e). For full-length E-Syt1, the 40-nm sorted ER-like proteoliposomes containing E-Syt1 K227E reduced, but did not abolish, lipid transfer (Fig. 1a and Supplementary Fig. 8).
The K227E mutant did not affect the lipid harboring ability of the SMP domain of E-Syt1, as the solubilized NBD-PE was loaded onto purified WT protein and K227E mutant at a similar level based on the fluorescence of NBD-PE in the band corresponding to SMP-C2AB of E-Syt1 in native polyacrylamide gel electrophoresis (Fig. 3c, f and g). The lipid harboring of K227E mutant was also demonstrated by the ability of PC to displace preloaded NBD-PE from the protein (Fig. 3g). As a control, mutating two hydrophobic residues to bulky hydrophobic amino acids that block the hydrophobic cavity of the SMP domain (V169W/L308W) impaired lipid harboring ((Fig. 3c and g)35. Collectively, these results suggest that the tip region of the SMP domain drives its association with the PM via a charge-based interaction for lipid extraction and release.
For the SMP-ER association, we hypothesized that the SMP domain associates with the ER membrane in the same way as it interacts with the PM. Y257 and F258 in the tip region of E-Syt2, corresponding to Y228 and F229 in E-Syt1 and I208 in E-Syt3 (Fig. 3c and Supplementary Fig. 6), are expected to be inserted into the bilayer. The importance of the tip region for membrane association was assessed by mutating these residues to Ala. The SMP of E-Syt1 bearing Y228A or F229A (Supplementary Fig. 3) had lower lipid transfer activity (Figs. 2a, 3c and e). F258A of E-Syt2 and I208A of E-Syt3 also reduced lipid transfer by SMP (Supplementary Fig. 7). Furthermore, if the SMP domain uses the same region to recognize the extreme membrane curvature and negatively charged lipids, these two membrane features are proposed to have a synergistic effect on its lipid transfer (Fig. 4a). To test this, the PM-like acceptor liposomes consisting of PC, PS, and PI[4,5]P2 was extruded through filters with 30 nm pores followed by DNA brick-assisted sorting. The 40-nm sorted PM-like liposomes were collected and concentrated (42.85 ± 12.35 nm in diameter, Fig. 4b). In the lipid transfer assays, replacement of 800-nm extruded PM-like liposomes with 40-nm sorted PM-like liposomes resulted in an increase in the lipid transfer activity of SMP (Figs. 2a, 4a and c). Lack of PI[4,5]P2 and PS in the 40-nm sorted PM-like liposomes (43.16 ± 15.11 nm in diameter, Fig. 4b) induced a slower NBD-PE dequenching (Fig. 4a and c). These results support a model in which the SMP domain uses its tip region to associate with not only the PM but also the tubular ER for lipid transfer. Fig. 4Extreme membrane curvature and acidic lipids have a synergistic effect on SMP-mediated lipid transfer.a, Schematic representation of SMP-mediated lipid transfer between curved ER-like and curved PM-like liposomes. b, Size distributions of PM-like acceptor liposomes with or without PI[4,5]P2 and PS prepared from extrusion through filters with 30 nm pores followed by DNA brick-assisted sorting. Diameters of liposomes were measured from negative-staining TEM images. Average diameters are presented as mean ± SD ($$n = 1258$$ and 1153 liposomes from left to right). c, Top, time courses of lipid transfer between ER-like donor liposomes and distinctly sized PM-like acceptor liposomes with or without PI[4,5]P2 and PS in the presence of SMP at 37 °C as assessed by dequenching of NBD-PE fluorescence. Bottom left, quantifications of NBD fluorescence after incubation for 15 min or 30 min. Bottom right, initial transfer rates. Data are presented as mean ± SD ($$n = 3$$ independent experiments). ** $P \leq 0.01$, *** $P \leq 0.001$, **** $P \leq 0.0001$ by two-way ANOVA with Bonferroni’s multiple comparisons test (bottom left) or by one-way ANOVA with Bonferroni’s multiple comparisons test (bottom right). P values: 2.3 × 10−4 (bottom left, 15 min), 2.4×10−7 (bottom left, 30 min), and 5.0 × 10−3 (bottom right) for [(SMP + 40-nm sorted PM-like liposomes) vs (SMP + 40-nm sorted PM-like liposomes no PS and PIP2)]; 1.8×10−5 (bottom left, 15 min), 1.6 × 10−8 (bottom left, 15 min), and 1.2 × 10−3 (bottom right) for [(SMP + 40-nm sorted PM-like liposomes) vs (SMP + 800-nm extruded PM-like liposomes)]. Source data are provided as a Source Data file.
## Molecular dynamics simulations of membrane association by the SMP domain of E-Syt
To further understand the molecular mechanism underlying the membrane association of the SMP domain, we performed molecular dynamics (MD) simulations of SMP dimer starting from the crystal structure of E-Syt228 (Fig. 5a) and investigated its interactions with an ER-like bilayer consisting of PC and PE (Fig. 5b) and a PM-like bilayer consisting of PC, PS and PI[4,5]P2 (Fig. 5c). To reach a longer timescale, we applied the latest optimized version of the coarse-grained (CG) Martini force field, which has been widely used to study protein-lipid interactions50,51. This allowed us to accumulate hundreds of microsecond data with dozens of simulations (Supplementary Table 2) that reflect the membrane binding frequency of the residues of the SMP domain to the ER-like membrane model and the PM-like membrane model (Fig. 5d and e). We observed that the SMP dimer mostly used the residues located at the two tips to interact with the membrane bilayer in both the ER-like and PM-like membrane models (Fig. 5d and e). The MD simulations also showed that the tip region of SMP dimer inserted into the bilayer to place the lipid binding pocket in close proximity to its cargo lipids (Fig. 5f).Fig. 5Membrane association by the SMP domain is supported by molecular dynamics simulations of E-Syt2 SMP dimer with an ER-like and a PM-like membrane.a, Representative conformations corresponding to the “standing-up” and “lying-down” bound states. b, Representative structure of an ER-like membrane model consisting of $80\%$ POPC:$20\%$ POPE. c, Representative structure of an PM-like membrane model consisting of $85\%$ POPC:$10\%$ POPS:$5\%$ PI[4,5]P2. d, e The membrane binding frequency of SMP residues mapped onto the crystal structure of E-Syt2 (PDB code 4P42) with the ER-like d, and the PM-like membrane e, respectively. The binding frequency is visualized by the thickness of the tube (thinner tube represents lower frequency) and different colors from blue (the lowest frequency), yellow, green, cyan, gray to magenta (the highest frequency). f Atomic model of SMP dimer inserting into the membrane. The depth of the insertion was obtained from the coarse-grained MD simulation. The system was energy minimized and equilibrated using all-atom MD simulation. g–m The free energy landscapes of SMP binding with the ER-like membrane g, SMP binding with the PM-like membrane h, SMP F258A mutant binding with the ER-like membrane i, SMP R256E mutant binding with the PM-like membrane j, SMP Y257A mutant binding with the ER-like membrane k, SMP F258A mutant binding with the PM-like membrane l, and SMP R256E mutant binding with the ER-like membrane m, as a function of the distance between the two ends of the SMP dimer projected to the membrane normal and the number of contacts between SMP and the membrane bilayer. Source data are provided as a Source Data file.
These membrane bindings were further supported by projecting the MD trajectories onto the two-dimensional free energy surfaces as a function of the distance between the two ends of the SMP dimer projected to the membrane normal (dz) and the number of contacts between SMP and the membrane bilayer (Fig. 5a, g and h). Given that the structure of the SMP dimer is symmetrical, the free energy surfaces were, as expected, almost symmetric along dz, indicating that the MD simulations converged well. The free energy surfaces revealed two free energy minima corresponding to the standing-up conformational state of SMP dimer on the membrane surface with either one of its tips interacting with the membrane surface (Fig. 5a, g and h). Collectively, these data confirmed that the SMP dimer prefers to bind perpendicularly to the membrane.
The comparison of the MD results suggested that the membrane binding of SMP was weaker in the uncurved ER-like bilayer model than that in the flat and acidic PM-like bilayer model (Fig. 5g, h). In principle, the MD simulations should allow us to simulate the SMP binding to highly curved vesicles. However, this is very computationally expensive, making it very challenging to get sufficient statistics. Alternatively, to further support our model, we performed five additional sets of CG MD simulations of the Y257A and F258A mutants of E-Syt2 SMP (corresponding to Y228A and F229A in E-Syt1 and I208A in E-Syt3, Fig. 3c and Supplementary Fig. 6) and the R256E mutant of E-Syt2 SMP (corresponding to K227E in E-Syt1 and K207E in E-Syt3, Fig. 3c and Supplementary Fig. 6) in the in the ER-like and PM-like membranes. All of these mutants, which impaired the lipid transfer activity of the SMP domain in vitro (Fig. 3 and Supplementary Fig. 7), had a significant decrease in the membrane binding probability of the SMP tip region (Fig. 5i–m).
The critical roles of the tip regions of SMP dimer in associating with the membranes lead to a plausible scenario in which the SMP dimer acts a static “tunnel” for lipid transfer at tight MCSs (<10 nm in distance). However, this hypothesis was not supported by our explicit solvent all-atom MD simulations (see Methods and Supplementary Table 2), which showed that the solvent-exposed hydrophobic channel in SMP dimer observed in the crystal structure28 was closed due to strong hydrophobic interactions and there was very likely a high energy barrier in the dimer interfacial region to prevent the sliding of the cargo lipid from one end to the other (Supplementary Fig. 9 and Supplementary Movie 1).
## Role of lipid transfer by the SMP domain in autophagosome biogenesis
Finally, we validated the importance of SMP-membrane association in the actions of E-Syts in cells. The hydrophobic hairpins, SMP, C2A, and C2B domains are highly conserved in all three E-Syts10,28. Fluorescence microscopy analyses of E-Syt-expressing cells revealed that E-Syt1 translocates to ER-PM contact sites upon cytosolic Ca2+ elevation via regulation by its C2C domain, whereas E-Syt2 and E-Syt3 are constitutive ER-PM tethers even at resting Ca2+ levels10,22,24. A previous study39 showed that E-Syt2 or E-Syt3 interacts with the class 3 PI3kinase complex (PI3KC3) partner VMP1 at ER-PM contact sites to regulate autophagy-associated PI3P synthesis, which is engaged in autophagosome biogenesis. Overexpression of E-Syt2 or E-Syt3, but not E-Syt1, in HeLa cells significantly increased the number of phagophore and autophagosome marker LC3 puncta even under fed conditions (Fig. 6 and Supplementary Fig. 10a)39.Fig. 6Lipid transfer by E-Syt3 contributes to autophagosome biogenesis.a Confocal images of HeLa cells co-expressing mCherry-LC3 and WT or mutated EGFP-E-Syt3. Scale bar, 10 μm. b Quantification of LC3 puncta per area compared to control. Data are presented as mean ± SEM ($$n = 42$$, 42, 47, 51, 45, and 42 cells from left to right). * $P \leq 0.05$, *** $P \leq 0.001$, ****$P \leq 0.0001$ by one-way ANOVA with Bonferroni’s multiple comparisons test. P values: 2.4 × 10−4 [(EGFP) vs (WT EGFP-E-Syt3)], 34 × 10−4 [(WT EGFP-E-Syt3) vs (EGFP-E-Syt3 ΔSMP)], 8.9 × 10−5 [(WT EGFP-E-Syt3) vs (EGFP-E-Syt3 V148W/I286W)], 0.020 [(WT EGFP-E-Syt3) vs (EGFP-E-Syt3 K207E)], 1.9 × 10−5 [(WT EGFP-E-Syt3) vs (STIM-E-Syt3-EGFP)]. Source data are provided as a Source Data file.
To better understand the role of E-Syt3 in autophagy, we overexpressed E-Syt3 construct lacking the SMP domain (E-Syt3 ΔSMP) or with mutated residues lining the lipid-harboring cavity of the SMP domain (V148W/I286W, corresponding to V169W/L308W in E-Syt1, Fig. 3c). No increase in LC3-positive structures was observed in these cells (Fig. 6). Therefore, the lipid transfer by E-Syt3 contributes to autophagosome biogenesis, which may be explained by the regulation of ER-PM contact site-associated PI3P synthesis. Importantly, when we mutated K207 in E-Syt3 to Glu (E-Syt3 K207E, corresponding to E-Syt1 K227E and E-Syt2 R256E, Fig. 3c and Supplementary Fig. 6), the overexpression of this mutant still increased and stabilized ER-PM contacts similar to WT E-Syt3 but failed to induce the formation of LC3 fluorescence puncta (Fig. 6). We also replaced the hydrophobic hairpin of E-Syt3 with a single transmembrane domain of STIM1 (STIM-E-Syt3) to further test the importance of specific ER localization of E-Syt in its physiological function. Unlike WT E-Syt3, STIM-E-Syt3 had no colocalization with another lipid transporter and ER-PM tether, ORP552–54, and failed to induce the formation of LC3 fluorescence puncta, though it still increased and stabilized ER-PM contacts (Fig. 6 and Supplementary Fig. 10b). The hydrophobic hairpin of E-Syt itself is dispensable for its lipid transfer activity, as the purified His-tagged E-Syt1cyto had comparable lipid transfer and membrane tethering levels as full-length E-Syt1 (Supplementary Fig. 1). These results confirm that an association of the tip region of SMP dimer with the acidic lipids in the PM and the subdomain of ER is required for its lipid transfer activity in cells.
## Discussion
A growing number of SMP domain-containing proteins have been found and reported to mediate non-vesicular lipid transfer at MCSs to control lipid homeostasis in cells8–17,19,21,26,30,32–37,40,41. The ER-PM tethering proteins, E-Syts (tricalbins in yeast), are better characterized members of the SMP family proteins8,10,16,22–24,26–28,30,31,34–41,45. However, it is technically challenging to investigate how E-Syt SMP dimer associates with membranes to extract and unload lipids because they do not stably interact with membranes23,30,31. In this study, we applied a recently developed DNA brick-assisted liposome sorting technique47 to in vitro lipid transfer assays and performed in silico MD simulations to study the mechanisms underlying the membrane association by the SMP domain of E-Syt. Unlike liposomes produced by extrusion or sonication, which have broad size distributions with mean diameters >60 nm, the liposomes coated with DNA bricks can be sorted into different homogeneous populations with mean diameters from 30 to 130 nm. Given that N-terminal hydrophobic hairpin localizes E-Syt to the tubular ER (25–90 nm in diameter)10,25–27,49, our DNA brick-aided lipid transfer system offers a solution for assessing the curvature-dependent activity of the protein across a physiologically relevant curvature range.
Here, we suggest that both the extreme curvature of ER-like liposomes (<50 nm in diameter) and the negatively charged lipids in PM-like liposomes are critical for efficient lipid transfer between them by the E-Syt1 SMP domain alone. The requirement of acidic lipids for lipid transfer by the SMP domain of tricalbin 3 has also been reported in a previous study34. In addition, consistent with our previously proposed shuttle model (Fig. 7a)31, our in vitro data showed that the E-Syt1 SMP domain alone delivers lipids between ER-like and PM-like liposomes without tethering them. For full-length E-Syt1, reconstituting it into larger ER-like liposomes (>50 nm in diameter) reduced the lipid transfer activity in the presence of Ca2+, but the activity was not negligible. We propose that the Ca2+- and membrane-binding C2A domain that is in close proximity to the SMP domain helps it stay at the lowly curved membrane surface for lipid extraction and release (Fig. 7).Fig. 7Model of SMP-mediated lipid transfer at ER-PM contact sites. E-Syt1 is anchored to the tubular ER via its N-terminal hydrophobic hairpin and interacts with the PM by its Ca2+-binding C2C and C2E domain. The tip region of the SMP dimer associates with the subdomain of the tubular ER and acidic-lipid-enriched region in the PM for transferring lipids as a “shuttle” at typical ER-PM contact sites a and as a “screw propeller” b at tight ER-PM contact sites. The Ca2+-binding C2A domain is in close proximity to the SMP domain and binds to either the ER or PM to help the SMP domain stay at the membrane. The Ca2+ ions are shown as small black circles.
As all three E-Syts are similar in regard to the corresponding domains, we inspected the structure of the SMP domain of E-Syt228 and found a small basic patch at its tip region. An important finding of the analyses of this region is that the SMP domain associates with the acidic lipids in the PM through the positively charged residues in this basic patch, which is not expected to be strong enough for a stable interaction (Fig. 7). For the SMP-ER association, it is plausible that the ER membrane is also recognized by the tip region of the SMP domain (Fig. 7). This was found to be the case in our experimental and in silico analyses, as the curvature-facilitated lipid transfer by the SMP domain and its membrane binding frequency to the ER-like membrane model were reduced when the large hydrophobic residues at its tip region were substituted with Ala. According to our MD simulations (Fig. 5f), we also propose that similar to the model of Mdm3455, the insertion of the tip region of the E-Syt SMP domain into the bilayer allows the lipid binding pocket, which is the potential opening of the hydrophobic channel of the SMP domain, to be close to the lipid cargos for lipid uptake and/or release.
In summary, together with previous studies, our results lead to a refined model for SMP-mediated lipid transfer, which has important implications for understanding the regulation of lipid homeostasis at MCSs (Fig. 7). At typical ER-PM contact sites (>10 nm in distance), the E-Syt SMP domain, together with the Ca2+-bound C2A and C2B domains, shuttles between the tubular ER and the acidic-lipid-enriched PM, and lipids enter and exit via its tip region (Fig. 7a). It is plausible that with the help of C2A domain lipid transfer with low efficiency still occurs at the region where the ER tubule is not extremely curved. This proposed model in which SMP dimer is oriented nearly perpendicular to the ER and PM is also supported by the observed architecture of tricalbin 3 in yeast by cryo-electron tomography (cryo-ET), displaying that its SMP dimer and C2 domains are sequentially arranged in a linear fashion along the axis of its rod-like structure and connect the ER and PM perpendicularly27. The shuttling lipid transfer proteins have been suggested to play roles in modulating lipid signaling56. Interestingly, the involvement of the acidic-lipid-enriched region in the PM in lipid transfer is compatible with the essential roles of E-Syts in controlling PS exposure and PIP signaling, including autophagy-associated PI3P synthesis in proximity to ER-PM contact sites30,35,39–41. We further demonstrate that this PI3P regulation, which contributes to autophagosome formation, requires lipid harboring and membrane association of the SMP domain. The lipid transfer by E-Syts may also be facilitated by autophagy protein VMP1, which is a scramblase and interacts with E-Syts39,57,58.
At tight ER-PM contact sites (<10 nm in distance), we propose that the E-Syt SMP dimer acts as a “screw propeller” (Fig. 7b) or a static “tunnel” to transfer lipids between apposed bilayers. According to our in silico MD simulations, as the lipid is difficult to pass through the SMP dimer interface, the “screw propeller” model is preferred, in which one SMP molecule extracts lipids and flips vertically to deliver them to the target membrane. This model is also supported by the results that the membrane association of the SMP domain is weak and unstable. In addition, our in vitro data here provide evidence that the extreme membrane curvature, which is a feature of the spherical ER membrane peaks (<30 nm in diameter) formed in a C2 domain-dependent manner at these regions26,27,45, accelerates lipid transfer by the SMP domain. Further elucidating the timescale of membrane binding of SMP domain coupled with lipid extraction by all-atom MD simulations will be important. Moreover, identification of potential partners of E-Syts and tricalbins for regulating the directionality of bulk lipid transfer deserve further investigation, as at cortical ER peaks in yeast the tricalbin SMP domain transfers lipids to repair PM damage upon heat shock26. In mammals, E-Syts together with Sec22b-Stx1 promote PM expansion for neuronal development37. The ER-PM distance and ER curvature during this process remain to be further explored. Finally, our DNA brick-assisted sorting of vesicles reconstituted with high-molecular-weight proteins and DNA brick-aided lipid transfer system can be adapted to gain further insights into other lipid transfer proteins or enzymes functioning at MCSs.
## Reagents
Reagents were obtained from the following sources: Ni Sepharose 6 Fast Flow (Cytiva, 17531802), tris(2-carboxyethyl)phosphine (TCEP, 75259) (Thermo), OptiprepTM Density Gradient Medium ($60\%$, w/v) (Sigma-Aldrich, D1556), glycerol (Sigma-Aldrich, G9012), Anapoe-X-100 (Anatrace, 9002-93-1). All DNA oligonucleotides were purchased from Sangon Biotech. All lipids were obtained from Avanti Polar Lipids: 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine (POPC, 850457); 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphoethanolamine (POPE, 850757); 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine (POPS, 840034); L-α-phosphatidylinositol- 4,5-bisphosphate [PI[4,5]P2, 840046]; 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-(7-nitro-2-1,3-benzoxadiazol-4-yl) (NBD-PE, 810144); 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine-N-(lissamine rhodamine B sulfonyl) (Rhodamine-PE, 810150); 1,2-dioleoyl-sn-glycero-3-[(N-(5-amino-1-carboxypentyl) iminodiacetic acid) succinyl] [DGS-NTA(Ni), 790404].
## Plasmids
The plasmids encoding E-Syt1cyto (a.a. 93–1104, pCMV6-AN-His), SMP-C2AB (a.a. 93-634, pCMV6-AN-His), SMP-C2AB K227E (a.a. 93–634, pCMV6-AN-His), SMP of E-Syt1 (a.a. 134–327, pET-28a), EGFP-E-Syt3 (a.a. 1–886, pEGFP-C1), mCherry-ORP5 (a.a. 1–879, pmCherry-C1) and mCherry-LC3 (pmCherry-C1) were described previously10,30,31,52. The region coding the full-length E-Syt1 (a.a. 1–1104) was cloned into the pCMV6-AN-His vector using the AscI and NotI sites. The region coding the SMP of E-Syt2 (a.a. 162–355) and the SMP of E-Syt3 (a.a. 113–304) were cloned into the pET-28a vector using the NheI and XhoI sites. Point mutations were introduced by site-directed mutagenesis. STIM-E-Syt3 (signal peptide and transmembrane domain of STIM1 and a.a. 71-886 of E-Syt3) was generated by overlap PCR and cloned into the pEGFP-N1 vector.
## Protein expression and purification
Full-length E-Syt1 was expressed in Expi293 cells (Thermo Fisher Scientific, A14527). Cells were harvested by centrifugation, washed twice with buffer A [25 mM Hepes, pH 7.4, 300 mM NaCl, 1× complete EDTA-free protease inhibitor cocktail (Roche), 0.5 mM TCEP], and lysed by three freeze-thaw cycles using liquid nitrogen. Membranes were pelleted by centrifugation at 200,000 × g for 1 h at 4 °C using a SW 41 rotor (Beckman Coulter) and subsequently solubilized in $2.5\%$ (w/v) Anapoe X-100 in buffer A for 1 h at 4 °C. The extract was centrifuged at 200,000 × g for 1 h at 4 °C using a SW 41 rotor and the protein was purified from the supernatant by an Ni-NTA column. After elution using $0.1\%$ (w/v) Anapoe X-100 and 200 mM imidazole in buffer A, the protein was passed through a desalting column with $0.1\%$ (w/v) Anapoe X-100 in buffer A to remove the imidazole. The purified E-Syt1 was mixed with liposomes at a protein to lipid ratio of 1:500 at 4 °C for 1 h with gentle shaking. The final Anapoe X-100 concentration was added to $0.1\%$ (w/v) and lipid concentration was 1 mM. The reconstitution of proteins into liposomes was achieved by removing the detergent with bio-beads SM-2 resin (Bio-Rad).
Soluble fragments of E-Syts were expressed in Expi293 cells or Rosetta (DE3) (Biomed) E. coli cells and purified as described previously30. Briefly, cells were harvested and lysed in buffer A by three freeze-thaw cycles using liquid nitrogen (for Expi293 cells) or by sonication (for bacteria). The suspension was clarified by centrifugation at 200,000 × g for 1 h at 4 °C using a TYPE 45 Ti rotor. The protein was isolated by a Ni-NTA column and further purified by gel filtration in buffer A. Fractions containing E-Syt1 fragments were pooled and concentrated.
## Liposome preparation
ER-like donor liposomes were composed as follows: 87:10:1.5:1.5 mole percent of POPC: POPE: NBD-PE: Rhodamine-PE or 77:10:1.5:1.5:10 mole percent of POPC: POPE: NBD-PE: Rhodamine-PE: DGS-NTA(Ni). PM-like acceptor liposomes were composed as follows: 85:10:5 mole percent of POPC: POPS: PI[4,5]P2 or 70:20:10 mole percent of POPC: POPS: PI[4,5]P2. ER-like acceptor liposomes were composed as follows: 90:10 mole percent of POPC: POPE. PM-like donor liposomes were composed as follows: 82:10:5:1.5:1.5 mole percent of POPC: POPS: PI[4,5]P2: NBD-PE: Rhodamine-PE. Liposome preparation was performed as described previously30. Briefly, lipid mixtures were dried with N2 to form a film. Buffer A was added to the tube to rehydrate the lipid films, and the suspension underwent ten freeze-thawing cycles using liquid nitrogen. Extruded liposomes were formed by extrusion through polycarbonate filters with a pore size of 30, 100, 400, or 800 nm (Avanti Polar Lipids). Sonicated liposomes were formed by sonication for 10 min with 1 s sonication on and 1 s pulse using a probe sonicator.
## DNA brick preparation
The DNA brick was assembled as described previously47. Briefly, the unmodified and cholesterol-modified oligonucleotides were mixed in buffer B (25 mM Hepes, pH 7.0, 400 mM KCl, 10 mM MgCl2) and assembled to form DNA bricks using thermal annealing from 95 to 4 °C (held at 95, 65, 50, 42, 37, 22, and 4 °C for 5 min each). The assembled DNA bricks were placed on top of a 5–$20\%$ glycerol gradient. The sample-loaded density medium was centrifuged at 205,000 × g for 4.5 h at 4 °C using a TLS 55 rotor (Beckman Coulter) and analyzed by agarose gel electrophoresis. Fractions containing well-folded DNA bricks were combined and concentrated. The purified products were stored at −20 °C.
## DNA-brick-assisted liposome sorting
The DNA bricks and liposomes or proteoliposomes were mixed at a DNA brick to lipid ratio of 1:375 and incubated at 4 °C over night with gentle shaking. The DNA brick-coated liposomes or proteoliposomes were subjected to centrifugation in iodixanol gradients. A quasi-linear gradient containing 0–$18\%$ (w/v) iodixanol was loaded on top of sample containing $22.5\%$ iodixanol. Gradients were then centrifuged at 215,000 × g for 4.5 h at 4 °C using a SW 55 Ti rotor (Beckman Coulter). After ultracentrifugation, the content of a tube was fractioned from top to bottom. The fractions were examined by negative-staining TEM and those containing liposomes or proteoliposomes were treated with DNase I (Thermo) to digest the DNA bricks. To remove iodixanol, the liposomes or proteoliposomes were concentrated by centrifugation at 10,000 × g at 4 °C on Amicon filtration units with 30 kD NMWL, and the concentrated samples were diluted in buffer B and concentrated again for a total of five times. To determine the protein to lipid ratio, the concentration of protein in each fraction was assessed by the density of the corresponding protein band on SDS-PAGE gel stained with Coomassie Blue using BSA concentration as standards, and lipid concentrations were measured by Rhodamine-PE absorbance at 574 nm, which was $1.5\%$ of total lipids.
## Transmission electron microscopy
To prepare negatively stained liposomes or proteolipsomes with or without DNA bricks, a drop of sample (5 μL) was deposited on a glow discharged formvar/carbon-coated copper grid and incubated for 1–3 min at room temperature. Fluid was then blotted away. The grid was immediately stained for 3 min with $2\%$ (w/v) uranyl formate. Grids were examined using a JEOL JEM-1400 Plus microscope (acceleration voltage: 80 kV). Images were acquired by an Advanced Microscopy Technologies bottom-mount 4k × 3k charge-coupled device camera using the AMT Image Capture Engine. Liposome sizes were measured from electron micrographs using ImageJ (NIH) as described previously47.
## Lipid transfer assays
All in vitro lipid transfer assays were performed as described previously35. Briefly, reactions were performed in 100 μL volumes. The final lipid concentration was 0.5 mM with donor and acceptor liposomes added at a 1:1 ratio. The reaction buffer was 25 mM Hepes, pH 7.4, 150 mM NaCl, 0.5 mM TCEP. Reactions were initiated by the addition of proteins or proteoliposomes to the mixtures (protein: lipid ratio of 1: 1000) in a 96-well plate (Corning). The fluorescence intensity of NBD was monitored at an excitation of 460 nm and emission of 538 nm every 10 or 30 s over 10 or 30 min at room temperature or 37 °C using a Cytation 5 Imaging Reader (BioTek). All data were corrected by setting the data point at 0 min to zero and subtracting the baseline values obtained at 0 min. The data were expressed as a percentage of the maximum fluorescence determined after adding 10 μL of $2.5\%$ dodecylmaltoside (Avanti Polar Lipids) to the reactions after 10 or 30 min. The slope of the initial linear portion (after the rapid uptake phase) of the lipid transfer curve was calculated to determine the initial rate.
## Liposome tethering assays
Liposome tethering assays were performed as described previously30. Briefly, the reaction conditions were same as the lipid transfer assays. The reactions were initiated by the addition of proteins or proteoliposomes to the mixture of liposomes in a 96-well plate (Corning). The absorbance at 405 nm was measured to assess turbidity every 10 s over 10 min at room temperature using a Cytation 5 Imaging Reader (BioTek). Data were expressed as absolute absorbance values subtracted by the absorbance at 0 min.
## Lipid harboring assays
Purified WT or mutated SMP-C2AB of E-Syt1 (19 μL at 40 μM) was mixed with or without 1 μL NBD-PE or a mixture of NBD-PE and POPC (1:10 ratio) in methanol and incubated at 4 °C for 1 h. The fluorescence of NBD-PE and Coomassie-stained proteins were visualized on a native PAGE gel.
## Liposome sedimentation assays
A total of 2 μM protein was incubated with liposomes (protein to lipid ratio of 1: 500) in buffer containing 25 mM Hepes, pH 7.4, 150 mM NaCl, 0.5 mM TCEP for 1 h at room temperature, followed by ultracentrifugation at 16,100 × g for 1 h at 4 °C. The membrane pellets were re-suspended in the same buffer. Equal volumes of supernatants and pellets were run on SDS-PAGE and stained with Coomassie Blue.
## Molecular dynamics simulations
The crystal structure of E-Syt228 was used to model the dimeric SMP (SMP2). The coarse-grained (CG) model of SMP2 used the most recent development version of the Martini 3 force field50. The CG model of SMP2 was prepared using the martinize.py program and subsequently embedded in an ER-like membrane model consisting of $80\%$ POPC:$20\%$ POPE and a PM-like model consisting of $85\%$ POPC:$10\%$ POPS:$5\%$ PI[4,5]P2 with dimensions of 12 × 12 × 18 nm3 (Table S2) using the INSANE protocol59. The mutants Y257A, F258A and R256E were prepared using martinize.py. The ElNeDyn elastic-network approach was employed to restrain the protein structure, using a force constant of 1000 kJ/mol/nm2 and the lower and upper limits of the cutoff distance of 0.5 and 0.9 nm, respectively. The systems were solvated using a CG Martini water model and neutralized by adding NaCl at a concentration of 0.15 M to mimic physiological conditions. To alleviate the dependence on the initial orientation of SMP, the principal axis of the SMP dimer was set to be either parallel or vertical to the normal of the lipid bilayers and 2.5 nm away from the membrane surface, resulting in two different initial conformations corresponding to the lying-down and standing-up models (Table S2). Note that a model without the membrane was also built as a control.
First, the CG system was minimized for 5000 steps with the steepest descent method and subsequently equilibrated by following the standard CHARMMGUI equilibrium protocol60. Finally, each production run was performed for 10 μs in the semi-isotropic NPT ensemble using a time step of 20 fs. The temperature of the system was kept at 310 K with the velocity rescaling thermostat. The pressure was kept at 1 bar using the Parrinello-Rahman barostat with a compressibility of 3 × 10−4 bar−1 and a coupling constant of 12 ps.
For the all-atom (AA) MD simulations, we used the CHARMM36m force field for the protein and lipids and prepared the systems using the CHARMM-GUI server. To explore lipid sliding along the SMP domain, we replaced one of the lipids in the crystal structure of E-Syt2 with a POPE lipid and removed the other lipids. To model the membrane binding conformation of the SMP domain, we inserted the SMP dimer in a mixed $80\%$ POPC:$20\%$ POPE bilayer based on the insertion depth obtained from the CG simulations.
The box size was 5.9 × 5.9 × 10.9 nm3 and 12.4 × 12.4 × 14.9 nm3 for the lipid sliding model and SMP insertion model, respectively. The systems were solvated using the TIP3P solvent model. Periodic boundary conditions were employed, and the particle-mesh Ewald method was used for the treatment of long-range electrostatic interactions. The simulations were conducted at a constant semi-isotropic pressure of 1 atm and a temperature of 310 K using the Parrinello–Rahman barostat and the Nosé–Hoover thermostat, respectively.
To check whether the loaded lipid can move along the hydrophobic channel of the SMP dimer in a reasonable MD time, we added an additional ratchet-and-pawl-like potential to the center of mass of the headgroup of the bound POPE lipid with a force constant of 1 kJ/(mol nm2) using the adiabatic bias molecular dynamics (ABMD) method61,62. The biasing potential is zero when the lipid moves to the other tip but provides a penalty as it moves back, so that we were able to accelerate its motion from one tip to the other. The ABMD simulation was run for 50 ns.
All simulations were performed with GROMACS (version 2021.5). Trajectories were saved every 1 ns. The results were analyzed with PLUMED63, VMD64 and in-house scripts.
## Cell culture and transfection
HeLa cells (ATCC, CCL-2) were cultured in Dulbecco’s modified *Eagle medium* (DMEM, Gibco) supplemented with 100 U/ml penicillin, 0.1 mg/ml streptomycin, and $10\%$ fetal bovine serum (FBS, Biological Industries) at 37 °C under $5\%$ CO2. Cells were transfected with plasmids using Polyethylenimine Linear (PEI, Yeasen Biotechnology) according to the manufacturer’s instructions.
## Fluorescence microscopy
Cells were grown to $60\%$ confluence on a 14-mm coverslip, washed twice with PBS, and fixed with freshly prepared $4\%$ formaldehyde at 37 °C for 15 min. Fixed cells were imaged using a laser scanning confocal microscope (Zeiss LSM 800 with Airyscan) with a 63× oil-immersion objective. EGFP was excited by a 488 nm laser and fluorescence was detected within the wavelength range of 490–575 nm. RFP was excited by a 568 nm laser and fluorescence was detected within the wavelength range of 570–700 nm. The number of LC3 puncta was determined with ImageJ (NIH) and analyzed by manually drawing regions at edges of cells.
## Statistical analysis
No statistical method was used to predetermine sample size. For fluorescence microscopy using cultured cells, values were obtained from three independent experiments. Data were compared by either the two-tailed Student’s t-test or the one-way or two-way ANOVA with Bonferroni’s or Sidak’s multiple comparisons test as appropriate with Prism 8 (GraphPad software).
## Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Description of Additional Supplementary Files Supplementary Movie 1 Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37202-8.
## Source data
Source Data
## Peer review information
Nature Communications thanks Timothy Levine and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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|
---
title: Telomere length associates with chronological age and mortality across racially
diverse pulmonary fibrosis cohorts
authors:
- Ayodeji Adegunsoye
- Chad A. Newton
- Justin M. Oldham
- Brett Ley
- Cathryn T. Lee
- Angela L. Linderholm
- Jonathan H. Chung
- Nicole Garcia
- Da Zhang
- Rekha Vij
- Robert Guzy
- Renea Jablonski
- Remzi Bag
- Rebecca S. Voogt
- Shwu-Fan Ma
- Anne I. Sperling
- Ganesh Raghu
- Fernando J. Martinez
- Mary E. Strek
- Paul J. Wolters
- Christine Kim Garcia
- Brandon L. Pierce
- Imre Noth
journal: Nature Communications
year: 2023
pmcid: PMC10023792
doi: 10.1038/s41467-023-37193-6
license: CC BY 4.0
---
# Telomere length associates with chronological age and mortality across racially diverse pulmonary fibrosis cohorts
## Abstract
Pulmonary fibrosis (PF) is characterized by profound scarring and poor survival. We investigated the association of leukocyte telomere length (LTL) with chronological age and mortality across racially diverse PF cohorts. LTL measurements among participants with PF stratified by race/ethnicity were assessed in relation to age and all-cause mortality, and compared to controls. Generalized linear models were used to evaluate the age-LTL relationship, Cox proportional hazards models were used for hazard ratio estimation, and the Cochran–Armitage test was used to assess quartiles of LTL. Standardized LTL shortened with increasing chronological age; this association in controls was strengthened in PF (R = −0.28; $P \leq 0.0001$). In PF, age- and sex-adjusted LTL below the median consistently predicted worse mortality across all racial groups (White, HR = 2.21, $95\%$ CI = 1.79–2.72; Black, HR = 2.22, $95\%$ CI = 1.05–4.66; Hispanic, HR = 3.40, $95\%$ CI = 1.88–6.14; and Asian, HR = 2.11, $95\%$ CI = 0.55–8.23). LTL associates uniformly with chronological age and is a biomarker predictive of mortality in PF across racial groups.
The association of telomere length with age and mortality across racially diverse pulmonary fibrosis populations is unknown. Here, the authors show that leukocyte telomere length associates with chronologic age and is predictive of mortality in pulmonary fibrosis across racial groups.
## Introduction
Pulmonary fibrosis (PF) is a complex group of lung diseases characterized by scarring in the interstitium of the lung; median survival in its most common subtype, idiopathic PF (IPF), is only 3–5years1–3. While numerous investigations over the past decade have illuminated the pathophysiology of PF, much remains unknown about the accuracy of current prognostic indices across individuals from different racial or ethnic groups. An improved understanding of the prognostic value of PF biomarkers across racial and ethnic groups would enhance pharmacotherapeutic precision and inform targeted management within the spectrum of diagnoses among patients of diverse backgrounds.
Recently discovered genomic biomarkers in the peripheral blood represent a promising avenue toward achieving precision medicine in patients with PF4. Short leukocyte telomere length (LTL), a marker of cellular senescence, is associated with germline mutations in rare and common gene variants involved in telomere biology and linked to PF incidence as well as mortality5–9. LTL expectantly shortens with increasing age, but PF patients are disproportionately enriched for short LTL10–12. However, the current understanding of the impact of genetic and genomic biomarkers on patient outcomes in PF is confounded by the lack of inclusion of racial and ethnic minorities in most genetic studies to date13. In particular, studies linking LTL to clinical outcomes in PF have not been performed in racially diverse cohorts. Due to these shortcomings, the optimal prognostic value of results derived from these genomic studies is unknown in diverse PF populations, as is whether telomere shortening is uniformly associated with increasing age in PF across racial/ethnic groups.
The primary objective of this study was to comparatively analyze differences in LTL between racial/ethnic groups within PF and within control populations. The secondary objectives were (a) to investigate whether LTL is associated with chronological age across racially and ethnically diverse PF cohorts and (b) to assess the relationship between LTL and mortality risk across ethnic groups with PF. We hypothesized that shorter telomere length is associated with increased age and greater mortality across diverse racial groups, underscoring the importance of this biomarker in all patients with PF.
## Results
A total of 7854 individuals were included in the study, 2046 participants with PF and 5808 HRS control participants. The number of participants with PF and control individuals analyzed across the different racial/ethnic groups is presented in Table 1 (characteristics of participants with PF stratified by enrollment site can be found in Supplementary Table 1 in the Supplement). The mean ± SD age was 65 ± 11 years for PF participants, and 69 ± 10 years for HRS controls. Black participants had the youngest mean age across all sites, and age at PF diagnosis was lower than in White participants (CHICAGO 54 vs. 65 years; $P \leq 0.001$; CALIFORNIA 60 vs. 68 years; $P \leq 0.001$; TEXAS 53 vs. 64 years; $P \leq 0.001$; IPFNet 61 vs. 67 years; $$P \leq 0.25$$; Supplementary Fig. 1). Male participants were $54\%$ of those with PF and $41\%$ of HRS controls. The mean BMI was high in participants with PF (28 ± 6 kg/m2) and control participants (29 ± 6 kg/m2). Tobacco use was common in PF ($58\%$) and control participants ($57\%$). The median follow-up time for PF participants was 31 (IQR 14–60) months. Table 1Pulmonary fibrosis (PF) and health retirement survey (HRS) cohorts stratified by racial ancestryCharacteristicsaWhiteBlackHispanicAsianP value#PF ($$n = 2046$$)($$n = 1613$$)($$n = 162$$)($$n = 187$$)($$n = 70$$) Age, years66.0 (10.4)54.9 (12.2)59.7 (12.8)65.7 (12.9)<0.001 Male918 (56.9)44 (27.2)90 (48.1)39 (55.7)<0.001 Ever Smoker978 (60.8)70 (43.2)96 (51.3)26 (37.7)<0.001 Body Mass Index29.2 (5.7)29.0 (6.5)29.8 (6.7)25.9 (5.1)0.001 Lung function FVC (% predicted)68.4 (19.1)59.4 (18.4)62.0 (19.2)66.7 (18.2)<0.001 FEV1 (% predicted)74.1 (19.7)62.9 (20.5)65.7 (19.6)74.4 (25.0)<0.001 DLCO (% predicted)48.4 (19.2)41.8 (18.9)45.0 (17.3)45.3 (17.1)<0.001 PF sub-category IPF653 (40.5)13 (8.0)49 (26.2)20 (28.6)<0.001 IPAF153 (9.5)30 (18.5)15 (8.0)8 (11.4)0.006 CTD-ILD200 (12.4)89 (54.9)46 (24.6)14 (20.0)<0.001 FHP359 (22.3)14 (8.6)34 (18.2)11 (15.7)0.001 Unclassifiable/others248 (15.4)16 (9.9)43 (23.0)17 (24.3)<0.001HRS ($$n = 5808$$)($$n = 4319$$)($$n = 779$$)($$n = 614$$)($$n = 96$$) Age, years70.2 (10.2)67.8 (9.9)66.0 (10.5)65.5 (11.0)<0.001 Male1817 (42.1)280 (35.9)235 (38.3)39 (40.6)0.007 Ever smoker2493 (58.0)442 (57.1)320 (52.7)40 (41.7)0.001 Body mass index27.9 (5.7)29.8 (6.6)29.0 (5.6)26.7 (5.9)<0.001aCategorical variables presented as n (%); continuous variables presented as means (SD).#P value for chi-squared (categorical data) or one-way ANOVA (continuous data) comparing all four main racial groups. Patients with mixed or other racial ancestry not depicted above, $$n = 14$.$ Exception for participants in PF cohort; smoking status, $$n = 2041$$; Body mass index = 1505; FVC forced vital capacity, $$n = 2015$$; FEV1 forced expiratory volume in the first second, $$n = 1254$$; DLCO diffusing capacity of the lungs, $$n = 1959$.$ ILD interstitial lung disease; IPF idiopathic pulmonary fibrosis, $$n = 735$$, IPAF interstitial pneumonia with autoimmune features, $$n = 207$$; CTD-ILD connective tissue disease associated-ILD, $$n = 349$$; FHP fibrotic hypersensitivity pneumonitis, $$n = 422$$; unclassifiable/other ILD, $$n = 333$.$
## Telomere length across racial/ethnic groups
Among participants with PF, LTL differed across racial groups and was longest in Black subjects. The mean standardized LTL at PF diagnosis was longer in Black participants 0.37 ± 0.49 than in White participants −0.06 ± 0.47 in the pooled PF cohort ($P \leq 0.0005$), and when stratified by participant recruitment site. Among HRS control participants, mean standardized LTL was also longer in Black participants 0.08 ± 0.48 compared to White participants −0.04 ± 0.49 ($P \leq 0.0005$). The magnitude of this difference in LTL across racial groups was 3.6-fold larger in the PF cohort than in controls (Fig. 1, Supplementary Table 2). Among the control cohort, the odds of having LTL below the median was lower in Black, Hispanic, and Asian subjects (OR = 0.7, $95\%$ CI = 0.6–0.8, $P \leq 0.001$; OR = 0.5, $95\%$ CI = 0.5–0.6, $P \leq 0.001$; and OR = 0.6, $95\%$ CI = 0.4–0.9, $$P \leq 0.015$$, respectively) when compared to White subjects. Similarly, among the PF cohort, the odds of having LTL below the median was lower in Black and Hispanic subjects (OR = 0.2, $95\%$ CI = 0.2–0.4, $P \leq 0.001$; and OR = 0.5, $95\%$ CI = 0.4–0.7, $P \leq 0.001$, respectively) but not different in Asian subjects (OR = 0.9, $95\%$ CI = 0.5–1.4, $$P \leq 0.55$$) when compared to White subjects (Fig. 1). The difference in mean LTL between Black and White subjects with PF was diminished in propensity-score matched analyses (0.37 ± 0.49 vs. 0.33 ± 0.56, respectively) (Supplementary Fig. 2). When assessing quartiles of standardized LTL stratified by race/ethnicity, the mean LTL uniformly increased from the lowest quartile (Q1) to the highest quartile (Q4) across all racial groups(Supplementary Table 3).Fig. 1Mean observed minus expected (O–E; age and gender-adjusted) leukocyte telomere length (TL) is longest in Black subjects with pulmonary fibrosis (PF).Study cohort stratified according to White(W), Black(Bl), Hispanic(H), and Asian(As); A CHICAGO cohort, (W, $$n = 332$$; Bl, $$n = 84$$; H, $$n = 35$$; and As, $$n = 8$$); mean TL (relative T/S): W, Bl, H, and As = 1.38(SD 0.27), 1.62(SD 0.23), 1.51(SD 0.34), and 1.48(0.21), which correspond to mean age and gender-adjusted TL(O–E) of −0.09(0.43), 0.34(0.46), 0.24(0.59), and −0.13(0.53), respectively. B CALIFORNIA cohort, (W, $$n = 499$$; Bl, $$n = 21$$; H, $$n = 70$$; and As, $$n = 36$$); mean TL, base pairs(bp): W, Bl, H, and As = 6058bp(SD 650), 6267bp(SD 679), 6206bp(SD 662), and 6255bp(SD 682), which correspond to mean age and gender-adjusted TL(O–E) of −0.06(0.49), 0.32(0.56), 0.28(0.49), and −0.08(0.58), respectively. C TEXAS cohort, (W, $$n = 527$$; Bl, $$n = 55$$; H, $$n = 68$$; and As, $$n = 19$$); mean TL (relative T/S): W, Bl, H, and As = 1.29(SD 0.28), 1.50(SD 0.26), 1.41(SD 0.29) and 1.37(0.34), which correspond to mean age and gender-adjusted TL(O–E) of −0.07(0.46), 0.46(0.51), 0.14(0.57), and 0.16(0.55), respectively. D IPFNet cohort, (W, $$n = 236$$; Bl, $$n = 2$$; H, $$n = 14$$; and As, $$n = 7$$); mean TL(relative T/S): W, Bl, H, and As = 1.11(SD 0.21), 1.35(SD 0.43), 1.17(SD 0.23), and 1.19(0.18), which correspond to mean age and gender-adjusted TL(O–E) of 0.013(SD 0.50), 0.27(SD 0.41), −0.13(SD 0.33), and −0.25 (0.60), respectively. E Pooled PF cohort, (W, $$n = 1611$$; Bl, $$n = 162$$; H, $$n = 183$$; and As, $$n = 70$$); mean age and gender-adjusted TL(O–E) of −0.06(0.47), 0.37(0.49), 0.19(0.54), and −0.04(0.57), respectively. F HRS cohort, (W, $$n = 4319$$; Bl, $$n = 779$$; H, $$n = 614$$; and As, $$n = 96$$); mean TL(relative T/S) W, Bl, H, and As = 1.34(SD 0.65), 1.57(SD 1.11), 1.41 (SD 0.59), and 1.39(0.63), which correspond to mean age and gender-adjusted TL(O–E) of −0.04(0.49), 0.08(0.48), 0.16(0.51), and 0.19(0.53), respectively. Thick short black lines show the median for each subgroup. The black dotted line shows the median TL for each cohort group; the blue dotted lines show approximate age-adjusted prediction bands in percentiles for each cohort. Group comparisons between white subjects (#) and other racial subgroups were conducted using the student’s T-test; ***$P \leq 0.0005$; NS not significant (P ≥ 0.05).
## Sex, disease subtypes, and telomere length
The mean unadjusted LTL was consistently shorter in males than females across all PF cohorts and HRS control participants(Supplementary Table 4). This sex disparity was also uniformly observed across all racial/ethnic groups assessed(Supplementary Table 4).
Subtypes of PF differed in their baseline demographics and in their standardized LTL measurements after adjusting for age and sex (Supplementary Table 5). Subjects with IPF were older (median age 68 years) and had the shortest median LTL −0.20 (IQR −0.49–0.08) (Fig. 2). Comparatively, subjects with interstitial pneumonia with autoimmune features (median age 63 years; median LTL −0.02, IQR −0.29–0.32), fibrotic hypersensitivity pneumonitis (median age 65 years; median LTL 0.06, IQR −0.24–0.36), connective tissue disease-related ILD (median age 57 years; median LTL 0.28, IQR −0.08–0.69), and other ILDs (median age 61 years; median LTL 0.06, IQR −0.17–0.47) were younger and had longer LTL ($P \leq 0.0005$). Observed LTL measurements for participants with the unclassifiable PF subtype (median age 70 years; median LTL −0.13, IQR −0.42–0.17) did not differ from those with IPF ($$P \leq 0.20$$) (Fig. 2). Across PF subtypes, White subjects had the shortest LTL (Supplementary Tables 6–8).Fig. 2Median standardized leukocyte telomere length is shortest in subjects with idiopathic PF and longest in subjects with connective tissue disease-related interstitial lung disease (CTD-ILD), and other ILDs. Patients with pulmonary fibrosis (PF) stratified by diagnostic subgroup and median age of subgroup (gray); idiopathic pulmonary fibrosis (IPF, $$n = 735$$), unclassifiable (UNCLASS, $$n = 260$$), interstitial pneumonia with autoimmune features (IPAF, $$n = 207$$), fibrotic hypersensitivity pneumonitis (FHP, $$n = 422$$), CTD-ILD, $$n = 349$$, and other ILDs (Others, $$n = 73$$). All telomere lengths depicted are observed minus expected (O–E; age and gender-adjusted). The notched colored box shows the interquartile range (25th to 75th percentile), the horizontal thick black line indicates the median, the vertical upper and lower whiskers represent values outside the middle $50\%$ (the upper $25\%$ of values and the lower $25\%$ of values, respectively), the whisker boundaries represent the maximum and minimum values, and the black dots represent outlier values. Boxplot notch displays the confidence interval around the median based on the median ± 1.58 × IQR/sqrt(n). Group comparisons between IPF (#) and other diagnostic subgroups were conducted using Mood’s median test; **$P \leq 0.0005$; NS not significant (P ≥ 0.05).
## Telomere length and chronological age
With increasing age, the unadjusted LTL decreased in participants with PF at all recruitment sites and among HRS control subjects (Supplementary Tables 7–9). Chronological age had a negative correlation with LTL across diverse racial groups in HRS control subjects and PF participants (Fig. 3). Assessment of LTL quartiles showed an expected increase in mean LTL measurements from the first (Q1) to the fourth quartile (Q4) consistently across age categories stratified by 5-year epochs (Fig. 4A). However, participants with PF had wider IQR for all age categories than the HRS control participants (Fig. 4A).Fig. 3Correlation of leukocyte telomere length (TL) with age. TL demonstrates a nonlinear negative correlation with age across diverse racial groups in healthy subjects A Whites; B Blacks; C Hispanics; and D Asians; that is altered in subjects with pulmonary fibrosis E Whites; F Blacks; G Hispanics; and H Asians. Statistical test: Restricted cubic spline regression of TL on age across racial/ethnic groups. Model goodness of fit indicated by R (correlation coefficient), RMSE (root mean square error) of restricted cubic spline (solid line), and P value reported for the respective populations. Lighter colored band indicates $95\%$ confidence intervals; vertical bars on the horizontal scale indicate individual observations. Fig. 4Standardized leukocyte telomere length (TL) measured by qPCR demonstrates wider interquartile range variation in pulmonary fibrosis (PF) and decreases with increasing age. A Age-stratified mean values of TL measurements increase within quartiles (Q) from the first quartile (Q1) to the fourth quartile (Q4) for 2046 subjects with PF and 5808 control subjects. Median TL for each race (White red, Black gray, Hispanic orange, and Asian green) stratified by sex (male = squares, female = triangles) among the B Control population (HRS); and C PF population); statistical test: generalized linear regression of sex-stratified median TL across racial/ethnic groups. R² (the coefficient of determination), RMSE (root mean squared error), and P value reported for each subgroup. D Shorter TL below the median (TL50) has greater prevalence with increasing age across all racial groups in the HRS control population (dashed lines), but this association was stronger in White and Black subjects with PF (solid lines). Subjects with PF (White $$n = 1613$$, Black, $$n = 162$$, Hispanic, $$n = 187$$, Asian, $$n = 70$$). Control subjects (White $$n = 4319$$, Black, $$n = 779$$, Hispanic, $$n = 614$$, Asian, $$n = 96$$). Other racial groups [$$n = 14$$] are not included in the graphs above. TL depicted are observed minus expected (O–E; age and gender-adjusted). Purple dotted line = fitted values for females and black dashed line = fitted values for males.
Standardized LTL was negatively associated with chronological age across White, Black, Hispanic, and Asian HRS control participants (R = −0.13, $95\%$ CI = −0.16 to −0.10; R = −0.15, $95\%$ CI = −0.22 to −0.09; R = −0.29, $95\%$ CI = −0.36 to −0.21; and R = −0.35, $95\%$ CI = −0.51 to −0.16, respectively). This modest negative association was strengthened across White, Black, Hispanic, and Asian PF subjects (R = −0.22, $95\%$ CI = −0.27 to −0.17; R = −0.33, $95\%$ CI = −0.46 to −0.18; R = −0.31, $95\%$ CI = −0.44 to −0.18; and R = −0.14, $95\%$ CI = −0.36 to 0.10, respectively) where the highest correlation occurred among Black PF participants (Table 2 and Supplementary Table 9). Assessment of the median LTL within each substratum of race and sex, showed a strong correlation across racial/ethnic groups of the age-telomere length relationship for male and female HRS control subjects (R2 = 1.0; $P \leq 0.001$) (Fig. 4B). PF disrupted this uniform linear relationship across racial/ethnic groups with a non-significant correlation among male PF participants (R2 = 0.84; $$P \leq 0.06$$), and LTL was shortest in older White or Asian males with PF (Fig. 4C). Among controls, Hispanic ethnicity significantly impacted the age-LTL relationship (interaction term $$P \leq 0.027$$) while race did not. In PF, Hispanic ethnicity, White, and Black race significantly impacted the age-LTL relationship (interaction term $P \leq 0.001$) while Asian race did not(interaction term $$P \leq 0.25$$). In an assessment of the overall age/LTL relationship between PF and HRS control subjects across racial/ethnic categories, our model shows that PF exerts an independent effect on this age/LTL relationship within the different racial/ethnic categories with significant interaction P values (Supplementary Table 7).Table 2Models depicting the association of leukocyte telomere length (LTL) with age and mortality across diverse racial populationsCharacteristicsWhiteBlackHispanicAsianCombinedRegression models for Age*Unadjusted LTLPF Cohort($$n = 1613$$)($$n = 162$$)($$n = 187$$)($$n = 70$$)($$n = 2046$$) All, R (Root MSE)−0.21 (0.27)−0.29 (0.24)−0.39 (0.28)−0.17 (0.29)−0.30 (0.28) $95\%$ CI−0.26 to −0.17−0.43 to −0.14−0.51 to −0.26−0.40 to 0.07+−0.33 to −0.26 HRS Cohort($$n = 4319$$)($$n = 779$$)($$n = 614$$)($$n = 96$$)($$n = 5808$$) All, R (Root MSE)−0.04 (0.65)−0.02 (1.11)−0.12 (0.59)−0.07 (0.63)−0.05 (0.73) $95\%$ CI−0.07 to −0.01−0.10 to −0.05−0.42 to −0.05−0.26 to −0.14−0.08 to −0.03Standardized LTL**PF cohort($$n = 1613$$)($$n = 162$$)($$n = 187$$)($$n = 70$$)($$n = 2046$$) All, R (Root MSE)−0.22 (1.08)−0.33 (0.96)−0.31 (1.07)−0.14 (1.10)−0.28 (1.08) $95\%$ CI−0.27 to −0.17−0.46 to −0.18−0.44 to −0.18−0.36 to 0.10+−0.32 to −0.24HRS cohort($$n = 4319$$)($$n = 779$$)($$n = 614$$)($$n = 96$$)($$n = 5808$$) All, R (Root MSE)−0.13 (1.09)−0.15 (1.10)−0.29 (1.09)−0.35 (1.09)−0.17 (1.10) $95\%$ CI−0.16 to −0.10−0.22 to −0.09−0.36 to −0.21−0.51 to −0.16−0.19 to −0.14PF mortality risk models($$n = 1613$$)($$n = 162$$)($$n = 187$$)($$n = 70$$)($$n = 2046$$)#Overall crude mortality rate8.925.279.626.628.49 ($95\%$ CI)(8.16–9.76)(3.91–7.10)(7.47–12.38)(3.84–11.40)(7.83–9.20)+Mortality incidence rate ratio, TL502.102.092.792.672.31 ($95\%$ CI)(1.71–2.59)(1.07–4.07)(1.66–4.69)(0.89–8.03)(1.93–2.78)*Mortality hazard ratio for TL502.212.223.402.112.47 ($95\%$ CI)(1.79–2.72)(1.05–4.66)(1.88–6.14)(0.54–8.23)(2.05–2.97)R Pearson’s bivariate correlation coefficient. Root MSE root mean squared error. PF pulmonary fibrosis. HRS health retirement survey.*Hazard Ratio for TL50 in multivariable Cox regression models adjusting for forced vital capacity, diffusing capacity of the lungs for carbon monoxide, interstitial lung disease subtype, and hospital center. For multivariable models, White $$n = 1534$$, Black $$n = 145$$, Hispanic $$n = 175$$, Asian $$n = 64$$, All (including patients with race categorized as other, $$n = 14$$) $$n = 1932$.$*$P \leq 0.001$ for all regression models except where denoted by +. ** Standardized telomere lengths in quartiles.#Overall crude mortality rate computed per 100 person-yrs. # P value for Mantel–Haenszel test statistic.+Mortality incidence rate ratios in subjects with age- and gender-adjusted leukocyte telomere length below the median (TL50) were estimated using a generalized linear model with a Poisson distribution and logistic regression link adjusting for age, gender, forced vital capacity, diffusing capacity of the lungs for carbon monoxide, and hospital center.
We analyzed the association between LTL and chronological age using age- and gender-adjusted LTL below the median (TL50). The association of LTL with chronological age remained modest with the use of this binary threshold across all racial/ethnic subgroups of the HRS population ($R = 0.11$–0.33) and among participants with PF($R = 0.17$–0.30) (Table 2 and Fig. 4D).
For each quartile decreases in LTL, the age-specific odds for developing predictors of respiratory impairment, including FVC and DLCO, differed across racial groups with PF (Fig. 5). In the sixth and seventh decades of life, decreasing LTL was associated with increased odds for IPF in White participants, and increased odds of being male in both Black and White participants. Fig. 5Racial differences in age-specific odds ratio (OR) for clinical predictors of respiratory impairment (y-axis) per quartile decrease in leukocyte telomere length (TL).Data stratified by race/ethnicity (panels) and age group (x-axis) at diagnosis of pulmonary fibrosis. OR and P value (in parenthesis) depicted for White subjects—top left panel (red), Black subjects—top right panel (gray), Hispanic subjects—bottom left panel (orange), and Asian subjects—bottom right panel (green). Binomial logistic regression models were used to compute OR and to determine the significance of association ($P \leq 0.05$) of TL with clinical variables. OR displayed within the corresponding box (OR on top and P value in parentheses) and the adjacent bar scales the statistical significance of the odds likelihood. The color of each box represents the magnitude of the statistical significance (darker color = greater statistical significance; lighter color = lesser statistical significance). FVC forced vital capacity below $50\%$ predicted; DLCO diffusing capacity of the lung for carbon monoxide below $50\%$ predicted; MALE SEX male, IPF idiopathic pulmonary fibrosis, AUTOIMMUNE autoimmune-related interstitial lung disease.
## Telomere length and mortality
Among participants with PF, the absolute mortality rate and mortality incidence rate ratios for individuals with age- and gender-adjusted LTL below the median (TL50) were lowest among Black participants and highest among Hispanic participants (Table 2). When stratifying all racial/ethnic PF subgroups by the ILD-GAP score as an index of disease severity, LTL was observed to be shorter with increasing PF severity (Supplementary Table 10).
In the pooled PF population, standardized LTL as a continuous measure was associated with mortality, and shorter LTL consistently predicted worsened mortality (Fig. 6A). When compared with participants who had LTL at or above the median (TL≥50), participants with TL50 had poorer survival across all study sites, across PF subtypes, and when substratified by racial/ethnic groups (Fig. 6B and Supplementary Figs. 3–5). These findings remained consistent even after adjusting for age, sex, and disease severity. Each quartile decrease in LTL was associated with a higher risk of death in participants with PF for each subtype of PF, and across the different racial/ethnic groups (Figs. 6C, D, 7).Fig. 6Shorter leukocyte telomere length (TL) consistently predicts worse survival patterns in pulmonary fibrosis (PF).A Scatter plot of mortality hazard ratios (HR)* in PF by transformed TL (negative log-transformed inverse of one minus percentile TL) comparing each centile of TL to the highest TL centile. The plot depicts increasing mortality hazard with shorter TL. B Survival stratified by age and gender-adjusted TL below the median (TL<$50\%$) vs. above the median (TL≥$50\%$) in the PF cohort. Unadjusted Cox proportional hazard ratio (HR) and $95\%$ confidence interval of this estimate are depicted with its respective P value. Fixed effect mortality hazard estimates for quartiles of leukocyte telomere length (TLQ) adjusted for age, gender, FVC, DLCO, ILD subtype, and hospital center categorized by C PF subtype, and D race/ethnicity. HR depicted per quartile increase in TL. FVC forced vital capacity, DLCO diffusing capacity of the lungs, ILD interstitial lung disease, IPF idiopathic pulmonary fibrosis, $$n = 735$$; IPAF interstitial pneumonia with autoimmune features, $$n = 207$$; CTD-ILD connective tissue disease associated-ILD, $$n = 349$$; FHP fibrotic hypersensitivity pneumonitis, $$n = 422$$; unclassifiable/other ILD, $$n = 333$.$ White $$n = 1613$$, Black $$n = 162$$, Hispanic $$n = 187$$, Asian $$n = 70$$, others $$n = 14$$, All patients $$n = 2046$.$ The navy-blue boxes within the forest plot represent the point estimate for the mortality hazard ratio for each cohort, the thin horizontal line represents its $95\%$ confidence interval, the vertical line is the line of no effect, and the diamond represents the overall effect estimate. Fig. 7Shorter leukocyte telomere length consistently predicts worse survival. A Kaplan–Meier survival curve according to age and gender-adjusted leukocyte telomere length in quartiles (TLQ). B Kaplan–Meier survival curves according to TLQ adjusted for age, gender, FVC, DLCO, ILD subtype, and hospital center. Cox proportional hazard ratio (HR) and $95\%$ confidence interval of this estimate are depicted with its respective P value in each plot. FVC forced vital capacity, DLCO diffusing capacity of the lung for carbon monoxide, ILD interstitial lung disease.
Compared to subjects with TL≥50 in multivariable Cox proportional hazards models adjusted for age, sex, FVC, DLCO, PF subtype, and study site, TL<50 was independently associated with a higher risk of death among PF subjects. This pattern remained consistent across different racial/ethnic groups (Hispanic, HR = 3.40, $95\%$ CI = 1.88–6.14; Asian, HR = 2.11, $95\%$ CI = 0.54–8.23; White, HR = 2.21 $95\%$ CI = 1.79–2.72; and Black participants, HR = 2.22, $95\%$ CI = 1.05–4.66).
## Discussion
Short LTLs are present in patients with PF and are associated with survival regardless of race/ethnicity. Distinct racial/ethnic differences exist in the extent of LTL shortening and in the correlation with chronologic age, supporting epidemiologic differences. Black subjects had a 3.6-fold increase in the odds of having longer LTL and were over a decade younger than White individuals at PF diagnosis. While LTL shortens with PF9,14, the results of this analysis show that this trend does not occur uniformly across all racial/ethnic groups but may reflect the chronological age at the time of PF diagnosis. To our knowledge, this original investigation uniquely adds to the literature in linking genomic markers to clinically identified epidemiological differences across patients with PF from diverse racial/ethnic groups.
The linear relationship between chronological age and telomere length is significantly disrupted in PF compared to healthy controls. The lack of congruence in this linear relationship amongst individuals with PF might suggest that acceleration of the biological aging processes, early senescence, and cellular apoptosis are contributory pathobiologic mechanisms underlying PF. We found that the shortest LTLs were present in older male Asian and White individuals with PF, and among these subjects, IPF was the most prevalent. Conversely, Black participants with PF had the least short LTL and the lowest prevalence of IPF. This supports the observation that telomere shortening may be causal for IPF5 and may explain why IPF is the most common form of PF encountered in kindred harboring pathogenic mutations in telomere-maintenance genes and short LTLs15,16. In contrast, we found that Black participants had less-short LTL and developed lung disease at a much younger chronological age, suggesting that pathologic alterations of telomere homeostasis may play a lesser role in PF development in this group17. These pathological alterations in telomere homeostasis, such as TERC or TERT mutations, may lead to defects in the telomerase complex18, and, subsequently, culminate in PF with very short telomeres. Comparatively, autoimmune and most other causes of PF do not typically result in such profoundly shortened telomeres. As telomere length is often not entirely dependent on genetic factors alone, but gene-by-environment factors, including smoking, environmental air pollution, and emotional stress, are often contributory, it is possible that when compared to White patients, LTL in Black patients are not as profoundly shortened at disease onset. It is also possible that the subset of black people in whom the LTL are not as profoundly shortened are more likely to have better medical follow-up (due to their social status, or from the presence of extrapulmonary symptoms) and therefore are more likely to have an earlier diagnosis of PF, and are for the same reasons at lower risk of death.
Notably, Black subjects had the highest prevalence of autoimmune-associated causes of PF, which was four times higher than that seen among White subjects and twice that of Asian and Hispanic subjects. This underscores the divergent contribution of lung stressors in the etiopathogenesis of PF across racial backgrounds. Non-hereditary factors such as inflammatory disease states, sporadic autoimmune conditions19–21, toxic inhalation of environmental antigens22, air pollution23,24, and various socioeconomic determinants of health25, are known to disproportionately impact Black individuals.
Black race has been associated with longer telomere length in healthy individuals across most tissues, including lungs and peripheral leukocytes26,27. However, the difference between Black and White subjects among those with PF in our study was several orders of magnitude higher than in controls reinforcing the idea that additional factors influence this disparity. Current formulas for age-based expected LTL appear to hold across diverse racial/ethnic groups and observed differences in LTL across races may reflect the cumulative effects of differential exposure to oxidative stress over the individual’s lifecourse28,29. Asians had the longest LTLs amongst healthy controls. However, in the pooled PF cohort, their LTL was almost as short as that of White subjects. This significant shortening of LTL in Asians with PF also suggests the influence of ethnic factors. Additionally, the high prevalence of IPF among Asians, which approaches seen in White populations, further supports this observation30.
In consolidating the plethora of evidence that points to LTL processes as being causal for PF, our results provide additional illumination by demonstrating that the magnitude of effect in this causal relationship may differ by subtype, as seen with autoimmune and idiopathic forms of PF in this study. The causal model of shorter LTL culminating in PF is further strengthened by our depiction of decreasing LTL as being directly linked to increased odds for PF, despite the variation in odds ratios observed across different racial groups.
Consistent with our study hypothesis, we observed a strong association of LTL with chronological age across racially and ethnically diverse PF cohorts. While many of the observed differences in LTL were related to age differences between subgroups, our study also demonstrated age-specific differences in the association of LTL with the risk of lung function impairment and specific PF subtypes across diverse races. As LTLs are not uniformly correlated with age across all age groups (Figs. 3, 4), it would be reasonable to expect that the association with clinical predictors of respiratory impairment would differ based on the age stratum. While our study evaluated LTLs in circulation, it has been shown that LTL measurements in peripheral blood correlate highly with that of the lungs26. White subjects in the seventh and eighth decades of life with shorter LTLs had an increased risk of lung function impairment. The observation that older Black or White individuals with PF and shorter LTLs were more likely to be male is consistent with data showing that telomerase’s in vitro activity is regulated by sex hormones, and circulating levels of sex hormones are associated with LTLs31. This is also consistent with population data showing that LTLs are shorter in males than females, highlighting the need for future studies to further examine the potential value of hormonal-based interventions in PF for ameliorating telomere loss or impacting clinically relevant outcomes26,32.
In recent studies demonstrating possible pharmacogenomic effects of short telomeres, we identified LTL as a biomarker that may identify subsets of patients with PF who are at risk for poor outcomes when exposed to immunosuppression33,34. The use of prednisone/azathioprine/N-acetylcysteine in patients who had IPF and LTL below the 10th percentile was associated with a higher composite endpoint of death, lung transplantation, hospitalization, or FVC decline33. In a separate study, we showed that mycophenolate therapy is not associated with improvement in survival or lung function among patients who have FHP and short telomeres34. However, in the absence of short LTL, mycophenolate therapy was associated with improved survival in FHP34. Given that the frequency and pretest probability of specific PF subtypes varies across populations, such as lower IPF prevalence among Black subjects and higher frequency of FHP among certain Asian subpopulations35,36, this knowledge of potential pharmacogenomic effects of short telomeres may prompt the measurement of LTL and influence the decision to treat as well as the choice of immunosuppressive therapy.
Despite LTL variation in this diverse PF population, we observed that shorter LTL is uniformly associated with worsened survival across all races independent of the PF subtype. As most genetic data stem from calculations derived primarily from a single race, their predictive accuracy, when applied to other racial/ethnic groups, has previously been uncertain. While the routine application of standardized reference LTL values that are derived primarily from white populations to all races may indeed result in decreased precision, our findings undermine the argument that inherent bias in genomic biomarkers entirely precludes their utility in other populations. Our findings of an overall negative relationship between mortality and LTL in non-White populations correspond to the already reported mortality in White populations as described in several previous investigations in the general population and appear to occur irrespective of the severity of fibrosis. We showed that among patients with PF, LTL measurements have substantial predictive value for mortality even after adjusting for known confounding variables. In our study, there was a “dose-dependent” relationship between shorter LTL in quartiles with mortality across racial/ethnic groups. Further, LTL below the median was a useful prognostic biomarker applicable to all racial/ethnic groups. Identifying at-risk individuals with short telomeres regardless of racial origin could inform pharmacotherapeutic interventions and guide targeted management, thus enhancing personalized medicine delivery to patients.
This study has some limitations. First, this cross-sectional cohort study was limited in its ability to assess LTL dynamics longitudinally. Second, our analyses were based on qPCR with measurements of the average LTL derived from the standard single-copy gene ratio. However, this technique allowed us to assess LTLs on archived samples. Third, our study is focused on differences across race/ethnic groups, complex social constructs that often reflect an individual’s perception of their familial origin, cultural environment, and genetic makeup37. Fourth, it is also possible that the observed association of short telomeres with a detrimental survival pattern is due to mortality from comorbidities in PF, including coronary artery disease, pulmonary hypertension, dyslipidemia, combined pulmonary fibrosis and emphysema, obstructive sleep apnea, gastro-esophageal reflux disease, among others38–40. Given that physiologic age (linked to short telomeres) may be more important than chronological age in driving mortality due to comorbidities, statistical adjustments for age would not necessarily account for these. However, given the retrospective nature of this analyses, we were unable to ascertain the cause of death for decedents in our study. Fifth, due to methodological heterogeneity in telomere length measurements, we could not directly quantify across-group averaged raw estimates of absolute LTLs between cases and controls, as data was measured differently between both populations and within different tissues41,42. However, using z-score standardization, we were able to show within-group racial/ethnic differences in standardized LTL and how these differences were compared across populations. Further, results among the Asian subgroup appeared less consistent. This study limitation likely stems from the consideration of all Asian participants as being in a single Asian ethnic group in the analyses despite substantial heterogeneity in factors such as socioeconomic position, cultural norms and behaviors, and immigration history across various Asian ethnicities. This limitation in data might have contributed to less precision in this subgroup as variations across ethnicities are averaged with their aggregate treatment. Additionally, our study utilized self-reported race for racial/ethnic categorizations and this precluded our ability to discuss potential genetic differences in risk of PF or PF-related mortality as we do not have genetic ancestry data with which to assess genetic differences. Finally, residual confounding variables that could influence LTLs to remain possible even after adjustment for identified factors. Nevertheless, the magnitude of our sample size and the widely disparate geographic regions covered should mitigate such potential effects.
Telomere length in subjects with PF is a heterogeneous but predictive biomarker that is associated with chronological age. Shorter LTL measurements hold promise for consistently identifying patients at the highest risk of death for earlier and more precise intervention irrespective of racial/ethnic background.
## Study design, age, and mortality ascertainment
This cohort study used data obtained from patients prospectively enrolled with a confident multidisciplinary diagnosis of fibrotic interstitial lung disease subtypes, collectively referred to as PF, at four geographically disparate US tertiary centers: University of Chicago (UChicago), University of California San Francisco (UCSF), the University of California Davis (UC Davis), and University of Texas Southwestern, Dallas (UTSW) between September 2003 and December 2019. Within their respective centers, all study participants underwent a confident multidisciplinary evaluation of PF performed using available clinical data, pulmonary function tests, high-resolution computed tomography (HRCT) scans, and surgical lung biopsies according to current American Thoracic Society/European Respiratory Society criteria43–47. An assessment of the multidisciplinary diagnosis of ILD was performed by pulmonologists in conjunction with rheumatologists, dedicated thoracic radiologists, and a thoracic pathologist.
All participants were recruited using consensus American Thoracic Society (ATS)/European Respiratory Society (ERS) criteria45,46 and were enrolled after obtaining written informed consent. All relevant hospital institutional review boards approved the study (IRB:UChicago#14163 A;UCSF#10-01592 & #10-00198; UCDavis#585448-7 & #875917-2; UTSW#082010-127 & #AAAS0753). Study participants were enrolled at the diagnosis of PF. Participants enrolled in the Idiopathic Pulmonary Fibrosis Clinical Research Network (IPFnet)48 clinical trials(NCT00650091/NCT00957242) and consented to participate in the optional genetics substudy with available genomic DNA were included in this study. Healthy participants enrolled in the “Health and Retirement Study”(HRS)[NIA-U01AG009740]49 who had available individual-level demographic data and LTL measurements by quantitative polymerase chain reaction (qPCR) assay were included as controls.
Race and ethnicity data were collected by self-report in prespecified fixed categories, and chronological age was determined at study enrollment. Categorization of self-reported race was implemented per federally defined US Census Bureau standards on race (White, Black or African-American, American-Indian or Alaska Native, Asian and Native Hawaiian or Pacific Islander) and ethnicity (Hispanic or not Hispanic)50. Patients meeting these pre-defined White (not Hispanic), Black (not Hispanic), Asian (not Hispanic), or Hispanic racial/ethnic categories were included in the analysis. Race and ethnicity comparisons were made between Hispanic participants, non-Hispanic White participants, non-Hispanic Black participants, and non-Hispanic Asian participants (hereafter referred to as Hispanic, White, Black, and Asian participants, respectively). Vital status was determined by reviewing medical records and confirmed using the United States Social Security death index. Lung transplantation status was determined from the EMR of individual sites. Analyses of LTL association with mortality were adjusted for age and sex, as these are known to influence mortality in other pulmonary diseases. In all cohorts, mortality refers to all-cause mortality unless otherwise specified.
## Telomere length analysis
Genomic DNA was isolated from peripheral blood leukocytes obtained at study enrollment. Peripheral blood LTL was measured using qPCR and the RotoGene real-time PCR system (Qiagen) in triplicate18,33,51,52, and age-adjusted LTL was calculated using normal controls34,51. For comparative analyses, LTL was assessed in salivary leukocytes from HRS control subjects. We derived standardized LTL values from the observed (qPCR-based) LTL minus the Cronkhite 2008 expected value and depicted these in the results as telomere length (adjusted O–E)51,52. LTL measurements are depicted as a T/S ratio for all cohorts except for California—where LTL in base pairs (bp) is reported. Given the potential variance in qPCR-based LTL measurement across study sites, standardized LTL values were calculated by applying z-score normalization with multivariable adjustment for age and sex, and categorization into quartiles53. This conversion enabled each sample to have the same distribution based on standard deviations empirically computed for each individual across each cohort54. In survival analyses, we used LTL below the median (TL50) and transformed TL (negative log-transformed inverse of one minus percentile TL), comparing mortality hazard ratios for each centile of TL to the highest TL centile among subjects with PF.
## Statistical analyses
In all cohorts, association analyses between pairs of variables were conducted using Fisher exact tests for categorical variables and two-tailed t-tests or analysis of variance (ANOVA) for continuous variables. Comparisons of LTL between independent diagnostic subgroups of PF were conducted using the nonparametric Mood’s median test. In sensitivity analyses to adjust for baseline demographic differences between Black and White participants with PF, propensity-score matching was performed in this selected subcohort and LTL measurements analyzed. Generalized linear models with a logistic regression link were used to evaluate the age-LTL relationship. The linearity assumption was assessed for continuous covariates using a lack-of-fit sum of squares test that compared the linear fit to restricted cubic spline fit with five knots, which ascertains that statistical models fit well with the selected set of observations. Linear regression models were fitted to assess congruence between median standardized LTL and chronological age for all included racial/ethnic groups stratified by sex, while rates of absolute mortality were analyzed using conditional logistic regression. Odds ratios for LTL comparisons across racial/ethnic groups were generated from transformed coefficients of linear combinations.
Cox proportional hazards models were used for hazard ratio estimation and analysis of time to mortality with robust standard errors to account for familial correlation within the cohort. All included patients were stratified by quartiles of standardized LTLs and the Cochran–Armitage test was used to assess trends across quartiles, using quartile integers (1, 2, 3, and 4) as scores18,54. The estimated survival functions for each quartile were plotted based on the Cox model. For the PF cohort, we assessed transplant-free survival over five years, and transplant-free survival time was calculated from study enrollment to death, lung transplantation, loss to follow-up, or end of the study period. Survival time was censored on December 31, 2019, or when a participant was lost to follow-up. In multivariable models assessing mortality, we sought to only adjust for established confounders previously linked to both the predictor and the outcome; therefore, covariate selection for inclusion in the model was based on potential confounding variables known to affect telomere length and PF mortality55. As smoking and BMI are inconsistently associated with mortality in interstitial lung disease56–58, and when present, seem to exert their effect on worsened PF mortality through worsening pulmonary function or accelerating the progression of fibrosis59,60, the analyses of all multivariable outcome models were adjusted for age, sex, measures of pulmonary function including forced vital capacity (FVC) and diffusing capacity of the lungs for carbon monoxide (DLCO), and hospital center. The relationship between LTL and the severity of PF using the most widely-used index for assessing PF severity, the ILD-GAP score61. Survival curves are plotted using the Kaplan–Meier survival estimator. In Cox models, we assessed all covariate effects over time and none violated the proportional hazards assumption.
All P values were two-sided, and a level of 0.05 was considered statistically significant. Data collation was performed using Microsoft Excel for Mac Version 16.65, 2019. Analyses were conducted using Stata (V 2019.R.16, V 2021.R17; StataCorp) and R.v.3.5.1.
## Reporting summary
All sexes were considered in this observational cohort study and the sex of participants was determined based on self-report as documented within the respective data registries. No consent was obtained for reporting and sharing individual-level data. Results of sex-based analyses, which were performed a-priori are reported accordingly. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
## Supplementary information
Supplementary Information Reporting Summary The online version contains supplementary material available at 10.1038/s41467-023-37193-6.
## Peer review information
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
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|
---
title: Rapid and effective preparation of clonal bone marrow-derived mesenchymal stem/stromal
cell sheets to reduce renal fibrosis
authors:
- Sumako Kameishi
- Celia M. Dunn
- Masatoshi Oka
- Kyungsook Kim
- Yun-Kyoung Cho
- Sun U. Song
- David W. Grainger
- Teruo Okano
journal: Scientific Reports
year: 2023
pmcid: PMC10023793
doi: 10.1038/s41598-023-31437-7
license: CC BY 4.0
---
# Rapid and effective preparation of clonal bone marrow-derived mesenchymal stem/stromal cell sheets to reduce renal fibrosis
## Abstract
Allogeneic “off-the-shelf” mesenchymal stem/stromal cell (MSC) therapy requires scalable, quality-controlled cell manufacturing and distribution systems to provide clinical-grade products using cryogenic cell banking. However, previous studies report impaired cell function associated with administering freeze-thawed MSCs as single cell suspensions, potentially compromising reliable therapeutic efficacy. Using long-term culture-adapted clinical-grade clonal human bone marrow MSCs (cBMSCs) in this study, we engineered cBMSC sheets in 24 h to provide rapid preparation. We then sought to determine the influence of cBMSC freeze-thawing on both in vitro production of pro-regenerative factors and in vivo ability to reduce renal fibrosis in a rat model compared to freshly harvested cBMSCs. Sheets from freeze-thawed cBMSCs sheets exhibited comparable in vitro protein production and gene expression of pro-regenerative factors [e.g., hepatocyte growth factor (HGF), vascular endothelial growth factor (VEGF), and interleukin 10 (IL-10)] to freshly harvested cBMSC sheets. Additionally, freeze-thawed cBMSC sheets successfully suppressed renal fibrosis in vivo in an established rat ischemia–reperfusion injury model. Despite previous studies reporting that freeze-thawed MSCs exhibit impaired cell functions compared to fresh MSC single cell suspensions, cell sheets engineered from freeze-thawed cBMSCs do not exhibit impaired cell functions, supporting critical steps toward future clinical translation of cBMSC-based kidney disease treatment.
## Introduction
Mesenchymal stem/stromal cells (MSCs) have been applied in hundreds of clinical trials to date based on their therapeutic secretome and paracrine potency. Many different cytokines and growth factors are implicated in MSC-based cell therapies, including immunomodulatory factors (e.g., interleukin-10: IL-10, prostaglandin E2: PGE-2)1–3, anti-fibrotic factors (e.g., hepatocyte growth factor: HGF, bone morphogenetic protein 7: BMP-7)4–6, and angiogenic factors (e.g., vascular endothelial growth factor: VEGF, basic fibroblast growth factor: bFGF)7–9. Specifically, MSC’s reportedly high immunomodulatory capacity has motivated several ongoing clinical studies for immune-related diseases, such as graft-versus-host disease (GvHD), Crohn's disease, and severe acute pancreatitis10,11, all of which lack effective conventional pharmaceutical treatment alternatives. Current MSC administration strategies utilize conventional injection-based delivery of MSC single-cell suspensions, considered advantageous for treating systemic diseases. However, for localized diseases, it is essential to employ local cell transplantation methods to enhance cell engraftment and survival rates in the targeted site, thus increasing the potential for sustained cell-based delivery of therapeutic factors12,13.
Cell sheet technology uses commercial thermo-responsive cell culture dishes (TRCDs) grafted with the temperature-responsive polymer, poly(N-isopropylacrylamide), allowing scalable harvest of cultured cells as a single, confluent sheet14 via non-enzymatic temperature-mediated detachment. By avoiding the use of enzymatic-mediated culture, cell sheets retain innate instructive ECM and cell–cell interactions15,16 that facilitate direct cell sheet transplantation without use of sutures. Our group has recently reported characterization of cell sheets engineered from human clinical-grade MSCs and demonstrated that MSC sheet transplantation in vivo prolongs cell retention at target tissue sites compared to single-cell injections17,18. Furthermore, our group has recently reported that MSC sheet formation enhances cytokine production compared to single-cell conditions in vitro19–21. Preclinical applications of directly transplanted MSC sheets demonstrate pro-regenerative therapeutic efficacy across several disease models and various tissues22, such as the heart23, periodontal ligament24,25, bone26, skin27, and kidney17. Interestingly, previous studies suggest that some transplanted GFP-labeled MSCs may transdifferentiate into endothelial cells, pericytes, and other cell types to support neovascularization to regenerate damage tissues22,23,27,28.
Despite the ongoing promise of MSC therapies, including MSC sheet therapy, major obstacles preclude clinical translation of MSC therapy, including notable scientific, practical, and economic challenges. Allogeneic “off-the-shelf” MSC strategies defined under scaled quality-controlled cell production methods now address several current issues, with anticipated improvements in cost and potency per cell dose29,30. Human MSC source standardization, mass manufacturing under quality control, product distribution, and clinical dosing regimens must be addressed29,30. In this study, we employed long-term culture-adapted human clonal bone marrow stem (stromal) cells (cBMSCs): MSC primary lines derived from a single human MSC and that exhibit stable cell proliferative capability beyond passage 10 with high regenerative capacity and low immunogenicity31–33. Human cBMSCs are clinically available through good manufacturing practice (GMP) production based on safety tests, including in vivo toxicity, biodistribution analysis, tumorigenicity tests, and karyotyping34. Therefore, cBMSCs used in this study represent a new opportunity to produce a potent, sustainably cell-banked allogeneic product with increased homogeneity for future clinical use.
Reliable, cost-effective clinical delivery of allogeneic MSC products to patients requires improvements in cell storage, cell manufacture, and transplantation systems. Currently, freshly isolated MSCs, called "non-cryopreserved MSCs" in this study, are employed in preclinical animal studies, while banked MSC products ("freeze-thawed MSCs") are prepared and directly transfused in human clinical trials to reduce procedural complexity10. Discrepancies between MSC protocols for preclinical animal studies and human clinical settings contribute to inconsistent clinical outcomes10,35,36; therefore, quality control of freeze-thawed human MSCs is necessary to validate off-the-shelf MSC products.
Previous studies reported that tri-lineage differentiation potency and MSC surface marker expression are well-preserved after cryopreservation37. However, freeze–thaw cycles often compromise MSC viability30,36,38. In addition, impaired immunomodulatory and blood regulatory properties are commonly reported in freeze-thawed MSCs35,38,39. Given changes to MSC function caused by cryo-handling and freeze–thaw cycles29,30, distinct challenges associated with freeze-thawed MSCs must be addressed before their direct utilization in human clinical products.
Current MSC sheet production including cell expansion and cell sheet fabrication typically requires 2–3 weeks19–21 before application. Therefore, to reduce production time and associated costs, MSC sheet clinical translation would benefit from improved processes that utilize freeze-thawed MSCs from large-scale cell banks to yield rapid cell sheet fabrication while maintaining therapeutic effects. In this study, cell sheets were developed using GMP-grade cBMSCs31,40 certified by MSC criteria and currently investigated to treat GvHD in phase II clinical trials in Korea41. Cell sheets made from cBMSCs revived from cryo-banking, passaged twice and used for cell sheets (i.e., "freshly harvested cBMSCs") were compared to cell sheets from cBMSCs immediately revived from a working cell bank ("freeze-thawed cBMSCs") (Fig. 1). We compared cBMSC sheets from these two cell sources at identical passage numbers for pro-regenerative cytokine production in vitro and therapeutic suppression of renal fibrosis in an in vivo rat ischemia–reperfusion injury (IRI) model17,42, a potential clinical application. This study seeks to fill the gap between MSC preclinical model work and human clinical performance as an allogeneic cell therapy using freeze-thawed GMP-produced cBMSCs, ultimately required to improve MSC therapeutic and clinical translational impact. Figure 1Preparation of freshly harvested and freeze-thawed clonal BMSC sheets. Clonal BMSC sheet preparation strategy using freshly harvested and freeze-thawed cells revived from each working cell bank. Cells were seeded onto thermo-responsive cell culture dishes (TRCDs) and cultured for 24 h to harvest as cell sheets.
## Evaluation of freshly harvested and freeze-thawed cBMSCs for cell viability and growth in vitro
Viability of freeze-thawed cBMSCs was determined immediately following thawing from cryogenic banking and compared to freshly harvested cBMSCs revived from the same initial working cell bank but passaged twice (see Fig. 1). Freeze-thawed cBMSCs exhibited $90.8\%$ cell viability, significantly lower than freshly harvested cBMSCs ($99.1\%$, Fig. 2a). After 5 days of culture, both freshly harvested and freeze-thawed cBMSCs exhibited similarly high cell viability and growth rates (Fig. 2b) and became ~ $70\%$ confluent after identical seeding conditions (Fig. 2c), indicating that freeze-thawed cBMSCs rapidly recover from cryopreservation. Cell size and morphology were comparable between the freeze-thawed and freshly harvested cBMSCs at both 2- and 5-day cultivation (Fig. 2c). These findings suggest that freeze-thawed cBMSCs adhere and proliferate equivalently to previously culture-rescued cBMSCs despite exhibiting reduced cell viability immediately following freeze-thawing. Figure 2Cell viability and proliferation of freshly harvested and freeze-thawed human clonal BMSCs. ( a) Cell viability of freshly harvested (Fresh) clonal BMSCs following culture and freeze-thawed (Freeze) clonal BMSCs following thawing from cell bank at passage 10, day 0. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, *$P \leq 0.05.$ ( b) Cell viability and doubling time of freshly harvested (Fresh) and freeze-thawed (Freeze) clonal BMSCs at passage 10, day 5. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.). ( c) Phase-contrast images of freshly harvested and freeze-thawed clonal BMSCs at 2-day and 5-day culture. Scale bars represent 200 μm.
## Cell adherence and spreading ability in freshly harvested and freeze-thawed cBMSC cultures in vitro
Rapid cell sheet fabrication depends on rapid cell adhesion and spreading to form a confluent monolayer; therefore, cell adhesion and spreading properties of freeze-thawed cBMSCs are critically important to their ability to be used for cell sheet fabrication. Initial cell adhesion and spreading was observed after seeding freshly harvested and freeze-thawed cBMSCs onto cell cultureware and incubating for 15, 30, and 60 min. After incubation, cell culture dishes were washed with PBS to remove non-adherent cells (i.e., floating cells), and the remaining adherent cells were then observed. Interestingly, freeze-thawed cBMSCs exhibited increased cell spreading versus freshly harvested cBMSCs after both 15- and 30-min incubations, as shown in Fig. 3a. Additionally, the number of adherent cells was approximately double in the freeze-thawed cBMSCs experimental group compared to freshly harvested cBMSCs after 15-min (Fig. 3b). Live cell time-lapse imaging further confirmed these trends, indicating that over time the freeze-thawed cBMSCs possessed higher intrinsic cell adhesion and spreading capabilities (Supplemental Figure S2 and Videos). To investigate differences during initial cell spreading processes, gene expression integrin β1, ITGB1, the primary cell adhesion receptor, was evaluated using qRT-PCR. No significant differences in integrin β1 gene expression were observed between freshly harvested and freeze-thawed cBMSCs after 10- and 60-min incubations, as shown in Fig. 3c. These findings indicate that freeze-thawed cBMSCs possess higher initial cell spreading and adhesion capability than freshly harvested cBMSCs, regardless of ITGB1 expression. Figure 3Cell spreading ability in freshly harvested and freeze-thawed human clonal BMSCs. ( a) Phase contrast images of freshly harvested and freeze-thawed clonal BMSCs at 15-, 30-, and 60-min incubation after seeding in culture. Scale bars represent 200 μm. ( b) Adherent cell numbers of freshly harvested (orange) and freeze-thawed (blue) clonal BMSCs are shown. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.), **$P \leq 0.01.$ ( c) Gene expression of ITGB1 in freshly harvested (orange) and freeze-thawed (blue) clonal BMSCs were shown. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.).
## Adherent cell shape correlates to actin organization and focal adhesion formation during maturation of cell adhesion in culture
Immunohistochemistry (IHC) staining of vinculin, a focal adhesion marker, was performed using F-actin phalloidin (Fig. 4a–c), and cell morphology of adherent cells at each culture time point was quantified using Image J, as shown in Fig. 4d,e43,44. Freshly harvested cBMSCs exhibited larger average adherent cell size and more elongated cell shape compared to freeze-thawed cBMSCs at both 3- and 5-h time points (Fig. 4a,b). These differences faded by the 24-h time point (Fig. 4c). Additionally, increased vinculin localization uniformity was observed at the cell edges, co-localizing with F-actin fibers, in the freshly harvested cBMSCs compared to freeze-thawed cBMSCs at the 3-h time point (Fig. 4a, overlaid fluorescent images). These findings indicate that freshly harvested adherent cBMSCs develop more mature focal adhesions and elongated cell morphologies consistent with stromal phenotypes compared to freeze-thawed cBMSCs after seeding and culture. Figure 4Actin organization and focal adhesion formation in freshly harvested and freeze-thawed human clonal BMSCs. ( a–c) Fluorescent images of freshly harvested and freeze-thawed clonal BMSCs at 3-, 5-, 24-h incubation after seeding respectively. Scale bars represent 50 μm. ( d) *Cell area* measurements of freshly harvested (orange) and freeze-thawed (blue) clonal BMSCs. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.) *$P \leq 0.05.$ ( e) Cell elongation measurements of freshly harvested (orange) and freeze-thawed (blue) clonal BMSCs. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.) *$P \leq 0.05.$
## Cell sheet fabrication and cytokine production in culture
Current cell sheet preparation for allogeneic cell therapy requires approximately 2–3 weeks19–21 for cell expansion and maintenance when working from an established working cell bank without donor cell isolation. One approach to reduce the barriers to cell sheet applications is to simplify and shorten production steps to yield more cost-effective cell sheets fabricated for on-demand, acute or ready emergency use. To address the need for shorter, more cost-effective cell sheet fabrication methods, this study uses an extensive cell bank of long-term culture-adapted human cBMSCs to produce allogenic cell sheets within 24 h. Freshly harvested and freeze-thawed cBMSCs were used directly to prepare cBMSC sheets in 24 h using a high initial seeding cell density of 4 × 105 and 1 × 106 cells/dish (Fig. 5a).Figure 5Cell sheet preparation using freshly harvested and freeze-thawed human clonal BMSC sheets as prepared in 24 h and comparison of cytokine production. ( a) Macroscopic images of freshly harvested and freeze-thawed clonal BMSC sheets at the seeding densities of 4 × 105 and 1 × 106 cells/dish. Scale bars represent 500 mm. ( b) Gene expression levels of HGF, VEGF, FGF2, IL10, and FN1 in freshly harvested (fresh, orange) and freeze-thawed (freeze, blue) clonal BMSC sheets. Data shown are mean ± SD ($$n = 4$$). Statistical significance: Student’s t-test, not significant (N.S.) *$P \leq 0.05.$ ( c) Measurements of released cytokine amounts from freshly harvested (orange, $$n = 5$$) and freeze-thawed (blue, $$n = 4$$) clonal BMSC sheets. Data shown are mean ± SD ($$n = 4$$ or 5). Statistical significance: Student’s t-test, not significant (N.S.) **$P \leq 0.01.$
We expected differences in rapid and functional cell sheet formation based on differences observed in adherent abilities of freshly harvested and freeze-thawed cBMSCs, specifically, increased cell sheet fabrication ability based on observed enhanced adhesion rates of freeze-thawed cBMSCs (see Figs. 3, 4). After 24-h incubation, cultured cells in each experimental group were successfully harvested from TRCDs as contiguous, contracted cell sheets via temperature-mediated detachment. As shown in Fig. 5a, freshly harvested cBMSCs produced a fragile cell sheet containing many defects/holes under the lower initial seeding density of 4 × 105 cells/dish.
In contrast, freeze-thawed cBMSCs reliably produced intact cell sheets under the same 4 × 105 (i.e., 0.4 M) cells/dish initial seeding density (Fig. 5a). At the higher initial seeding density of 1 × 106 (1 M) cells/dish, both freshly harvested and freeze-thawed cBMSCs produced intact cell sheets (Fig. 5a). Resulting diameters of fresh and freeze-thawed harvested sheets (seeding: 1 M cells/35-mm dish) were 13.8 and 14.1 mm, respectively ($$n = 3$$/group, not statistically different). These results indicate that rapid (24 h) cell sheet formation is possible from freeze-thawed banked cBMSCs at multiple seeding densities, distinct from freshly harvested cBMSCs. Taken together, rapid adhesion of freeze-thawed cBMSCs may contribute to the enhanced ability to rapidly fabricate cell sheets, which is advantageous for reducing the time and costs of cell sheet production.
MSCs secrete multiple pro-regenerative cytokines (i.e., HGF, VEGF), making them an attractive alternative to single-molecule drugs in treating diseases such as renal fibrosis45–48. However, freeze-thawed MSCs have been reported to exhibit impaired cytokine production, specifically those related to immunomodulatory effects based on indoleamine 2,3-dioxygenase (IDO) expression35,38,39. Therefore, in this study, we sought to compare production of tissue-regenerative cytokines from freshly harvested and freeze-thawed cBMSC sheets at 1 × 106 cells/dish seeding density. No differences in gene expression levels of HGF and IL-10 were observed between freshly harvested and freeze-thawed cBMSC sheets (Fig. 5b). Expression of VEGF and FGF2 was significantly higher, while FN was significantly lower in freeze-thawed cBMSC sheets, although these differences were slight (Fig. 5b). Overall, the cytokine production in freeze-thawed cBMSC sheets is comparable to freshly harvested cBMSC sheets, indicating no adverse effects of using freeze-thawed cBMSCs (Fig. 5b). We further evaluated the actual protein production from harvested cell sheets by replating detached cell sheets onto insert wells, incubating for 3 days, and collecting the media supernatant for ELISA assay. HGF concentration was significantly higher in freshly harvested cBMSC sheets at 1 and 2 days, but no differences were seen at 3 days (Fig. 5c). In contrast, VEGF concentration was significantly higher (around 2-fold higher) in freeze-thawed cBMSC sheets at each timepoint (Fig. 5c). No differences in IL-10 concentration were observed. These findings suggest that using freeze-thawed cBMSCs is a promising method to rapidly produce cell sheets with equivalent production of therapeutically relevant pro-regenerative cytokines to freshly harvested cBMSC sheets. This strategy could prove useful to suppress renal fibrosis, as shown in the rat IRI model previously17,42.
## Freeze-thawed cBMSC sheet transplantation in a rat ischemia–reperfusion injury (IRI) model to evaluate cell sheet therapeutic effects on acute renal fibrosis
The efficacy of allogeneic freeze-thawed cBMSC sheets in preventing/treating renal fibrosis was determined previously by transplantation of freshly harvested rat BMSC sheets17 and cBMSC sheets42 using a published acute renal fibrosis rat IRI. In this study, to evaluate therapeutic efficacy of freeze-thawed cBMSC sheets, freeze-thawed GFP-labeled rat cBMSC sheets were fabricated by seeding cells onto TRCDs using the same established method as the human freeze-thawed cBMSC sheets. Sheet GFP fluorescent signal was observed in Fig. 6a,b just prior to kidney transplantation. The average diameter of freeze-thawed rat cBMSC sheets is analogous to sheets prepared from human BMSCs (approx. 1 cm, compare Figs. 5, 6), as shown in Fig. 6b. Freeze-thawed rat cBMSC sheets were transplanted directly to the renal capsule of the IRI kidney, as shown in Fig. 6b. Visualization of the cell sheet via GFP signal confirmed that transplanted cell sheets covered the entirety of the dorsal side and stably adhered to the kidney surface (Fig. 6b). On day 28 post-surgery, the kidneys were harvested for histological analysis. Disease progression of renal fibrosis was determined by ECM deposition and assessed using periodic acid-Schiff (PAS) and Masson’s trichrome (MT) staining (Fig. 6d). The rat IRI model without cell sheet transplantation (control, disease) group exhibited increased fibrotic area (indicated by the black arrowheads in Fig. 6d) compared to the cell sheet transplantation group. To quantify fibrotic components in respective kidneys, we investigated gene expression levels of fibronectin (FN1), collagen type 1 (COL1A1, COL1A2), and collagen type 3 (COL3). Importantly, expression levels of fibronectin (FN1) and collagen type 1 (COL1A1, COL1A2), common fibrotic tissue makers, were lower in the freeze–thaw cBMSC cell sheet transplantation group compared to IRI-only group (no cell sheet transplantation) (Fig. 6e). Additionally, collagen type 3 (COL3), a renal fibrosis marker, was significantly lower in the freeze–thaw cell sheet transplantation group compared to the IRI-only group (Fig. 6e). These findings suggest that allogeneic freeze-thawed cBMSC sheet transplantation suppresses renal fibrosis in the rat IRI model, demonstrating feasibility to address human fibrotic disease pathology, as indicated by reduced fibrotic areas. Figure 6Therapeutic effects of rat freeze-thawed GFP-labeled clonal BMSC sheets in rat IRI model. ( a) Macroscopic and fluorescent images of GFP-labeled rat freeze-thawed clonal BMSC sheets at 0-day harvest prior to cell sheet in vivo transplantation. Scale bars represent 500 μm. ( b) Hematoxylin and eosin staining and GFP immunohistochemistry staining of rat freeze-thawed clonal BMSC sheets. Scale bars represent 50 μm. ( c) Macroscopic and fluorescent images of cell sheet transplantation and attachment on rat kidney at 0-day cell sheet in vivo transplantation. ( d) Periodic acid–Schiff (PAS) and Masson's trichrome staining. Black arrows indicate thick basement membrane. White arrows indicate fibrotic component deposition. Scale bars represent 50 μm. ( e) Gene expression levels of fibrotic markers (Fn1, Col3, Col1A1, Col1A2) in kidneys collected from native ($$n = 3$$), IRI ($$n = 8$$), and IRI + freeze-thawed cBMSC sheet transplantation ($$n = 8$$) groups. Statistical significance: one-way ANOVA, Tukey’s multiple comparisons, not significant (N.S.) *$P \leq 0.05$, and **$P \leq 0.01.$
## Discussion
Currently, there are many clinical trials investigating various uses of allogeneic MSCs to treat human disease and in regenerative medicine. However, unreliable critical quality attributes, lack of potency standards, insufficient product validation, and costly cell manufacturing costs remain critical barriers to clinical advancement of MSC-based cell therapies49,50. Progress with allogeneic MSC therapies requires improved, sustainable cell banking systems and efficient, cost-effective production methods that reliably and consistently yield a validated, potent cell therapy product. In this study, to reduce MSC sheet production time, we employed cryopreserved, freeze-thawed cBMSCs to shorten key cell sheet production steps from several weeks to 24 h (Fig. 5), facilitating urgent or emergency off-the-shelf use.
Freeze-thawed MSCs are the preferred cell preparation in clinical settings due to the convenience and accessibility, contributing to clinical and economic benefits10,35,36,51,52. However, clinical translation of freeze-thawed MSCs remains stalled by inconsistent efficacy, attributed to inappropriate optimization and criteria in using and validating properties of freeze-thawed cells10,37,51,53. Evaluation of GvHD clinical trial outcomes, defined as the loss of all symptoms or improvements against acute GvHD54, shows that patient benefits are doubled when using freshly isolated, non-cryopreserved MSCs cultured up to four passages compared to clinical studies using freeze-thawed MSCs38. *Although* general MSC phenotypic indicators, such as surface antigen expression and tri-lineage differentiation potency, are well-preserved in freeze-thawed MSCs39,53, low cell viability and impaired blood regulatory properties have been described following freeze–thaw cycles38,52. MSC immunomodulatory properties are also reported to be impaired after cryopreservation35,38,39 but can be rescued when exposed to IFN-γ for a 24-h culture35. Analogous IFN-γ priming of human cBMSCs to improve immunomodulatory factor productions in cell sheets similar to those reported here has recently been reported55.
In contrast to previous studies evaluating cryogenically preserved MSCs10,35,36,51,52, we have successfully engineered cBMSC sheets from freeze-thawed cells in 24 h with in vitro cytokine production comparable to freshly harvested cBMSC sheets (Fig. 5). This finding is different from previous contrasting studies reporting impaired functionality of freeze-thawed MSCs as single cells10,35,36,51,52. Furthermore, freeze-thawed cBMSC sheets demonstrate the therapeutic capacity to reduce renal fibrosis in a rat IRI kidney disease model (Fig. 6), similar to non-cryopreserved MSC sheets, as previously reported17,42. Thus, cell sheet technology may overcome the common disadvantages of freeze-thawed single cell MSC formulations as used in previous studies10,35,36,51,52. Our prior reports of cell sheet technology demonstrate that the MSC sheet three-dimensional (3D) structure enhances cytokine production compared to both single cells20 and 2D monolayer cultures before sheet detachment from cell cultureware21. Given supporting cell sheet data from these past studies, intrinsic three-dimensionality of freeze-thawed cBMSC sheets is reasonably inferred to contribute to production of multiple therapeutic cytokines (compare Figs. 5 and 6) and immunomodulatory factors55 that drive forward their therapeutic utility in kidney fibrosis models17,42.
Additionally, we found that freeze-thawed cBMSCs, compared to freshly harvested cBMSCs, exhibit greater initial cell adhesion and cell spreading ability after plating ($t = 15$ min) (Fig. 3a,b, supplemental Figure S1). Similarly, Pollock et al. showed that MSC cryopreservation in DMSO does not affect cell adhesion ability at 2 h post-plating53. However, MSCs incubated for 1-h in DMSO before freezing, exhibited a significantly reduced cell adhesion ability without affecting cell viability53. We believe this heightened cell spreading ability correlates to the observed improved rapid (24 h) cell sheet formation, enabling flexible cell sheet production from multiple different seeding densities ranging from low (0.4 × 106 cells) to high (1 × 106 cells) seeding densities per 35-mm TRCD dish (Fig. 5a). Cell adhesion molecule, ITGB1, expression was unchanged after freeze-thawing (Fig. 3c). Chinnadurai et al. showed that cell surface expression of adhesion molecules tetraspanin (CD63), integrin alpha V (CD51), MHC class I (HLA-ABC), integrin beta 1(CD29), integrin alpha 4 (CD49d), integrin alpha IIb (CD41), ICAM 1 (CD54), integrin beta 3 (CD61), and integrin alpha 5 (CD49e), do not differ between non-cryopreserved and freeze-thawed MSCs36. Taken together, this study shows that freeze-thawed cBMSCs possess greater initial cell adhesion ability compared to freshly harvested cBMSCs (Fig. 3a,b) while expression of cell adhesion molecule ITGB1 is comparable (Fig. 3c)36,53.
Increased cell spreading rate of freeze-thawed cBMSC, shown in Fig. 3, may be related to actin fiber formation associated with cell adhesion. Pollock et al.36,53 and Chinnadurai et al.36,53 demonstrate reduced formation of polymerized long-form actin stress fibers, F-actin, and elongated cells after freeze-thawing, also corroborated by our data (Fig. 4a–c). Instead of long-form actin fibers, freeze-thawed cBMSCs possess short-form actin fibers usually involved in early stages of cell adhesion (Fig. 4a–c), nascent adhesion, known as non-muscle myosin II-independent adhesion during cell spreading56–59. Notably, previous studies showed that mature, long actin fibers are implicated in focal adhesion contractile forces, reducing rates of cell spreading60–62. Therefore, it is reasonable to suggest that freeze-thawed cBMSCs exhibit enhanced cell spreading using short-form actin fibers, without mature actin fibers, compared to freshly harvested cBMSCs, advantageous for rapid cell sheet production. Additionally, due to lack of observed mature actin fibers in freeze-thawed cBMSCs, sizes of freeze-thawed cBMSC sheets are slightly larger, attributed to impaired actin fiber formation and thus reducing cell sheet contractile forces21,63 after cryopreservation without diminishing the sheet’s transplantability (Figs. 5a, 6c). Despite the observed lack of mature actin formation after freeze-thawing (Fig. 4)36,53, the actin reorganization pathway is reported to be upregulated in freeze-thawed cells52, a likely reason for why they adequately recover from cryo-induced stress and proliferate well after culture, as shown in Fig. 2b,c.
Variable MSC quality and biodistribution induced by freeze–thaw cycles reduces consistent and reliable cell systemic administration, complicating treatment of localized diseases and likely contributing to inconsistent and often insufficient therapeutic effects10,30,35,36,38,51,52. When MSCs are treated with cytochalasin D, a disruptor of actin fiber formation similar to that observed in freeze-thawed MSCs (Fig. 4), MSC in vivo biodistribution changes, instead engrafting largely in the lung and colon after systemic infusion and intraperitoneal injection, respectively30,36,38. Lack of actin fibers and unpredictable biodistribution of freeze-thawed MSCs may be responsible for insufficient clinical outcomes after systemic administration of freeze-thawed cBMSCs10,38. In contrast, retention of topically transplanted cell sheets is reported in many preclinical models of disease22. Cell sheets directly adhere and stably engraft to, and are retained on, targeted tissue sites spontaneously without suturing17,18. Cell sheet transplantation, a local MSC delivery method without systemic administration challenges, may overcome current issues associated with freeze-thawed cell suspensions to facilitate broader future clinical utility.
## Conclusions
This study reports successful MSC sheet production using long-term culture-adapted human and rat cBMSCs in both freshly harvested and freeze-thawed conditions. The high intrinsic cell spreading ability of freeze-thawed cBMSCs relates to accelerated cell sheet formation without compromising in vitro cytokine production and therapeutic ability to suppress renal fibrosis formation in vivo. Most investigators have focused on the disadvantages of freeze-thawed cells such as impaired immunomodulatory properties35,38 and biodistribution30,36,38. In this study, however, we show that cell sheet technology can overcome these limitations by fabricating cBMSC sheets generated directly from cryo-banked passaged stocks. Based on our recent reports showing immunomodulatory properties for cBMSC sheets55 and demonstrating therapeutic cBMSC sheet efficacy in the rodent IRI kidney fibrosis model42, we combined with the unique cBMSC sheet properties shown in this current study herein involving cryo-banked cell revival, expansion, rapid and reliable sheet formation and therapeutic cytokine production all fit a consistent picture for feasible future off-the-shelf, consistent clinical applications. This study proposes the strategic combination of clinical grade, long-term culture-adapted clonal BMSCs and cell sheet technology to improve therapeutic properties and promote further clinical translation of freeze-thawed MSCs. These findings represent an important first step for initiating use of rapidly fabricated cell sheets from freeze-thawed MSCs to treat renal fibrosis and possibly other organ pathologies in the future.
## Preparation of freshly harvested and freeze-thawed cBMSCs
Human clonal BMSC cell lines were provided by SCM Lifescience (Republic of Korea) for this study as reported31. Briefly, human bone marrow aspirate was mixed with growth media [Dulbecco's Modified Eagle's Medium (DMEM, Thermo Fisher Scientific, 11885076) supplemented with $20\%$ Fetal Bovine Serum (FBS, Thermo Fisher Scientific, 16000044), $0.05\%$ MycoZap Prophylactic (Lonza, VZA-2023), $1\%$ penicillin streptomycin (Thermo Fisher Scientific, 15140163)] and cultured at 37 °C, $5\%$ CO2 in a culture incubator for isolation of single-cell derived clonal BMSC cell line using their patented Subfractionation Culturing Method (SCM)31. Isolated human cBMSCs (clonal cell line: A106 D127, SCM Lifescience, Korea) were cultured in Dulbecco's Modified Eagle's Medium (DMEM, Thermo Fisher Scientific, 11885076) supplemented with $10\%$ Fetal Bovine Serum (FBS, Thermo Fisher Scientific, 16000044), $0.05\%$ MycoZap Prophylactic (Lonza, VZA-2023), $1\%$ penicillin–streptomycin (Thermo Fisher Scientific, 15140163) at 37 °C, $5\%$ CO2. Expanded cBMSCs were then collected using $0.05\%$ trypsin EDTA (Thermo Fisher Scientific, 25200114), centrifuged at 210xg for 5 min, and resuspended in STEM-CELLBANKER cryogenic media (Amsbio, 11890) to establish working cell banks. cBMSCs in cryogenic media were then put in a freezing container (Corning, 432007) for 2 days at − 80 °C before being moved to a liquid nitrogen tank. Working cell banks were produced at both passage 8 (P8) and 10 (P10) (Fig. 1). Total cell number, cell viability, and doubling times of cultured cBMSCs were determined by trypan blue staining (MilliporeSigma, 72-57-1) and hemocytometers. MSC phenotypic validation of P10 cBMSCs was performed by cryopreserved cBMSCs at P8, culturing to P10 and testing for tri-lineage differentiation potential (osteogenesis, adipogenesis, and chondrogenesis), and surface antigen expression, CD73 + (BioLegend, 344014), CD90+ (BioLegend, 202507), CD105+ (BioLegend, 323208), CD44+ (BioLegend, 338808), CD34− (BioLegend, 343521), CD31− (BioLegend, 303110), CD45− (BioLegend, 304021) using flow cytometry (BD, Canto) (Supplemental Figure S1).
## Cell sheet preparation using freshly harvested and freeze-thawed cBMSC stocks
Freshly harvested cBMSC sheets were prepared by reviving cryopreserved cells from a P8 working cell bank in a 37 °C water bath more than 20 min, collecting the cells in a pre-warmed cell culture medium, and centrifuging at 210×g for 5 min. P8 cBMSCs were then seeded onto conventional cell culture flasks (CELLTREAT, 229351) and passaged twice prior to seeding harvested cells at P10 onto 35-mm temperature-responsive culture dishes (TRCDs, Thermo Fisher Scientific, 03150025) (Fig. 1). Freeze-thawed cBMSC sheets were prepared by reviving cryopreserved cells from the P10 working cell bank and seeding them directly onto a 35-mm TRCD without prior cultivation (Fig. 1). Both freshly harvested and freeze-thawed cBMSC sheets were prepared on TRCDs at seeding densities of 4 × 105 and 1 × 106 cells/dish and cultured for 24 h at 37 °C, $5\%$ CO2 incubator in cell culture medium comprising DMEM (Thermo Fisher Scientific, 11885076) supplemented with $10\%$ FBS (Thermo Fisher Scientific, 16000044), $0.05\%$ MycoZap Prophylactic (Lonza, VZA-2023), $1\%$ penicillin–streptomycin (Thermo Fisher Scientific, 15140163), and 50 µg/ml of l-ascorbic acid phosphate magnesium salt n-hydrate (Fujifilm Wako Pure Chemical, 013-19641).
## cBMSC in vitro adhesion assay
To investigate cell adhesion abilities under culture, single cell preparations of each experimental cBMSC group (i.e., freshly harvested and freeze-thawed stocks) were seeded onto 35-mm tissue culture treated dishes (CELLTREAT, 229635) at a seeding density of 5 × 104 cells/dish. After 15–60 min, dishes were washed with PBS twice to remove any non-adherent cBMSCs and determine the number of adherent cells by counting the remaining adhered cells using phase contrast microscopy images (Zeiss, AXIOVert. A1, 5 random positions in each sample, $$n = 4$$). Additionally, adhesion rates of freshly harvested and freeze-thawed cBMSCs after seeding were observed using time-lapse imaging (Olympus, IV-83: $$n = 3$$) using a stage-top incubator (Tokai Hit, INU) and counting the number of adherent cells on 35-mm tissue culture treated dishes. Additionally, the gene expression of integrin β1 was examined at varying time points during cell adhesion and spreading. cBMSC were seeded onto 100-mm diameter dishes at 5000 cells/cm2 and incubated for 15, 30, and 60 min. After incubation, the dishes were washed with PBS twice, and total RNA was collected using RNeasy mini kits (Qiagen, 74104). cDNA synthesis was performed with 1 µg of total RNA using a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, 4368814). Gene expression was examined by quantitative Real-Time PCR (qRT-PCR) using TaqMan® Gene Expression Assays (Thermo Fisher Scientific: ITGB1; Hs01127536_m1), normalized to expression of the internal control gene, B2M, and compared between freshly harvested and freeze-thawed cBMSC sheets.
## Fluorescent microscopy analysis of cell actin fiber formation and focal adhesion molecule localization
Freshly harvested and freeze-thawed cBMSCs were each seeded onto FBS-coated chamber slides (CELLTREAT, 229164) at a seeding density of 2000 cells/cm2 to observe single cells, and incubated for 3, 5, and 24 h to follow their adherent cell morphology and focal adhesion formation using phalloidin and the focal adhesion marker, vinculin. At each time point, incubated cells were washed with PBS and fixed with $4\%$ paraformaldehyde (PFA) for 10 min. Samples were then incubated with $5\%$ goat serum (Jackson Immunoresearch, 005-000-121) and $0.1\%$ Triton X (Sigma-Aldrich, T8787) in PBS for 1 h at room temperature, followed by incubation with primary antibody, anti-human vinculin (MilliporeSigma, V9131), for 2 h at room temperature. After washing with PBS for 30 min, samples were incubated with secondary antibody, anti-mouse IgG Alexa Fluor 568 (Thermo Fisher Scientific, A-11004), and Alexa Fluor 488 phalloidin (Thermo Fisher Scientific, A12379) for 1 h at room temperature. Stained samples were mounted with ProLong™ Gold Antifade Mountant with DAPI (Thermo Fisher Scientific, P36935). Images were taken by a fluorescent microscope (Zeiss, AXIOVert. A1: 5 random positions in each sample, $$n = 4$$), and fluorescent signal areas were quantified by Image J (National Institutes of Health). Feret’s diameters, a measure of the longest and shortest diameter of adherent cells, were measured by image J to quantify cell shapes43,44. Error bars indicate standard deviations.
## Cytokine production analysis
Total RNA from cBMSC sheets was collected, and cDNA synthesis was performed as described above. Gene expressions were examined by qRT-PCR using TaqMan® Gene Expression Assays (Thermo Fisher Scientific: B2M; Hs00187842_m1, HGF; Hs00379140_m1, VEGFA; Hs99999070_m1, FGF2; Hs00266645_m1, IL10; Hs00961622_m1, FN1; Hs01549976_m1), normalized to the expression level of the internal control gene, B2M, and compared between freshly harvested and freeze-thawed cBMSC sheets. In addition, specific protein production was measured by evaluating protein concentrations in the media using ELISA (R&D Systems; Human HGF Quantikine ELISA Kit; DHG00B, Human VEGF Quantikine ELISA Kit; DVE00, Human IL-10 Quantikine ELISA Kit; D1000B). To investigate cytokine release from fabricated cell sheets detached freshly harvested and freeze-thawed cBMSC sheets were replated onto 6-well inserts and incubated for 3-days in cell culture media with supernatant collection each day.
## Freeze-thawed rat BMSC sheet transplantation in rodent kidney IRI model
Rat cBMSCs derived from SDTg (CAG-EGFP) rats were provided by SCM Lifescience (Republic of Korea) and confirmed for rat MSC phenotype by tri-lineage differentiation and surface maker expression of CD90+, CD29+, MHC class II−, CD11−, and CD45− at P5. Rat freeze-thawed cBMSC sheets derived from SDTg (CAG-EGFP) rats were prepared following protocols identical to the human freeze-thawed cBMSC sheets described above. The rat kidney fibrosis model study was conducted under approval of the Animal Care & Use Committee, IACUC, University of Utah (assigned ID: 19-03011). All experiments were conducted in accordance with relevant guidelines and regulations. Study procedures were reported previously for the rat BMSC sheet transplantation model17, except in this study, the renal capsule remained intact, and no right kidney nephrectomy occurred42. Briefly, Lewis rats (6-week-old, males, Charles River Laboratories) were acclimatized in facilities for one week and randomly divided into three groups: [1] native tissue ($$n = 3$$), [2] IRI procedure without freeze-thawed cBMSC sheet transplantation ($$n = 8$$) and [3] IRI with freeze-thawed cBMSC sheet transplantation ($$n = 8$$), with no animal exclusions. Under isoflurane anesthesia, the IRI model was performed by clamping the left renal pedicle for 60 min. Allogeneic rat cBMSC GFP-cell sheets were transplanted onto the intact left renal capsule; transplanted cell sheets covered the kidney dorsal side and stably adhered to the entire kidney surface without suturing. At 4-weeks post-surgery, all kidneys were collected for histological analysis using periodic acid–Schiff (PAS) and Masson's trichrome (MT) staining to evaluate excess extracellular matrix deposition indicative of acute renal fibrosis. Additionally, the renal parenchymas were collected from each rat for gene expression analysis to evaluate fibrotic marker expression. Tissue homogenization by forceps mincing and syringing allowed total RNA extraction using RNeasy Fibrous Tissue Mini Kits (Qiagen; 74704). cDNA synthesis was performed as described above. Gene expression was examined by qRT-PCR using TaqMan® Gene Expression Assays and normalized to expression levels of the internal control gene, B2m, and compared between freshly harvested and freeze-thawed cBMSC sheets. One surgical and non-surgical, blinded, operators performed analysis separately. All procedures were performed in accordance with ARRIVE guidelines.
## Statistical analysis
All statistical analysis for in vitro experiments was conducted with data sets of $$n = 4$$ (Figs. 2, 3, 4, 5b) and $$n = 4$$ or 5 (Fig. 5c) using unpaired, two-tailed, Student’s t-test. Different statistical analysis for in vivo experiments was conducted with data sets of native ($$n = 3$$), IRI ($$n = 8$$), IRI+ freeze-thawed cBMSC sheet transplantation ($$n = 8$$) groups (Fig. 6e). Statistical analysis was conducted by one-way ANOVA, with Tukey’s multiple comparisons. Statistical significance was defined as **$P \leq 0.01$, *$P \leq 0.05$ and not significant (N.S.) P ≥ 0.05 using GraphPad Prism (http://www.graphpad.com).
## Supplementary Information
Supplementary Information 1.Supplementary Information 2.Supplementary Video 1.Supplementary Video 2. The online version contains supplementary material available at 10.1038/s41598-023-31437-7.
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|
---
title: Neuromodulatory effect of 4-(methylthio)butyl isothiocyanate against 3-nitropropionic
acid induced oxidative impairments in human dopaminergic SH-SY5Y cells via BDNF/CREB/TrkB
pathway
authors:
- Prabhjot Kaur
- Shivani Attri
- Davinder Singh
- Farhana Rashid
- Sharabjit Singh
- Avinash Kumar
- Harjot Kaur
- Neena Bedi
- Saroj Arora
journal: Scientific Reports
year: 2023
pmcid: PMC10023800
doi: 10.1038/s41598-023-31716-3
license: CC BY 4.0
---
# Neuromodulatory effect of 4-(methylthio)butyl isothiocyanate against 3-nitropropionic acid induced oxidative impairments in human dopaminergic SH-SY5Y cells via BDNF/CREB/TrkB pathway
## Abstract
Mitochondrial impairment, energetic crisis and elevated oxidative stress have been demonstrated to play a pivotal role in the pathological processes of Huntington’s disease (HD). 3-Nitropropionic acid (3-NPA) is a natural neurotoxin that mimics the neurological dysfunctions, mitochondrial impairments and oxidative imbalance of HD. The current investigation was undertaken to demonstrate the neuroprotective effect of 4-(methylthio)butyl isothiocyanate (4-MTBITC) against the 3-NPA induced neurotoxicity in human dopaminergic SH-SY5Y cells. The experimental evidence of oxidative DNA damage by 3-NPA was elucidated by pBR322 DNA nicking assay. In contrast, the 4-MTBITC considerably attenuated the DNA damage, suggesting its free radical scavenging action against 3-NPA and Fenton's reagent. The dose and time-dependent increase of 3-NPA revealed its neurotoxic dose as 0.5 mM after 24 h of treatment of SH-SY5Y cells in MTT assay. In order to determine the optimal dose at which 4-MTBITC protects cell death, the 3-NPA (IC50) induced cells were pretreated with different concentrations of 4-MTBITC for 1 h. The neuroprotective dose of 4-MTBITC against 3-NPA was found to be 0.25 μM. Additionally, the elevated GSH levels in cells treated with 4-MTBITC indicate its propensity to eliminate reactive species generated as a result of 3-NPA-induced mitochondrial dysfunction. Likewise, it was determined through microscopic and flow cytometric experiments that 3-NPA's induced overproduction of reactive species and a decline in mitochondrial membrane potential (MMP) could be efficiently prevented by pre-treating cells with 4-MTBITC. To elucidate the underlying molecular mechanism, the RT-qPCR analysis revealed that the pre-treatment of 4-MTBITC effectively protected neuronal cells against 3-NPA-induced cell death by preventing Caspase-3 activation, Brain-derived neurotrophic factor (BDNF) upregulation, activation of cAMP response element-binding protein (CREB) and Nrf2 induction. Together, our findings lend credence to the idea that pre-treatment with 4-MTBITC reduced 3-NPA-induced neurotoxicity by lowering redox impairment, apoptotic state, and mitochondrial dysfunction. The present work, in conclusion, presented the first proof that the phytoconstituent 4-MTBITC supports the antioxidant system, BDNF/TrkB/CREB signaling, and neuronal survival in dopaminergic SH-SY5Y cells against 3-NPA-induced oxidative deficits.
## Introduction
According to World Health Organization (WHO), the entire global population having age group of 60 years-and-above is being predicted to become double over the next three decades1. With increasing global life expectancy rate, the prevalence of heterogeneous group of neurodegenerative diseases (NDDs) such as Alzheimer’s disease, Amyotrophic lateral sclerosis, Huntington’s disease, and Parkinson’s disease is also rising day by day. As a result of these neurological disorders, an estimated range of 6–8 million people die annually2. Excessive levels of reactive oxygen species (ROS), neuroinflammation, mitochondrial impairment, and disturbances in protein homeostasis (proteostasis) contribute to the progression of various NDDs. Huntington's disease (HD) is a progressive, terminal, incurable NDD caused by an extended repeat of CAG codon in the *Huntingtin* gene that encodes an abnormally long sequence of polyglutamine in the Huntingtin protein (Htt)3,4. This autosomal dominant disorder is characterized by motor, cognitive, and psychiatric disturbances5. Atypical involuntary and voluntary movements linked with corticostriatal neuronal loss are the additional hallmarks6. The mutant protein mHtt, according to a number of investigations, may facilitate the breakdown of the mitochondrial electron transport chain. As a result of mitochondrial dysfunction, a predominant increase in the free radical production and oxidative stress occurs, which causes gradual brain damage7,8.
Experimental evidences collected in recent years suggest that activation of the BDNF/CREB/TrkB pathway is beneficial in HD9. Brain-derived neurotrophic factor (BDNF) is essential for the control of brain functioning. BDNF stimulates tyrosine kinase B (TrkB), which helps neurons differentiation and survival10. TrkB causes the upregulation of cAMP response element-binding protein (CREB), a downstream effector, to become active. multiple biological processes, including neuron survival, differentiation, and synaptic transmission in the brain, depend on CREB11,12. β-Nitropropionic acid (3-nitropropionic acid, 3-NPA, C3H5NO4) is a mitochondrial toxin that produces striatal alterations similar to those observed in the brain of patients suffering from Huntington’s disease. It irreversibly inhibits the complex II of the electron transport chain13. 3-NPA down-regulates Glutathione (GSH) and initiate pro-oxidant effects which causes bioenergetics deficits in human cells by affecting mitochondrial function and redox balance14,15. Moreover, it reduces the levels of neurotrophic factors such as BDNF which is critical for normal health of brain cells16.
Since, the pathophysiology of HD is still unknown and existing treatment options provide symptomatic relief which is not sufficiently feasible to restore the quality of life. In light of this, antioxidants are the most desirable alternative natural multi-target therapeutic candidates with minimum side effects particularly for the brain, a tissue rich in fatty acids and is especially vulnerable to ROS-mediated mitochondrial damage and oxidative stress than other organs17. As indicated by Melrose et al.18, the antioxidant properties of activated glucosinolate compounds are known to guard the brain health. Food rich antioxidants, especially isothiocyanates present in Brassica vegetables, have been reported to display neuroprotective effects in several experimental paradigms19.
Eruca sativa (Mill) Thell. is an annual plant of the family Brassicaceae commonly known as rogula, salad rocket, or taramira. It is rich in gluco-4-methylthiobutyl isothiocyanate, a glucosinolate which breaks down to form a highly volatile glucosinolate hydrolytic product i.e. 4-(methylthio)butyl isothiocyanate (4-MTBITC) or erucin20. 4-MTBITC is a structural analog of sulforaphane (SFN) which known for its neuroprotective potential. A comparative study done by Morroni et al.21 demonstrates the adaptive neuroprotective benefits of SFN and 4-MTBITC against in vitro and in vivo PD model through induction of GSH via nuclear factor erythroid 2–related factor 2 (Nrf2) activation. In recent experimental study, SFN has been confirmed to exert neuroprotective actions through normalizing mitochondrial function and suppressing oxidative stress via Nrf2/HO-1 pathway in SH-SY5Y cell line22. Similar to this, 4-MTBITC can support healthy neuronal function in wide range of animal and human tissues23. Contrary to SFN, the biological effects of 4-MTBITC are not well supported by experimental data. 4-MTBITC exhibits a cytoprotective, anti-inflammatory and antioxidant tendency against numerous NDDs24–26. Therefore, the current study addresses the ameliorating effect of 4-MTBITC against the modulation of molecular and biochemical markers in neurodegeneration caused by 3-NPA in neuroblastoma (SH-SY5Y) cells.
## Isolation and characterization of 4-MTBITC
A quick and efficient hydro-distillation method resulted in the extraction of 4-MTBITC from seeds of plant *Eruca sativa* with > 99 percent purity. It was detected by the UHPLC-PDA as a single peak. The m/z ions were also examined and their results were contrasted with published data. The characterization was done with 1H NMR (Supplementary Fig. S1).
## 4-MTBITC protects against pBR322 DNA damage
DNA Nicking assay is a reliable method to quantify the pBR322 DNA damage with conversion of its native supercoiled form into open circular and nicked linear forms. In the present study, we have analyzed 3-NPA as a nicking agent. 4-MTBITC showed the DNA protective potential against the free radicals generated by Fenton’s reagent and protection increased with increase in concentration of 4-MTBITC from 0.3 mg/ml to 0.9 mg/ml. At highest concentration, 4-MTBITC showed $80\%$ of preserved native supercoiled plasmid DNA (Fig. 1). Similarly, the protective effect of 4-MTBITC was observed against the 3-NPA induced oxidative damage. The intensity of supercoiled form of plasmid was found to be preserved with increase in the concentration of 4-MTBITC against the damage caused by 1 mg/ml concentration of 3-NPA for 1 h. These results are well presented in the band profile of plasmid (pBR322) DNA in Fig. 2.Figure 1(A) Lane 1–6 represent protective effect of 4-MTBITC in pBR322 DNA nicking with Fenton’s reagent (FR). 1: native supercoiled DNA; 2: FR treated DNA; 3: FR treated DNA and rutin (100 μg/ml); 4–6: the FR treated DNA with varying concentrations (0.3, 0.6 and 0.9 mg/ml) of 4-MTBITC; (B) Tabular; and (C) Graphical representation of quantitative proportion of different DNA plasmid forms after treatment using Lab Image software. All the treatments in each lane of gel were run in parallel. The supplementary file is provided with the raw full-length gel corresponding to figure number used here in the manuscript. Figure 2(A) Protective effect of 4-MTBITC on pBR322 DNA damage triggered by Fenton’s reagent (FR) and 3-NPA. Lane 1, native supercoiled DNA; Lane 2, FR treated DNA; Lane 3, FR treated DNA + 0.3 mg/ml 4-MTBITC; lane 4, FR treated DNA and rutin (100 μg/ml); lane 5, DNA + 3-NPA (1 mg/ml); lanes 6–8, 3-NPA treated DNA with varying concentrations (0.3, 0.6 and 0.9 mg/ml) of 4-MTBITC; (B) Tabular; and (C) Graphical representation of quantitative proportion of different DNA plasmid forms after treatment. All the treatments in each lane of gel were run in parallel. The supplementary file is provided with the raw full-length gel corresponding to figure number used here in the manuscript.
## 3-NPA treatment induced neurotoxicity in SH-SY5Y cells
To determine the effect of 3-NPA on viability of the human neuroblastoma (SH-SY5Y), the cells were treated with different concentrations of 3-NPA. Figure 3A shows the concentration–response study of 3-NPA (0.1–15 mM) for 24 h. It exhibited concentration-dependent cell death with IC50 of 0.5 mM. As a result, the 0.5 mM dose was selected to induce neurotoxicity in SH-SY5Y cells for further studies. Figure 3(A) Dose-dependent cytotoxicity profile of 3-NPA (0.1–5 mM) in SH-SY5Y cells; (B) Dose-dependent cellular viability of 4-MTBITC alone; (C) Dose-dependent study of cellular viability of pretreatment of 4-MTBITC (0.625–1000 μM) against 0.5 mM 3-NPA exposed cells for 24 h; (D) Representation of cellular viability of optimal cytotoxic dose of 3-NPA (0.5 mM) and optimal cytoprotective dose of 4-MTBITC (1.25 µM) used in combination with 3-NPA. MTT assay results detected the cellular viability as the percentage of reduction in MTT with comparison to control. The measurements are represented as the mean ± SE at the level of significance p ≤ 0.05. Small letters as data labels indicate statistical difference comparatively to control by one-way ANOVA, followed by the Tukey post hoc test.
## 4-MTBITC attenuated 3-NPA neurotoxicity in neuroblastoma (SH-SY5Y) cells
The neuroprotective potential of 4-MTBITC against 3-NPA toxicity was evaluated by the MTT reduction assay. A 1 h prior exposure of cells with different doses of 4-MTBITC (0.625–1000 μM) against 0.5 mM 3-NPA for 24 h was observed (Fig. 3C). It was found that more than $50\%$ cells were significantly viable at the lower doses (1.25–5 μM) of 4-MTBITC (Fig. 3C). The protective concentration of 4-MTBITC at which roughly $60\%$ of SH-SY5Y cells survived the toxicity generated by 3-NPA was determined to be 1.25 μM. The dose of 1.25 μM was selected throughout the subsequent studies as the protective concentration at which approximately $60\%$ SH-SY5Y cells survived the toxicity induced by 3-NPA. Moreover, the optimal cytotoxic dose of 3-NPA (0.5 mM) and optimal cytoprotective dose of 4-MTBITC (1.25 µM) used in combination with 3-NPA in MTT assay were statistically significant comparatively to control [F-ratio: 22.2801; $$p \leq 0.0091$$] (Fig. 3D). However, the results of 4-MTBITC alone exhibited dose-dependent reduction in the metabolic ability of MTT as depicted in the Fig. 3B indicating the cell survival at lower doses only.
## 4-MTBITC increased the levels of intracellular reduced glutathione (GSH) in SH-SY5Y cells
GSH is a significant non-enzymatic antioxidant that safeguards cells against exogenous and endogenous toxins, including ROS, and preserves the cellular redox balance. On investigation of changes in intracellular GSH level by using MCB probe, it was found to be depleted (0.61 -fold) in the cells exposed to 3-NPA neurotoxin in comparison to untreated cells. However, it significantly increased in 4-MTBITC treated cells approximately by 1.72-folds. Moreover, the pretreatment of cells with 4-MTBITC prior to 3-NPA exposure reduces the oxidative damage partly by restoring the GSH level close to control with approximately 1.29-folds increase (Fig. 4).Figure 4The effect of 4-MTBITC on SH-SY5Y cells treated with or without 3-NPA on the intracellular GSH level measured by using MCB fluorescent probe. Data (mean ± SEM; p ≤ 0.05) are expressed as fold change for three independent experiments (one-way ANOVA, followed by the Tukey post hoc test).
## Phase-contrast microscopy
The changes in the cell morphology of SH-SY5Y cells, induced by 3-NPA, 4-MTBITC and their co-administration, were visualized using microscopy (Fig. 5A(a–d)). Observed morphological alterations were rounding-off of the cells, loss of neurites and detachment from the surface forming clusters in the 3-NPA treated cells (Fig. 5A(b)). 4-MTBITC treatment (Fig. 5A(d) also showed reduced viability and loss of contact of cells relative to control (Fig. 5A(a)). Whereas, Fig. 5A(c) showed the co-administration of 4-MTBITC with 3-NPA where cell survival was restored with few morphological alterations in comparison to 3-NPA treated cells. Figure 5Representative microscopic images of neuroblastoma SH-SY5Y cells. ( A) Brightfield images. ( B) Fluorescent images of Hoechst staining showing 4-MTBITC mediated neuroprotection against SH-SY5Y cell death induced by 3-NPA exposure. ( C) Scanning electron micrographs (SEM) of SH-SY5Y cells. ( D) Fluorescence microscopic images of Acridine orange and ethidium bromide (AO/EtBr) double staining. Arrow indicated live (L) cells, early apoptosis (EA), late apoptosis (LA) and necrotic (N) cells in treated groups. ( a) Untreated cells; (b) 3-NPA treated cells; (c) 3-NPA + 4-MTBITC treated cells; (d) 4-MTBITC alone treated cells ($$n = 3$$ coverslips per group). All images were processed using Microsoft Word “Corrections” for brightness and contrast applied equally including controls.
The SH-SY5Y cells exposed to different experimental treatments were monitored and morphological changes were detected under a phase-contrast microscope, Nikon Eclipse Ti2, Japan.
## Fluorescence microscopy by Hoechst staining
The characteristic fragmentation and condensation of the nuclear material in SH-SY5Y cells (arrows) appeared by Hoechst 33258 staining after treatment of 4-MTBITC (0.25 μm), with or without 3-NPA for 24 h. The images 5B(a-d) were captured with a fluorescent microscope NIKON Eclipse Ti2. The nuclei of cells treated with 1.25 µM 4-MTBITC were found to be intact fine structure nearly similar to untreated cells. However, when SH-SY5Y cells were exposed to 3-NPA, they exhibited typical symptoms of apoptosis as nuclear DNA condensation and nuclear disintegration. The nuclear analysis of cells co-treated with 4-MTBITC and 3-NPA revealed lower apoptotic damage in contrast to the 3-NPA treated cells (Fig. 5Bc).
## Scanning electron microscopy (SEM)
The SEM of SH-SY5Y neuronal cells revealed the marked ultrastructural changes in different groups as shown in Fig. 5C(a–d). In 3-NPA exposed cells, the evident defects were reduced size, rounding-off as well as detachment of cells from substrate, membrane blebbing and appearance of small apoptotic bodies. These defects were found to be halted to some extent in 4-MTBITC treated cells prior to 3-NPA exposure indicating the cellular protection. Collectively, the morphological alterations associated with apoptosis show that 3-NPA causes SH-SY5Y cells to undergo apoptosis, and lower dosages of 4-MTBITC ensure protection.
SH-SY5Y cells (4 × 105 cells/well) were plated overnight over 12 mm circular coverslips and subjected to different experimental doses based on the method given by Chen et al.30. After that cells were harvested and subsequently fixed with a mixture of $4\%$ paraformaldehyde (PFA) and $2.5\%$ glutaraldehyde for 4 h at − 20 °C. Dehydration of cells was done with a series of chilled ethanol for time period of 5 min. The cell-adhered coverslips were mounted on the stubs and coated with silver using sputter coater (Quorum Q150R ES). The images were captured with a SEM, Carl Zeiss, EVO LS10, Germany.
## Double staining with Acridine orange and ethidium bromide (AO/EtBr)
The evidence of apoptosis is represented by double staining with Acridine orange and ethidium bromide (AO/EtBr) in 3-NPA and 4-MTBITC treated cells comparable to untreated cells. As shown in Fig. 5D(a–d), the live cells are green stained in control group whereas, nuclear changes speculated early as well as late apoptotic cell death (indicated with arrows) in 3-NPA treated SH-SY5Y cells. The lesser proportion of apoptotic nuclei is visible in 4-MTBITC treated cells (Fig. 5D(c–d)).
## 4-MTBITC pretreatment decreased the levels of 3-NPA induced Intracellular Reactive Oxygen Species (ROS)
The ROS production was investigated in all the experimental groups by using 2’, 7’-dichlorofluorescein diacetate (DCFH-DA) dye. The intensity of fluorescence monitored with the flow cytometer is proportional to the accumulated ROS (M2) within the cell cytoplasm (Fig. 6). The application of 3-NPA and 4-MTBITC elevated the reactive species production (M2) in SH-SY5Y cells by $69.2\%$ and $61\%$ respectively. However, the pre-treatment of 4-MTBITC against 3-NPA significantly defends the SH-SY5Y cells by decreasing the ROS levels by $54.5\%$ comparable to 3-NPA exposed cells. Figure 6(A) The flow cytometric representation of Intracellular reactive species level detected by the fluorescent probe DCFH-DA. M2 represents the proportion (%) of ROS accumulated cells in comparison to live cells depicted as M1 phase. ( B) Quantification of flow cytometry results. Results are expressed as the mean ± SE of three replicates.
### Δψm) damage in SH-SY5Y cells
The collapse of MMP was estimated by Rhodamine 123 (Rh-123) dye. In the flow cytometry dot plots, M1 represents the proportion (%) of cells with disrupted potential (Δψm) in comparison to intact cells depicted as M2 phase (Fig. 7). The higher mitochondrial depolarization was evident in the 3-NPA and 4-MTBITC treated SH-SY5Y cells with approximate of $61\%$ and $34.1\%$ respectively. Whereas, approximately $40\%$ mitochondrial protection was observed for 4-MTBITC (1.25 μM) against the depolarization of membrane induced by 3-NPA.Figure 7(A) Representative dot plots of flow cytometric loss of MMP for the 3-NPA exposed SH-SY5Y cells with or without 4-MTBITC in contrast to control cells. M1 represents the proportion (%) of cells with disrupted potential (Δψm) in comparison to intact cells depicted as M2 phase (B) Quantification of the membrane potential and intact cell population. Values are represented as the mean ± SEM; $$n = 3$$ set of independent experiments. MMP, mitochondrial membrane potential.
## 4-MTBITC protects against 3-NPA-induced apoptosis in SH-SY5Y cells
The cell apoptosis analysis was performed on SH-SY5Y cells treated with 3-NPA with or without the 4-MTBITC and incubated for 24 h. The results of Annexin V-FITC/PI dual staining via flow cytometry showed the increased apoptotic rates (EA: $46.6\%$; LA: $5.1\%$) for 3-NPA induced cells compared to untreated cells (Fig. 8). The significant apoptosis of about $1.8\%$ was also detected for such lowest concentration of 4-MTBITC (1.25 μM). However, the co-treatment of 4-MTBITC prior to 3-NPA significantly reduced the apoptosis level (EA: $19.7\%$; LA: $3.2\%$).Figure 8(A) Flow cytometric representation of apoptosis by dot plots of double annexin V/FITC-PI staining and flow cytometry in SH-SY5Y cells. Data are presented as the proportion (%) of cells in four different quadrants: live cells (Annexin V and PI negative), early apoptosis (EA = Annexin V-positive, PI negative), late apoptosis (LA = Annexin V-positive, PI positive) and necrotic (Annexin V-negative, PI positive). ( B) The corresponding bar diagram of flow cytometry results as percentage of apoptosis. Results are expressed as the mean ± SEM of three set of independent experiments. AV, Annexin V.
## Neuronal survival via BDNF/TrkB/CREB signaling, Caspase-3 down-regulation and Nrf2 induction: role of 4-MTBITC
The analysis of RT‑qPCR results revealed a significant down regulation in the expression of genes such as Nrf2, BDNF, TrkB and CREB, whereas significant upregulation of Caspase 3 at IC50 of 3-NPA compared to control in the SH-SY5Y cell line (Fig. 9). In contrast, the 4-MTBITC treatment counteracts the action of 3-NPA significantly by upregulating the gene expression of Nrf2, BDNF, TrkB and CREB. The obtained results with 4-MTBITC suggested the enhanced neuroprotection via BDNF as neurotrophic factor and Nrf2 as anti-oxidative factor. The significant lowered levels of Caspase-3 are consistent with supporting action of cell survival by optimal dose of 4-MTBITC. The values are presented as mean ± SE for $$n = 3$$ set of experiments. Figure 9Relative gene expression of different genes Nrf2, BDNF, TrkB, CREB and Caspase-3 in SH-SY5Y cells by RT-qPCR. The values are represented as mean ± SE of three replicates and small letters as data labels indicate significant difference ($p \leq 0.05$) between different treatment groups.
## Discussion
Askeland and colleagues35 documented that HD was linked to significant nuclear DNA damage and alterations in mitochondrial features. The observed DNA damage raises the possibility that mHTT compromises genomic integrity in HD patients. Previous studies by Leba et al. have shown that in vitro DNA nicking tests enable more efficient and clinically meaningful screening of potential in vivo antioxidant agents36. Therefore, DNA nicking assay was used to investigate the action of 4-MTBITC as free radical scavenger against plasmid DNA subjected to both Fenton's reagent as well as 3-NPA, separately. This study is the first to use the effectiveness of 3-NPA as a nicking agent. The DNA damage was found to occur at a dose of 1 mg/ml 3-NPA for 1 h. The different doses of 4-MTBITC demonstrated its capacity to guard DNA from free radicals produced by Fenton's reagent, and that potential rises with concentration. Similarly, the results of co-administration of 4-MTBITC and 3-NPA for 1 h revealed efficacy of 4-MTBITC as a potent free radical scavenger as it established its scavenging ability in DNA nicking assay against 3-NPA induced genomic toxicity.
An increasing body of evidence highlights the tremendous potential of isothiocyanates, like SFN and 4-MTBITC, as an alternative and preventive therapy approach to limit the onset and progression of neurodegenerative illnesses19,21,24,37. In the current investigation, we looked for the mechanisms that would underlie the putative protective benefits of 4-MTBITC against mitochondrial malfunction and excessive oxidation stress brought by 3-NPA, a mitochondrial toxin employed in HD experimental models38. The neuron-like SH-SY5Y cells were exclusively chosen as the study subject since they have various essential characteristics of neurons, including the expression of dopaminergic marker genes and the physical traits of adrenergic neurons. Moreover, it is well known that post-mitochondrial processes like autophagy and apoptosis play a crucial role in inducing 3-NPA toxicity in this cell line39. In the present study, the cytotoxicity of 3-NPA was investigated on SH-SY5Y cells. The experimental outcomes show that the cytotoxicity of 3-NPA was dose-dependent, and IC50 of 3-NPA as 0.5 mM was selected for subsequent experiments (Fig. 3A). However, as shown in Fig. 3B, 4-MTBITC alone resulted in a dose-dependent drop in the metabolic ability to reduce MTT, revealing that cell survival was possible only at lower concentrations. These results support the reported findings in the literature24,40. Subsequent evaluation revealed that 1-h pre-treatment with 4-MTBITC considerably reduced the loss of cellular viability caused by 3-NPA over the course of 24 h (Fig. 3C). Accordingly, treatment with 4-MTBITC was able to dramatically raise (by 1.72-fold) the levels of GSH (Fig. 4). This ability of 4-MTBITC to enhance GSH levels is clear from previous experiments on human and rat non-neuronal cells as well as on neuronal cells24,41,42. It should be noted that when cells were pre-treated with 4-MTBITC, the considerable drop in levels of GSH seen in 3-NPA-treated cells was not observed. The fact that GSH levels in this situation (4-MTBITC + 3-NPA) were significantly lower than in cells treated simply with 4-MTBITC definitely suggests that the stimulatory actions of 4-MTBITC toward GSH levels help to clear out ROS produced following 3-NPA-mediated mitochondrial damage.
In the recent past, various studies indicated that several neurodegenerative diseases, including HD, are accompanied by mitochondrial dysfunction43. The research advances have demonstrated the number of beneficial pharmacological lines shielding these mitochondrial deficiencies in different neurological illnesses. However, clinical data shows that mitochondrial modulators have not been successful in showing positive effects in HD patients44,45. An alternate theory for this condition is that providing neuroprotection in HD may involve pharmaceutical measures to reduce adverse events referred to as post mitochondrial events caused by mitochondrial dysfunction. Decrease in MMP is a marked early apoptotic event and 3-NPA was able to reduce the MMP indicating that 3-NPA mediated cell death in SH-SY5Y cells was at least partially attributed to apoptosis40. In agreement with this, our in vitro data demonstrate that 4-MTBITC attenuated 3-NPA's induction of reactive species, decrease in ΔΨm, and induction of cell viability loss.
There are lots of evidences pointing to the major effect of mitochondrial complex inhibition being an increase in reactive species production46. Based on this and our reported results (Figs. 6 and 7), we looked into the possibility of role of antioxidant pathways in 4-MTBITC's protective actions against 3-NPA-induced cytotoxicity. Pre-treatment with 4-MTBITC totally attenuated the considerable increase in reactive species production caused by 3-NPA treatment (0.5 mM) (Fig. 6A). It is feasible to assume that the beneficial effects of 4-MTBITC in reducing the number of reactive species caused by 3-NPA may be reliant on its scavenging abilities given that such an effect was seen at lower 4-MTBITC concentrations (1.25 μM). That’s how the 3-NPA-induced negative events including the production of reactive species and a drop in MMP were hindered by 4-MTBITC pre-treatment. Moreover, the transient rise in intracellular ROS levels was observed with treatment of 4-MTBITC alone in comparison to control (Fig. 6). It is well known that induction of ROS was found to be responsible for the activation of Nrf2, which is master regulator of antioxidant defense system. Thereby, increasing the intracellular GSH as a part of protective entity against 3-NPA toxicity47. Additionally, the literature supports that GSH further interacts with ROS and acts as a substrate for the activation of downstream antioxidant defense against oxidative damage48. Thus, the initial rise of ROS as well as enhanced GSH content at the experimental dose of 4-MTBITC may be attributed to the adaptive neuroprotective mechanism of isothiocyanates for maintaining the redox cellular homeostasis21,24. The further investigation of mechanistic action of 4-MTBITC was done by RT-qPCR. Keeping in mind the possible potency of 4-MTBITC for different endogenous targets, the analysis of gene expression levels of Nrf2, BDNF, TrkB, CREB and Caspase-3 was carried out in the present study. The results demonstrated that 4-MTBITC reduced the oxidative stress caused by 3-NPA in SH-SY5Y cells by upregulating the expression of transcription factor Nrf2. The previous studies have reported that the expression of Nrf2 tends to decline as the disease progresses, it is upregulated in the primary stages of HD by the creation of reactive species brought on by the toxicity of 3-NPA. The protective impact of Nrf2 against oxidative insults may be greatly enhanced by the stimulation of transcription of protective genes involved in antioxidative potential, such as HO-1 synthetase49. Additionally, Zhang et al. mentioned the requirement of Nrf2 in stress-induced neurogenesis and how up-regulation can lessen the impact of mitochondrial malfunction, control the generation of ROS, and lessen neuronal apoptosis50.
The motor, cognitive, and psychiatric decline are primarily caused by the dysfunction and mortality of the medium sized spiny neurons (MSNs) of the striatum and this selective susceptibility of MSNs in HD has been attributed to a deficit in BDNF/TrkB signalling51. According to Tejeda et al., HD patients have been found to have lower levels of striatal BDNF protein, which is caused by decreased neurotrophin expression and impaired corticostriatal transportation52. BDNF has been shown to play a major role in learning and memory and is regarded as a key synaptic regulator of synapse development and synaptic plasticity. BDNF activates intracellular signaling pathways by binding to the particular receptor TrkB, which results in receptor dimerization and autophosphorylation53,54. In consistent to these investigations, the 4-MTBITC treatment against 3-NPA induced toxicity significantly boosted BDNF expression and thus may potentially confer neuroprotection. Furthermore, growing data suggests that BDNF-mediated neuronal activity may be the root cause of AKT activation. AKT signaling pathway can be activated by persistent BDNF induction55. The AKT pathway, one of the most well studied cellular signaling pathways, has a strong connection to HD's pathogenesis and is very vulnerable to oxidative stress56. *Target* genes are activated by an active transcription complex created by the action of AKT, which can also cause the phosphorylation of CREB. A transcription factor called CREB is crucial for neurogenesis and neural plasticity57. Aside from the fact that oxidative stress exposure is directly linked to a decrease in CREB expression, research have suggested that CREB phosphorylation is decreased during the clinical pathophysiology of HD and that CREB phosphorylation is increased during the anti-HD response in people52,58. BDNF is also vital CREB target, and CREB activation is necessary for BDNF expression. These results imply that the HD pathogenesis may be closely related to the BDNF/TrkB/CREB cycle signalling system59,60. Despite the fact that current findings suggested that 4-MTBITC had no effects on the protein expression in the BDNF/TrkB/CREB signalling pathway in normal SHSY5Y cells, the situation was very different under the condition of oxidative stress, where 3-NPA-triggered decreased phosphorylation of TrkB, AKT, and CREB as well as reduced expression of BDNF could be reversed by 4-MTBITC. In constrast, the activation of downstream targets, such as the anti-apoptotic protein Caspase-3, may come from the overexpression of CREB and prevent neuronal apoptosis61,62. Similar results were obtained in the current study, where 4-MTBITC reduced the 3-NPA-induced Caspase-3 activation and delayed apoptotic neuronal death. Moreover, the effects of 4-MTBITC on ROS generation and MMP deficit were dependent on the stimulation of the BDNF/TrkB/CREB signaling, suggesting that this pathway is likely to be a crucial stage in the development of 4-MTBITC's anti-oxidative effects.
## Conclusion
According to this study, the 3-NPA is a pBR322 DNA nicking agent and 4-MTBITC is a powerful free radical scavenger against 3-NPA-induced DNA damage. Moreover, the present study provided the first evidence that dietary phytoconstituent 4-MTBITC has the neuroprotective effects on 3-NPA induced cytotoxicity in cultured human dopaminergic SH-SY5Y cells. The outcomes showed that 4-MTBITC treatment reduced the redox impairment, apoptotic state and mitochondrial dysfunction caused by 3-NPA in SH-SY5Y cells. Additionally, it may function to strengthen the antioxidant system, stimulates BDNF/TrkB/CREB signaling, and supports neuronal survival (Fig. 10).Figure 10Diagrammatic representation summarizing the key findings of the study. 4-MTBITC ensuing neuromodulatory action against the 3-NPA-induced cytotoxicity via activation of BDNF/TrkB/CREB signalling and the downstream targets, such as the anti-apoptotic protein Caspase-3. Also, the 4-MTBITC induced Nrf2 upregulation lessen the impact of mitochondrial malfunction and control the generation of ROS, thereby, conferring cytoprotection..
## Chemicals and reagents
Dulbecco's Modified Eagle Media (DMEM) and Ham’s F12 Media (DMEM/Ham’s F12) and 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, MTT were purchased from Himedia Lab, Mumbai, India. 3-NPA, DNase I, L-Glutamine, Fetal bovine serum (FBS), ethidium bromide, penicillin, streptomycin, acridine orange, 2,7-dichloro dihydro fluorescein diacetate (DCFH-DA), glutaraldehyde, propidium iodide, rhodamine 123 (Rh123), DAPI, annexin V-FITC apoptosis detection kit and fluoromount were bought from Sigma-Aldrich (St. Louis, MO, USA). The iScript cDNA synthesis kit was purchased from Bio-rad, California, USA.
## Isolation and characterization of 4-(methylthio)butyl isothiocyanate (4-MTBITC)
The seeds of plant *Eruca sativa* variety named RTM2002 were purchased from Sri Karan Narendra Agriculture University, Jobner, Rajasthan, India. The plant material was further identified and authenticated by Herbarium, Department of Botanical and Environmental Sciences (DOBES), Guru Nanak Dev University, Amritsar with voucher number 7297. The use of plant material in the current study complies with the international, national, and institutional guidelines. Following a standard protocol established in our lab by Arora et al. [ 2018], the hydro distillation method using the *Clevenger apparatus* was used to extract the 4-MTBITC from the seeds of Eruca sativa27. To one liter of double distilled water in the flat bottom flask, 100 g of crushed seeds of E. sativa along with a magnetic bead were added. The flask was placed on a hot plate with a magnetic stirrer and boiled for 3 h. The mixture of oil and water was put in the separating funnel and oil was extracted with solvent dichloromethane (DCM). The DCM was recovered in a rotary evaporator at 30ºC to obtain the crude oil which was kept at -40ºC storage. The collected phytochemical was characterized using UHPLC (Shimadzu, Japan), Mass spectrometry (LC-5050, Shimadzu) and Nuclear Magnetic Resonance (NMR) spectroscopy (500 MHz, Brucker).
## DNA nicking assay
DNA free radical scavenging assay was performed to evaluate ability of 4-MTBITC to protect supercoiled pBR322 DNA from the devastating effects of the Fenton reagent that produces the hydroxyl radicals. 0.5 μl of plasmid DNA was mixed with 10 μl of Fenton’s reagent (30 mM H2O2, 50 μM ascorbic acid, and 80 μM FeCl3) followed by the addition of 10 μl of 4-MTBITC (0.3, 0.6 and 0.9 mg/ml)28. Using distilled water, the final volume of the mixture was brought to 20 μl. The reaction mixture was incubated at 37 °C for 30 min and the sample was loaded on $1\%$ agarose gel (prepared by dissolving 0.5 g agarose in 50 ml 1X TBE buffer) and electrophoresed followed by staining with ethidium bromide. Rutin was taken as a positive control. The gel was observed under the Gel Doc XR system and quantification was done by using LabImage 1D software (Kapelan Bio-Imaging, Leipzig, Germany).
## Cell culture and treatment
SH-SY5Y cell line was purchased from the National Centre for Cell Science (NCCS), Pune, India. The cells were routinely grown in T-25 flask containing a mixture of Dulbecco’s Modified Eagle Media (DMEM) and Ham’s F12 Media (DMEM/Ham’s F12) in ratio 1:1, enriched with $10\%$ fetal bovine serum, streptomycin (100 μg/ml), penicillin (100 U/ml) and $1\%$ 2 mM nonessential amino acid (L-Glutamine). The cells were kept at 37 °C in a humified CO2 incubator having $5\%$ CO2. Cells were further sub-cultured at 70–$80\%$ confluences and media was regularly replaced with fresh media.
3-NPA was uniformly solubilized in phosphate buffer saline (PBS, pH 7.4). 4-MTBITC was dissolved in $0.05\%$ DMSO. To study the neuroprotective effect against 3-NPA induced neurotoxicity, cells were pretreated with 4-MTBITC for 1 h.
## Determination of neurotoxicity of 3-NPA
The neuroblastoma (SH-SY5Y) cells were cultured in 96-well plates to assess the cell viability in terms of 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) reduction to formazan. To determine the cytotoxicity induced by 3-NPA, cells were treated with different doses of 3-NPA (0.1–15 mM) for 24 h. The more the amount of formazan, higher is the viability of cells. The MTT (5 mg/ml) dissolved in PBS was added to media for 4 h. After incubation at 37 °C in $5\%$ CO2, the media was discarded and crystals of formazan were solubilized in DMSO (100 μl). The crystals of formazan were quantified at 570 nm using a microplate reader. The toxicity of 3-NPA was determined as a percent of decrease in intracellular granules of MTT.
## Determination of neuroprotective activity of 4-MTBITC
To estimate the cytoprotective potential of 4-MTBITC, SH-SY5Y cells were subjected to different doses of 4-MTBITC for 24 h. Further, to determine the optimal dose at which 4-MTBITC protects cell death, the 3-NPA (IC50) induced cells were pretreated with different concentrations of 4-MTBITC (0.625–1000 μM) for 1 h. The neuroprotective activity in terms of increase in intracellular MTT granules was measured by MTT formazan exocytosis assay as explained above. The absorbance of DMSO soluble MTT was measured at 570 nm. Data were expressed as a percentage of untreated control cultures.
## Experimental design
After estimating neurotoxicity induction by 3-NPA and optimal protective concentration of 4-MTBITC, the following experimental design was set up. Group I: Control treated with DMSO ($0.05\%$).Group II: 3-NPA (0.5 mM).Group III: 3-NPA (0.5 mM) + 4-MTBITC (1.25 µM).Group IV: 4-MTBITC alone (1.25 µM).
## Estimation of GSH level
The glutathione (GSH) levels were determined in SHSY-5Y cells (2 × 105 cells/ml) seeded in 6-well plates and incubated in CO2 incubator (humidified $5\%$) for 24 h at 37 °C by modified method of29. The SH-SY5Y cells were subjected to different experimental doses. After the incubation, the treatment of each well was replaced with 100 μl of monochlorobimane (MCB), a fluorescent probe of concentration 50 μM. Subsequently, after keeping samples for 30 min at ambient temperature, the levels of GSH were estimated using a multiplate reader with excitation at 355 nm and emission at 460 nm. The readings were determined as concentrations of GSH (μM) in terms of fold increases comparable to untreated cells.
## Morphological studies for apoptotic detection
The SH-SY5Y cells after treatment with 3-NPA with or without 4-MTBITC were observed for various morphological modifications under the phase-contrast microscope, scanning electron microscope (SEM) and fluorescence microscope.
## Fluorescence microscopy
The morphology of nucleus in SH-SY5Y cells was analyzed for various alterations by Hoechst staining31. The SH-SY5Y cells (4 × 105/well) were cultured in six-well plate with coverslips till confluency (70–$80\%$). Thereafter cells were treated with different experiment doses. After that, cells were twice washed with cold PBS and incubated with $4\%$ paraformaldehyde for about 30 min in dark. The cells were washed with PBS followed by staining with DAPI (10 μg/ml) dye and kept for time period of 15 min under dark conditions. After that, cells were twice washed to remove traces of dye with cold 1 × PBS and slides were mounted using fluoromount. The morphological changes of nucleus were imaged under a fluorescent microscope (Nikon Eclipse Ti2, Japan).
## Acridine orange and ethidium bromide (AO/EtBr) double staining
To estimate the process of cell death, fluorescence microscopy with AO/EtBr dual staining was done as per the protocol recommended by Liu et al.32. SH-SY5Y cells (4 × 105/well) were grown in T-25 flask and after confluency, cells were treated with different doses. After 24 h, suspended and adherent cells were trypsinized and harvested to make pellet cells were centrifuged at 2500 rpm for 5 min. The cell pellet was then mixed with PBS (100 μl). From these cell suspensions, 25 μl of cells suspension mixed with 5 μl of dual fluorescent staining solution (100 μg/ml AO and 100 μg/ml EB (AO/EB)) and placed on glass slides and then covered with a coverslip. The morphology of apoptotic and necrotic cells was immediately assessed using a fluorescence microscope.
## Intracellular reactive oxygen species (ROS)
The ROS formation was assessed using fluorescent probe 2′,7′-dichlorodihydroflurescein diacetate, named DCFH-DA according to method recommended by LeBel et al.33. The SHSY-5Y cells (4 × 105 cells/well) were incubated with different treatment groups. After 24 h, the cells were incubated with the DCFH-DA (10 μg/mL) dye for 30 min. The cells were harvested after trypsinization and centrifuge at 2500 rpm to obtain pellet. The pellet was suspended into PBS and intracellular levels of ROS were estimated by using C6 Flow Cytometer (BD Accuri TM) and the data was determined as % intracellular reactive species. The experiment was performed in triplicates and BD Biosciences software was used to analyze the results.
### Δψm)
To check the MMP variations in SHSY-5Y cells, the cells were incubated with different doses in 24-well plates and were analyzed as per the method suggested by Carlson and Ehrich34. After incubation, the cell pellets of all the groups were obtained using trypsinization and centrifugation at 2500 rpm for 4 min. This was followed by cell fixation with $70\%$ of chilled ethanol and cell staining using 2 µg/ml of Rhodamine 123 (Rh-123) for time period of 30 min. The SH-SY5Y cells were washed thrice using PBS and analyzed using the flow cytometer.
## Annexin V-Fluorescein isothiocyanate (FITC) staining
The SH-SY5Y cells were cultured till confluency and treated with various experimental doses. The trypsinized cells were washed, centrifuged and dissolved in the binding buffer (Annexin V-FITC Apoptosis Detection Kit (Sigma)). Further, in the cell suspension (500 µL), 5 µL of annexin-V-FITC and 10 µL of propidium iodide (PI) dyes were mixed and gently vortexed followed by incubation in dark for 10 min at ambient temperature. Finally, the analysis was carried out on AccuriTMC6 Flow Cytometer.
## Analysis of gene expression using Quantitative Real‑Time PCR (RT‑qPCR)
RNA was isolated from SH-SY5Y cells exposed to 3-NPA with or without 4-MTBITC using Trizol Reagent. The extracted RNA was mixed in TE buffer and kept at 60 °C for 5 min to avoid any DNA impurity, samples were incubated with DNase I for 30 min at room temperature. The OD of isolated RNA was observed by Nano-Drop spectrophotometer at 260 nm and 280 nm. Then, same quantity of RNA was utilized for the preparation of complementary DNA using iScriptTM synthesis kit. The quantification of cDNA was performed and similar amount was further used for performing RT-qPCR by iQSYBR Supermix system. The sequence of particular primers (Nrf2, BDNF, TrkB, CREB and Caspase-3) taken for analysis are given in Table 1. To estimate the relative expression, the RT-qPCR reaction was carried out and data was quantified using the ΔΔCT, comparative threshold cycle method. β-actin was taken as housekeeping gene. The CT value of each target gene was normalized by CT value of housekeeping gene, β-actin. The relative expression of genes was represented as 2−ΔΔCT ± SE.Table 1The list of RT-qPCR primers and their sequences. S. NoPrimerAccession NoProduct lengthSequence of Oligonucleotides (5′-3′)Source1Nrf2NM_006164.5118Forward:AGGTTGCCCACATTCCCAAAReverse:AGTGACTGAAACGTAGCCGANCBI2BDNFNM_001143812.2142Forward:GAAAGCTAGGGGAGCGAGACReverse:CTTCGAGGGGTGTTCCAGCNCBI3TrkBS76473.1124Forward: AAAGAAGAAGCCGCAAAGCGReverse:GGGTCCATGCCACCTTATCCNCBI4CREBNM_001382431.1186Forward:CAGTGGGACAGAGGAGCAAGReverse:AAGGTCAAGTGCTACCGTGGNCBI5Caspase 3NM_001354777.2176Forward:CTCCTAGCGGATGGGTGCTATTGReverse:TTATTAACGAAAACCAGAGCGCCGNCBI6β-actinNM_001101.572Forward:AGACCTGTACGCCAACACAGReverse:TTCTGCATCCTGTCGGCAATNCBI
## Statistical analysis
Data was expressed as Mean ± standard error (SE) and statistically analyzed for variances and interactions using one-way analysis of variance (ANOVA). Statistical evaluation was performed in triplicates and p ≤ 0.05 were considered significant.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31716-3.
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|
---
title: Kinin B1 and B2 receptors mediate cancer pain associated with both the tumor
and oncology therapy using aromatase inhibitors
authors:
- Indiara Brusco
- Gabriela Becker
- Tais Vidal Palma
- Micheli Mainardi Pillat
- Rahisa Scussel
- Bethina Trevisol Steiner
- Tuane Bazanella Sampaio
- Daniel Mendes Pereira Ardisson-Araújo
- Cinthia Melazzo de Andrade
- Mauro Schneider Oliveira
- Ricardo Andrez Machado-De-Avila
- Sara Marchesan Oliveira
journal: Scientific Reports
year: 2023
pmcid: PMC10023805
doi: 10.1038/s41598-023-31535-6
license: CC BY 4.0
---
# Kinin B1 and B2 receptors mediate cancer pain associated with both the tumor and oncology therapy using aromatase inhibitors
## Abstract
Pain caused by the tumor or aromatase inhibitors (AIs) is a disabling symptom in breast cancer survivors. Their mechanisms are unclear, but pro-algesic and inflammatory mediators seem to be involved. Kinins are endogenous algogenic mediators associated with various painful conditions via B1 and B2 receptor activation, including chemotherapy-induced pain and breast cancer proliferation. We investigate the involvement of the kinin B1 and B2 receptors in metastatic breast tumor (4T1 breast cancer cells)-caused pain and in aromatase inhibitors (anastrozole or letrozole) therapy-associated pain. A protocol associating the tumor and antineoplastic therapy was also performed. Kinin receptors’ role was investigated via pharmacological antagonism, receptors protein expression, and kinin levels. Mechanical and cold allodynia and muscle strength were evaluated. AIs and breast tumor increased kinin receptors expression, and tumor also increased kinin levels. AIs caused mechanical allodynia and reduced the muscle strength of mice. Kinin B1 (DALBk) and B2 (Icatibant) receptor antagonists attenuated these effects and reduced breast tumor-induced mechanical and cold allodynia. AIs or paclitaxel enhanced breast tumor-induced mechanical hypersensitivity, while DALBk and Icatibant prevented this increase. Antagonists did not interfere with paclitaxel's cytotoxic action in vitro. Thus, kinin B1 or B2 receptors can be a potential target for treating the pain caused by metastatic breast tumor and their antineoplastic therapy.
## Introduction
Breast cancer is the most frequently diagnosed cancer in women worldwide, reaching $24.7\%$ of new cases in 2020, a year that for the first time surpassed lung cancer1. An increase of $47.5\%$ in these cases is expected for 2040, according to the 2020 GLOBOCAN Cancer Tomorrow tool. Bone metastases are responsible for causing the most debilitating breast cancer pain once it usually does not cause excruciating pain in its native tissue2–4. Chronic pain occurs in $74\%$ of breast cancer survivors, of which $84\%$ present moderate-intensity pain from 1 to 3 days per week5. These pain symptoms are characterized by mechanical and cold allodynia, ongoing pain, paresthesia, and phantom sensations, which compromise the patients’ quality of life once they are often improperly treated2,5,6. Thus, it is essential to deepen the knowledge regarding cancer pain mechanisms to develop more appropriate analgesics2,7.
Cancer pain etiology includes direct tumor infiltration/expansion, tumoral metastasis, and tumor- or stromal cell-derived mediators, such as bradykinin, which can sensitize or activate nociceptors2,8. Cancer pain also results from diagnostic and surgical methods or as an adverse effect of anticancer therapy2,4,6–10. Paclitaxel, for example, is one of the main chemotherapeutic agents used in breast cancer therapy11. Nevertheless, it causes acute and neuropathic pain syndrome that limits its use9,12–14.
Aromatase inhibitors (AIs), indicated for estrogen receptor-positive breast cancer, also cause painful symptoms10,11,15. More than a third of patients that receive AIs report muscle and joint pain (34–$50\%$) characterized by morning stiffness and pain in the hands, knees, hips, lower back, and shoulders8,10,16–19, in addition to neuropathic pain20. The most commonly prescribed AIs are anastrozole and letrozole21, often recommended for 5–10 years18,22. However, nearly $20\%$ of patients discontinue or do not adhere to treatment due to painful symptoms10,20, thus compromising the success of anticancer therapy. Therefore, studies investigating therapeutic interventions for relieving these painful symptoms in patients16 without interfering with anticancer treatments are needed.
Compelling evidence showed that transient receptor potential ankyrin 1 (TRPA1) channels and protease-activated receptor 2 contribute to AIs-associated pain,15,23 but the mechanisms underlying this pain have not yet been fully clarified. These same studies suggest that pro-inflammatory and pro-algesic mediators may activate signalling pathways contributing to pain development caused by AIs15,23. Since kinins are potent endogenous algogenic peptides involved in inflammatory and pain processes via B1 and B2 receptors24,25, and these receptors can interact intracellularly with TRPA126,27, they may be involved in AIs-induced pain. Kinin receptors are G protein-coupled whose activation stimulates phospholipase C and intracellular mediators’ formation, with increased intracellular calcium, thus exciting the cells where these receptors are expressed25,28. Nociceptive neurons associated with pain modulation in the periphery and spinal cord and non-neuronal cells that express kinin B1 and B2 receptors contribute to painful processes28–35. This explains the ability of kinins to produce pain directly when bound to their B1 and B2 receptors. Due to this distribution in peripheral and central structures involved in the nociceptive transmission, these receptors can mediate acute and systemic chronic pain conditions such as fibromyalgia, neuropathy, and others31,36–40, including pain caused by chemotherapy drugs such as paclitaxel12,13,41. These characteristics of kinin receptors are also suggestive of a possible contribution to the AIs-caused pain symptoms.
Kinins also regulate breast cancer progression by acting as cell proliferation agents42–45. Interestingly, breast cancer patients present elevated kinin levels46, the human breast expresses both kinin B1 and B2 receptors, and antagonists of these receptors can inhibit breast cancer cell proliferation42,43,45. Thus, we hypothesized that kinins are involved in breast cancer pain and that kinin receptor antagonists attenuate breast tumor proliferation while treating the pain caused by the tumor and anticancer therapy. Therefore, using a breast cancer pain model in mice, we evaluated the involvement of the kinin B1 and B2 receptors in pain induced by breast tumor at metastatic-stage, AIs, or breast tumor combined with AIs or paclitaxel.
## AIs cause mechanical allodynia and reduce muscle strength
AIs anastrozole (0.2 mg/kg, p.o.) and letrozole (0.5 mg/kg, p.o.) reduced the mechanical paw withdrawal threshold (PWT) of mice, indicating the development of mechanical allodynia. Mechanical allodynia was observed at 2, 3, and 6 h after anastrozole or letrozole administration with maximum PWT reduction of 79 ± $2\%$ and 77 ± $3\%$, respectively, at 3 h after administrations ([F[5,50] = 11.85; $P \leq 0.0001$; Fig. 1A] and [F[5,50] = 9.58; $P \leq 0.0001$; Fig. 1C]). Anastrozole and letrozole also reduced the animals' muscle strength at 34 ± $3\%$ and 35 ± $6\%$, respectively, at 3 h after its administrations (maximum nociception time observed in mechanical allodynia ([F[1,10] = 56.55; $P \leq 0.0001$; Fig. 1B] and [F[1,10] = 23.08; $P \leq 0.001$; Fig. 1D]).Figure 1Aromatase inhibitors induce mechanical allodynia, reduce the muscle strength of mice and increase the kinin B1 and B2 receptor protein expression. Time-response curves to the mechanical allodynia (A,C) and the muscle strength at 3 h (B,D) after vehicle (10 mL/kg, p.o.), anastrozole (0.2 mg/kg, p.o. A,B), or letrozole (0.5 mg/kg, p.o. C,D) administration. ( B) Denotes baseline mechanical threshold or muscle strength before drug administrations. Representative imagens of kinin B1 receptor protein expression in the plantar tissue (E,F), kinin B2 receptor protein expression in sciatic nerve (G,H) and spinal cord (I,J) at 3 h after vehicle (10 mL/kg, p.o.), anastrozole (0.2 mg/kg, p.o.) or letrozole (0.5 mg/kg, p.o.) administration. Data are expressed as the mean + SEM. * $P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$ when compared to the vehicle group; two-way repeated-measures ANOVA followed by Bonferroni’s post hoc test to the behavioral tests ($$n = 6$$/group; C57BL/6 mice) or Student’s t-test to the protein expression ($$n = 4$$–7/group; C57BL/6 mice). PWT paw withdrawal threshold. The original western blot images are available in Supplementary Fig. S2.
## AIs increase kinin B1 and B2 receptor protein expression
The letrozole increased the kinin B1 receptor protein expression only in the plantar tissue, but anastrozole was not able to cause this increase (Fig. 1E,F). Both anastrozole and letrozole increased the kinin B2 receptor protein expression in the sciatic nerve and spinal cord (Fig. 1G–J) but not in plantar tissue (data not shown). However, the AIs did not alter bradykinin-related peptide levels in the plantar tissue (data not shown). Once AIs increased the kinin B1 and B2 receptors protein expression in peripheral and central structures of mice, despite limitations due to the lack of validity of antibodies specificity in knockout animals, we next evaluated the involvement of these receptors in nociceptive behaviors induced by AIs.
## Kinin B1 or B2 receptor antagonists relieve the AIs-induced mechanical allodynia and reduction on the muscle strength
Pretreatment with the kinin B1 receptor antagonist DALBk reduced the anastrozole-induced mechanical allodynia at 2 and 3 h after administration with maximum inhibition (Imax) of 66 ± $13\%$ at 3 h [F[4,40] = 9.99; $P \leq 0.0001$; Fig. 2A]. The kinin B2 receptor antagonist Icatibant was effective from 1 to 3 h after administration with an Imax of 50 ± $8\%$ in 3 h [F[4,40] = 9.67; $P \leq 0.0001$; Fig. 2B]. Post-treatment with DALBk and Icatibant also reduced anastrozole-induced mechanical allodynia from 0.5 to 2 h after administrations with Imax of 37 ± $7\%$ and 33 ± $5\%$ at 0.5 h, respectively ([F[5,50] = 8.72; $P \leq 0.0001$; Fig. 2C] and [F[5,50] = 11.05; $P \leq 0.0001$; Fig. 2D]). The reduction in muscle strength caused by anastrozole was prevented (DALBk inhibition = $100\%$; Icatibant inhibition = 90 ± $3\%$) and reversed (DALBk inhibition = 70 ± $4\%$; Icatibant inhibition = 61 ± $5\%$) by kinin B1 and B2 receptor antagonists ([F[2,15] = 23.25; $P \leq 0.0001$; Fig. 2E] and [F[4,30] = 20.31; $P \leq 0.0001$; Fig. 2F]).Figure 2Kinin B1 (DALBk 150 nmol/kg, i.p.) and B2 (Icatibant 100 nmol/kg, i.p.) receptor antagonists relieve mechanical allodynia and reduction on the muscle strength caused by the aromatase inhibitor anastrozole (0.2 mg/kg, p.o.). Time-response curve for mechanical allodynia after treatment with DALBk or Icatibant injected 15 min before anastrozole administration (A,B). Time-response curve for mechanical allodynia after treatment with DALBk or Icatibant injected at 2 h after anastrozole administration (C,D). Measure muscle strength after treatment with DALBk or Icatibant injected 15 min before (E) or 2 h after (F) anastrozole administration. ( B) Denotes baseline mechanical threshold or muscle strength before drug administration; B2 indicates baseline threshold at 2 h after anastrozole administration. Results are presented as mean + SEM ($$n = 6$$/group; C57BL/6 mice). ## $P \leq 0.01$; ###$P \leq 0.001$ compared to baseline threshold (B). * $P \leq 0.05$; ***$P \leq 0.001$ compared to the vehicle group. Two-way ANOVA repeated-measures followed by Bonferroni's post hoc test. PWT paw withdrawal threshold.
Similarly, pretreatment with DALBk reduced letrozole-induced mechanical allodynia from 1 to 3 h after administration with an Imax of 74 ± $16\%$ in 2 h [F[4,40] = 11.89; $P \leq 0.0001$; Fig. 3A]. Icatibant was effective at 2 and 3 h after administration with an Imax of 58 ± $15\%$ at 3 h [F[4,40] = 4.74; $P \leq 0.01$; Fig. 3B]. Post-treatment with DALBk and Icatibant also reduced letrozole-induced mechanical allodynia from 0.5 to 2 h after administrations with an Imax of 50 ± $8\%$ at 1 h and 29 ± $5\%$ at 0.5 h, respectively ([F[5,50] = 13.23; $P \leq 0.0001$; Fig. 3C] and [F[5,50] = 7.25; $P \leq 0.0001$; Fig. 3D]). The reduction in muscle strength caused by letrozole was also prevented (DALBk inhibition = 96 ± $3\%$; Icatibant inhibition = $100\%$) and reversed (DALBk inhibition = 76 ± $9\%$; Icatibant inhibition = 84 ± $6\%$) by kinin B1 and B2 receptor antagonists ([F[2,15] = 14.32; $P \leq 0.01$; Fig. 3E] and [F[4,30] = 8.97; $P \leq 0.0001$; Fig. 3F]).Figure 3Kinin B1 (DALBk 150 nmol/kg, i.p.) and B2 (Icatibant 100 nmol/kg, i.p.) receptor antagonists relieve mechanical allodynia and reduction on the muscle strength caused by the aromatase inhibitor letrozole (0.5 mg/kg, p.o.). Time-response curve for mechanical allodynia after treatment with DALBk or Icatibant injected 15 min before letrozole administration (A,B). Time-response curve for mechanical allodynia after treatment with DALBk or Icatibant injected 2 h after letrozole administration (C,D). Measure muscle strength after treatment with DALBk or Icatibant injected 15 min before (E) or 2 h after (F) letrozole administration. ( B) Denotes baseline mechanical threshold or muscle strength before drug administration while B2 indicates baseline threshold at 2 h after letrozole administration. Results are presented as mean + SEM ($$n = 6$$/group; C57BL/6 mice). # $P \leq 0.05$; ###$P \leq 0.001$ compared to baseline threshold (B). * $P \leq 0.05$; ***$P \leq 0.001$ compared to the vehicle group. Two-way ANOVA repeated-measures followed by Bonferroni's post hoc test. PWT paw withdrawal threshold.
## Breast tumor-bearing mice develop mechanical and cold allodynia
Metastatic breast tumor-bearing mice developed mechanical allodynia at 10, 15, 20, and 25 days after tumor induction with a maximum PWT reduction of 69 ± $4\%$ at 20 days after injection [F[5,90] = 5.14; $P \leq 0.01$; Fig. 4A]. Cold allodynia was observed at 15, 20, and 25 days after tumor induction with greater nociception at 25 days [F[5,90] = 4.48; $P \leq 0.01$; Fig. 4B].Figure 4Metastatic breast tumor-bearing mice develop mechanical (A) and cold (B) allodynia and present increased bradykinin levels and kinin B1 and B2 receptor protein expression. The nociceptive parameters were evaluated at 5, 10, 15, 20, and 25 days after vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. ( B) denotes baseline mechanical threshold/nociception time before injection. Bradykinin-related peptide levels in the plantar tissue (C). Representative images of kinin B1 and B2 receptor protein expression in the plantar tissue (D,E), in sciatic nerve (F,G), and spinal cord (H,I) at 20 days after vehicle (50 µL/site) or 4T1 breast cancer cells (1 × 104, 50 mL/site) injection. Data are expressed as the mean + SEM. * $P \leq 0.05$; ***$P \leq 0.001$ when compared to the vehicle group; two-way repeated-measures ANOVA followed by Bonferroni’s post hoc test to the behavioral tests ($$n = 10$$/group; BALB/c mice) or Student’s t-test to the protein expression and kinin levels ($$n = 4$$/group; BALB/c mice). PWT paw withdrawal threshold. The original western blot images are available in Supplementary Fig. S3.
## Breast tumor-bearing mice present increased kinin B1 and B2 receptor protein expression and high bradykinin levels
The plantar tissue of metastatic breast tumor-bearing mice showed an increase in the bradykinin-related peptide levels of approximately twice (4.7 ± 0.8 ng/mL/mg protein) compared to the vehicle-injected mice (2.7 ± 0.2 ng/mL/mg protein) (Fig. 4C). Although there are limitations due to the lack of validity of the antibody specificity in kinin receptors knockout animals in our study, the protein expression of kinin B1 and B2 receptors increased in plantar tissue and spinal cord of breast tumor-bearing mice (Fig. 4D,E,H,I). Only the kinin B1 but not the B2 receptor protein expression increased in the sciatic nerve of these animals (Fig. 4F,G). Thus, we next evaluate the involvement of kinin receptors in metastatic breast cancer-associated nociceptive behaviors.
## Kinin B1 or B2 receptor antagonists relieve the breast tumor–induced mechanical and cold allodynia
Kinin B1 (DALBk) and B2 (Icatibant) receptor antagonists caused an antinociceptive effect in mice 20 days after tumor induction, the day the maximum nociception was established. DALBk decreased the mechanical and cold allodynia induced by breast tumor at 1 h after its administration with inhibition of 24 ± $4\%$ and $100\%$, respectively ([F[8,144] = 2.17; $P \leq 0.05$; Fig. 5A] and [F[8,144] = 2.97; $P \leq 0.01$; Fig. 5B]). Icatibant also decreased the mechanical allodynia at 1 and 2 h (Imax = 26 ± $7\%$ at 1 h) and cold allodynia from 0.5 up to 2 h (inhibition = $100\%$ at all times) after its administration ([F[8,144] = 2.22; $P \leq 0.05$; Fig. 5C] and [F[8,144] = 5.20; $P \leq 0.0001$; Fig. 5D]).Figure 5Kinin B1 (DALBk, 150 nmol/kg, i.p.) or B2 (Icatibant, 100 nmol/kg, i.p.) receptor antagonists reduce the mechanical (A,C,E,G) and cold (B,D,F,H) allodynia in breast tumor-bearing mice. Time-response curves caused by the post-treatment with the vehicle, DALBk (A,B) or Icatibant (C,D) at 20 days after 4T1 breast cancer cells (104 cells, 50 µL/site) injection. Early-stage repeated treatment (6–15 days) with vehicle DALBk (E,F) or Icatibant (G,H) in animals that received 4T1 breast cancer cells (104 cells, 50 µL/site) injection. ( B) Denotes baseline mechanical threshold/nociception time before tumor induction. The dotted arrows indicate the timings of antagonist administration. Data are expressed as the mean + SEM ($$n = 10$$/group; BALB/c mice). # $P \leq 0.05$; ##$P \leq 0.01$; ###$P \leq 0.001$ when compared to the baseline (B); *$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$ when compared to the vehicle group; two-way repeated-measures ANOVA followed by Bonferroni’s post hoc test. PWT paw withdrawal threshold.
The repeated (from 6 up to 15 days after tumor injection) administration of kinin B1 and B2 receptor antagonists from the initial stage of tumor induction also promoted an antinociceptive effect in mice. DALBk decreased the mechanical and cold allodynia in breast tumor-bearing mice from 10 up to 20 days (Imax = 65 ± $10\%$ at 15 days) and from 15 up to 20 days (inhibition = $100\%$ at all times) after its administration, respectively ([F[5,90] = 7.99; $P \leq 0.0001$; Fig. 5E] and [F[5,90] = 9.01; $P \leq 0.0001$; Fig. 5F]). Likewise, Icatibant also decreased the mechanical allodynia in breast tumor-bearing mice at 10 and 15 days (Imax = 42 ± $11\%$ at 15 days) and cold allodynia at 20 days (inhibition = 87 ± $15\%$) after its administration ([F[5,90] = 6.55; $P \leq 0.0001$; Fig. 5G] and [F[5,90] = 4.46; $P \leq 0.01$; Fig. 5H]).
## Anticancer drugs anastrozole, letrozole, and paclitaxel enhance the breast tumor-induced mechanical hypersensitivity
Breast cancer-bearing mice presented mechanical allodynia at 10 days (B2) after 4T1 breast cancer cells injection compared to the basal values (B). The administration of low doses of AIs anastrozole (0.15 mg/kg, p.o.) and letrozole (0.3 mg/kg, p.o.) enhanced the mechanical hypersensitivity induced by breast tumor from 3 up to 7 h after its administrations. The maximum PWT reduction was 63 ± $4\%$ and 55 ± $10\%$ at 4 h and 5 h after administration of anastrozole and letrozole, respectively ([F[10,75] = 5.61; $P \leq 0.0001$; Fig. 6A and [F[10,75] = 4.27; $P \leq 0.001$; Fig. 6B]). Likewise, low dose paclitaxel (0.001 mg/kg, i.p.) administration also enhanced the mechanical hypersensitivity in breast cancer-bearing mice from 24 to 28 h after its administration with a maximum PWT reduction of 55 ± $6\%$ at 24 h [F[10,70] = 4.17; $P \leq 0.001$; Fig. 6C]. The cold hypersensitivity induced by breast tumor was not enhanced by the anticancer drugs (data not shown).Figure 6Anticancer drugs enhance the mechanical hypersensitivity in breast cancer-bearing mice, and kinin B1 (DALBk, 150 nmol/kg, i.p.) or B2 (Icatibant, 100 nmol/kg, i.p.) receptor antagonists reduce the mechanical hypersensitivity induced by breast tumor plus anticancer therapy. Time-response curves caused by the vehicle (10 mL/kg, p.o. or i.p.), anastrozole (0.15 mg/kg, p.o.; A), letrozole (0.3 mg/kg, p.o.; B) or paclitaxel (0.001 mg/kg, i.p.; C) administration at 10 days after vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. Time-response curves caused by the vehicle, DALBk (D–F) or Icatibant (G–I) treatment in animals previously injected with 4T1 breast cancer cells (104 cells, 50 µL/site) plus anastrozole (0.15 mg/kg, p.o.; D;G), letrozole (0.3 mg/kg, p.o.;E;H) or paclitaxel (0.001 mg/kg, i.p.;F;I). ( B) Denotes the baseline mechanical threshold before cells injection and drugs administration. ( B2) denotes the baseline mechanical threshold at 10 days after cells injection and before drugs administration. ( B3) denotes the baseline mechanical threshold at 10 days after cells injection and 3 h after anastrozole or letrozole administration, or 24 h after paclitaxel administration. The closed arrows indicate the timing of anticancer therapy administration, and the dotted arrows indicate timings of antagonist administration. Data are expressed as the mean + SEM ($$n = 5$$–6/group; BALB/c mice). ### $P \leq 0.001$ when compared to the baseline (B); +$P \leq 0.05$ when compared to the 4T1 cells plus vehicle group; *$P \leq 0.05$; **$P \leq 0.01$; ***$P \leq 0.001$ when compared 4T1 cells plus anastrozole, letrozole or paclitaxel group; two-way repeated-measures ANOVA followed by Bonferroni’s post hoc test. PWT paw withdrawal threshold.
Although two animal strains were used in our study due to different pain models and histocompatibility of BALB/c mice with 4T1 breast cancer cells, the mechanical PWT reduction caused by anticancer drugs was reproduced in both mice strains. This is because BABL/c mice injected with the vehicle in the breast and treated with anastrozole and letrozole developed mechanical allodynia (Fig. 6A,B), as well as C57BL/6 mice treated with anastrozole and letrozole (Fig. 1A,C).
## Kinin B1 or B2 receptor antagonists reduce the mechanical hypersensitivity in a cancer pain model associated with the breast tumor and anticancer therapy
Low doses of AIs anastrozole (0.15 mg/kg, p.o.) and letrozole (0.3 mg/kg, p.o.) enhanced the breast tumor-induced mechanical hypersensitivity (B2) at 3 h after its administrations (B3). The treatment with kinin B1 (DALBk; 150 nmol/kg, i.p.) or B2 (Icatibant; 100 nmol/kg, i.p.) receptor antagonists reduced the mechanical hypersensitivity induced by breast tumor plus anastrozole at 1 and 2 h after antagonist administrations with Imax of 51 ± $9\%$ for DALBk and 36 ± $7\%$ for Icatibant at 1 h ([F[15,100] = 6.96; $P \leq 0.0001$; Fig. 6D] and [F[15,95] = 7.21; $P \leq 0.0001$; Fig. 6G]). DALBk and Icatibant also reduced the mechanical hypersensitivity induced by breast tumor plus letrozole at 1 and 2 h after its administrations with Imax of 25 ± $5\%$ and 31 ± $7\%$ at 2 h, respectively ([F[15,95] = 4.43; $P \leq 0.0001$; Fig. 6E] and [F[15,100] = 4.97; $P \leq 0.0001$; Fig. 6H]).
A low dose of paclitaxel (0.001 mg/kg, i.p.) also enhanced the breast tumor-induced mechanical hypersensitivity (B2) at 24 h after its administration (B3). The treatment with DALBk or Icatibant reduced the mechanical hypersensitivity induced by breast tumor plus paclitaxel at 1 and 2 h after its administrations with Imax of 27 ± $5\%$ for DALBk and 31 ± $4\%$ for Icatibant at 1 h ([F[15,95] = 5.19; $P \leq 0.0001$; Fig. 6F] and [F[15,95] = 4.60; $P \leq 0.0001$; Fig. 6I]).
Although the mechanical threshold has been partially restored to the baseline values (B) by kinin B1 (DALBk) or B2 (Icatibant) receptor antagonists, both DALBk and Icatibant reduced at $100\%$ the potentiation of mechanical hypersensitivity induced by anastrozole, letrozole, or paclitaxel in breast cancer-bearing mice when compared to these mice treated with vehicle (Fig. 6).
## Expression of kinin B1 or B2 receptor in 4T1 breast cancer cells from Sequence Read Archive (SRA) analysis
We found reads mapped against the kinin B1 or B2 receptor coding DNA sequence (CDS) for the four independent transcriptome experiments (Supplementary Table S1). The PUM1 gene was used as an internal control and presented > 4900 mapped reads for each individual experiment. Kinin B1 receptor was transcribed in 4T1 cells at a residual level (only 28.5 ± 7.3 mapped reads) compared to the transcription of the kinin B2 receptor (1608.3 ± 46.3 mapped reads). Therefore, 4T1 cells grown in vitro seem to transcribe about 60-fold more kinin B2 receptor mRNA than the kinin B1 receptor. Once 4T1 cells appear to express kinin receptors, we next evaluated the effect of kinin receptor antagonists on these cells in vitro.
## Kinin B1 or B2 receptor antagonists did not alter the cytotoxic action of paclitaxel on 4T1 breast cancer cells
As expected, the chemotherapy paclitaxel (100 μM) reduced the 4T1 breast cancer cell viability by inhibiting 47 ± $3\%$. In the concentration tested, the kinin B2 receptor antagonist (Icatibant 100 μM) did not alter the 4T1 breast cancer cells viability. Interestingly, the kinin B1 receptor antagonist (DALBk, 100 μM) reduced the 4T1 breast cancer cells viability by inhibiting 40 ± $2\%$, similar to the paclitaxel. Relevantly, Icatibant (100 μM) or DALBk (100 μM) did not interfere with the cytotoxic action of paclitaxel (100 μM) when both were associated (Supplementary Fig. S1A).
Once DALBk, but not Icatibant, reduced the viability of 4T1 breast cancer cells in the MTT assay, we evaluated the effect of this antagonist in the apoptosis and cell migration assay. DALBk (100 μM) did not cause apoptosis of 4T1 cells in the annexin/7AAD assay (Supplementary Fig. S1B), nor did it reduce cell migration (Supplementary Fig. S1C). In contrast, paclitaxel (100 μM) caused early apoptosis in 40 ± $6\%$ of cells and reduced cell migration by 83 ± $2\%$ (Supplementary Fig. S1B,C). Notably, DALBk (100 μM) did not interfere with the effect of paclitaxel (100 μM) in inducing apoptosis (at 38 ± $5\%$ of cells) and reducing migration (84 ± $4\%$) of 4T1 breast cancer cells when both were combined (Supplementary Fig. S1B,C).
## Discussion
Breast cancer patients experience chronic pain that may be associated with the tumor itself, especially after metastasis, and/or the anticancer therapy2,4,5. Cancer pain significantly affects the patient’s functional capacity, survival, and quality of life since the available treatments are partially effective or cause adverse effects2,8,47. Treatment guidelines from the World Health Organization (WHO) and other national or international bodies are broad and do not relate treatment approaches to pain classification8. This can occur because cellular and molecular mechanisms associated with cancer pain are still uncertain. In this sense, the kinins and their B1 and B2 receptors appear attractive targets to alleviate cancer-associated pain since they are involved in various painful conditions, including those induced by chemotherapy12,13,39–41 and contribute to tumor cell proliferation42,43. Here, our findings extend the use of kinin B1 and B2 receptor antagonists to treating the pain caused by the metastatic breast tumor, antineoplastic therapy with AIs, or both besides reducing breast tumor progression.
AIs are widely used as endocrine therapy for breast cancer11. However, breast cancer patients undergoing AIs treatment often report musculoskeletal symptoms, particularly arthralgias and myalgias characterized by pain and stiffness11,16,17,19. Here, the AIs anastrozole and letrozole elicited mechanical allodynia (pain from normally non-painful stimuli)2,5 and reduced the muscle strength of mice, in agreement with previous reports15,23.
In addition to TRPA1 channels, the contribution of pro-inflammatory and painful mediators in AIs-caused nociception was previously suggested15. Bradykinin and kallidin are endogenous peptides that mediate inflammatory and painful processes via the kinin B2 receptor, while their active metabolites (des-Arg-kinins) are kinin B1 receptor agonists24,25. Notably, activation of kinin receptors can lead to intracellular sensitization of TRPA1 through downstream vias (phospholipase C, protein kinase C, and protein kinase A), which can contribute to the pronociceptive effects of kinins26,27. Thus, AIs could be causing pain directly by activating kinin receptors and indirectly by sensitizing TRPA1, which is under investigation (Fialho et al., unpublished data). Here, we found that kinin B1 and B2 receptor antagonists attenuated the mechanical allodynia and loss of muscle strength caused by AIs. However, bradykinin-related peptide levels did not alter in the plantar tissue of mice after AIs administration.
Although the bradykinin content has not changed, both anastrozole and letrozole increased the kinin B2 receptor protein expression in the sciatic nerve and spinal cord but not plantar tissue, and letrozole increased the kinin B1 receptor protein expression in the plantar tissue of the mice. Kinin receptors are expressed in peripheral nociceptive neurons such as C- and Aδ-fibers, sensory ganglions, and the spinal cord, mediating nociceptive transmission28–31. Kinin receptors also are found or upregulated in astrocytes and microglia in the central nervous system, contributing to chronic pain and inflammatory states32–35. In response to nociceptive inputs, kinins also are released in the spinal cord, where they act in postsynaptic receptors potentiating glutamatergic synaptic transmission to produce pain hypersensitivity48,49. Additionally, the interaction of kinins with endothelial cells and leukocytes in peripheral tissues may contribute to conditions such as cancer, inflammation, and pain31,50,51. These data explain the ability of kinin B1 and B2 receptor antagonists to attenuate the AIs-induced mechanical allodynia and changes in muscle strength test, a sensitive test for painful parameters52. Assays of pharmacological antagonism, genetic manipulation, and receptor proteins expression showed kinins’ contribution to the pathogenesis of various acute and chronic pain models31,36–40, including chemotherapy-induced as vincristine41 and paclitaxel12,13. Paclitaxel is widely used in breast cancer management11, although it causes acute and neuropathic pain syndrome limiting its use. Notably, kinins contribute to developing both of these processes9,12,13. Thus, kinin receptor antagonists could advantage breast cancer patients by reducing the paclitaxel-induced pain syndrome and subsequently alleviating the pain associated with endocrine therapy with AIs.
Once kinins are involved in breast cancer cell proliferation42,43 and pain caused by antineoplastic therapy13,15, we assessed the role of kinin receptors in metastatic breast tumor-caused pain. The cancer pain model was induced by injecting 4T1 breast cancer cells into the mammary fat pads of female mice. This model mimics breast carcinoma's clinical signs and progression, inducing naturally bone metastases and nociception53,54, without the need for direct bone injection as performed on other models. Thus, the tumor progression is similar to human breast cancer54,55.
Hypersensitivity to touch and cold are clinical characteristics of cancer pain56. We found that metastatic breast tumor-bearing mice develop mechanical and cold allodynia as previously reported3,54,57–59. Most studies describe the development of breast cancer-induced bone pain3,4,57,58,60 and, in general, solid tumors, including mammary carcinoma, do not result in pain in their native tissue but cause excruciating pain once they metastasize to bone3. This observation reinforces the validity of this pain model in mimicking the clinical symptoms of breast cancer once 4T1 cells have a metastatic profile.
Tumor metastases cause bone remodeling and tissue injury, which presumably induces the release of bradykinin2. Additionally, tumor cells and the associated immune cells release algogenic substances, such as bradykinin, prostaglandins, and growth factors, which may be involved in the cancer pain2,4,30,61. Bradykinin, for example, was considered a potential target in developing analgesics for cancer pain treatment2. Here, metastatic breast tumor-bearing mice presented high kinin levels in the plantar tissue and a greater expression of kinin receptors protein in plantar tissue, sciatic nerve, and spinal cord than control mice. Interestingly, patients with breast cancer present higher serum levels of bradykinin and its metabolites compared to healthy individuals46. Similarly, kinin B1 receptor mRNA expression increased in mice's dorsal root ganglion in a melanoma-like skin cancer pain model62.
Kinin B1 and B2 receptor antagonists also reduced metastatic breast tumor-induced mechanical and cold allodynia. This effect was observed even during the maximal nociception stage (20 days after breast tumor induction). Further, antagonists seem more effective when administered in the early stages of tumor development. These findings corroborate previous studies in which pharmacologic blockade of the kinin B1 receptor attenuated pain-related behaviors during both early and advanced stages of bone cancer in mice63. The kinin B1 and B2 receptors also appear to be partially involved in nociceptive behaviors associated with melanoma-like skin cancer in mice62.
The tumor and the antineoplastic therapy were individually sufficient to trigger cancer pain. Additionally, anastrozole, letrozole, and paclitaxel enhanced the mechanical but not cold hypersensitivity caused by tumor cells in metastatic breast tumor-bearing animals. These results were already expected since AIs have been associated with the development of mechanical allodynia and changes in muscle strength, but not cold allodynia15,23. However, paclitaxel and the breast tumor cause mechanical and cold allodynia13,54,59. Still, the development of mechanical allodynia caused by the metastatic breast tumor occurred before (from day 10) the development of cold allodynia (from day 15), according to previous data54. Thus, the submaximal dose of paclitaxel was probably not enough to cause cold allodynia when both paclitaxel and the breast tumor were associated on day 10. Importantly, increased pain due to the use of anticancer therapies is correlated with a worse prognosis, suggesting that adequate analgesic interventions could improve the survival of cancer patients64.
Once it is difficult to define if the pain of cancer patients undergoing anticancer therapy is caused by the tumor or by the antineoplastic treatment, an analgesic capable of reducing the pain from both etiologies is clinically relevant. Here, kinin B1 and B2 receptor antagonists reduced the mechanical hypersensitivity in metastatic breast cancer-bearing mice, which is enhanced by anastrozole, letrozole, or paclitaxel. These results are relevant once kinin receptor antagonism reduces the pain symptoms caused by the tumor and by chemotherapy treatment with paclitaxel13, as well as by endocrine therapy with AIs, alone or associated with the breast tumor.
It is essential to observe whether analgesic drugs used to relieve cancer pain could interfere with tumor progression, impacting patient survival2. Although controversial, morphine’s long term use, for example, seems to impair survival and contribute to chemotherapy resistance and cell proliferation in breast cancer models in vitro and mice66–68. In this sense, kinin receptor antagonists seem advantageous since kinins exercise regulatory control over breast cancer progression by acting as cell proliferation agents and mediating pain. Consequently, kinin receptor antagonists exhibit antiproliferative and pro-apoptotic effects against breast carcinoma cells42–45. Nevertheless, when subjectively evaluated by visual observation and palpation, peptide kinin B1 and B2 receptor antagonists did not seem to interfere with tumor growth in our in vivo experiments. A larger 4T1 breast cancer cell concentration69, a more extended experimental period, and measurements that more accurately assess the tumor size and its metastases are necessary to observe tumor growth changes in vivo. However, it was previously discussed that at 30 days after cell injection, the advanced-stage tumor could reduce the animals' locomotor activity, thus compromising the detection of evaluated nociceptive parameters54. Thus, a careful experimental design is need to clarify these issues.
Although previous publications have established breast cancer models induced by 4T1 cells as metastatic53,54, a limitation of our study is the absence of these data since it is essential to confirm the tumor formation and its metastases to the bone and correlate them with pain behaviors. Besides breast tumors, it remains to be explored whether other metastatic tumors and different animal strains reproduce our data since our study was limited to BALB/c mice because this strain is histocompatible with 4T1 breast cancer cells. Another limitation of our study is the lack of confirmation of the specificity of the antibodies and data on the kinin receptors expression in a more specific manner in cells and tissues, such as the dorsal root ganglion or dorsal horn of the spinal cord, for example, which are essential in the processing of pain. Altogether, these investigations could better clarify our findings, especially the responsiveness to the pain of each animal evaluated.
Cell culture experiments showed that kinin B1 or B2 receptor peptide antagonists did not affect the cytotoxic action of paclitaxel on 4T1 breast cancer cells in the cell viability, migration, and apoptosis assays. Additionally, kinin B1 receptor peptide antagonist (DALBk), but not B2 (Icatibant), reduced 4T1 cell viability. Although SRA analysis showed that these cells present both kinin receptors, the B2 receptor is 60 times more expressed. Perhaps a higher concentration of its antagonist would be necessary for experiments in these cells. Furthermore, DALBk alone was ineffective against cell migration and apoptosis. The peptide characteristic of these antagonists may have influenced this lack of antiproliferative effect since kinin receptor peptide antagonists cannot cross cell membranes and, thus, may fail to produce anticancer effects. However, non-peptide antagonists and cell-permeable peptide antagonists have shown potential value against breast cancer proliferation and cooperate with suboptimal doses of chemotherapy (doxorubicin and paclitaxel) to promote anticancer effects42,43. Notably, kinin receptor antagonists appear more cytotoxic to cancer cells than normal breast cells, an important aspect of new anticancer therapies43. The breast cancer cells express various members of the kallikrein-kinin system, supporting the hypothesis that kinins may be formed in the tumor microenvironment and autoregulate functionality of tumor cells70. Therefore, the role of kinin receptor antagonists in breast cancer proliferation should be further explored in human cells and tissue samples.
The pain associated with the tumor and its therapy is often treated as a distinct entity from cancer itself. However, it must be an integral part of the treatment to improve patients’ quality of life and survival2,47,64. Analgesics recommended to treat cancer pain, such as opioids and non-steroidal anti-inflammatory drugs, do not always lead to complete pain relief and may also cause various adverse effects47. Consequently, new cellular and molecular mechanism-based therapies are necessary to reduce cancer-associated pain2,7. Our findings show the involvement of the kinin B1 and B2 receptors in pain caused by tumor and antineoplastic therapy with AIs. Since cancer pain is a mixed type of inflammatory and neuropathic pain8, future investigations into the relationship of kinins at specific sites in the spinal cord and with peripheric inflammatory components may further clarify their role in breast cancer pain associated with metastatic-stage tumor and AIs. Importantly, Icatibant, a kinin B2 receptor antagonist approved for hereditary angioedema, is described as safe and well tolerated by patients71. Therefore, kinin B1 or B2 receptors present a potential target in relieving the pain associated with metastatic breast cancer and its therapy without compromising the effect of antineoplastic agents while still contributing to disease progression control.
## Materials
Anastrozole and letrozole were purchased from Tocris Bioscience (Bristol, UK) and were diluted in $0.5\%$ carboxymethylcellulose and $99.5\%$ of NaCl ($0.9\%$). Paclitaxel (6 mg/mL of paclitaxel in Cremophor EL and dehydrated ethanol) was purchased from Glenmark (Buenos Aires, ARG) and was dissolved in NaCl ($0.9\%$). Icatibant (kinin B2 receptor peptide antagonist), des-Arg9-[Leu8]-bradykinin (DALBk; kinin B1 receptor peptide antagonist) were purchased from Sigma-Aldrich Chemical Company (St. Louis MO, USA) and were also prepared in NaCl ($0.9\%$). The enzyme immunoassay kit for bradykinin was obtained from Phoenix Pharmaceuticals, Inc. (California, USA). Specific anti-B1 (bs-8675R–lot 9C20V14) or anti-B2 (bs-2422R–lot AG08307921) antibodies were acquired from Bioss Antibodies (Massachusetts, USA) and secondary antibody (sc-2357–lot L1218) was obtained from Santa Cruz Biotechnology (California, USA). Paclitaxel from semisynthetic ≥ $97\%$ (in vitro assays) was acquired from Sigma-Aldrich Chemical Company (St. Louis MO, USA). Annexin-V and 3- (4, 5-dimethyl-2-thiazolyl) -2, 5-diphenyl-2H-tetrazolium bromide] salt (MTT; 5 mg/mL solution) were obtained from Life Technologies (São Paulo, BR). The 7-Amino-Actinomycin D (7AAD) was acquired from BD Biosciences (California, USA). The doses of the drugs used in this study were based on previous studies13,15,23,40,72.
## Animals
Experiments were conducted using female C57BL/6 mice (20–25 g) and female BALB/c mice (20–30 g), which were maintained in a temperature-controlled room (22 ± 1 °C) under a 12 h light/12 h dark cycle with free access to food and water. Behavioral assessments were conducted between 8:00 a.m. and 5:00 p.m. C57BL/6 mice were used in the pain protocols induced by AIs. In contrast, BALB/c mice were used in the pain protocols caused by tumor alone, tumor plus AIs, and tumor plus paclitaxel once 4T1 cells have histocompatibility with BALB/c mice. All protocols were approved by the Institutional Animal Care and Use Committee of the Federal University of Santa Maria (process #$\frac{4647180719}{2019}$ for C57BL/6 mice) or by the Institutional Animal Care and Use Committee of the University of Extreme South Catarinense (process #$\frac{71}{2019}$-1-UNESC for BALC/c mice). Experimental protocols followed ethical guidelines established for investigations of experimental pain in conscious animals73. The experiments also were performed following the national and international legislation (guidelines of Brazilian Council of Animal Experimentation Control—CONCEA—and of U.S. Public Health Service’s Policy on Humane Care and Use of Laboratory Animals—PHS Policy) and the Animal Research: Reporting in vivo Experiments (ARRIVE) guidelines74. The number of animals and the intensities of noxious stimuli used were the minimum necessary to demonstrate the consistent effects of the treatments. Animals were allocated according to the baseline thresholds before and after the drugs administration or cells injection, according to the experimental protocol. All experiments were also performed by experimenters blinded to drug administration or the group to be tested.
## Mechanical allodynia
The mechanical allodynia was evaluated with von Frey filaments of increasing stiffness (0.02–10 g) using the Up-and-Down method75,76. The mechanical paw withdrawal threshold (PWT) was calculated according to Dixon [1980]77 and expressed in grams (g). The mechanical allodynia was considered a decrease in the PWT compared to the baseline (B) values (before induction of the pain models).
## Cold allodynia
The cold allodynia was assessed through the nocifensive response to the acetone-evoked evaporative cooling78. A droplet (20 µL) of acetone was gently applied to the plantar surface of the animals’ hind paws, and the time spent in elevation and licking of the plantar region was measured for 60 s. Cold allodynia was considered as an increase in the nociceptive response caused by exposure to acetone compared with basal (B) values (before induction of the pain models).
## Muscle strength—grip test
The muscle strength of mice was measured by an automated grip strength meter (Model EFF305, Insight, São Paulo, Brazil). The apparatus consists of a raised metal grid from the floor and is connected to a power transducer. Mice were placed on the grid and allowed to grip it with their paws, and then they were gently pulled backward in a horizontal plane from the tail base. The maximum strength exerted by the paws of each mouse was automatically recorded in grams by the device, and results were expressed as muscle strength in grams (g). The test was repeated three times per mouse with at least 1 min resting period between each test79.
## AIs-induced pain model
For the induction of the pain model, the mice received the oral administration (p.o.) via gavage of the AIs anastrozole (0.2 mg/kg, p.o) or letrozole (0.5 mg/kg, p.o.). A group of animals received only the oral vehicle administration ($0.5\%$ carboxymethylcellulose in saline ($0.9\%$ NaCl))15.
## Evaluation of the mechanical allodynia and muscle strength after AIs administration
The mechanical PWT and muscle strength were measured before (baseline values; B) or after vehicle (10 mL/kg, p.o.), anastrozole (0.2 mg/kg, p.o) or letrozole (0.5 mg/kg, p.o.) treatment15.
## Effect of kinin B1 or B2 receptor antagonists on AIs-induced mechanical allodynia and reduction of the muscle strength
The effect of kinin receptor antagonists on AIs-induced mechanical allodynia and reduced muscle strength was evaluated in a pre-treatment and post-treatment protocol. For the pre-treatment protocol, the mechanical PWT and muscle strength of animals were measured (baseline values; B). After that, animals were intraperitoneally treated with vehicle (10 mL/kg, i.p.), or kinin B1 (DALBk, 150 nmol/kg, i.p.), or B2 (Icatibant, 100 nmol/kg, i.p.) receptor antagonists. After 15 min, the animals received anastrozole (0.2 mg/kg, p.o.) or letrozole (0.5 mg/kg, p.o.), and the mechanical PWT was evaluated at 1, 2, 3, and 6 h after AIs administration. The muscle strength was measured at 3 h after AIs administration.
For the post-treatment protocol, the baseline mechanical threshold and muscle strength (baseline values; B) were assessed before anastrozole (0.2 mg/kg, p.o.) or letrozole (0.5 mg/kg, p.o.) treatments and at 2 h after its administrations (baseline values B2). The animals presenting mechanical allodynia and reduced muscle strength were treated with vehicle (10 mL/kg, i.p.), or kinin B1 (DALBk, 150 nmol/kg, i.p.), or B2 (Icatibant, 100 nmol/kg, i.p.) receptor antagonists. Mechanical PWT was evaluated at 0.5, 1, 2, and 4 h after antagonists’ administrations, while the muscle strength was measured at 1 h after the antagonist’s administrations.
## Breast tumor-induced pain model
The metastatic breast cancer pain model associated with the tumor was induced by injecting 4T1 murine breast carcinoma cells as previously determined54. The 4T1 cells (ATCC® CRL2539TM®) were obtained from Banco de Células do Rio de Janeiro, Brazil (code 0022) and were free of mycoplasma contamination. The 4T1 cells were cultured using DMEM medium in monolayer supplemented with $5\%$ fetal bovine serum and $1\%$ penicillin/streptomycin. For tumor induction, the cells were resuspended in PBS, and 50 μL of the cell suspension (104 cells) or vehicle (PBS) was injected into the right fourth caudal mammary fat pad of female mice.
## Evaluation of metastatic breast tumor-induced mechanical and cold allodynia
Mechanical PWT and cold sensitivity were measured before (baseline values; B) the injection of the vehicle or 4T1 breast cancer cells. Next, animals received vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) in the right fourth mammary gland, and mechanical PWT and cold sensitivity were again assessed at 5, 10, 15, 20 and 25 days after injection54.
## Effect of kinin B1 or B2 receptor antagonists on breast tumor-induced mechanical and cold allodynia
A reversion protocol was performed to assess whether kinin receptor antagonists could reduce mechanical and cold allodynia during a stage of maximum nociception induced by breast tumor. The mechanical PWT and cold sensitivity were measured before (baseline values; B) and several days (5, 10, 15, and 20) after injection of 4T1 breast cancer cells (104 cells, 50 µL/site). At 20 days after cells injection (day of maximum nociception observed in previous tests), animals received intraperitoneally (i.p.) vehicle (10 mL/kg, i.p.) or kinin B1 (DALBk; 150 nmol/kg, i.p.) or B2 (Icatibant; 100 nmol/kg, i.p.) receptor antagonists, and the mechanical PWT and cold sensitivity were again evaluated from 0.5 up to 4 h after treatments.
Another protocol was carried out to assess whether treatment with kinin receptor antagonists during an early stage could reduce the mechanical and cold allodynia induced by breast tumor. Mechanical PWT and cold sensitivity were measured before (baseline values; B) and 5 days after the injection of 4T1 breast cancer cells (104 cells, 50 µL/site) to show that nociception was not yet established. Next, animals received vehicle (10 mL/kg, i.p.) or kinin B1 (DALBk; 150 nmol/kg, i.p.) or B2 (Icatibant; 100 nmol/kg, i.p.) receptor antagonists from 6 up to 15 days after tumor injection, totaling ten administrations. The mechanical PWT and cold sensitivity were re-evaluated at 10, 15, 20, and 25 days after inoculation of the tumor, always after treatment with antagonists.
## Cancer pain model associated with the breast tumor and anticancer therapy
To assess whether treatment with AIs and the chemotherapy paclitaxel could potentiate the pain caused by the breast tumor, we performed a cancer pain protocol associated with the tumor and anticancer therapy. The mechanical PWT was measured before (baseline values; B) and at 10 days (baseline values; B2) after the vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. Next, the animals received vehicle (10 mL/kg, p.o.), low doses of anastrozole (0.15 mg/kg, p.o.) or letrozole (0.3 mg/kg, p.o.), or a low dose of paclitaxel (0.001 mg/kg, i.p.). The mechanical PWT was again evaluated at 3, 4, 5, and 7 h after anastrozole or letrozole administration; or at 24, 25, 26, and 28 h after paclitaxel administration.
Once the mechanical PWT was partially reduced at 10 days after the tumor cells injection, this time was selected to observe the mechanical hypersensitivity potentiation effects with anticancer drugs. For the same reason, low doses of anticancer drugs were used in this protocol according to previous studies13,23.
## Effect of kinin B1 or B2 receptor antagonists on the potentiation of mechanical hypersensitivity caused by breast tumor combined with anticancer therapy
The mechanical PWT was measured before (baseline values; B) and at 10 days (baseline values; B2) after the vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. The animals then received vehicle (10 mL/kg, p.o.) or low doses of anastrozole (0.15 mg, p.o.), letrozole (0.3 mg/kg, p.o.) or paclitaxel (0.001 mg/kg, i.p.). The mechanical PWT was evaluated at 3 h after anastrozole or letrozole administration or 24 h after paclitaxel administration corresponding to the third baseline value (B3). Next, animals were treated with vehicle (10 mL/kg, i.p.) or kinin B1 (DALBk; 150 nmol/kg, i.p.) or B2 (Icatibant; 100 nmol/kg, i.p.) receptor antagonists, and the mechanical PWT was again assessed from 1 up to 4 h after antagonist administrations.
## Determination of bradykinin-related peptide levels
The plantar tissue of the mice was collected at 3 h after vehicle (10 mL/kg, p.o.), anastrozole (0.2 mg/kg, p.o.), or letrozole (0.5 mg/kg, p.o.) administrations, and at 20 days after vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. Samples were homogenized in a buffer containing kininase inhibitors. The kinin levels were measured by enzyme immunoassay using a high-sensitivity kit for bradykinin. The results were expressed as bradykinin-related peptide levels in ng/mL of the sample40,80, normalized for mg/protein81.
## Protein expression of kinin B1 and B2 receptors
The plantar tissue, sciatic nerve, and spinal cord (T1-L6 approximately) of the mice were collected at 3 h after vehicle (10 mL/kg, p.o.), anastrozole (0.2 mg/kg, p.o.), or letrozole (0.5 mg/kg, p.o.) administrations, and at 20 days after vehicle (50 µL/site) or 4T1 breast cancer cells (104 cells, 50 µL/site) injection. Briefly, samples were homogenized in a lysis buffer containing protease and phosphatase inhibitors. Protein content was determined using bovine serum albumin as the standard by the bicinchoninic acid method (Thermo Fisher Scientific, Massachusetts, USA). Proteins were submitted to SDS-PAGE ($8\%$ resolving gels) and transferred to nitrocellulose membranes. The membranes were blocked with $3\%$ bovine serum albumin and incubated overnight at 4 °C with rabbit anti-B1 (1:1000) and anti-B2 (1:1000) polyclonal primary antibodies and for 1 h at room temperature with anti-rabbit secondary antibody (1:5000). The chemiluminescence was developed with ECL solution in a ChemiDoc™ MP Image System (Bio-Rad, California, USA)40. The blots were cut before hybridization to save the primary antibodies, approximately between 60 and 30 kDa (ColorBurst™ Electrophoresis Marker, # C1992, Sigma Aldrich). Also, due to unspecific bands, some blots were covered to the adequate obtention of protein immunoreactivity. Protein immunoreactivity was measured with ImageJ software. Ponceau S was used as a loading control. Each value was expressed as the ratio between arbitrary units obtained by the kinin B1 or B2 receptors bands and the respective ponceau area82. The results are shown as protein expression % related vehicle-treated controls.
## Sequence read archive (SRA) experiment analysis
Through Sequence Read Archives (SRAs) available at the NCBI platform, we search for kinin B1 and B2 receptor transcripts in deep-sequencing 4T1 breast cancer cells grown in vitro. Bioproject PRJNA533191 contains the transcriptomes of 4T1 cells. Briefly, total RNAs from 4T1 cells were extracted in quadruplicate; four barcoded mRNA-seq cDNA libraries were individually prepared and deep-sequenced using the Illumina HiSeq 2000 platform. The transcriptome reads were downloaded using the SRA Toolkit, trimmed, and individually mapped against the entire coding DNA sequence (CDS) of kinin B1 receptor (NM_007539), kinin B2 receptor (NM_009747), and as internal control PUM1 (AY027917) of mice using the Geneious R9.0 Software83. The reads were mapped to the sequence with a pairwise nucleotide identity of $99\%$.
## Cell viability measurement
The possible cytotoxic effect of the kinin B1 or B2 receptor antagonists was evaluated through MTT assay84. The 4T1 breast cancer cells were cultured overnight in a 96-well plate (2 × 105 cells/mL) in a culture medium at 37 °C and $5\%$ CO2. Next, the medium was discarded, and cells were treated with vehicle/control (only DMEM medium), kinin B1 (DALBk; 100 μM) or B2 (Icatibant; 100 μM) receptor antagonists, or paclitaxel (100 μM) and incubated at 37 °C and $5\%$ CO2 for 72 h. In another set of experiments, cells were incubated for 72 h with DALBk (100 μM) or Icatibant (100 μM) associated with the paclitaxel (100 μM) to observe if the kinin B1 or B2 receptor antagonists interfere with the chemotherapeutic action of paclitaxel. The in vitro protocol was performed with paclitaxel since it has direct cytotoxic action, unlike AIs considered an adjuvant endocrine treatment for breast cancer.
The drugs' concentrations were defined using a previously performed concentration–response curve (1, 10, and 100 μM). After the incubation period, the medium was discarded, and 40 μL of MTT solution was added to each well. One hour later, the MTT was removed, and 120 μL of dimethylsulfoxide was added to solubilize the formazan salts. The experiments were repeated in triplicate. The results were colorimetrically determined at 570 nm and expressed as the cell viability percentage compared with the control.
## Cell apoptosis and migration assay
Once DALBk (kinin B1 receptor antagonist) reduced the viability of 4T1 breast cancer cells in the MTT assay, we also tested this antagonist in the cell apoptosis and migration assay. For apoptosis assay, 4T1 breast cancer cells were cultured in 6-well plates (2 × 105 cells/well) in the presence of vehicle/control (only DMEM medium), DALBk (100 µM), paclitaxel (100 µM), or DALBk (100 µM) combined with paclitaxel (100 µM) for 48 h. After the treatment period, the supernatant was discarded, and the cells were removed from the wells with a trypsin/EDTA solution ($0.2\%$/$0.02\%$). The trypsin activity was neutralized with the addition of a medium with $10\%$ fetal bovine serum. Subsequently, cells were centrifuged, supernatants were discarded, and cell pellets were resuspended with 200 μL of annexin V binding buffer (10 mM HEPES, 140 mM NaCl, 2.5 mM CaCl2, and 1 μL annexin-V) plus 7AAD (5 μL). The samples were incubated at room temperature for 30 min and immediately acquired in a BD FACSCalibur flow cytometer. Data were analyzed with Flowjo V10 software and expressed as the percentage of cells in early apoptosis.
In the cell migration assay, the 4T1 breast cancer cells were incubated in 24-well plates with DMEM medium supplemented with $10\%$ fetal calf serum until confluency. Then, a scratch was made with a sterile tip in the central region of the well for mechanical removal of the cells. The treatment with DALBk (100 µM), paclitaxel (100 µM), or DALBk (100 µM) combined with paclitaxel (100 µM) was added for 24 h85. Cell migration capacity was assessed by comparing images of the wells before and after treatments using ImageJ software (NIH, USA). The results were expressed as the percentage of cell migration concerning the control.
## Statistical analyses
Statistical analyses were performed using GraphPad Prism 6.0 software. Results were expressed as the mean and standard error of the mean (SEM). The significance of differences between groups was evaluated with a Student’s t-test, one-way or two-way (time and treatment as factors; F values indicate the interaction between these factors) analysis of variance (ANOVA) followed by Bonferroni’s post hoc test. Mechanical threshold data were transformed to log before analyses to attend to parametric assumptions. P values lower than 0.05 ($P \leq 0.05$) were considered significant.
## Supplementary Information
Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-023-31535-6.
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|
---
title: Additive prognostic value of longitudinal myocardial deformation to SCORE2
in psoriasis
authors:
- George Makavos
- Ignatios Ikonomidis
- Vaia Lambadiari
- Georgia-Angeliki Koliou
- George Pavlidis
- John Thymis
- Pinelopi Rafouli-Stergiou
- Gavriella Kostelli
- Konstantinos Katogiannis
- Konstantinos Stamoulis
- Aikaterini Kountouri
- Emmanouil Korakas
- Kostas Theodoropoulos
- Alexandra Frogoudaki
- Pelagia Katsimbri
- Evangelia Papadavid
journal: European Heart Journal Open
year: 2023
pmcid: PMC10023827
doi: 10.1093/ehjopen/oead016
license: CC BY 4.0
---
# Additive prognostic value of longitudinal myocardial deformation to SCORE2 in psoriasis
## Abstract
### Aims
Psoriasis has been associated with increased cardiovascular (CV) risk. We investigated whether markers of CV function and their change after treatment have a prognostic value for adverse outcomes.
### Methods and results
In a prospective study, at baseline and after 6 months of treatment with biological agents, we assessed in 298 psoriasis patients (i) left ventricular global longitudinal strain (GLS) and (ii) carotid-femoral pulse wave velocity (PWV), to evaluate their prognostic value for major adverse cardiovascular events (MACEs), including coronary artery disease, stroke, hospitalization for heart failure, and all-cause death over a 4-year follow-up period. During follow-up, 26 ($8.7\%$) MACEs were recorded. By univariate analysis, decreasing absolute GLS values [hazard ratio (HR): 0.73, $P \leq 0.001$], decreasing GLS change after treatment (HR: 0.53, $$P \leq 0.008$$), and increasing PWV values (HR: 1.16, $$P \leq 0.049$$) were associated with adverse outcomes. Baseline GLS and its change post-treatment remained independent predictors of adverse events after adjusting for several confounders ($P \leq 0.05$). The addition of baseline GLS and its absolute change post-treatment to SCORE2 increased Harrell’s C from 0.882 to 0.941. By multivariable analysis, for each $1\%$ increase in absolute baseline GLS values, the risk of MACE decreased by $33\%$ and for each $1\%$ absolute increase of GLS post-treatment compared with the baseline value, the risk of MACE decreased by $58\%$.
### Conclusion
Global longitudinal strain has an independent and additive prognostic value to SCORE2 for adverse CV events in psoriasis, providing timely decision-making for intensive anti-inflammatory treatment and aggressive modification of risk factors to reduce CV risk.
## Graphical Abstract
Graphical Abstract
## Introduction
Psoriasis is a common, immune-mediated disease affecting up to $3\%$ of the general population, characterized by skin lesions and chronicity. Systemic manifestations may also occur, associated with accelerated atherosclerosis and increased cardiovascular (CV) risk.1-3 Atherosclerosis and psoriasis share common pathophysiological pathways including inflammation, oxidative stress, and common genetic susceptibility.4 Global longitudinal strain (GLS) and pulse wave velocity (PWV) are both similarly impaired in psoriasis and coronary artery disease (CAD) patients,5 findings consistent with subtle myocardial and vascular dysfunction, possibly attributed to a similarly increased inflammatory and oxidative stress burden.5 Psoriasis has been proposed as an independent risk factor for adverse CV events.3,6-9 Epidemiological studies have demonstrated higher rates of established CV risk factors in psoriasis compared with the general population,10-15 and current recommendations suggest CV risk estimation according to CV disease risk prediction models.16 However, the prognostic value of markers of subclinical myocardial and vascular dysfunction for adverse CV events has not been adequately studied in psoriasis. We aimed to evaluate the prognostic value of markers of myocardial function, namely GLS (assessed by two-dimensional speckle-tracking echocardiography), and arterial stiffness (assessed by PWV) in psoriatic patients at baseline and after anti-inflammatory treatment with biological agents and whether these markers may provide additive prognostic value over traditional CV risk factors and the recently updated SCORE216 for CV events.
## Study population
We prospectively enrolled 298 patients (from September 2014 until December 2017) with moderate-to-severe psoriasis. Baseline clinical characteristics of the study population are presented in Table 1. All patients had received treatment with biological agents (either anti-TNF-α or anti-interleukin-$\frac{12}{23}$ or anti-interleukin-17 inhibitors) in the 2nd Department of Dermatology and Venereology, Attikon Hospital, University of Athens, for at least 6 months regardless of the discontinuation or combined use of other oral agents or phototherapy during the follow-up period. Baseline assessment date was defined as the initiation date of treatment with biological agents. One hundred and thirty-eight patients were treated with anti-TNF-α agents, 89 patients with anti-interleukin $\frac{12}{23}$, and 71 patients with anti-interleukin 17a agents.
**Table 1**
| Unnamed: 0 | All patients (N = 298) | Patients without MACE (N = 272) | Patients with MACE (N = 26) | P-value |
| --- | --- | --- | --- | --- |
| Age, year | 51.6 ± 12.7 | 50.5 ± 12.6 | 62.5 ± 5.6 | <0.001 |
| Sex (male), n (%) | 179 (60.1) | 161 (59.2) | 18 (69.2) | 0.318 |
| PASI score | 12.30 ± 3.6 | 12.30 ± 3.6 | 11.90 ± 3.8 | 0.905 |
| Disease duration (years) | 17 ± 12 | 17.3 ± 12 | 12.3 ± 9 | 0.116 |
| Risk factors, n (%) | Risk factors, n (%) | Risk factors, n (%) | Risk factors, n (%) | Risk factors, n (%) |
| Hypertension | 100 (33.6) | 80 (29.4) | 20 (76.9) | <0.001 |
| Hyperlipidaemia | 98 (32.9) | 82 (30.1) | 16 (61.5) | 0.001 |
| Diabetes | 53 (17.8) | 34 (12.5) | 19 (73.1) | <0.001 |
| Current smoking | 142 (47.7) | 130 (47.8) | 12 (46.2) | 0.846 |
| SCORE2 (median; IQR) | 2 (0–9) | 1 (0–6) | 10 (9–12) | 0.001 |
| Medications, n (%) | Medications, n (%) | Medications, n (%) | Medications, n (%) | Medications, n (%) |
| ACE inhibitors/ARBs | 91 (30.4) | 73 (26.8) | 18 (69.2) | <0.001 |
| Diuretics | 20 (6.7) | 17 (6.3) | 3 (11.5) | 0.303 |
| Lipid-lowering drugs | 96 (32.2) | 84 (30.9) | 12 (46.2) | 0.111 |
| SBP (mmHg) | 135.2 ± 20.6 | 134.2 ± 20 | 145.6 ± 21 | 0.009 |
| DBP (mmHg) | 82.6 ± 12.3 | 82.2 ± 12.5 | 86.7 ± 9.3 | 0.040 |
| hs-CRP (mg/L) | 5.9 ± 4.5 | 5.8 ± 4.9 | 6 ± 2.8 | 0.940 |
| HbA1c (%) | 6.7 ± 0.8 | 6.6 ± 0.7 | 6.8 ± 1 | 0.818 |
| Total cholesterol (mg/dL) | 193.5 ± 42.9 | 194.3 ± 42.8 | 186.6 ± 45 | 0.514 |
| LDL-C (mg/dL) | 126.4 ± 36.5 | 127.4 ± 35.8 | 117.2 ± 43.2 | 0.309 |
| HDL-C (mg/dL) | 47.8 ± 13 | 48 ± 13.4 | 46.4 ± 7.5 | 0.474 |
| Triglycerides (mg/dL) | 139 ± 74.2 | 137.2 ± 74.8 | 154.6 ± 68.6 | 0.391 |
At baseline and after 6 months of treatment with biological agents (within 1 week after completion of the 6-month period), we assessed (i) left ventricular ejection fraction (LVEF), (ii) GLS, and (iii) PWV. We also assessed left ventricular (LV) diastolic function parameters, namely peak mitral inflow velocity (E), deceleration time (DT) of E-wave isovolumic relaxation time (IVRT), early diastolic mitral annulus velocity (Eʹ) by tissue Doppler imaging, E/Eʹ, Left atrial (LA) volume index, as well as right ventricular (RV) function parameters, namely systolic tricuspid annulus velocity (SʹRV), by tissue Doppler imaging. The mean follow-up period was 4 years (48 ± 2 months) for incidence of major adverse cardiovascular events (MACEs) defined as the composite endpoint of one of the following: (i) CAD including angina pectoris and acute coronary syndrome, (ii) stroke, (iii) hospitalization for heart failure, and (iv) all-cause mortality. The patients were observed from the 1st day after the completion of the 6-month treatment with biological agents, until the occurrence of their 1st MACE or until they reached the end of the follow-up period without MACE. Cardiovascular risk factors included smoking, hypertension, hyperlipidaemia, and diabetes mellitus. The recently updated SCORE2 for CV events based on age, sex, blood pressure, smoking, and non-HDL cholesterol was also calculated in all patients.
Baseline exclusion criteria were history of CAD diagnosed before baseline assessment, presence of LV wall motion abnormalities and ejection fraction of <$50\%$, severe valvular heart disease, primary cardiomyopathies, malignancies, psoriatic arthritis as it represents a different disease entity and treatment with b-blockers, as b-blockers may induce or exacerbate psoriasis. Coronary artery disease with significant epicardial stenosis (>$70\%$) at baseline was excluded in psoriatic patients by absence of clinical history, angina, and reversible myocardial ischaemia, as assessed by treadmill test and stress echocardiography or computed tomography coronary angiography. The disease duration from initial diagnosis until inclusion in the study was 17 ± 12 years. After the exclusion of 28 patients because of inadequate speckle-tracking echocardiography images for analysis ($91.4\%$ feasibility), the final cohort included in the study was 298 patients. Echocardiographic and vascular function markers are listed in Table 2.
**Table 2**
| Unnamed: 0 | All (Ν = 298) | All (Ν = 298).1 | All (Ν = 298).2 | Without MACE (Ν = 272) | Without MACE (Ν = 272).1 | Without MACE (Ν = 272).2 | With MACE (Ν = 26) | With MACE (Ν = 26).1 | With MACE (Ν = 26).2 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Baseline | After treatment | P-value | Baseline | After treatment | P-value | Baseline | After treatment | P-value |
| E (cm/s) | 72.7 ± 16.8 | 71.6 ± 16.2 | 0.311 | 72.7 ± 16.6 | 71.7 ± 15.4 | 0.312 | 72.8 ± 19.2 | 71.2 ± 22.3 | 0.508 |
| Eʹ (cm/s) | 11.3 ± 3.6 | 11.3 ± 3.7 | 0.723 | 11.6 ± 3.6 | 11.7 ± 3.7 | 0.821 | 8.7 ± 1.7 | 9 ± 2 | 0.511 |
| E/Eʹ | 6.91 ± 2.7 | 6.81 ± 2.4 | 0.621 | 6.66 ± 2.5 | 6.57 ± 2.2 | 0.743 | 8.83 ± 3.8 | 8.62 ± 3.4 | 0.623 |
| Sʹ (cm/s) | 9.6 ± 4.1 | 9.9 ± 6.8 | 0.600 | 9.75 ± 4.2 | 10.1 ± 7.3 | 0.644 | 8.7 ± 3.1 | 8.4 ± 2 | 0.732 |
| RV Sʹ (cm/s) | 13.1 ± 2.7 | 13.2 ± 3.3 | 0.601 | 13.2 ± 2.7 | 13.4 ± 3.3 | 0.501 | 12.1 ± 2 | 11.8 ± 2.7 | 0.667 |
| DT (ms) | 209.4 ± 51.7 | 209.3 ± 57.5 | 0.911 | 206.3 ± 51.3 | 207.6 ± 56 | 0.811 | 235.6 ± 49.1 | 223.5 ± 69.4 | 0.402 |
| IVRT (ms) | 88.9 ± 22.2 | 88.5 ± 24.1 | 0.812 | 89.1 ± 22.8 | 87.8 ± 23.7 | 0.546 | 87.2 ± 17.1 | 94.6 ± 27 | 0.05 |
| LA volume index (mL/m2) | 24.8 ± 10.3 | 25.2 ± 10.7 | 0.331 | 24.1 ± 10.3 | 24.4 ± 10.7 | 0.412 | 30.5 ± 8.9 | 31.7 ± 8.6 | 0.308 |
| LVEF (%) | 57.6 ± 4.6 | 58.1 ± 4.4 | 0.721 | 58.1 ± 4.5 | 58.5 ± 4.4 | 0.644 | 57.5 ± 4.4 | 57.9 ± 4.5 | 0.711 |
| GLS (%) | 17.58 ± 2.5 | 18.72 ± 2.1 | <0.001 | 17.8 ± 2.4 | 19.1 ± 1.9 | <0.001 | 15.21 ± 1.9 | 15.72 ± 1.8 | 0.060 |
| PWV (m/s) | 10.75 ± 2.5 | 10.20 ± 1.8 | <0.001 | 10.67 ± 2.5 | 10.13 ± 1.8 | <0.001 | 11.50 ± 2.5 | 11.02 ± 1.7 | 0.051 |
The psoriasis area severity index (PASI) was used to monitor the extent of disease and was calculated at baseline and after a 6-month treatment with biological agents to monitor the effect of treatment. In all patients, LV function and vascular function assessment were performed on the same day. The study protocol was approved by the Institute’s Ethics Committee, and written informed consent was obtained from all patients.
## Arterial stiffness
Carotid-femoral PWV was measured using a previously published methodology (Complior; Alam Medical, Vincennes, France).5 Pulse wave velocity was calculated as the distance divided by transit time between waves (m/s).
## Two-dimensional and speckle-tracking echocardiography
Studies were performed in the echocardiography laboratory of the 2nd Department of Cardiology, Attikon Hospital, University of Athens, using a Vivid E95 (GE Medical Systems, Horten, Norway) ultrasound system. All studies were digitally stored in a computerized station (Echopac 204; GE Medical Systems, Horten, Norway) and were analysed by two observers, blinded to clinical and laboratory data. From cross-sectional echocardiographic images, we measured LV end-diastolic and end-systolic diameter (mm), interventricular septal and posterior wall thickness (mm), LV end-diastolic volume (mL), LV end-systolic volume (mL), and ejection fraction (%) using Simpson method of discs. Left atrial volume (mL) was measured from four- and two-chamber views, using the disk summation method and indexed to body surface area as LA volume index (mL/m2).
Using a dedicated software package (EchoPAC), two-dimensional strain was measured using speckle-tracking analysis. We acquired LV apical four-, two-, and three-chamber views at frame rates ≥50 frames/s. Subsequently, we calculated the GLS from the apical views (four, two, and three chambers) according to previously published methodology.17 All variables represent the mean value of measurements taken in three consecutive cardiac cycles. Patients with ≥2 segments with poor image quality were rejected from the analysis. The inter- and intra-observer variabilities of GLS were 10 and $7\%$, respectively. The change of GLS post-treatment was calculated as baseline GLS values minus GLS value at 6 months. For purposes of clarity in presentation of results, the GLS values are presented as absolute positive values.
## Doppler echocardiography
The early mitral inflow E wave was measured by using pulsed-wave Doppler. The DT of E mitral wave and the IVRT between aortic closure and beginning of mitral E wave were measured in Doppler mitral inflow recordings. Myocardial velocities were recorded with tissue Doppler imaging. The sample volume was placed in the septal and lateral sites of the mitral annulus in the apical four-chamber view to record the LV systolic velocity (Sʹ) and early diastolic velocity (Eʹ). The average value of the velocities at the two annular sites was used to calculate Sʹ and Eʹ. The ratio of the mitral E, the Eʹ (E/Eʹ), was also calculated. The sample volume was also placed in the anterior site of the tricuspid annulus to record the RV systolic velocity (RV Sʹ).
## Statistical analysis
Continuous variables were tested for normality using the Kolmogorov–Smirnov test. Normally distributed variables were expressed as mean ± standard deviation (SD). Hazard ratios (HRs) with the respective $95\%$ confidence intervals (CIs) were obtained through univariate and multivariable Cox regression analyses to estimate the risk of MACE for the examined markers of myocardial and vascular function and their change after treatment, age, sex, PASI, CV risk factors, and medication. In multivariable analysis, three different models were examined using both forward and backward selection procedure with the following parameters included in Model 1: SCORE2, diabetes, PASI, LVEF, PWV, treatment with angiotensin-converting enzyme inhibitors (ACEis)/angiotensin II receptor blockers (ARBs), lipid-lowering drugs, Model 2: all parameters of Model 1 plus GLS baseline to assess its additive predictive value and Model 3: all parameters of Model 2 plus GLS absolute change post-treatment. This analysis was applied for each one of the examined echocardiography markers.
Harrell’s C statistic was calculated to evaluate the improvement in risk prediction.
Survival curves were estimated using the Kaplan–Meier method for GLS (using as cut-off the lowest tertile of GLS) and for the absolute GLS difference (according to the tertiles of the change of GLS post-treatment). The log-rank test for time-to-event data with respect to the total events was used for comparison among groups. We planned a study with an accrual interval of 1 year, and additional follow-up after the accrual interval of 4 years. Based on our previous studies, we speculated that the median survival time of patients with normal GLS (greater than absolute value $17\%$ median of the overall population) would be 5 years. If the true HR (relative risk) of patients with normal GLS relative to those with impaired GLS is 2, we estimated that we would need to study 128 patients with impaired and 128 patients with normal GLS to be able to reject the null hypothesis that the normal and abnormal GLS survival curves are equal with probability (power) 0.850. The Type I error probability associated with this test of this null hypothesis is 0.05.
Statistical analysis was conducted using SPSS (version 26; SPSS, Chicago, IL, USA), Stata (version 16 Stata Corp LP, College Station, TX, USA), and SAS (version 9.3, SAS Institute, Inc., Cary, NC, USA). All tests were two-sided. Significance was set at $5\%$.
## Results
We prospectively enrolled 298 patients (51.6 ± 12.7 years, 179 men) with PASI disease activity score: 12.3 ± 3.6 SD. During the 4-year follow-up period, 26 MACEs occurred ($8.72\%$ of the study population). Coronary artery disease occurred in eight patients ($2.68\%$), stroke in eight patients ($2.68\%$), hospitalization for heart failure in eight patients ($2.68\%$), and all-cause death in two patients ($0.67\%$). At the end of treatment with biological agents, all patients had improved GLS (from −17.58 ± 2.5 at baseline to −18.72 ± 2.1 after treatment, $P \leq 0.001$) and PWV (from 10.75 ± 2.5 at baseline to 10.20 ± 1.8 after treatment, $P \leq 0.001$) values (Table 2). No significant effect was observed after treatment in mitral E, IVRT, DT, Eʹ, E/Eʹ, RV Sʹ, and LA volume index ($P \leq 0.05$). In patients free of MACE, there was a greater improvement of GLS compared with those with MACE (GLS increase 1.30 vs. $0.51\%$, respectively, $$P \leq 0.001$$). Lower PWV values were observed after 6 months of treatment in patients without MACE (from 10.67 ± 2.5 at baseline to 10.13 ± 1.8 after treatment, $P \leq 0.001$) but not in patients with MACE (from 11.50 ± 2.5 at baseline to 11.02 ± 1.7 after treatment $$P \leq 0.050$$) (Table 2). Psoriasis area severity index decreased from 12.30 ± 3.6 at baseline to 2.30 ± 2 at 6 months of treatment ($P \leq 0.001$) in all patients. Psoriasis area severity index was similarly decreased in patients with (from 11.90 ± 3.8 to 2.54 ± 2 $P \leq 0.001$) or without MACE (from 12.30 ± 3.6 to 2.30 ± 2, $P \leq 0.001$). Baseline GLS and GLS change correlated modestly with disease duration (r = −0.17, $$P \leq 0.012$$ and $r = 0.23$, $$P \leq 0.001$$, respectively). By univariate Cox regression analysis, age and the presence of hypertension, diabetes mellitus, and hyperlipidaemia were associated with incidence of MACE ($P \leq 0.05$ for all associations) (Table 3). SCORE2 was also a univariate predictor of MACE (HR: 1.27, $95\%$ CI: 1.18–1.36, $P \leq 0.001$). There was no association between disease duration and incidence of MACE ($$P \leq 0.2$$). Among the markers of myocardial and vascular function, decreasing absolute baseline values of GLS (HR: 0.73, $95\%$ CI: 0.65–0.83, $P \leq 0.001$) and lower values of GLS absolute change after treatment (HR: 0.53, $95\%$ CI: 0.33–0.84, $$P \leq 0.008$$) were associated with incidence of MACE (Table 3). No association was found between decreasing LVEF with MACE incidence ($$P \leq 0.15$$). Although increasing PWV values had a borderline predictive value for MACE (HR: 1.16, $95\%$ CI: 1.00–1.33, $$P \leq 0.049$$) (Table 3), the change of PWV after treatment did not show a significant association with MACE ($$P \leq 0.820$$). By multivariable analysis, using the backward procedure, SCORE2, decreasing baseline GLS and lower GLS absolute change after treatment remained independent predictors of increased risk for MACE ($P \leq 0.05$) (Table 4). For each $1\%$ increase in absolute baseline GLS values, the risk of MACE decreased by $33\%$, and for each $1\%$ absolute increase of GLS change post-treatment compared with the baseline value, the risk of MACE decreased by $58\%$. ( multivariable Model 3).
The addition of baseline GLS to SCORE2 increased Harrell’s C from 0.882 to 0.913 ($P \leq 0.001$), and in a next step, the addition of GLS absolute change post-treatment to SCORE2 and baseline GLS further increased Harrell’s C to 0.941 ($P \leq 0.001$).
The HR and significance of baseline GLS and its absolute increase after treatment remained similar after addition of disease duration ($P \leq 0.05$; data not given). There was no difference in terms of MACE incidence between the type of biological treatment (either anti-TNF-α or anti-IL-$\frac{12}{23}$ or anti-IL-17a) after adjustment for age, sex, PASI, CV risk factors, treatment with ACEis/ARBs, lipid-lowering drugs, and vascular and myocardial function markers ($P \leq 0.05$; data not available).
We also conducted Kaplan–Meier survival time analysis for participants with absolute baseline GLS ≥ $16.4\%$ and those with baseline GLS <$16.4\%$ (value of the lowest tertile) (Figure 1). We observed a lower-cumulative MACE-free survival in patients with baseline GLS < $16.4\%$ compared with the group with baseline GLS ≥ $16.4\%$ (log rank P = <0.001). In detail, only 5 of 226 patients with baseline GLS ≥ 16.4 had a MACE (absolute risk $2.2\%$), whereas 21 of 72 with a baseline GLS < $16.4\%$ had a MACE (absolute risk $29.2\%$).
**Figure 1:** *Kaplan–Meier curve for global longitudinal strain (using the −16.4% as a cut-off value) with respect to survival without a major adverse cardiovascular event.*
By Kaplan–Meier analysis, we also analysed the tertiles of the GLS absolute increase after 6 months of treatment. Participants with a GLS absolute increase of ≥$1.44\%$ (upper tertile) after 6 months of treatment had a lower cumulative MACE incidence compared with participants with GLS absolute increase <$1.44\%$ (log rank $$P \leq 0.015$$) (Figure 2). In detail, only 1 out of 99 patients with GLS absolute increase of ≥$1.44\%$ had a MACE (absolute risk $1.01\%$), whereas 25 out of 199 with a GLS absolute increase of <$1.44\%$ had a MACE (absolute risk $12.5\%$).
**Figure 2:** *Kaplan–Meier curve for global longitudinal strain change with respect to survival without a major adverse cardiovascular event.*
Patients with MACE had an impaired DT, E, E/Eʹ, and LA volume index compared with those without MACE ($P \leq 0.05$, Table 2). Conversely, E, IVRT, Sʹ, and RV Sʹ were similar between the two study groups ($P \leq 0.05$). By univariate analysis, DT, Eʹ, E/Eʹ, and LA volume index were predictors of adverse outcome ($P \leq 0.05$, Table 3). However, in multivariable analysis including SCORE2, diabetes, PASI, LVEF, PWV, treatment with ACEis/ARBs, lipid-lowering drugs, and using a backward procedure, none of the above markers showed an additive value to SCORE2 for the prediction of MACE (data not available, $P \leq 0.05$).
## Discussion
In the present study, we have demonstrated that GLS has a predictive value for MACE in psoriasis patients during a 4-year follow-up period. Baseline GLS, as well as GLS absolute change after 6 months of treatment with biological agents, had an independent and additive predictive value for MACE to SCORE2. A cut-off value of $16.4\%$ for baseline GLS (lower tertile) and GLS absolute increase after treatment with a cut-off value of a GLS change ≥$1.44\%$ (upper tertile) were indicative of higher MACE-free survival during the follow-up period.
To our knowledge, this is the first study to assess the prognostic value of GLS and PWV for MACE in psoriasis patients. Psoriasis is characterized by a higher incidence of MACE compared with the general population.18 It has been demonstrated that incidences of myocardial infarction per 1000 person-years were 4.04 ($95\%$ CI: 3.88–4.21) for patients with mild and 5.13 ($95\%$ CI: 4.22–6.17) for patients with severe psoriasis.6 Results from a meta-analysis have indicated an elevated risk of CV events in psoriatic patients compared with controls without psoriasis [odds ratio (OR) 1.28; $95\%$ CI: 1.18–1.38].19 The association between psoriasis and CV disease seems to be multifactorial. The increased prevalence of CV risk factors such as diabetes, hyperlipidaemia, and hypertension may partially explain the accelerated atherosclerosis in psoriasis patients.12,20 Psoriasis patients are at an increased risk for developing hypertension, diabetes, hypertriglyceridaemia, and quantitative and qualitative alterations in LDL and HDL cholesterol, which have been linked to accelerated atherosclerosis in these patients.20-22 Indeed, in our study, we observed that the presence of diabetes, hyperlipidaemia, hypertension, and a validated score to assess CV risk are univariate predictors of MACE at follow-up. However, most of the above risk factors have been shown to exert a detrimental effect on myocardial deformation even in the presence of normal LVEF as well as on arterial elasticity.23 Another important factor of the atherosclerotic process in psoriasis is systemic inflammation affecting CV function independently from the presence of CV risk factors. It has been demonstrated that psoriasis is associated with an increased risk for CAD regardless of age and the presence of diabetes, hypertension, or smoking.24 Moreover, psoriatic patients, particularly those with severe disease, have an increased risk of stroke and CV mortality regardless of the presence of traditional CV risk factors.25,26 Similarly, low-grade inflammation has been shown to adversely affect myocardial deformation and vascular function at an early stage.5 Indeed, in our study, we observed an association between impaired myocardial deformation as assessed by GLS and psoriatic disease duration. This finding suggests that exposure to an inflammatory state for a long period may directly compromise myocardial function and thus affect prognosis. There is growing evidence regarding the link between psoriasis and atherosclerosis, including the presence of activated T helper1 and T helper17 lymphocytes, macrophages, and monocytes in both psoriatic and atherosclerotic plaques. The inflammatory cytokines released in psoriatic lesions, including interleukin-6, 12, 17, 23, and TNF-α, are also implicated in myocardial and vascular dysfunction and atherosclerotic plaque formation and destabilization.27-29 Several studies have described early abnormalities of myocardial and vascular function in psoriasis related to disease duration, oxidative stress, and inflammatory markers.5,30,31 Therefore, markers of myocardial deformation and arterial stiffness including GLS and PWV, respectively, may identify subtle abnormalities of CV function attributed to an elevated inflammatory burden and/or the presence of atherosclerotic risk factors at an early phase of the disease and despite a normal LVEF.
Interestingly, treatment with biological agents resulted in a significant improvement of GLS and PWV in parallel with a reduction in disease severity, inflammatory, and oxidative stress markers.32,33 Indeed, previous studies have shown that the reduction of malondialdehyde, a marker of oxidative stress, and inflammatory cytokines, namely interleukin-12 and 6, after treatment with biological agents were associated with a concomitant improvement of PWV and GLS in psoriasis.32,33 Furthermore, in the above studies, improved PWV after treatment was also related to an increase of GLS. These findings support that reduction of oxidative stress and inflammatory burden may improve LV performance either directly and/or through improvement of arterial elasticity. Circulating cytokines including interleukin-6 and tumour necrosis factor TNF-α, and oxidative stress may have negative inotropic effects, result in apoptosis, and LV dysfunction.34,35 Increased arterial stiffness leading to early systolic arrival of wave reflections promotes a dysfunction of the subendocardial fibres of LV. Dysfunction of the subendocardial layer is a major determinant of impaired longitudinal myocardial deformation.36 Conversely, improved arterial elasticity permits arrival of wave reflection at diastole instead of systole and thus increases diastolic perfusion pressure to coronary arteries and reduces LV afterload.36 Thus, improved arterial elasticity after treatment with biological agents may improve longitudinal myocardial function by reducing afterload and increasing myocardial perfusion in psoriatic patients.
Global longitudinal strain is the most validated and widely applied parameter of myocardial deformation and is considered a useful and clinically applicable marker of subclinical myocardial dysfunction.37 A cut-off value of GLS ≤ −$20\%$ is expected in healthy subjects.37 Global longitudinal strain has an additive prognostic value to LVEF after myocardial infarction.38 Impaired GLS values have also been reported in psoriatic arthritis39 and rheumatoid arthritis.40 Abnormal GLS is considered to reflect the results of systemic inflammation, fibrosis, and coronary microcirculatory dysfunction on myocardial function in autoimmune rheumatic diseases (ARDs).5,41 Since cardiac involvement in psoriasis is a serious complication likely leading to increased morbidity and mortality,3,6-8 early detection of CV abnormalities is crucial to permit appropriate therapy and intensive modification of CV risk factors to reduce CV risk. The role of imaging parameters for the risk stratification for adverse cardiac events in ARDs remains unclear. It has been indicated that impaired myocardial deformation is associated with poor clinical outcomes in rheumatoid arthritis patients.42 In concordance with these data, we have demonstrated in the current study that GLS may a serve as an independent and additive prognostic marker to SCORE2 for CV events in psoriasis patients, as it may reflect the cumulative effects of an increased inflammatory burden and coexisting comorbidities on cardiac function at an early phase of the disease.
Interestingly, in our study, patients free of MACE had a greater improvement of GLS values after treatment with biological agents compared with those who developed MACE, suggesting that the beneficial effects of anti-inflammatory treatment on myocardial deformation may be translated to improved prognosis in patients with moderate to severe psoriasis. Targeting inflammation may have beneficial effects in terms of MACE reduction, as has been recently shown by the Canakinumab Anti-inflammatory Thrombosis Outcomes Study (CANTOS) trial.43 Pulse wave velocity has been also proposed as a parameter that may re-stratify patients to a higher CV risk beyond established CV factors.44 In our study, we found that PWV was also a univariate predictor of MACE in psoriasis patients. However, PWV did not maintain its predictive value in our multivariable model, possibly due to the co-existence of diabetes and hypertension, which are known to significantly affect arterial stiffness.44 However, in our study, improved PWV values after treatment with biological agents were observed only in patients free of MACE but not in those with MACE, suggesting that improvement of arterial function may have also contributed to improved cardiac performance, as previously shown,32,33 and consequently, to improved outcome in the long term. Echocardiography markers of LV diastolic function were univariate predictors of outcome in our study. Similar to PWV, these markers did not show an additive predictive value to SCORE2 by multivariable analysis, likely because of the co-existence of diabetes and hypertension, which are known to significantly affect LV diastolic function.
## Study limitations
This study is a single-centre study, with a relatively small size study population and a relatively low prevalence of MACE; thus, a limited number of covariates may have been included in multivariable analysis. The lower and non-statistically significant improvement of PWV compared with the respective statistically significant improvement of GLS after treatment in patients with MACE might be attributed to the much lower power of the test in the small sample size of patients with MACE.
Non-invasive imaging techniques such as stress echocardiography and coronary CT angiography were used to exclude significant obstructive coronary artery disease but may not rule out non-obstructive atherosclerosis. Thus, it is difficult to assess the impact of intermediate coronary artery stenosis on GLS values and increased risk of CV events. This study design does not permit us to fully elucidate the causality between baseline GLS values and its changes after treatment with MACE. Our study includes patients with moderate to severe psoriasis, and thus, the findings may not be extrapolated to psoriatic patients with mild disease, where the prevalence of traditional risk factors and calculation of SCORE2 may be the most important factors to assess CV risk and thus define proper treatment (e.g. statins) to alter prognosis.
In our study population, incidence of hypertension, diabetes, hyperlipidaemia, and smoking was relatively high, limiting the number of patients without any CV risk factors. Thus, an assessment of the prognostic value of GLS in psoriatic patients without risk factors was not feasible. As the presence of CV risk factors is common in psoriatic patients in the real world, the additive prognostic value of GLS assessment to that of SCORE2 in our study enhances the clinical utility of this echocardiography marker.
## Conclusions
Impaired baseline GLS and GLS change after treatment with biological agents had an independent and additive prognostic value to SCORE2 for MACE during a 4-year follow-up in psoriasis, after adjusting for several confounders and medication. Among all echocardiographic parameters in our study, GLS was the only one to show additive predictive value to SCORE2. Therefore, GLS may serve as a risk stratification marker, which enables taking a timely decision for providing an intensive anti-inflammatory treatment and aggressively modify CV risk factors to reduce CV risk in psoriasis patients.
## Lead author biography
Dr Ignatios Ikonomidis, MD, PhD, is Professor of Cardiology, Director of Echocardiography, Laboratory of Preventive Cardiology Clinic of Cardiometabolic Diseases, 2nd Cardiology Department, National and Kapodistrian University of Athens (NKUA), Attikon Hospital, Athens, Greece. In 1997, he completed his training in Cardiology and cardiac imaging, in Hammersmith Hospital, Imperial College, London, UK. Since then, he has been working as a consultant cardiologist in University Hospital of Patras, Department of Clinical Therapeutics and 2nd Cardiology Department, Medical School, Athens. His research fields include cardiovascular imaging, microcirculation, inflammation, diabetes, smoking control, and ventricular–arterial coupling. He authored 300 publications in indexed journals and several book chapters.
## Data availability
The authors state that data are avilable on reasonable demand.
## Funding
None declared.
Conflict of interest: None declared.
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|
---
title: 'Racial Disparities in Patients With COVID-19 Infection: A National Inpatient
Sample Analysis'
journal: Cureus
year: 2023
pmcid: PMC10023870
doi: 10.7759/cureus.35039
license: CC BY 3.0
---
# Racial Disparities in Patients With COVID-19 Infection: A National Inpatient Sample Analysis
## Abstract
Introduction Evidence suggests the COVID-19 (coronavirus disease 2019) pandemic highlighted well-known healthcare disparities. This study investigated racial disparities in patients with COVID-19-related hospitalizations utilizing the US (United States) National Inpatient Sample (NIS).
Methodology This was a retrospective study conducted utilizing the NIS 2020 database. The NIS was searched for hospitalization of adult patients with COVID-19 infection as a principal diagnosis using ICD-10 (International Classification of Diseases, Tenth Revision) codes. We divided the NIS into four major racial/ethnic groups: White, Black, Hispanic, and others. The primary outcome was inpatient mortality, and the secondary outcomes were the mean length of stay, mean total hospital charges, development of sepsis, septic shock, use of vasopressors, acute respiratory failure, acute respiratory distress syndrome, acute kidney failure, acute myocardial infarction, cardiac arrest, deep vein thrombosis, pulmonary embolism, cerebrovascular accident, and need for mechanical ventilation.
Results Compared to White patients, Hispanic patients had higher adjusted inpatient mortality odds (aOR [adjusted odds ratio]: 1.25, $95\%$ CI 1.19-1.33, $p \leq 0.001$); however, Black patients had similar adjusted mortality odds (aOR: 0.96, $95\%$ CI 0.91-1.01, $$p \leq 0.212$$). Black patients and Hispanic patients had a higher mean length of stay (8.01 vs 7.13 days, $p \leq 0.001$ and 7.67 vs 7.13 days, $p \leq 0.001$, respectively), adjusted odds of cardiac arrest (aOR: 1.53, $95\%$ CI 1.37-1.71, $p \leq 0.001$ and aOR: 1.73, $95\%$ CI 1.54-1.94, $p \leq 0.001$), septic shock (aOR: 1.23, $95\%$ CI 1.13-1.33, $p \leq 0.001$ and aOR: 1.88, $95\%$ CI 1.73-2.04, $p \leq 0.001$), and vasopressor use (aOR: 1.32, $95\%$ CI 1.14 - 1.53, $p \leq 0.001$ and aOR: 1.87, $95\%$ CI 1.62 - 2.16, $p \leq 0.001$).
Conclusion Our study showed that Black and Hispanic patients are at higher risk of adverse outcomes compared to White patients admitted with COVID-19 infection.
## Introduction
The initial outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection occurred in December 2019. Since then, SARS-CoV-2 has spread rapidly and caused a global pandemic [1]. It continues to impact the health, economy, and quality of life of individuals and societies globally. By October 12, 2022, there were 96,581,755 confirmed cases and 1,057,975 deaths in the United States alone [2].
Health disparities, defined as a higher burden of illness, injury, disability, or mortality among one group relative to another, are well known and documented in the United States [3]. The COVID-19 pandemic highlighted the prominent impact of social determinants of health [4]. The health effects of COVID-19 have been unevenly distributed across the United States [5]. Racial/ethnic disparities are observed in healthcare utilization and outcomes of populations during the COVID-19 pandemic, and evidence has emerged that the pandemic is disproportionately affecting people from Black, Hispanic, and minority ethnic communities [6,7].
The primary objective of this study was to identify how COVID-19 affected major racial/ethnic groups in the United States on a national level utilizing the National Inpatient Sample (NIS) database for 2020.
## Materials and methods
Design and data source This was a retrospective cohort study, using the NIS database for 2020. The NIS is the largest database of hospital inpatient stays in the United States derived from billing data submitted by hospitals to statewide data organizations across the United States, covering $98\%$ of the US population. It contains discharge data from a $20\%$ stratified sample of community hospitals and is a part of the Healthcare Quality and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality. The International Classification of Diseases, Tenth Revision, Clinical Modification/Procedure Coding System (ICD-10-CM/PCS) was used in the coding. Diagnoses are divided into principal diagnosis and secondary diagnosis. A principal diagnosis is the main ICD-10 code for hospitalization. Secondary diagnoses are any ICD-10 code other than the principal diagnosis billed for that hospitalization.
Study population We queried the NIS 2020 database for patients who had a principal discharge diagnosis of COVID-19 infection. Patients who were younger than 18 years of age or who had elective admissions were excluded from our study. The ICD-10-CM codes used for COVID-19-related hospitalizations were U.071, U.00, U.49, U.50, U.85, J.1282, and B.342. Per coding guidelines, these codes were based on documentation by the provider or documentation of a positive COVID-19 test result. The ICD-10-CM diagnosis codes for COVID-19 were implemented beginning April 1, 2020 [8]. Coding for race in NIS combines “race” and “ethnicity” provided by the data source into one data element (RACE). If both “race” and “ethnicity” were available, ethnicity was preferred over race in setting the HCUP value for “RACE". For this analysis, race/ethnicity was classified as White, Black, Hispanic, and others (Asian or Pacific Islander, Native American, others) as employed in prior NIS-based publications [9-11].
Outcome measures The primary outcome of our study was comparing inpatient mortality among race groups. Secondary outcomes included mean length of stay, mean total hospital charges, development of sepsis, septic shock, use of vasopressors, acute respiratory failure, acute respiratory distress syndrome (ARDS), acute kidney failure, acute myocardial infarction, cardiac arrest, deep vein thrombosis, pulmonary embolism, cerebrovascular accident, and need for mechanical ventilation.
Statistical analysis *The data* were analyzed using Stata® Version 14 software (StataCorp, Texas, USA). Comorbidities were calculated as proportions of the cohorts and the chi-square test was used to compare these characteristics among different racial groups. The Deyo modification of the Charlson Comorbidity Index (CCI) was used to identify the burden of comorbid diseases [12]. CCI, as a summary comorbidity measure, was used as a potential confounder in the multivariate regression analysis, given comparable performance to each component of CCI used separately [13,14]. Variables obtained from the literature search (age, sex, race/ethnicity, median yearly income in the patient’s zip code, hospital location [rural or urban], geographic region [Northeast, Midwest, West, or South], hospital teaching status, and hospital bed size, smoking history, obesity, malnutrition, anemia, and pulmonary hypertension) were tested with a univariate screen. Variables with p-values <0.01 were subsequently included in the final multivariate regression model (stricter entry criteria were implemented given large database analysis). This was done to avoid overpowering and avoid variables attaining statistical significance while only marginally changing the outcome [15].
Using predictive margins analysis in our multivariate regression model, we obtained adjusted mortality rates, mean total hospital charges, mean length of stay, and the rates of the other secondary outcomes. We present both the crude and adjusted values for these outcomes in this study.
Ethical considerations The NIS is a retrospective database that protects patient confidentiality, lacking individual or hospital identifiers. This study was therefore exempt from our Institutional Review Board approval.
## Results
Patient characteristics NIS database for 2020 contained over 32 million weighted hospital discharges, of which 1,019,325 were adults (persons 18 years and above), non-elective admissions, and had a principal diagnosis of COVID-19 infection, which satisfied the inclusion criteria.
Out of all patients admitted with COVID-19 infection, White patients comprised $52.4\%$ and were significantly older compared to Black patients and Hispanic patients (68.9 years vs 61.2 years vs 58.1 years, $p \leq 0.001$). Black patients had a higher proportion of females compared to White patients and Hispanic patients ($53.3\%$ vs $46.9\%$ vs $43.6\%$). Black patients had the highest proportion of diabetes mellitus, hypertension, obesity, and malignancy, while White patients had the highest proportion of history of smoking, chronic obstructive pulmonary disease, and dependency on oxygen. Baseline patient and hospital characteristics are shown in Table 1.
**Table 1**
| Variable | White Patients | Black Patients | Hispanic Patients | Others | p-Value |
| --- | --- | --- | --- | --- | --- |
| COVID-19 infection (N=1,019,325) | n=534,126 | n=189,575 | n=210,980 | n=84,644 | |
| Mean age (years) | 68.9 | 61.2 | 58.1 | 61.3 | <0.001 |
| Female (%) | 46.9 | 53.3 | 43.6 | 44.8 | <0.001 |
| Insurance type (%) | | | | | <0.001 |
| Medicare | 65.7 | 51.4 | 38.5 | 42.7 | |
| Medicaid | 5.4 | 16.4 | 26.3 | 19.6 | |
| Private | 27.1 | 30.9 | 32.4 | 35.3 | |
| Uninsured | 1.8 | 1.3 | 2.8 | 2.4 | |
| Charlson Comorbidity Index Score (%) | | | | | <0.001 |
| 0 | 25.5 | 23.1 | 35.3 | 31.8 | |
| 1 | 26.5 | 26.2 | 31.2 | 31.3 | |
| 2 | 17.7 | 16.5 | 13.6 | 14.1 | |
| ≥3 | 30.3 | 34.2 | 19.9 | 22.8 | |
| Median household income (%) | Median household income (%) | | | | <0.001 |
| < $49,999 | 27.1 | 51.9 | 37.5 | 26.2 | |
| ≥ $50,000 to < $64,999 | 29.8 | 22.7 | 27.3 | 23.9 | |
| ≥ $65,000 to < $85,999 | 24.1 | 15.5 | 22.9 | 24.3 | |
| ≥ $86,000 | 19.0 | 9.9 | 12.3 | 25.6 | |
| Hospital region (%) | | | | | <0.001 |
| Northeast | 17.2 | 18.1 | 17.7 | 19.2 | |
| Midwest | 29.9 | 19.9 | 9.8 | 17.3 | |
| South | 40.3 | 57.1 | 37.6 | 27.8 | |
| West | 12.6 | 4.9 | 34.9 | 35.7 | |
| Hospital bed size (%) | | | | | <0.001 |
| Small | 26.3 | 24.7 | 22.9 | 22.2 | |
| Medium | 28.9 | 27.8 | 30.5 | 29.7 | |
| Large | 44.8 | 47.5 | 46.6 | 48.1 | |
| Location/teaching status of hospital (%) | | | | | <0.001 |
| Rural | 15.7 | 8.1 | 3.6 | 5.0 | |
| Urban non-teaching | 21.0 | 14.4 | 20.8 | 19.3 | |
| Urban teaching | 63.3 | 77.5 | 75.6 | 75.7 | |
| Comorbidities (%) | | | | | |
| Diabetes mellitus | 36.0 | 47.9 | 45.0 | 40.8 | <0.001 |
| Hypertension | 70.2 | 76.8 | 56.0 | 61.5 | <0.001 |
| Smoking | 27.0 | 18.2 | 13.7 | 14.6 | <0.001 |
| Congestive heart failure | 19.5 | 19.6 | 9.7 | 11.3 | <0.001 |
| Chronic kidney disease | 20.6 | 27.1 | 14.5 | 16.8 | <0.001 |
| Obesity | 25.8 | 33.6 | 29.2 | 27.6 | <0.001 |
| Pulmonary hypertension | 2.8 | 3.0 | 1.5 | 1.8 | <0.001 |
| History of cerebrovascular accident | 3.9 | 3.5 | 2.7 | 3.8 | 0.072 |
| Chronic obstructive pulmonary disease | 17.5 | 11.4 | 4.9 | 6.7 | <0.001 |
| Dependency on oxygen | 4.9 | 3.1 | 2.3 | 2.5 | <0.001 |
| Liver disease | 3.5 | 3.1 | 5.5 | 5.0 | <0.001 |
| Anemia | 18.3 | 26.6 | 18.8 | 19.5 | <0.001 |
| Malignancy | 4.2 | 3.3 | 2.1 | 2.5 | <0.001 |
Primary outcome: inpatient mortality The crude inpatient mortality rate of COVID-19 infection for the total cohort was $11.10\%$. White patients had the highest crude mortality rate ($11.63\%$); however, when the mortality rate was adjusted for other variables using predictive margins analysis in our logistic regression model, Hispanic patients had the highest adjusted mortality rate ($10.41\%$). When compared to White patients (taken as reference), the adjusted odds ratio for mortality was 0.96 ($95\%$ CI 0.91-1.01, $$p \leq 0.212$$) for Black patients, 1.25 ($95\%$ CI 1.19-1.33, $p \leq 0.001$) for Hispanic patients, and 1.22 ($95\%$ CI 1.14-1.31, $p \leq 0.001$) for other races.
Secondary outcomes The total length of hospitalization and the total hospital charges between the Black patient and the Hispanic patient groups were compared to White patients using a multivariate linear regression model. Compared to White patients, Black patients and Hispanic patients had a higher mean length of stay (8.01 vs 7.13 days, $p \leq 0.001$, and 7.67 vs 7.13 days, $p \leq 0.001$, respectively). Hispanic patients had significantly higher mean total hospital charges compared to White patients ($104,826 vs $67,682, $p \leq 0.001$). However, there was no difference in mean total hospital charges between Black patients and White patients. Among different racial/ethnic groups, the remaining secondary outcomes are demonstrated in Table 2, and adjusted odds ratios are visualized in Table 3.
## Discussion
According to our study, the proportion of White patients admitted for COVID-19 was significantly less than the adult White population of the US represented in the whole NIS ($52.4\%$ vs $63.6\%$, $p \leq 0.001$). Strikingly, the proportions of Black patients and Hispanic patients with COVID-19-related hospitalizations were significantly higher compared to their proportions in the whole NIS ($18.6\%$ vs $15.8\%$, $p \leq 0.001$, and $20.6\%$ vs $13.1\%$, $p \leq 0.001$). This signifies that a lower proportion of White patients and a higher proportion of Black patients and Hispanic patients were hospitalized for COVID-19 compared to the entire US hospitalizations. This finding was comparable to the latest available CDC data, which suggest that race and ethnicity are risk markers for worse outcomes for COVID-19 patients [16]. The above finding is likely tied to a complex mechanism driving different healthcare outcomes of different races, connections, and consequences of socioeconomic disparities, including the type of work, location, and access to health care [17,18]. A study conducted by Abedi et al. [ 19] suggests that counties in the United States with higher and more diverse populations have a higher rate of SARS-CoV-2 infection, whereas counties with grocery mobility (lack of food deserts) and work mobility are associated with lower rates of infection. Another study by Dey et al. [ 20] hypothesized that air pollution might increase vulnerability to COVID-19 resulting in hospitalization and/or death, which may be linked to racial disparities since zip codes with high levels of fine particulate matter (PM2.5) mostly predominantly consisted of racial/ethnic minorities.
Crude mortality and adjusted mortality were significantly different among racial/ethnic groups in patients with COVID-19-related hospitalizations. This discrepancy between the two approaches is likely secondary to other confounders affecting mortality, which were also significantly different among our cohort groups. For example, White patients were significantly older than the other groups and older age is associated with higher mortality. These findings are comparable to prior studies, such as findings from a cross-sectional study by Acosta et al. [ 21] involving 143,342 patients which demonstrated significant differences in inpatient mortality among different racial groups, and Hispanic patients and Black patients had a higher risk of inpatient mortality; however, a study conducted by Yehia et al. [ 22], with 92 US hospitals involved, suggested that there was no mortality difference between Black patients and White patients (HR 0.93, $95\%$ CI 0.80-1.09).
We found that Black patients and Hispanic patients had a significantly higher length of stay and increased odds of sepsis, septic shock, and use of vasopressors compared to White patients. Hispanic patients had higher mean total hospital charges and increased odds of developing ARDS compared to White patients, a well-known complication of COVID-19 infection. Our findings build on previous COVID-NET studies, and other assessments that found that Black and Hispanic populations are disproportionately affected by COVID-19-related hospitalizations [23,24].
Our study has several strengths. The data were sourced from a large national database, providing a large sample size that enabled us to compare mortality outcomes. The nature of the database allows us to provide insights into the comparison of baseline demographics and hospital outcomes between different racial/ethnic groups to statistically significant levels.
There are some limitations to our study. First, the database we utilized has limitations related to coding, missing data, and dependency on inpatient admissions (does not involve outpatient COVID-19 infections), among others. NIS database studies involve hospitalizations and not individual patients, so patients admitted multiple times will be counted separately. Second, COVID-19 treatment evolved significantly over the past years. This database includes patients from 2020; hence, treatments involved in our cohort could be outdated compared to current guidelines and management of COVID-19 infections. Previous studies conducted in 2020 and 2021 reveal similar disparities in racial/ethnic populations regarding COVID-19 outcomes, and this may suggest that the newly developed therapies may also be poorly distributed among different groups, tied closely to availability and access to health care [25,26].
## Conclusions
In summary, among adults excluding elective admissions, Black, Hispanic, and other non-White ethnic/racial groups are at a higher risk of adverse outcomes including inpatient mortality, respiratory complications, and length of hospital stay when compared to White patients in patients with COVID-19-related hospitalizations. There were significant disparities in outcomes across various sociodemographic groups. This discrepancy identified warrants further randomized studies into possible unadjusted confounders to address healthcare disparities. Our findings may help identify potential gaps in health care involving COVID-19 patients.
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|
---
title: 'Predictors of outcome after catheter ablation for atrial fibrillation: Group
analysis categorized by age and type of atrial fibrillation'
authors:
- Tetsuya Uemura
- Hidekazu Kondo
- Hiroki Sato
- Masaki Takahashi
- Tetsuji Shinohara
- Kazuki Mitarai
- Akira Fukui
- Kei Hirota
- Tomoko Fukuda
- Nozomi Kodama
- Miho Miyoshi
- Naoko Ogawa
- Masato Wada
- Hirochika Yamasaki
- Kenzo Iwanaga
- Akihiro Uno
- Katsunori Tawara
- Keisuke Yonezu
- Hidefumi Akioka
- Yasushi Teshima
- Kunio Yufu
- Mikiko Nakagawa
- Naohiko Takahashi
journal: Annals of Noninvasive Electrocardiology
year: 2022
pmcid: PMC10023880
doi: 10.1111/anec.13020
license: CC BY 4.0
---
# Predictors of outcome after catheter ablation for atrial fibrillation: Group analysis categorized by age and type of atrial fibrillation
## Abstract
The patients with atrial fibrillation (AF) divided by age and the type AF showed significant difference of AF recurrence after catheter ablation. The significant factors associated with AF recurrence were found in each category.
### Background
The outcome of catheter ablation could probably differ among patients with atrial fibrillation (AF), depending on age and AF type. We aimed to investigate the difference in predictors of outcome after catheter ablation for AF among the patient categories divided by age and AF type.
### Methods and Results
A total of 396 patients with AF (mean age 65.69 ± 11.05 years, 111 women [$28.0\%$]) who underwent catheter ablation from January 2018 to December 2019 were retrospectively analyzed. We divided the patients into four categories: patients with paroxysmal AF (PAF) or persistent AF (PeAF) who were 75 years or younger (≤75 years) or older than 75 years (>75 years). Kaplan–Meier survival analysis demonstrated that patients with PAF aged ≤75 years had the lowest AF recurrence among the four groups (log‐rank test, $$p \leq .0103$$). In the patients with PAF aged ≤75 years ($$n = 186$$, $46.7\%$), significant factors associated with recurrence were female sex ($$p \leq .008$$) and diabetes ($$p \leq .042$$). In the patients with PeAF aged ≤75 years ($$n = 142$$, $35.9\%$), the only significant factor associated with no recurrence was medication with a renin‐angiotensin system inhibitor ($$p \leq .044$$). In the patients with PAF aged >75 years ($$n = 53$$, $14.4\%$), diabetes was significantly associated with AF recurrence ($$p \leq .021$$). No significant parameters were found in the patients with PeAF aged >75 years ($$n = 15$$, $4.1\%$).
### Conclusions
Our findings indicate that the risk factors for AF recurrence after catheter ablation differed by age and AF type.
## INTRODUCTION
Catheter ablation for atrial fibrillation (AF), especially pulmonary vein antrum isolation (PVAI), is effective for maintaining sinus rhythm; however, the efficacy is limited by the type of AF (paroxysmal or persistent) (Bhargava et al., 2009). The ability for catheter ablation to maintain sinus rhythm is greater in patients with paroxysmal AF (PAF) than in those with persistent AF (PeAF) (Parkash et al., 2010; Brooks et al., 2010). This could be due to progression of AF substrates out of the pulmonary vein (PV) and the presence of non‐PV targets that remain after PVAI (Terricabras et al., 2020).
The impact of age on the outcomes after AF ablation is controversial. In studies that investigated the outcomes of AF catheter ablation focusing on differences in age, no significant variations in the overall success rate by age were noted (Natale et al., 2021; Bunch et al., 2010; Bahnson et al., 2022). In contrast, a small study that included patients with persistent AF undergoing cryoballoon catheter ablation showed that the older group (>75 years) achieved a lower success rate ($36.1\%$) than the younger group (≤75 years) ($47.0\%$) (Vermeersch et al., 2021).
There are several different predictors for AF recurrence after catheter ablation between patients with PAF and PeAF or younger and older patients (Bhargava et al., 2009; Buiatti et al., 2016; Fujino et al., 2020). The purpose of this study was to investigate the difference in predictors of outcome after catheter ablation for AF among patient categories divided by age and AF type.
## MATERIALS AND METHODS
The data supporting our findings of this study are available from the corresponding author upon reasonable request.
## Patient selection
The retrospective study enrolled 396 patients with PAF ($$n = 239$$) and PeAF ($$n = 157$$) who underwent radiofrequency catheter ablation for AF at our institute between January 2018 and December 2019. Their mean age was 65.69 ± 11.05 years. The study group comprised 111 females and 285 males. Sixty‐eight patients were aged 76 years and older (labeled as “older”) and 328 were aged 75 years and younger (“younger”).
Patients with prior AF ablation, cardiovascular implantable electronic devices, cardiopulmonary disease, or structural heart disease were excluded from the study. PAF and PeAF were defined according to the 2017 HRS/EHRA/ECAS/APHRS/SOLAECE expert consensus statement (Calkins et al., 2018). Briefly, PAF is defined as an episode of AF that terminates spontaneously or with intervention in <7 days, and PeAF is defined as episodes that are sustained for >7 days and are not self‐terminating. Transthoracic and transesophageal echocardiograms were performed using the Vivid 7 ultrasound system (GE Vingmed) before ablation to evaluate the left ventricular function and left atrial diameter (LAD) and to exclude the presence of thrombi.
Experienced physicians recorded the medical history, medication regimes, and body mass index of all patients. All patients underwent physical examination, ECG, and blood testing (including renal function, Hb, HbA1c, NT‐proBNP, and C‐reactive protein).
## Follow‐up
Follow‐up was performed at 1, 3, 6, and 12 months after catheter ablation using a 12‐lead electrocardiogram and 24‐h Holter monitoring at each visit. Any atrial tachyarrhythmia lasting ≥1 min was considered a recurrence. In addition to palpitation, patients were asked to check whether their pulse was regular in their free time. If recurrence was suspected, additional 24‐h Holter monitoring was performed. The discontinuation of antiarrhythmic drugs was recommended at the 3‐month follow‐up.
## Pulmonary vein antrum isolation by catheter ablation
Contact force‐guided PVAI was performed by two operators. Circumferential PVAI was performed with integrated 3D images using the open‐irrigated ThermoCool SmartTouch catheter (Biosense Webster). The ablation catheter was advanced into the left atrium (LA) using a long sheath. Radiofrequency energy was delivered at 30 W in the anterior aspect of the circumferential PVAI line and at 25 W in the posterior aspect using the Stockert 70 generator system (Biosense Webster) radiofrequency generator. The operator attempted to maintain a contact force between 10 and 20 g during PVAI. While radiofrequency energy was being delivered, the catheter tip was dragged by approximately 2 mm every 5–15 s. The endpoint of PVAI was the elimination of all PV potentials recorded by a circular catheter (Lasso, Biosense Webster) placed at the ostium of the PV and the PV‐to‐LA block during pacing from 10 pairs of the circular catheter at 10 V output with 1‐ms pulse width. Isoproterenol (4 μg) was injected intravenously to induce AF in the non‐PV foci. When a non‐PV focus was identified, focal ablation was performed at the foci, except for one in the superior vena cava (SVC) where segmental isolation was performed. SVC isolation was performed if the length of the SVC sleeve was regarded >30 mm (Higuchi et al., 2010). Cavotricuspid isthmus (CTI) linear ablation was also performed if atrial flutter was documented before ablation or induced during the ablation procedure.
## Statistical analysis
Baseline clinical characteristics are presented as mean with standard deviation or frequency with percentage, as appropriate. For continuous variables, normality of the distribution was tested using Shapiro–Wilk test. For continuous variables, an unpaired t‐test was used to test a difference between the PAF and PeAF groups. For categorical variables, chi‐square test and Fisher's exact test were used. A value of $p \leq .05$ was considered significant. Potential risk factors for AF recurrence were investigated using univariate logistic regression. All computations were performed using the SPSS statistical software (version 26.0) running on Windows 10 (Microsoft).
## Patient characteristics
The characteristics of patients with AF in this study are shown in Table 1. This study enrolled a total of 396 patients (mean age 65.69 ± 11.05 years; 111 females). Of the 396 patients, 157 ($39.6\%$) had PeAF, and 68 were aged >75 years, with an average age of 78.9 ± 2.9 years. The average age of patients aged ≤75 years was 62.9 ± 10.1 years ($p \leq .001$). All patients underwent PVAI, and 208 patients ($52.5\%$) underwent isolation of the SVC. There were no significant differences in the execution rate of SVC isolation among the four groups. Based on their age and type of AF, patients were classified into group 1: ≤75 years and PAF (labeled as “younger PAF,” $$n = 186$$), group 2: ≤75 years and PeAF (“younger PeAF,” $$n = 142$$), group 3: >75 years and PAF (“older PAF,” $$n = 53$$), and group 4: >75 years and PeAF (“older PeAF,” $$n = 15$$). The basic demographics of the four groups are listed in Table S1. There were significant differences in the number of male patients, creatinine clearance, plasma NT‐proBNP level, height, body weight, the mean CHADS2 score, amiodarone medication, antiarrhythmic medication, LAD, left ventricular ejection fraction, and E/e′ among the four groups. In contrast, there were no significant differences in the prevalence of hypertension, diabetes, serum creatinine levels, HbA1c, C‐reactive protein, BMI, treatment with a renin‐angiotensin system (RAS) inhibitor, and β‐blocker use among the groups.
**TABLE 1**
| Male sex, n (%) | 285 (72.0) |
| --- | --- |
| Age—years | 65.7 ± 11.1 |
| Type of AF | |
| Paroxysmal, n (%) | 239 (60.4) |
| Persistent, n (%) | 157 (39.6) |
| Stroke, n (%) | 34 (8.6) |
| Hypertension, n (%) | 238 (60.1) |
| Diabetes, n (%) | 61 (15.4) |
| Laboratory data | |
| Creatinine—mg/dl | 0.91 ± 0.45 |
| Creatinine clearance—ml/min | 79.6 ± 31.3 |
| Median NT‐proBNP (IQR)—pg/ml | 267 (98–604) |
| HbA1c—% | 5.87 ± 0.81 |
| C‐reactive protein—mg/dl | 0.19 ± 0.59 |
| Height—m | 1.64 ± 0.09 |
| Weight—kg | 67.2 ± 13.8 |
| Body mass index—kg/m2 | 24.8 ± 4.04 |
| CHADS2 | |
| 0, n (%) | 104 (26.3) |
| 1, n (%) | 152 (38.4) |
| 2, n (%) | 98 (24.7) |
| 3, n (%) | 30 (7.6) |
| 4, n (%) | 11 (2.8) |
| 5, n (%) | 1 (0.2) |
| Medication | |
| ACEI/ARB, n (%) | 162 (40.9) |
| Beta‐blocker, n (%) | 174 (43.9) |
| Amiodarone, n (%) | 51 (12.9) |
| Antiarrhythmic, n (%) | 72 (18.2) |
| Measurements by echocardiogram | |
| Left atrial diameter—mm | 40.3 ± 0.5 |
| Left ventricular ejection fraction—% | 63.3 ± 10.2 |
| E/e′ | 11.4 ± 4.5 |
## Kaplan–Meier MACCE‐free estimation
Kaplan–Meier survival analysis revealed that the AF recurrence rate was significantly different among the four groups at the 12‐month follow‐up (log‐rank $$p \leq .0103$$, Figure 1). The AF‐free survival rate of the younger PAF group was the highest, whereas that of the younger PeAF group was the lowest among the four groups.
**FIGURE 1:** *Kaplan–Meier curves showing AF recurrence free survival among four groups. There is a significant difference among four groups (log‐rank p = .0103).*
## Predictor of AF recurrence in the younger PAF group, aged ≤75 years
The baseline clinical, echocardiographic, and biochemical characteristics of patients with or without AF recurrence are shown in Table 2. The category of patients with PAF aged ≤75 years included 186 patients ($72.6\%$ males). Univariate analysis revealed that the significant factors associated with recurrence were female sex ($$p \leq .008$$) and diabetes ($$p \leq .042$$). In addition, multivariate analysis revealed that female sex ($$p \leq .005$$) and the prevalence of diabetes ($$p \leq .019$$) were independent predictive factors for AF recurrence.
**TABLE 2**
| Unnamed: 0 | AF recurrence (−) | AF recurrence (+) | p value | Multivariate analysis | Multivariate analysis.1 | Multivariate analysis.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | (n = 169) | (n = 17) | p value | Odds ratio | 95% CI | p value |
| Male sex, n (%) | 128 (75.7) | 7 (41.2) | .008 | 0.21 | 0.070–0.622 | .005 |
| Age—years | 63.8 ± 10.2 | 66.2 ± 8.4 | .335 | 1.03 | 0.91–1.03 | .417 |
| Stroke, n (%) | 15 (8.9) | 1 (5.9) | 1.000 | | | |
| Hypertension, n (%) | 99 (58.6) | 9 (52.9) | .797 | | | |
| Diabetes, n (%) | 18 (10.7) | 5 (29.4) | .042 | 4.54 | 1.219–16.667 | .019 |
| Creatinine—mg/dl | 0.9 ± 0.2 | 0.8 ± 0.2 | .275 | | | |
| Creatinine clearance—ml/min | 85.0 ± 32.8 | 80.5 ± 27.4 | .592 | | | |
| Median NT‐proBNP (IQR)—pg/ml | 121 (57–269) | 182 (84–543) | .650 | | | |
| HbA1c—% | 5.8 ± 1.0 | 6.0 ± 0.7 | .357 | | | |
| C‐reactive protein—mg/dl | 0.3 ± 0.4 | 0.2 ± 0.3 | .730 | | | |
| Height—m | 1.6 ± 0.1 | 1.6 ± 0.1 | .446 | | | |
| Weight—kg | 67.2 ± 13.1 | 64.7 ± 12.1 | .456 | | | |
| Body mass index—kg/m2 | 24.7 ± 3.9 | 24.5 ± 3.2 | .858 | | | |
| CHADS2 | | | | | | |
| 0, n (%) | 59 (34.9) | 5 (0.0) | .950 | | | |
| 1, n (%) | 70 (41.4) | 8 (47.1) | | | | |
| 2, n (%) | 25 (14.8) | 3 (17.6) | | | | |
| 3, n (%) | 12 (7.1) | 1 (5.9) | | | | |
| 4, n (%) | 3 (1.8) | 0 (0.0) | | | | |
| 5, n (%) | 0 (0.0) | 0 (0.0) | | | | |
| Medication | | | | | | |
| ACEI/ARB, n (%) | 62 (36.7) | 7 (41.2) | .794 | | | |
| Beta‐blocker, n (%) | 72 (42.6) | 6 (35.3) | .616 | | | |
| Amiodarone, n (%) | 14 (8.3) | 2 (11.8) | .644 | | | |
| Antiarrhythmic, n (%) | 42 (24.9) | 6 (35.3) | .386 | | | |
| Echocardiographic parameter | | | | | | |
| Left atrial diameter—mm | 38.5 ± 5.6 | 38.2 ± 3.9 | .811 | | | |
| Left ventricular ejection fraction—% | 65.4 ± 8.8 | 66.8 ± 5.7 | .499 | | | |
| E/e′ | 10.9 ± 4.3 | 11.3 ± 4.9 | .706 | | | |
## Predictor of AF recurrence in the older PAF group, aged >75 years
The baseline clinical, echocardiographic, and biochemical characteristics of patients with or without AF recurrence are shown in Table 3. The group included 53 patients ($45.3\%$ males). Univariate analysis revealed that the only significant factor associated with recurrence was diabetes ($$p \leq .021$$), which was also confirmed as an independent predictive factor for AF recurrence in multivariate analysis ($$p \leq .010$$).
**TABLE 3**
| Unnamed: 0 | AF recurrence (−) | AF recurrence (+) | p value | Multivariate analysis | Multivariate analysis.1 | Multivariate analysis.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | (n = 44) | (n = 9) | p value | Odds ratio | 95% CI | p value |
| Male sex, n (%) | 21 (47.7) | 3 (33.3) | .488 | 0.75 | 0.113–4.348 | .752 |
| Age—years | 79.0 ± 2.7 | 79.4 ± 3.5 | .656 | 1.15 | 0.833–1.587 | .377 |
| Stroke, n (%) | 8 (0.2) | 0 (0) | .324 | | | |
| Hypertension, n (%) | 31 (70.5) | 6 (66.7) | 1.000 | | | |
| Diabetes, n (%) | 4 (9.1) | 4 (44.4) | .021 | 16.6 | 1.219–16.667 | .010 |
| Creatinine—mg/dL | 0.9 ± 0.4 | 0.9 ± 0.2 | .964 | | | |
| Creatinine clearance—ml/min | 55.8 ± 16.9 | 52.0 ± 16.6 | .541 | | | |
| Median NT‐proBNP (IQR)—pg/ml | 251 (156–433) | 538 (345–794) | .276 | | | |
| HbA1c—% | 5.9 ± 0.5 | 6.1 ± 0.5 | .266 | | | |
| C‐reactive protein—mg/dl | 0.1 ± 0.2 | 0.1 ± 0.1 | .205 | | | |
| Height—m | 1.6 ± 0.1 | 1.6 ± 0.1 | .749 | | | |
| Weight—kg | 58.9 ± 10.3 | 58.4 ± 13.8 | .913 | | | |
| Body mass index—g/m2 | 24.0 ± 3.2 | 24.1 ± 4.6 | .930 | | | |
| CHADS2 | | | | | | |
| 0, n (%) | 0 (0) | 0 (0) | .119 | | | |
| 1, n (%) | 6 (13.6) | 2 (22.2) | | | | |
| 2, n (%) | 27 (61.4) | 2 (22.2) | | | | |
| 3, n (%) | 7 (15.9) | 4 (44.4) | | | | |
| 4, n (%) | 3 (6.8) | 1 (11.1) | | | | |
| 5, n (%) | 1 (2.3) | 0 (0.0) | | | | |
| Medication | | | | | | |
| ACEI/ARB, n (%) | 20 (45.5) | 4 (44.4) | 1.000 | | | |
| Beta‐blocker, n (%) | 17 (38.6) | 4 (44.4) | 1.000 | | | |
| Amiodarone, n (%) | 1 (2.3) | 1 (11.1) | .314 | | | |
| Antiarrhythmic, n (%) | 5 (11.4) | 1 (11.1) | 1.000 | | | |
| Echocardiographic parameter | | | | | | |
| Left atrial diameter—mm | 39.6 ± 5.8 | 39.6 ± 4.8 | .987 | | | |
| Left ventricular ejection fraction—% | 65.6 ± 10.2 | 65.7 ± 8.2 | .972 | | | |
| E/e′ | 14.6 ± 6.1 | 15.8 ± 5.2 | .584 | | | |
## Predictor of AF recurrence in the younger PeAF group, aged ≤75 years
The baseline clinical, echocardiographic, and biochemical characteristics of patients with or without AF recurrence are shown in Table 4. The group included 142 patients ($83.1\%$ males). No independent factors predicting AF recurrence were identified by multivariate analysis. The only significant factor associated with no recurrence was medication with RAS inhibitors ($$p \leq .044$$).
**TABLE 4**
| Unnamed: 0 | AF recurrence (−) | AF recurrence (+) | p value | Multivariate analysis | Multivariate analysis.1 | Multivariate analysis.2 |
| --- | --- | --- | --- | --- | --- | --- |
| | (n = 110) | (n = 32) | p value | Odds ratio | 95% CI | p value |
| Male sex, n (%) | 91 (82.7) | 27 (84.4) | 1.000 | 1.03 | 0.313–3.012 | .964 |
| Age—years | 61.7 ± 10.0 | 61.2 ± 10.1 | .812 | 0.99 | 0.95–1.04 | .788 |
| Stroke, n (%) | 6 (5.5) | 2 (6.3) | 1.000 | | | |
| Hypertension, n (%) | 67 (60.9) | 14 (43.8) | .105 | | | |
| Diabetes, n (%) | 23 (20.1) | 5 (15.6) | .619 | | | |
| Creatinine—mg/dl | 1.0 ± 0.7 | 1.0 ± 0.4 | .901 | | | |
| Creatinine clearance—ml/min | 85.5 ± 30.5 | 83.2 ± 27.0 | .712 | | | |
| Median NT‐proBNP (IQR)—pg/ml | 530 (277–902) | 478 (237–663) | .335 | | | |
| HbA1c—% | 6.0 ± 0.6 | 5.8 ± 0.5 | .360 | | | |
| C‐reactive protein—mg/dl | 0.2 ± 0.5 | 0.2 ± 0.5 | .947 | | | |
| Height—m | 1.7 ± 0.1 | 1.7 ± 0.1 | 0.839 | | | |
| Weight—kg | 71.6 ± 14.9 | 70.7 ± 12.1 | .770 | | | |
| Body mass index—kg/m2 | 25.6 ± 4.7 | 25,2 ± 3.6 | .649 | | | |
| CHADS2 | | | | | | |
| 0, n (%) | 28 (25.5) | 11 (34.4) | .409 | | | |
| 1, n (%) | 49 (44.5) | 15 (46.9) | | | | |
| 2, n (%) | 28 (25.5) | 5 (15.6) | | | | |
| 3, n (%) | 4 (3.5) | 0 (0.0) | | | | |
| 4, n (%) | 1 (1.0) | 1 (3.1) | | | | |
| 5, n (%) | 0 (0.0) | 0 (0.0) | | | | |
| Medication | | | | | | |
| ACEI/ARB, n (%) | 53 (48.2) | 9 (28.1) | .044 | 0.42 | 0.164–1.010 | .059 |
| Beta‐blocker, n (%) | 54 (49.1) | 11 (34.4) | .162 | | | |
| Amiodarone, n (%) | 25 (22.7) | 7 (21.9) | 1.000 | | | |
| Antiarrhythmic, n (%) | 12 (10.9) | 5 (15.6) | .537 | | | |
| Echocardiographic parameter | | | | | | |
| Left atrial diameter—mm | 42.2 ± 5.1 | 43.8 ± 4.7 | .129 | | | |
| Left ventricular ejection fraction—% | 58.8 ± 11.2 | 62.4 ± 8.9 | .102 | | | |
| E/e′ | 10.5 ± 3.6 | 10.6 ± 3.5 | .864 | | | |
## Predictor of AF recurrence in the older PeAF group, aged >75 years
The baseline clinical, echocardiographic, and biochemical characteristics of patients with or without AF recurrence are shown in Table S2. The group included 15 patients ($53.3\%$ males), and no significant parameters were found.
## Predictor of AF recurrence in the PeAF group regardless of age
The group of patients with persistent AF is small and with low number of events, therefore, we combined the two PeAF groups regardless of age. The basic demographics of the three groups (PAF ≤75, PAF >75 and PeAF) are listed in Table S3. Kaplan–Meier survival analysis revealed that the AF recurrence rate was significantly different among the three groups at the 12‐month follow‐up (log‐rank $$p \leq .0053$$, Figure S1). The PeAF group included 157 patients. The baseline clinical, echocardiographic, and biochemical characteristics of all PeAF patients with or without AF recurrence are shown in Table S4. Univariate analysis revealed that the factor associated with recurrence was medication with RAS inhibitors ($$p \leq .078$$), but it was not significant. No independent factors predicting AF recurrence were identified by multivariate analysis, even if we included medication with RAS inhibitors ($$p \leq .059$$).
## DISCUSSION
The main findings of this study are as follows: [1] the clinical success rates of AF ablation after 1 year were significantly different among the four groups categorized by age and AF type; [2] the AF‐free survival of the younger PAF group was the greatest, whereas that of the younger PeAF group was the lowest among the groups; [3] univariate analysis to predict AF recurrence in each group revealed that the prevalence of diabetes was significantly associated with AF recurrence in the younger and older PAF groups, whereas female sex was a significant predictor in the younger PAF group. In addition, medication with RAS inhibitors was significantly associated with no recurrence in the younger PeAF group; and [4] multivariate analysis revealed that the prevalence of diabetes and female sex were independent predictors of AF recurrence in the younger PAF group, whereas only the prevalence of diabetes was an independent predictor in the older PAF group.
Consistent with the results of our study, previous studies have shown that female sex and the presence of diabetes are independent predictors of AF recurrence after catheter ablation (Wang et al., 2020; Creta et al., 2019; Arora et al., 2018). Female sex has been reported to be associated with the presence of left atrial low‐voltage areas (Huo et al., 2018). In addition, Takigawa et al. reported that the prevalence of non‐PV triggers was significantly higher in women than in men ($16\%$ vs. $8.4\%$) (Takigawa et al., 2013). Another study reported that parasympathetic nervous activity, potentially affecting PAF vulnerability (Chen et al., 2014), is significantly enhanced in women than in men before and after AF ablation (Yu et al., 2018). The presence of these arrhythmogenic factors may contribute to higher AF recurrence rates in the younger PAF group. Sex‐related differences in parasympathetic regulation diminish with age (Kuo et al., 1999), which could be a reason why sex differences were detected only in the younger PAF group.
Diabetes is known to promote atrial remodeling associated with AF recurrence (Wang et al., 2019). In our study, diabetes was associated with recurrence in patients with PAF regardless of age but not associated in patients with PeAF. As described previously, the cardiac autonomic nervous system more greatly contributes to the pathogenesis of PAF than to that of PeAF. Based on these findings, diabetes might enhance AF vulnerability by deteriorating the cardiac autonomic nervous function.
Interestingly, treatment with a RAS inhibitor was significantly associated with no recurrence of AF only in the younger PeAF group in this study. The cardioprotective effects of RAS inhibitors have been widely accepted. RAS inhibitors attenuate cardiac remodeling by suppressing atrial inflammation and fibrosis (Schieffer et al., 1994; Nunez et al., 1997; Zhu et al., 1997). However, the preventive effect of RAS inhibitors on AF recurrence after catheter ablation remains controversial. Previous studies have suggested that RAS inhibitors are effective for the prevention of AF recurrence after radiofrequency catheter ablation (Wang et al., 2016; Cui et al., 2015; Tian et al., 2019), while other studies have reported that RAS inhibitors have no preventive effect (Tayebjee et al., 2010; Patel et al., 2010). Atrial remodeling progresses depending on age. Our study may provide the potential efficacy of RAS inhibitor therapy in the younger patient category before remodeling develops with advancing age. The PeAF‐limited effect of RAS inhibitors might be explained by the fact that the causes of PAF are often multifactorial (parasympathetic nervous activity, etc.) compared to those of PeAF, which are mainly considered to be atrial substrates. RAS inhibitors might not be able to affect factors such as increased parasympathetic nervous activity, resulting in no predictive value in patients with PAF. This study has some limitations. First, we followed up the patients for only 1 year. This period might be too short to monitor for AF recurrence. Second, the patients were not assessed for recurrent AF using an insertable cardiac monitor. Therefore, we might have missed AF recurrence in asymptomatic patients. Third, this study was not a multicenter study and the number of patients was relatively small. Fourth, some results of this study are different from previous reports, but it seems to be related to study population. In conclusion, our findings suggest that the predictors for AF recurrence after radiofrequency catheter ablation should be considered depending on age and AF type. Appropriate management for diabetes would improve the success rate of catheter ablation for PAF irrespective of age. In addition, RAS inhibitors might play a favorable role in efficacy of catheter ablation in younger PeAF group. However, due to limitations of the study, further follow‐up and a multicenter study are needed to validate the consistency and reproducibility of the results.
## AUTHOR CONTRIBUTIONS
Tetsuya Uemura and Hidekazu Kondo wrote the draft of this article and designed the study. Hiroki Sato helped to perform correct statistical analysis. Masaki Takahashi, Tetsuji Shinohara, Kazuki Mitarai, Akira Fukui, and Kei Hirota performed catheter ablation and followed‐up the patients. Tomoko Fukuda, Nozomi Kodama, Miho Miyoshi, Naoko Ogawa, Masato Wada, Hirochika Yamasaki, Kenzo Iwanaga, Akihiro Uno, Katsunori Tawara, Keisuke Yonezu, Hidefumi Akioka, Yasushi Teshima, Kunio Yufu, and Mikiko Nakagawa gave the advice in terms of interpretation of the data and planning the clinical research. Naohiko Takahashi made the decision regarding final approval of this article.
## FUNDING INFORMATION
None.
## CONFLICT OF INTEREST
The authors declared no conflict of interest for this article.
## COMPLIANCE WITH ETHICAL STANDARDS
The study was conducted in accordance with the ethics review board of Oita University. Informed consent was obtained from all subjects.
## CLINICAL TRIAL REGISTRATION
This study does not meet the definition of clinical trial. This study is a retrospective study and is not classified as an interventional trial.
## ETHICS STATEMENT
This retrospective study was conducted in accordance with the ethical standards of the institutional and national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The Institutional Review Board (IRB) of Oita University approved this study. Informed consent was obtained from all patients by the opt‐out method.
## DATA AVAILABILITY STATEMENT
The data supporting our findings of this study are available from the corresponding author upon reasonable request.
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|
---
title: β-cell–selective inhibition of DNA damage response signaling by nitric oxide
is associated with an attenuation in glucose uptake
authors:
- Chay Teng Yeo
- Erin M. Kropp
- Polly A. Hansen
- Michael Pereckas
- Bryndon J. Oleson
- Aaron Naatz
- Jennifer S. Stancill
- Kyle A. Ross
- Rebekah L. Gundry
- John A. Corbett
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10023961
doi: 10.1016/j.jbc.2023.102994
license: CC BY 4.0
---
# β-cell–selective inhibition of DNA damage response signaling by nitric oxide is associated with an attenuation in glucose uptake
## Body
The DNA damage response (DDR) is comprised of three kinases that belong to the family of PI3K-related kinases [1, 2]. In response to double-strand DNA breaks, ataxia–telangiectasia mutated protein (ATM) and DNA-dependent protein kinase are activated, whereas ATM- and Rad3-related protein (ATR) responds to a broader range of DNA damage such as replication stress and single-strand DNA breaks [2]. Once activated, transducer kinases phosphorylate downstream effector proteins, leading to multiple physiological responses that include DNA repair, cell cycle arrest, cellular senescence, or apoptosis if the damage is beyond repair [1, 3].
Pancreatic β-cells play a primary role in the regulation of whole-body glucose homeostasis through the synthesis and secretion of insulin. When exposed to inflammatory cytokines such as interleukin-1 (IL-1), β-cells express the inducible isoform of nitric oxide (NO) synthase (iNOS) and generate micromolar levels of NO. NO mediates the inhibitory actions of cytokines on insulin secretion, oxidative metabolism, protein synthesis, and induces DNA damage [4, 5, 6]. Based on these inhibitory and destructive actions, cytokines and NO have been implicated in the loss of functional β-cell mass during the development of diabetes [4, 5, 6]. In exploring the response of β-cells to DNA damage, we made the unexpected observation that NO, when generated at iNOS-derived levels [7, 8], has a dual action on DDR signaling that is cell type–selective [9, 10, 11, 12]. In all cell types examined to date, NO activates ATM-dependent DDR signaling via the induction of DNA-strand breaks and ATR-dependent DDR signaling by inhibiting ribonucleotide reductase [9, 12]. Surprisingly, when produced or provided at iNOS-derived low micromolar levels, NO inhibits ATM- and ATR-dependent DDR signaling in a β-cell selective manner [10, 12]. In fact, NO limits DDR signaling in the presence of persistent DNA damage and attenuates DDR-dependent β-cell apoptosis, while stimulating DDR signaling and DDR-dependent apoptosis in all other cell types examined to date [10]. While cytokines like IL-1 have been implicated in the loss of functional β-cell mass during the induction of diabetes [13], the inhibition of DDR signaling and attenuation of DDR-directed apoptosis suggests that IL-1 signaling may play physiological roles in protecting β-cells from DDR-mediated apoptosis.
One of the well-known actions of iNOS-derived levels of NO is the inhibition of mitochondrial oxidative metabolism. NO inhibits the tricarboxylic acid (TCA) cycle enzyme aconitase and complex IV of the electron transport chain (ETC) [14, 15]. The inhibitory actions of NO on mitochondrial metabolism are not cell type selective, but the cellular response to this inhibition is cell type selective [12, 16]. Most cell types have the metabolic flexibility to increase glycolytic flux when mitochondrial oxidation is inhibited [17, 18]; however, glycolytic and mitochondrial oxidative metabolism are coupled in β-cells as almost all the carbons of glucose are oxidized to CO2 in a concentration-dependent manner [19, 20, 21]. Because of this coupling, β-cells lack the flexibility to compensate for impaired mitochondrial oxidation with an increase in glycolysis [12, 16]. This lack of metabolic flexibility contributes to the β-cell–selective inhibition of DDR signaling by NO [12, 16]. Classical inhibitors of mitochondrial respiration such as rotenone (complex I), antimycin A (complex III), and carbonyl cyanide p-trifluoro-methoxyphenyl hydrazine (FCCP) (uncoupler) also attenuate DDR signaling in a β-cell–selective manner [12, 16].
In this study, targeted metabolomic analyses were used to identify potential metabolites and/or metabolic pathways that contribute to the β-cell–selective inhibition of DDR signaling by NO and inhibitors of mitochondrial oxidative metabolism. We show that NO and rotenone decrease the levels of ATP, NAD+, NADH, NADPH, several glycolytic intermediates, and citrate in β-cells, whereas the levels of these metabolites are not modified in non–β-cells. Because metabolites in glycolysis and the pentose phosphate pathway (PPP) are decreased in a β-cell–selective manner, we hypothesized that metabolic events upstream of the formation of glucose-6-phosphate, which resides at the branchpoint between glycolysis and the PPP, are decreased in β-cells in response to NO and rotenone. The first step in glucose metabolism is its phosphorylation by hexokinase (most non–β-cells) and glucokinase (β-cells) [22]. In this study, we show that the cell type–selective decrease in nucleotides such as ATP, NAD+, and NADPH and the inhibition of DDR signaling are associated with the inhibition of glucose uptake in a β-cell–selective manner.
## Abstract
Nitric oxide (NO) plays a dual role in regulating DNA damage response (DDR) signaling in pancreatic β-cells. As a genotoxic agent, NO activates two types of DDR signaling; however, when produced at micromolar levels by the inducible isoform of NO synthase, NO inhibits DDR signaling and DDR-induced apoptosis in a β-cell–selective manner. DDR signaling inhibition by NO correlates with mitochondrial oxidative metabolism inhibition and decreases in ATP and NAD+. Unlike most cell types, β-cells do not compensate for impaired mitochondrial oxidation by increasing glycolytic flux, and this metabolic inflexibility leads to a decrease in ATP and NAD+. Here, we used multiple analytical approaches to determine changes in intermediary metabolites in β-cells and non–β-cells treated with NO or complex I inhibitor rotenone. In addition to ATP and NAD+, glycolytic and tricarboxylic acid cycle intermediates as well as NADPH are significantly decreased in β-cells treated with NO or rotenone. Consistent with glucose-6-phosphate residing at the metabolic branchpoint for glycolysis and the pentose phosphate pathway (NADPH), we show that mitochondrial oxidation inhibitors limit glucose uptake in a β-cell–selective manner. Our findings indicate that the β-cell–selective inhibition of DDR signaling by NO is associated with a decrease in ATP to levels that fall significantly below the KM for ATP of glucokinase (glucose uptake) and suggest that this action places the β-cell in a state of suspended animation where it is metabolically inert until NO is removed, and metabolic function can be restored.
## A lack of metabolic flexibility is associated with the β-cell–selective inhibition of DDR signaling by NO
In response to the topoisomerase inhibitor camptothecin, ATM is activated and phosphorylates H2A histone family member X (H2AX) (γH2AX when phosphorylated) and Krüppel-associated box-associated protein (KAP)1 in INS $\frac{832}{13}$ cells and mouse embryonic fibroblasts (MEF) (Fig. 1, A and B). Consistent with our previous findings [10, 12, 16, 23], NO, supplied by the donor (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate (DPTA)/NO, attenuates ATM-dependent formation of γH2AX and the phosphorylation of KAP1 selectively in β-cells (Fig. 1, A and B). Like ATM-dependent signaling, NO attenuates hydroxyurea (ribonucleotide reductase inhibitor)-stimulated checkpoint kinase (Chk)1 phosphorylation in INS $\frac{832}{13}$ cells but not MEF (Fig. 1, C and D) [12].Figure 1Nitric oxide (NO) and rotenone inhibit DDR signaling in a β-cell–selective manner. INS $\frac{832}{13}$ cells (A and C), MEF (B and D), and dispersed rat islet cells (E) were treated with camptothecin (CPT) (A and B), hydroxyurea (HU) (C and D), or hydrogen peroxide (H2O2; 30 min treatment at 100 μM) (E) with or without DPTA/NO or rotenone for 2 h at the indicated concentrations. Phosphorylation of H2AX (γH2AX), KAP1, and Chk1 was determined by Western blot analysis, and GAPDH was determined as a loading control (A–D). Rat islet cells were treated as outlined above (E), plated onto slides, fixed, and then were stained for insulin (green), γH2AX (red), and nuclei (DAPI, blue). Images were visualized using a Nikon Eclipse 90i confocal microscope (60× with 2× field zoom). Results are representative of three to four independent experiments. Chk1, checkpoint kinase 1; DAPI, 4′,6-diamidino-2-phenylindole; DDR, DNA damage response; DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate; H2AX, H2A histone family member X; KAP1, Krüppel-associated box-associated protein; MEF, mouse embryonic fibroblasts.
NO is an effective inhibitor of mitochondrial respiration (aconitase and complex IV of the ETC) [14, 15], and consistent with mitochondrial metabolism as a target for the inhibition of DDR signaling by NO, complex I inhibitor rotenone attenuates ATM- and ATR-dependent DDR signaling only in INS $\frac{832}{13}$ cells (Fig. 1, A and C) [12, 16]. We have shown that the inhibitory actions of NO and rotenone on DDR signaling are selective for β-cells [10, 12, 16], and consistent with these previous findings, inhibitors of mitochondrial respiration do not modify ATM or ATR signaling in MEF (Fig. 1, B and D). In fact, NO activates ATM and ATR signaling in non–β-cells, whereas it fails to stimulate γH2AX formation or KAP1 phosphorylation in INS $\frac{832}{13}$ cells (Fig. 1, A–D) [10, 12]. Importantly, these cell type–selective responses have been observed in rat islet cells (Fig. 1E) [23]. Using hydrogen peroxide to induce DNA-strand breaks and DDR activation (note that camptothecin does not activate DDR signaling in primary β-cells as they are terminally differentiated and do not readily divide), γH2AX formation is observed in both insulin-containing and non–insulin-containing cells (Fig. 1E). DPTA/NO and rotenone attenuate hydrogen peroxide–induced γH2AX formation in insulin-containing primary β-cells but do not inhibit γH2AX formation in non–insulin-containing islet cells (Fig. 1E). These findings show that inhibitors of mitochondrial respiration (NO and rotenone) attenuate DDR signaling in a β-cell–selective manner in both insulinoma cells and primary β-cells [10, 12, 16, 23].
Unlike most cell types, pancreatic β-cells lack the ability to compensate for impaired mitochondrial respiration with an increase in glycolytic metabolism [12, 16]. Consistent with this view, NO decreases the oxygen consumption rate (OCR) of INS $\frac{832}{13}$ cells, fluorescence-activated cell sorting (FACS)–purified primary rat β-cells, and non–β-cells (MEF) in a concentration-dependent manner (Fig. 2, A–C). However, MEF compensate for this inhibition of mitochondrial respiration with an increase in extracellular acidification rate (ECAR; an index of glycolytic flux; Fig. 2F). INS $\frac{832}{13}$ cells and FACS-purified rat β-cells lack this metabolic flexibility and do not increase ECAR (Fig. 2, D and E). In fact, DPTA/NO, at concentrations above 200 μM, decreases ECAR in INS $\frac{832}{13}$ cells and FACS-purified β-cells. Like the actions of NO, rotenone also decreases OCR in both β-cells and non–β-cells, yet only non–β-cells (MEF) maintain the metabolic flexibility to increase glycolytic flux as assessed by ECAR (Fig. 2F). Using both insulinoma cells and primary rat β-cells, these findings correlate the inhibition of DDR signaling with an inability of β-cells to increase glycolytic flux in the presence of inhibitors of mitochondrial oxidative metabolism. Figure 2Cell type–selective effects of nitric oxide (NO) and rotenone on glycolytic flux and mitochondrial respiration. The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of INS $\frac{832}{13}$ cells (A and D), FACS-purified rat β-cells (B and E), and MEF (C and F) were measured over a 105 min incubation by extracellular flux analysis. DPTA/NO (100–400 μM) or rotenone (1 μM) was added following a 15 min incubation as indicated by the vertical dotted line. Results are the average ± SD of three independent experiments. DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate; FACS, fluorescence-activated cell sorting; MEF, mouse embryonic fibroblasts.
## Effects of inhibition of mitochondrial respiration on ATP, NAD+, and NADH
In most cell types, glycolytic metabolism is increased when mitochondrial oxidation is impaired [17, 18]; however, the coupling of these pathways in β-cells allows for the rates of mitochondrial oxidation of glucose to determine the amount of insulin to be released for the clearance of blood glucose [19, 20, 21]. One consequence of this coupling is that the inhibition of mitochondrial oxidation causes a decrease in the levels of ATP in β-cells [12, 16, 24]. As shown in Figure 3A, DPTA/NO decreases ATP levels in INS $\frac{832}{13}$ cells in a concentration-dependent manner that correlates with the concentration-dependent inhibition of OCR (Fig. 2A). In response to 400 μM DPTA/NO, there is a time-dependent loss of ATP that is first observed following a 30 min incubation and maximal following a 120 min incubation of INS $\frac{832}{13}$ cells (Fig. 3B). ATP levels are maintained in MEF treated with increasing concentrations of DPTA/NO (Fig. 3, A and B), consistent with an increase in glycolytic metabolism when mitochondrial respiration is impaired (Fig. 2F). Like NO, rotenone also decreases ATP levels over three fold in INS $\frac{832}{13}$ cells, whereas it is less effective at decreasing the levels of ATP in MEF (Fig. 3C) [12, 16].Figure 3Effects of nitric oxide (NO) and rotenone on ATP, NAD+, and NADH levels in INS $\frac{832}{13}$ cells and mouse embryonic fibroblasts(MEF). INS $\frac{832}{13}$ cells and MEF were treated with DPTA/NO or rotenone at the indicated concentrations for 2 h (A, C, D, F, H, and I) or were treated with 400 μM DPTA/NO for the indicated times (B, E, and G). ATP (A–C), NAD+ (D, E, and H), and NADH (F, G, and I) were determined by HPLC and normalized to total protein. Results are the average ± SD of three independent experiments. Statistically significant decreases in nucleotides by DPTA/NO or rotenone compared with the untreated condition in each cell type are indicated (∗$p \leq 0.05$). DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate.
The switch to glycolytic metabolism when oxygen is limiting or when mitochondrial oxidative metabolism is impaired requires the generation of NAD+ by lactate dehydrogenase (LDH) for continued activity of the NAD+-dependent enzyme GAPDH [17]. β-cells express low levels of LDH and the monocarboxylic acid transporter (MCT), which transports pyruvate and lactate [20, 25], and lack the capacity to regenerate NAD+ under conditions of impaired mitochondrial function or limitations in the amount of oxygen [12, 16]. In fact, LDH and MCT are considered disallowed genes in healthy adult β-cells [26]. Consistent with this regulation of metabolism, NO decreases NAD+ levels in a concentration- and time-dependent manner in INS $\frac{832}{13}$ cells but does not decrease NAD+ levels in MEF (Fig. 3, D and E). NO also decreases NADH levels in INS $\frac{832}{13}$ cells but not MEF (Fig. 3, F and G). Like NO, rotenone also decreases NAD+ and NADH in INS $\frac{832}{13}$ cells without modifying the levels in MEF (Fig. 3, H and I). These findings correlate the β-cell–selective inhibition of DDR signaling by NO and rotenone with a β-cell–selective decrease in ATP, NAD+, and NADH.
## The effects of NO and rotenone on the steady-state levels of glycolytic, TCA cycle, and PPP intermediates
Targeted metabolomics was performed to identify changes in the levels of metabolites that may contribute to the NO-mediated cell type–selective impairment in DDR signaling. Specifically, our goal was to identify changes in metabolite levels that occur in a similar manner following treatment with NO and rotenone, as both mitochondrial inhibitors attenuate DDR signaling selectively in β-cells. Consistent with an impairment in glycolytic metabolism as evidenced by decreases in both ATP and NAD+ (Fig. 3), both mitochondrial toxins decrease the levels of glycolytic intermediates fructose-6-phosphate, 3-phosphoglycerate, and phosphoenolpyruvate in INS $\frac{832}{13}$ cells (Fig. 4, A–C). The levels of the remaining intermediates of glycolysis were either below the limits of detection of the assay or not significantly changed following treatment with NO and rotenone (Fig. 4, D and E). When examining mitochondrial intermediates, only citrate levels are decreased by both NO and rotenone (Fig. 5A). Citrate synthase catalyzes the irreversible reaction of oxaloacetate with acetyl-CoA (produced in glycolysis) to produce citrate, suggesting that the loss of this intermediate is possibly because of the inhibition of a pathway providing substrate to the TCA cycle, specifically the oxidation of pyruvate to acetyl-CoA by pyruvate dehydrogenase complex. Rotenone causes a statistically significant decrease in the TCA cycle intermediates α-ketoglutarate, fumarate, and malate, and there is a small decrease in these metabolites in response to NO that did not achieve statistical significance (Fig. 5, B–D). NO inhibits aconitase and complex IV of the ETC, whereas rotenone inhibits complex I of the ETC but does not inhibit the TCA cycle, suggesting that carbons derived from glucose entering the TCA cycle can be oxidized in the presence of rotenone; however, this oxidation is impaired by NO because of aconitase inhibition. Overall, these findings correlate the inhibition of DDR signaling by NO and rotenone with decreases in ATP, NAD+, NADH, glycolytic intermediates, and citrate. Figure 4Effect of nitric oxide (NO) and rotenone on the steady-state levels of glycolytic metabolites in INS $\frac{832}{13}$ cells. INS $\frac{832}{13}$ cells (A–E) were treated with 400 μM DPTA/NO or 1 μM rotenone for 2 h. Samples were collected, and glycolytic metabolite levels were determined by mass spectroscopic analysis. Metabolites that were significantly different in response to both DPTA/NO and rotenone are boxed in the glycolytic pathway (F). Results are the average ± SD of three independent experiments. Statistically significant decreases in metabolites by DPTA/NO or rotenone compared with the untreated control condition are indicated (∗$p \leq 0.05$). DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate. Figure 5Effect of nitric oxide (NO) and rotenone on the steady-state levels of tricarboxylic acid (TCA) cycle intermediates in INS $\frac{832}{13}$ cells. INS $\frac{832}{13}$ cells (A–D) were treated with 400 μM DPTA/NO or 1 μM rotenone for 2 h. Metabolites were collected, and TCA cycle metabolite levels were determined by mass spectroscopic analysis. Citrate was the only metabolite that changed in the same manner by both DPTA/NO and rotenone and is boxed in the schematic of the TCA cycle (E). Results are the average ± SD of three independent experiments. Statistically significant decreases in metabolites by DPTA/NO or rotenone compared with the untreated condition are indicated (∗$p \leq 0.05$). DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate.
The oxidative branch of the PPP produces NADPH (for antioxidant defense), and the nonoxidative branch provides phosphorylated intermediates that can be used to support several metabolic pathways including intermediary metabolism. The decreases in glycolytic intermediates fructose-6-phosphate, 3-phosphoglycerate, and phosphoenolpyruvate coupled with decreases in ATP and NAD+ led us to hypothesize that the PPP activity may be inhibited by NO and rotenone in a β-cell–selective manner. Consistent with this hypothesis, NADPH levels are decreased, whereas GSSG levels are increased in INS $\frac{832}{13}$ cells treated with NO and rotenone (Fig. 6, A and B). Because we failed to detect NADPH in one of the INS $\frac{832}{13}$ cell control metabolomic samples (Fig. 6A), HPLC analysis was used to confirm these findings. NO decreases NADPH levels in a concentration- and time-dependent manner, and rotenone also decreases NADPH levels in INS $\frac{832}{13}$ cells (Fig. 6, C–E). These effects appear to be selective for β-cells as NO and rotenone do not decrease NADPH levels in MEF as measured by HPLC (Fig. 6, C–E). These findings correlate the β-cell–selective inhibition of DDR signaling by inhibitors of mitochondrial oxidative metabolism with decreases in ATP, NAD+, NADH, NADPH, glycolytic, TCA cycle, and PPP intermediates (see schematic, Figs. 4F, 5E, and 6F; boxes are intermediates decreased by NO and rotenone).Figure 6Mitochondrial inhibitors decrease NADPH levels in INS $\frac{832}{13}$ cells but not MEF.A and B, INS $\frac{832}{13}$ cells were treated with 400 μM DPTA/NO or 1 μM rotenone for 2 h. Samples were collected, and changes in NADPH and GSSG levels were determined by mass spectroscopic analysis. C–E, INS $\frac{832}{13}$ cells and MEF were treated with DPTA/NO or rotenone at the indicated concentrations for 2 h (C and E) or with 400 μM DPTA/NO for the indicated times (D). NADPH was determined via HPLC and normalized to total proteins. F, A schematic diagram of pentose phosphate pathway (PPP) (oxidative and nonoxidative phases) is shown with metabolites of the pathway that are decreased by NO and rotenone highlighted in dashed boxes. Results are the average ± SD of three independent experiments. Statistically significant decreases in metabolites by DPTA/NO or rotenone compared with the untreated condition in each cell type are indicated (∗$p \leq 0.05$). DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate; MEF, mouse embryonic fibroblasts.
## Effects of inhibitors of mitochondrial respiration on glucose uptake
Glucose is transported across the plasma membrane by facilitated diffusion through specific glucose transporters. Once transported, glucose is trapped within cells by phosphorylation catalyzed by hexokinase (KM for glucose of 0.003–0.3 mM; isoform dependent) [27] in most cell types and glucokinase (KM for glucose of ∼8 mM) [28] in β-cells and hepatocytes. Because glucose-6-phosphate resides at the branchpoint between glycolysis and the PPP (Fig. 7A) and intermediates of both pathways are decreased, the effects of inhibitors of mitochondrial respiration on glucose uptake (glucose transport and phosphorylation) in β-cells and non–β-cells were examined. Glucose uptake was determined by measuring the accumulation of 2-deoxyglucose-6-phosphate, which cannot be further metabolized in cells [29]. In a concentration- and time-dependent manner, NO decreases glucose uptake in INS $\frac{832}{13}$ cells (Fig. 7, B and C) and in FACS-purified primary rat β-cells (Fig. 7D). Like NO, rotenone also inhibits glucose uptake in INS $\frac{832}{13}$ cells (Fig. 7E). The inhibitory actions of NO on glucose uptake appear to be selective for β-cells, as this free radical does not inhibit glucose uptake in MEF (Fig. 7, F and G), and rotenone has only a minor inhibitory effect on glucose uptake in MEF (∼$25\%$ decrease, Fig. 7E versus Fig. 7H). Importantly, glucose transport into INS $\frac{832}{13}$ cells is not modified by NO or rotenone (Fig. 7I), indicating that effects of NO on metabolism are due to a decrease in glucose uptake (phosphorylation by glucokinase) in β-cells. In further support of this conclusion, the steady-state levels of glucokinase are not changed by treatment with NO or rotenone (Fig. 7J). Together, these findings suggest that NO limits glucokinase activity and decreases glucose phosphorylation, leading to shutdown of glucose metabolism that is associated with the β-cell–selective inhibition of DDR signaling by NO.Figure 7Inhibitors of mitochondrial respiration attenuate glucose uptake selectively in β-cells. A, A schematic diagram representing the sites and pathways with decreased levels of metabolites in β-cells in response to nitric oxide (NO). Glucose-6-phosphate is the product of glucokinase and resides at a branchpoint between two pathways in which intermediates are decreased in response to NO and rotenone treatment (glycolysis and pentose phosphate pathway (PPP)). B–H, INS $\frac{832}{13}$ cells (B, C, and E), FACS-purified rat β-cells (D), and MEF (F–H) were treated with DPTA/NO or rotenone at the indicated concentrations for 2 h (B, D, E, F, and H) or with 400 μM DPTA/NO at the indicated times (C and G). Glucose uptake was measured and normalized to total protein. I, Changes in INS $\frac{832}{13}$ cell glucose levels in response to treatment with 400 μM DPTA/NO or 1 μM rotenone (2 h) were determined by mass spectroscopic analysis. J, INS $\frac{832}{13}$ cell glucokinase levels are not modified following a 2 h treatment with DPTA/NO or rotenone as determined by Western blot analysis and quantified by densitometry (levels detected in untreated cells were normalized to 1). GAPDH is shown as a loading control. Results are representative (J) or the average ± SD (B–I, and J [top]) of three independent experiments. Statistically significant decreases in glucose uptake compared with the untreated condition in each cell type (B–H) are indicated (∗$p \leq 0.05$). DPTA, (Z)-1-[N-(3-aminopropyl)-N-(3-ammoniopropyl)amino]diazen-1-ium-1,2-diolate; FACS, fluorescence-activated cell sorting; MEF, mouse embryonic fibroblasts.
## Discussion
Pancreatic β-cells are found in the highly vascularized islets of Langerhans. They are responsible for the sensing of blood glucose levels and secreting the appropriate amount of insulin to facilitate the clearance of glucose from the blood stream. Glucose is sensed through the activity of the low-affinity high-turnover Glut2 transporter that allows for glucose entry into β-cells in a concentration-dependent manner, and glucokinase (high KM for glucose), which phosphorylates glucose, trapping it in β-cells [30]. In addition to this sensing blood glucose levels, the amount of insulin that is secreted is controlled by the rates of glucose oxidation [20, 21]. Nearly all the carbons of glucose are oxidized completely to CO2 in β-cells, and the rates of oxidation increase with increases in glucose levels [19, 20, 21]. Most cell types only utilize mitochondrial oxidative metabolism when there is a demand for ATP or intermediates produced by anaplerotic reactions in the mitochondria [17]. The coupling of glycolysis to mitochondrial oxidative metabolism in β-cells is facilitated by the relative absence of LDH [20] and the inability to regenerate NAD+ for continued glycolysis (GAPDH reaction) when oxygen is limiting or mitochondrial oxidative metabolism is impaired [12, 16]. This metabolic coupling serves as an important physiological regulator that prevents inappropriate insulin production under conditions where hepatic glucose production is necessary [31]. In this report, we identify a second potential physiological role by which the regulation of intermediary metabolism controls β-cell responses to external stimuli. In this case, NO limits DDR signaling and DDR-dependent β-cell apoptosis by inhibiting mitochondrial oxidative metabolism [10, 12, 16].
The activation of DDR signaling in β-cells in response to DNA damage leads to apoptosis [10], and we have shown that NO, when produced at micromolar levels or iNOS-derived levels, inhibits DDR signaling (Fig. 1, A and C) and prevents DDR-induced apoptosis in a β-cell–selective manner [10, 12, 16]. This action of NO is selective for β-cells and has been correlated with the inhibition of mitochondrial oxidative metabolism and loss of ATP [10, 12, 16]. In support of this conclusion, we have shown that inhibitors of mitochondrial respiration (rotenone, oligomycin, antimycin A, and FCCP) attenuate DDR signaling selectively in β-cells (Fig. 1, A and C) [12, 16]. As described previously, there is a coupling of glycolysis and mitochondrial oxidative metabolism such that most of the carbons of glucose are oxidized to CO2 and the rates of oxidation increase with increasing concentrations of glucose [19, 20, 21]. This allows the β-cell to couple the rate of insulin secretion to the rate of mitochondrial glucose oxidation [19, 20, 21]. Because of this coupling, β-cells lack the ability to compensate for impaired mitochondrial respiration by increasing glycolytic flux (Fig. 2, D and E), resulting in a decrease in ATP (Fig. 3, A–C) [12, 16]. Non–β-cells lack this coupling and have the metabolic flexibility to increase glycolytic flux when mitochondrial oxidative metabolism is impaired (Fig. 2F) and by this mechanism maintain ATP levels (Fig. 3, A–C) [12, 16].
It is well known that β-cells express LDH at low levels, and they do not express the lactate/pyruvate transporter MCT [20, 25, 26]. As expected, NAD+ levels fall in β-cells treated with NO and mitochondrial respiratory inhibitors (Fig. 3, D and E). In addition to LDH, the cytosolic pool of NAD+ is generated through the glycerol-3-phophate shuttle and malate–aspartate shuttle, whereas the mitochondrial pool of NAD+ can be generated by the ETC. Under impaired mitochondrial oxidative metabolism, NAD+ cannot be generated through glycerol-3-phosphate shuttle or malate–aspartate shuttle, as both pathways are linked to mitochondrial oxidative metabolism. Consequently, the inhibition of mitochondrial respiration shuts down pathways that generate NAD+ in β-cells. Because NAD+ is a required cofactor of the glycolytic enzyme GAPDH (see schematic, Fig. 4F) [18], the decreased NAD+ in response to NO contributes to the inability to sustain glycolysis in β-cells when mitochondrial respiration is impaired (Fig. 2, D and E) [12, 16].
It is surprising that the decrease in NAD+ does not correlate with an increase in NADH. In fact, we show that NADH levels are also decreased in β-cells treated with NO and inhibitors of mitochondrial respiration (Fig. 3, F, G, and I). NADH is generated in glycolysis and the TCA cycle through the reduction of NAD+, and the shutdown of glycolysis and the TCA cycle may explain the decreased NADH levels in β-cells. Furthermore, these results suggest that the decrease in NAD+ and NADH is not simply associated with the redox state of the nucleotide pools. Enzymes such as sirtuins (Sirts), poly(ADP-ribose) polymerase (PARP), and CD38 utilize NAD+ without oxidizing it back to NADH; instead, nicotinamide is generated [32]. It is possible that the decreases in NAD+ and NADH in β-cells are due to an increase in the activity of these enzymes, resulting in the depletion of the NAD+ pool, whereas the inhibition of mitochondrial oxidative metabolism attenuates NADH regeneration. NO has been shown to activate PARP in many cell types, including β-cells; however, it has been our experience that PARP does not contribute to the actions of cytokine or cytokine-derived NO on β-cell viability [33].
Targeted metabolomic analysis in INS $\frac{832}{13}$ cells exposed to NO or rotenone was employed to identify metabolites that are changed in a similar manner in β-cells. NO and rotenone decrease glycolytic (Fig. 4), TCA cycle (Fig. 5), and PPP (Fig. 6) intermediates in INS $\frac{832}{13}$ cells. Glucose-6-phosphate is found at a metabolic branchpoint in glucose metabolism as it is a substrate for glycolysis and the PPP. Decreases in glycolytic intermediates and NADPH (PPP) suggest that NO and rotenone inhibit glucose metabolism upstream of glucose-6-phosphate. This hypothesis is also consistent with decreases in NAD+ and NADH. Glucose is transported into cells by facilitated diffusion via GLUT transporters (Glut2 in β-cells), and this process is not inhibited by NO or rotenone in β-cells (Fig. 7I); however, glucose uptake is inhibited (Fig. 7, B–E). The first step in the metabolism of glucose is phosphorylation by hexokinase (four different isoforms I–IV), trapping glucose in cells [22]. β-cells express glucokinase (hexokinase IV), which has KM for glucose of ∼8 mM and ATP ∼0.4 mM [28]. Under normal conditions, basal ATP levels in β-cells are ∼1 to 2 mM [34, 35] or well above the KM for ATP of glucokinase, leaving glucose as the limiting factor regulating glucokinase activity [28]. However, under conditions where mitochondrial respiration is inhibited (by NO or respiratory chain inhibitors), ATP levels decrease 8-fold to 10-fold or to levels (∼0.1–0.2 mM) well below the KM of glucokinase for ATP. This results in a decrease in the activity of glucokinase and a decrease in glucose uptake. In support of this conclusion, we showed that NO and rotenone decrease glucose uptake selectively in INS $\frac{832}{13}$ cells and primary rat β-cells purified by FACS (Fig. 7, B–H) without reducing glucokinase expression (Fig. 7J). Like glucokinase, hexokinase I, II, and III have a high KM for ATP of 0.5 to 1.0 mM [27] and should also be sensitive to inhibition in the absence of sufficient ATP levels; however, non–β-cells possess the metabolic flexibility to enhance glycolytic flux in the presence of mitochondrial inhibitors (Fig. 2F) and maintain ATP to levels sufficient to support continued glycolysis (Fig. 3, A–C) [12, 16]. In further support of this hypothesis, we have shown that when MEF are forced to generate ATP via mitochondrial oxidative metabolism of glutamine (culturing in glucose-free galactose-containing medium), inhibitors of mitochondrial respiration (NO and rotenone) attenuate DDR signaling (ATM and ATR), and this inhibition correlates with a loss of ATP [12, 16]. Also, NO no longer activates DDR signaling or stimulates apoptosis in galactose-cultured MEF [12, 16].
The mechanisms by which decreased levels of ATP and glucose uptake attenuate DDR signaling are unknown. While protein phosphatase 1 is a regulator of DDR signaling, we have shown that it does not contribute to the inhibitory actions of NO on ATM signaling [23]. Furthermore, we do not believe that it is simply a decrease in ATP to levels below the KM of DDR kinases (e.g., KM of ATM for ATP ∼25 μM), as the levels of this nucleotide are more than sufficient to support DDR signaling [36]. NO at concentrations that limit DDR signaling and DDR-directed apoptosis also stimulates growth arrest and DNA damage–inducible protein (GADD)45α-dependent DNA repair in β-cells [37]. GADD45α expression and GADD45α-dependent repair of damaged β-cell DNA in response to NO is Forkhead Box O1 (FoxO1)- and Sirt1-dependent [38]. Under normal conditions, FoxO1 is phosphorylated by PI3K/Akt signaling and is sequestered in the cytoplasm by the scaffolding protein 14-3-3 [39]. When dephosphorylated, FoxO1 is released and translocates to the nucleus to activate gene expression [39]. NO is an inhibitor of PI3K signaling at low micromolar levels, allowing for FoxO1 dephosphorylation and nuclear translocation [38]. Furthermore, increases in Sirt1 activity appear to be required for NO-stimulated FoxO1-dependent GADD45α expression [38]. We hypothesize that NO functions to inhibit DDR signaling while activating base excision repair of damaged DNA in β-cells [37]. Additional studies directed at the regulation of Sirt1, FoxO1, and DDR signaling will be required to fully understand the interplay between these pathways and the cell type specificity of this regulation.
NO appears to place β-cells in a state of “suspended animation,” or a condition in which glucose uptake (Fig. 7), oxidative metabolism, and glucose-stimulated insulin secretion are impaired, but β-cells remain viable. The concentrations of NO that are required to induce this state are levels known to be produced by β-cells following iNOS induction and fall in the high nanomolar to low micromolar levels (0.8–5 μM). These physiological concentrations of NO are also generated using DPTA/NO at 200 to 400 μM [10]. While β-cells become metabolically impaired under these conditions, they do not die. Removal of NO by washing or the addition of NO synthase inhibitors to islets pretreated for 18 to 24 h with cytokines results in the complete recovery of mitochondrial oxidative metabolism and insulin secretory function and the repair of damaged DNA [40, 41, 42]. NO also activates heat shock and unfolded protein responses in β-cells [42, 43], and one consequence of the activation of these stress responses is the impairment in cytokine signaling. We have shown that heat shock, NO, and endoplasmic reticulum stress inducers impair the activation of nuclear factor κB in response to IL-1 and the phosphorylation of signal transducers and activators of transcription 1 in response to interferon-γ [42, 43, 44]. In the context of “suspended animation” induced by iNOS-derived levels of NO, intermediary metabolism is impaired, stress responses are stimulated, and β-cells become refractory to proinflammatory cytokines and resistant to apoptosis.
In addition to the aforementioned roles in the inhibition of apoptosis and cytokine singling, NO also limits picornavirus replication in a β-cell–selective manner [45, 46]. The inhibition of virus replication requires iNOS-derived levels of NO and is associated with an inhibition of mitochondrial oxidative metabolism and decrease in ATP [45, 46]. Much like DDR signaling, multiple inhibitors of mitochondrial respiration (e.g., rotenone, antimycin A, FCCP) also attenuate picornavirus replication in a β-cell–selective manner [45, 46]. Members of the picornavirus family have been proposed to participate in the initiation events or triggering events that initiate autoimmune diabetes [47].
Overall, these studies provide additional evidence that NO, via the inhibition of intermediary metabolism, attenuates DDR signaling in β-cells by decreasing ATP to levels that fail to support the phosphorylation of glucose by glucokinase (Fig. 8). The loss of ATP is associated with a coupling of glycolysis and mitochondrial respiration, which is essential for β-cell function. In non–β-cells, NO does not limit DDR signaling or decrease the levels of ATP, as non–β-cells enhance glycolytic metabolism as a compensatory mechanism for impaired mitochondrial oxidative metabolism [12, 16]. This cell type–selective regulation or coupling of glycolysis and mitochondrial oxidative metabolism, while essential for insulin secretion, is used by β-cells to defend against apoptosis [10] and to limit the replication of viruses from a family thought to be important in the initiation of autoimmune diabetes [48, 49]. NO is produced at micromolar levels by β-cells following cytokine stimulation and iNOS induction, and many groups have reported that cytokine-induced β-cell apoptosis [50, 51] is a contributory factor in the development of autoimmune diabetes [52, 53]. It is our experience, as well as that of others, that it is challenging to kill primary β-cells by apoptosis [54, 55, 56], and that NO is a potent inhibitor of apoptosis [10, 16, 43]. Furthermore, recent single-cell RNA-sequencing of mouse islet cells show that cytokines fail to stimulate the expression of proapoptotic factors [57, 58]. In fact, IL-1 and interferon-γ stimulate antiviral genes in all endocrine cells, not just β-cells [57, 58]. Given that β-cells reside in a highly vascularized micro-organ that is essential in the regulation of blood glucose levels, and that they are exposed to cytokines during infections (e.g., inflammatory viruses such as picornavirus and coronavirus), cytokine signaling in β-cells likely serves important physiological roles. Our findings suggest that these roles may include the expression of iNOS and production of micromolar levels of NO, which place β-cells in a state of “suspended animation” where cellular function and oxidative metabolism are decreased, they become resistant to cytokine signaling and are capable of limiting virus replication; however, they do not die but maintain the capacity to fully recover metabolic and secretory functions [40, 41, 59]. We hypothesize that it is when the cytokine storm is persistent or the presence of genetic defects that limit the protective host responses activated by cytokines and NO that β-cells are lost. Our findings begin to identify additional roles for the regulation of intermediary metabolism, in addition to its critical role in the regulation of insulin secretion, in the protection of β-cells from damage and infection. Figure 8Schematic. Nitric oxide inhibits mitochondrial respiration through inhibiting aconitase of the tricarboxylic acid (TCA) cycle and complex IV of the electron transport chain (ETC), resulting in a loss of ATP in β-cells (action 1). Because of the high KM of glucokinase for ATP (∼0.4 mM), ATP levels become limiting for glucokinase activity. This results in decreases in the rates of oxidation of glucose-6-phosphate in glycolysis and the pentose phosphate pathway (PPP) (action 2) decreasing NAD+, NADH, and NADPH levels in β-cells. The cell type–selective action is associated with the lack of glycolytic compensation for impaired mitochondrial oxidation in β-cells, whereas ATP, NAD+, NADH, and NADPH levels are maintained in non–β-cells because of glycolytic compensation for impaired mitochondrial oxidation (action 3). The net effect is a β-cell–selective metabolic regulation of DNA damage response signaling that is controlled by the actions of nitric oxide.
## Cell lines, animals, and materials
INS $\frac{832}{13}$ cells were obtained from Dr Christopher Newgard (Duke University). MEF were purchased from American Type Culture Collection. Male Sprague–Dawley rats were purchased from Harlan. Connaught Medical Research Laboratories (CMRL) 1066 medium and β-mercaptoethanol were purchased from Thermo Fisher Scientific. RPMI1640 medium, Dulbecco's modified Eagle's medium, trypsin ($0.05\%$ in 0.53 mM EDTA), l-glutamine, sodium pyruvate, Hepes, and penicillin–streptomycin were purchased from Corning. Fetal bovine serum was purchased from HyClone. DPTA/NO was purchased from Cayman Chemical. 2-Deoxyglucose (2-DG), camptothecin, hydroxyurea, and rotenone were purchased from MilliporeSigma. Primary and secondary antibodies used for Western blot and immunofluorescence were purchased as follows: mouse anti-γH2AX (Ser139) from MilliporeSigma; rabbit anti-phospho-KAP1 (Ser824) and rabbit anti-glucokinase from Abcam; rabbit anti-phospho-Chk1 (Ser345) from Cell Signaling Technology; mouse anti-GAPDH from Thermo Fisher Scientific; guinea pig anti-insulin from DakoCytomation; horseradish peroxidase (HRP)–conjugated donkey anti-mouse, HRP-conjugated donkey anti-rabbit, and Cy3-conjugated donkey anti-mouse from Jackson ImmunoResearch Laboratories, Inc; and, Alexa Fluor 488–conjugated donkey anti-guinea pig from Molecular Probes.
## Culture of cell lines and primary islet cells
INS $\frac{832}{13}$ cells and MEF were cultured as previously described [60]. Islets were isolated from male Sprague–Dawley rats by collagenase digestion as previously described [61, 62] and cultured overnight in complete CMRL (CMRL-1066 containing $10\%$ fetal bovine serum, 2 mM glutamine, 50 U/ml penicillin, and 50 μg/ml streptomycin). Islets were dispersed into individual cells by treatment with trypsin in Ca2+- and Mg2+-free Hanks [63]. Dispersed islet cells were incubated for 60 min at 37 °C in complete CMRL prior to cell sorting. β-cells were isolated from rat islets by FACS as previously described [64] using a FACSMelody Cell Sorter and cultured overnight in complete CMRL prior to experimentation. Animal welfare was approved by the Institutional Animal Care and Use Committees at the Medical College of Wisconsin (A3102-01).
## Western blot analysis
Western blot analysis was performed as previously described [38], using the following antibody dilutions: 1:1000 dilution for anti-phospho-Chk1 (Ser345) and anti-glucokinase; 1:2000 dilution for anti-phospho-KAP1 (Ser824); 1:10,000 dilution for anti-γH2AX (Ser139); 1:40,000 dilution for anti-GAPDH; and 1:20,000 dilution for HRP-conjugated donkey anti-mouse and HRP-conjugated donkey anti-rabbit. Antigen was detected by chemiluminescence [65].
## Immunofluorescence
Immunofluorescence was performed as previously described [45]. Images were taken using a Nikon 90i confocal microscope. Antibody dilutions were as follows: 1:500 dilution for anti-γH2AX (Ser139) and 1:1000 dilution for anti-insulin and all secondary antibodies.
## Cellular bioenergetics
OCR and ECAR were measured in FACS-purified β-cells (50,000–75,000 cells/well), INS $\frac{832}{13}$ cells (20,000 cells/well), and MEF (10,000 cells/well) using the Seahorse XFe96 analyzer (Agilent Technologies). Measurements were made in Dulbecco's modified Eagle's medium containing 5.5 mM glucose, 2 mM pyruvate, and 1 mM glutamine and were normalized to total protein determined using the Pierce BCA protein assay kit (Thermo Scientific). Results are expressed as percent of baseline for each cell type.
## Metabolomic analysis
INS $\frac{832}{13}$ cells were cultured for 24 h prior to treatment with the medium replaced 2 h before sample extraction. Samples were extracted in $80\%$ methanol containing heavy labeled internal standards on dry ice–ethanol bath and were transferred to precooled 1.5 ml low-binding microfuge tubes. Extracts were incubated on dry ice–ethanol bath for 20 min and were centrifuged at 14,000g for 5 min at 4 °C. Supernatants were collected for analysis, whereas pellets were dissolved in 0.5 N NaOH, and total protein was quantified using the Bradford assay (Thermo Scientific).
Targeted metabolomic analysis was performed using a 1200 Infinity Series HPLC (Agilent) in-line with a 6430 QqQ (Agilent) using dynamic multiple reaction monitoring scheduling. Samples were analyzed separately for nucleotides and cofactors (assay 1) and energetic, anabolic, and catabolic intermediates (assay 2). Raw data were processed in Skyline [66]. Peak areas were exported from Skyline and normalized to total protein and heavy labeled internal standards. Data were analyzed using MetaboAnalyst, no filtering was applied, and data were normalized to the control group with centering around the mean [67, 68]. Statistical analysis was performed using one-way ANOVA with Fisher’s least significant difference post hoc analysis. Details of the mass spectrometry and analysis parameters are outlined in Supporting information 1.
## Nucleotide measurement
Nucleotides (ATP, NAD+, NADH, and NADPH) were quantified by HPLC using a SUPELCOSIL LC-18-T column (3 μm, 150 × 4.6 mm internal diameter) as previously described [69, 70, 71, 72]. Nucleotide levels were normalized to total protein determined using the Pierce BCA protein assay kit and expressed in nanomoles per milligram protein.
## Glucose uptake measurement
The Glucose Uptake-Glo Assay from Promega was used to measure glucose uptake according to the manufacturer’s instructions. For these studies, INS $\frac{832}{13}$ cells and FACS-purified rat β-cells were incubated with 15 mM 2DG for 15 min, whereas MEF were incubated with 5 mM 2DG for 10 min (incubation times and concentrations of 2DG were optimized for each cell type). Glucose uptake, expressed as relative luminescence unit, was normalized to total protein as determined using the Pierce 660 nm protein assay kit supplemented with ionic detergent compatibility reagent (Thermo Scientific).
## Statistical analysis
Statistical significance was evaluated using paired t test, one-way or two-way ANOVA, and Tukey’s or Sidak’s multiple comparison post hoc analysis as indicated (∗$p \leq 0.05$).
## Data availability
All data not included in this article will be shared upon request. Contact Dr Corbett (jcorbett@mcw.edu) for data requests.
## Supporting information
This article contains supporting information.
Supporting information Enzyme abbreviation
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
C. T. Y. and J. A. C. conceptualization; C. T. Y. and J. A. C. methodology; C. T. Y., E. M. K., M. P., B. J. O., A. N., K. A. R., J. S. S., and P. A. H. investigation; C. T. Y., E. M. K., M. P., B. J. O., R. L. G., and J. A. C. data curation; C. T. Y. and E. M. K. formal analysis; C. T. Y. and J. A. C. resources; C. T. Y. and J. A. C. writing–original draft; C. T. Y., E. M. K., P. A. H., M. P., B. J. O., A. N., J. S. S., K. A. R., R. L. G., and J. A. C. writing–review & editing; C. T. Y. and J. A. C. supervision.
## Funding and additional information
This work was supported by the $\frac{10.13039}{100000002}$National Institutes of Health grants DK-052194 and AI-44458 (to J. A. C.), T32 HL134643 and K99DK129709 (to J. S. S.), and HL134010 and HL126785 (to R. L. G.); $\frac{10.13039}{100000901}$Juvenile Diabetes Research Foundation grant 2-SRA-2019-829-S-B (to J. A. C./R. L. G.); and gifts from the Forest County Potawatomi Foundation and the Scott Tilton Foundation (to J. A. C).The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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|
---
title: Saturated fatty acid– and/or monounsaturated fatty acid–containing phosphatidic
acids selectively interact with heat shock protein 27
authors:
- Naoto Yachida
- Fumi Hoshino
- Chiaki Murakami
- Masayuki Ebina
- Yuri Miura
- Fumio Sakane
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10023972
doi: 10.1016/j.jbc.2023.103019
license: CC BY 4.0
---
# Saturated fatty acid– and/or monounsaturated fatty acid–containing phosphatidic acids selectively interact with heat shock protein 27
## Body
Diacylglycerol kinase (DGK) phosphorylates diacylglycerol (DG) to convert to phosphatidic acid (PA) [1, 2, 3, 4] and participates in a great variety of pathological and physiological functions [5, 6]. Mammalian DGK consists of ten isoforms, which are divided into five groups (types I–V) [1, 2, 3, 4]. Type I DGK consists of the α, β, and γ isozymes. DG-binding proteins are C1 domain-containing proteins, including conventional protein kinase C (PKC), novel PKC, and Ras guanine nucleotide-releasing protein [7, 8, 9, 10]. There are many PA-binding proteins (PABPs) (more than 70), such as atypical PKC (PKCζ), novel PKC (PKCδ and ε), C-Raf, cAMP phosphodiesterase (PDE) 4A1, Opi1p, sporulation-specific protein 20p, α-synuclein, creatine kinase muscle type, L-lactate dehydrogenase A, Praja-1, and synaptojanin-1 [11, 12, 13, 14, 15]. Moreover, the number of PABPs is still growing.
DGKα [16, 17] has tandem Ca2+-binding EF-hand motifs and is activated by Ca2+ [16, 18, 19, 20, 21, 22]. DGKα is abundantly expressed in T lymphocytes/thymus [16] and cancer cells, including melanoma [23], hepatocellular carcinoma [24] and mesenchymal glioblastoma [25]. DGKα facilitates the immune nonresponsive (nonproliferation) state known as anergy in T lymphocytes [26, 27, 28]. In contrast to T cells, DGKα attenuates apoptosis and promotes the proliferation of melanoma [23, 29, 30] and hepatocellular carcinoma cells [24] and enhances epithelial–mesenchymal transition (metastasis) of glioblastoma [25]. In addition, DGKα has been reported to activate angiogenesis signaling [31]. Therefore, the inhibition of DGKα activity may suppress cancer progression [32, 33, 34]. As expected, DGKα-selective inhibitors and DGKα-specific siRNA caused apoptosis and inhibited the proliferation and epithelial–mesenchymal transition of several cancer cell lines [23, 24, 25, 30, 32]. Therefore, DGKα has reverse roles in T cells (attenuator) and cancer cells (enhancer) [6, 34]. It remains unclear how DGKα differently functions in these cells.
We recently found that DGKα preferentially generated saturated fatty acid (SFA)- and/or monounsaturated fatty acid (MUFA)-containing PAs, such as 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:0-, and 16:$\frac{0}{16}$:1-PA in serum-starved AKI melanoma cells [30]. Moreover, DGKα was indicated to produce broad SFA-, MUFA-, and/or polyunsaturated fatty acid (PUFA)-containing PA species, such as 14:$\frac{1}{16}$:1-, 14:$\frac{0}{16}$:1-, 14:$\frac{0}{16}$:0-, 16:$\frac{1}{16}$:2-, 16:$\frac{1}{16}$:1-, 16:$\frac{0}{16}$:1-, 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:1-, and 16:$\frac{0}{18}$:0-PA, in serum-starved Jurkat T cells [35]. PA species generated by DGKα in melanoma and T cells overlap each other. However, unsaturation levels of fatty acids of PA species in T cells are moderately higher than those in melanoma cells. These results suggest that the overlap PA species, for example, 16:$\frac{0}{16}$:0-PA, has different targets specifically expressed in melanoma and T cells, respectively. Alternatively, nonoverlap PA species, for example, 16:$\frac{1}{16}$:2-PA, may selectively regulate T-cell functions.
In the present study, to explore how DGKα plays reverse roles in cancer cells and T lymphocytes, we searched for the target proteins of 16:$\frac{0}{16}$:0-PA in human melanoma cells. We identified heat shock protein (HSP) 27, which acts as a molecular chaperone and is a biomarker of cancer [36], as a novel SFA- and/or MUFA-containing PA (SFA/MUFA-PA)-binding protein. Moreover, 16:$\frac{0}{16}$:0-PA induced oligomer dissociation of HSP27, which is an indication of its activation. Furthermore, constitutively active DGKα recruited HSP27 to the plasma membrane and colocalized it in a DGK activity (PA)-dependent manner. Intriguingly, HSP27 protein was barely expressed in Jurkat T cells, while the protein was enriched in AKI melanoma cells. Therefore, these results strongly suggest that SFA- and/or MUFA-containing PA species generated by DGKα interact with HSP27 and selectively regulate its cancer-progressive function in melanoma cells but not in T cells.
## Abstract
Diacylglycerol kinase (DGK) α, which is a key enzyme in the progression of cancer and, in contrast, in T-cell activity attenuation, preferentially produces saturated fatty acid (SFA)– and/or monounsaturated fatty acid (MUFA)–containing phosphatidic acids (PAs), such as 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:0-, and 16:$\frac{1}{16}$:1-PA, in melanoma cells. In the present study, we searched for the target proteins of 16:$\frac{0}{16}$:0-PA in melanoma cells and identified heat shock protein (HSP) 27, which acts as a molecular chaperone and contributes to cancer progression. HSP27 more strongly interacted with PA than other phospholipids, including phosphatidylcholine, phosphatidylserine, phosphatidylglycerol, cardiolipin, phosphatidylinositol, phosphatidylinositol 4-monophosphate, and phosphatidylinositol 4,5-bisphosphate. Moreover, HSP27 is more preferentially bound to SFA- and/or MUFA-containing PAs, including 16:$\frac{0}{16}$:0- and 16:$\frac{0}{18}$:1-PAs, than PUFA-containing PAs, including 18:$\frac{0}{20}$:4- and 18:$\frac{0}{22}$:6-PA. Furthermore, HSP27 and constitutively active DGKα expressed in COS-7 cells colocalized in a DGK activity–dependent manner. Notably, 16:$\frac{0}{16}$:0-PA, but not phosphatidylcholine or 16:$\frac{0}{16}$:0-phosphatidylserine, induced oligomer dissociation of HSP27, which enhances its chaperone activity. Intriguingly, HSP27 protein was barely detectable in Jurkat T cells, while the protein band was intensely detected in AKI melanoma cells. Taken together, these results strongly suggest that SFA- and/or MUFA-containing PAs produced by DGKα selectively target HSP27 and regulate its cancer-progressive function in melanoma cells but not in T cells.
## Identification of 16:0/16:0-PA-binding proteins in melanoma cells
We previously reported that DGKα preferentially generated SFA/MUFA-PA species, such as 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:0-, and 16:$\frac{0}{16}$:1-PA, in AKI human melanoma cells [30]. In the present study, we chose 16:$\frac{0}{16}$:0-PA and sought its target proteins in AKI melanoma cells. 16:$\frac{0}{16}$:0-PA-containing liposomes and 16:$\frac{0}{16}$:0-phosphatidylserine (PS)-containing liposomes (as a control) were reacted with a soluble fraction of AKI cell lysates and then ultracentrifuged. An intense band with a molecular mass of approximately 28 kDa was found in the precipitates of 16:$\frac{0}{16}$:0-PA-liposomes by silver staining (Fig. 1A). By in-gel digestion and liquid chromatography/tandem mass spectrometry (LC-MS/MS) analysis, the proteins of this band were identified, and we focused on HSP27 (also known as HSPβ1, UniProt accession number: P04792, 205 aa), which was reproducibly detected in the precipitates of 16:$\frac{0}{16}$:0-PA-liposomes as a candidate 16:$\frac{0}{16}$:0-PA-binding protein from the ∼28 kDa band (Fig. 1B).Figure 1Identification of HSP27 as a 16:$\frac{0}{16}$:0-PA target protein in melanoma cells. A, AKI cell lysates were incubated with 16:$\frac{0}{16}$:0-PA-containing liposomes or 16:$\frac{0}{16}$:0-PS-containing liposomes for comparison and then ultracentrifuged. The 16:$\frac{0}{16}$:0-PA-binding and 16:$\frac{0}{16}$:0-PS-binding proteins were separated by SDS-PAGE and detected by silver staining. The band marked with a black arrow was excised, in-gel digested, and analyzed by LC-MS/MS. B, LC-MS/MS identified HSP27. The accession number of the protein registered in the UniProt FASTA database (Accession), probability of identification (–10lgP), the percentage of the protein sequence covered by identified peptides (Coverage), and the number of unique supporting peptides for the protein (#Unique peptides) are shown. C, the 6×His-HSP27 protein expressed in E. coli cells was purified, separated by SDS-PAGE ($15\%$ acrylamide), stained with Coomassie Brilliant Blue (CBB), and detected by Western blotting (WB) using an anti-6×His antibody. HSP, heat shock protein; LC-MS/MS, liquid chromatography-tandem mass spectrometry; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis HSP27 is a molecular chaperone and is known to be a biomarker of cancer, renal injury and fibrosis, and neurodegenerative and cardiovascular disease [36]. In particular, the levels of HSP27 are increased in hepatocellular carcinoma cells, and moreover, HSP27 promotes proliferation and invasion, which consequently confer aggressiveness to cancer cells [37, 38].
Next, we cloned human HSP27 cDNA (accession number: AB020027) from the mRNAs of AKI cells and ligated it with the pET-28a vector. 6× His-tagged HSP27 protein was produced in *Escherichia coli* cells and purified by affinity chromatography using nickel-nitrilotriacetic acid agarose. We confirmed that 6× His-HSP27 was detected as a single band with a molecular mass of approximately 30 kDa, which was recognized by an anti-6× His antibody and was thus successfully purified (Fig. 1C).
## HSP27 binds to 16:0/16:0-PA with high selectivity and affinity
We next verified the interaction activity of 6 ×His-HSP27 with 16:$\frac{0}{16}$:0-PA using a liposome precipitation assay as described previously [39]. Only approximately $25\%$ of HSP27 was precipitated with liposomes containing phosphatidylcholine (PC) (neutral phospholipid) alone as a background control. Moreover, 16:$\frac{0}{16}$:0-PS-containing liposomes, as an acidic phospholipid control, cosedimented approximately $55\%$ of HSP27 (Fig. 2, A and B). However, approximately $90\%$ of HSP27 was sedimented with 16:$\frac{0}{16}$:0-PA-containing liposomes (Fig. 2, A and B), indicating that HSP27 more intensely binds to 16:$\frac{0}{16}$:0-PA liposomes than PC liposomes (background control) and 16:$\frac{0}{16}$:0-PS liposomes (acidic phospholipid control). Moreover, we analyzed 16:$\frac{0}{16}$:0-PC-binding activity of HSP27 and confirmed that it was almost the same with 16:$\frac{0}{16}$:0-PS (Fig. 2, C and D). Endogenous HSP27 expressed in AKI melanoma cells was mainly recovered in 200,000g sup (soluble fractions, ∼$80\%$) (Fig. 2, E and F). The endogenous HSP27 recovered in soluble fractions also showed stronger interaction activity with 16:$\frac{0}{16}$:0-PA liposomes than 16:$\frac{0}{16}$:0-PS and PC liposomes (Fig. 2, G and H).Figure 2Binding activity of 6×His-HSP27 to 16:$\frac{0}{16}$:0-PA.A, Liposome-binding assay of 6×His-HSP27 using 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS and PC liposomes was conducted. The purified 6×His-HSP27 (0.5 μM) was incubated with PC, 16:$\frac{0}{16}$:0-PA, or 16:$\frac{0}{16}$:0-PS liposomes (PA or PS: 200 μM) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were stained with Coomassie Brilliant Blue. The position of 6×His-HSP27 is indicated with a black arrowhead. C, A liposome-binding assay of 6×His-HSP27 using 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS, 16:$\frac{0}{16}$:0-PC, and PC liposomes was conducted. The purified 6×His-HSP27 (1.0 μM) was incubated with PC, 16:$\frac{0}{16}$:0-PC, 16:$\frac{0}{16}$:0-PA, or 16:$\frac{0}{16}$:0-PS liposomes (PA or PS: 200 μM) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were stained with Coomassie Brilliant Blue. The position of 6×His-HSP27 is indicated with a black arrowhead. E, AKI cells were washed two times with phosphate-buffered saline and lysed in HEPES buffer containing 25 mM HEPES (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol by sonication. After sonication, cell lysates were separated into soluble (supernatant (s)) and membrane (precipitate (p)) fractions by ultracentrifugation (200,000g for 30 min at 4 °C). The precipitate (p) was dissolved in HEPES buffer. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were detected by Western blotting using an anti-HSP27 antibody. F, the amounts of protein in the supernatant (s) and precipitate (p) were quantified by densitometry using ImageJ software. HSP27 expression was calculated as the percentage of the supernatant or precipitate band intensity compared to the total band intensity. Values are presented as the mean ± SD of three independent experiments. ∗∗$p \leq 0.01$, two-tailed t test. G, Liposome-binding assay of endogenous HSP27 using 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS, and PC liposomes was conducted. AKI cell lysates were incubated with PC, 16:$\frac{0}{16}$:0-PA, or 16:$\frac{0}{16}$:0-PS liposomes (PA or PS: 200 μM) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were detected by Western blotting using an anti-HSP27 antibody. The position of endogenous HSP27 is indicated with a black arrowhead. B, D, and H, The amounts of protein in the supernatant (s) and precipitate (p) were quantified by densitometry using ImageJ software. Binding activity was calculated as the percentage of the precipitate band intensity compared to the total band intensity. Values are presented as the mean ± SD of three independent experiments. ∗$p \leq 0.05$, ∗∗∗$p \leq 0.005$, one-way ANOVA followed by Tukey's post hoc test. ANOVA, analysis of variance; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
We also determined the affinity of HSP27 for 16:$\frac{0}{16}$:0-PA by measuring binding activity at 0 to 25 μM PA. Liposome-binding HSP27 was augmented in a 16:$\frac{0}{16}$:0-PA and 16:$\frac{0}{16}$:0-PS concentration-dependent manner (Fig. 3A). However, HSP27 did not show marked binding activity to liposomes at 25 μM PC (Fig. 3A). The dissociation constant (Kd) of HSP27 for 16:$\frac{0}{16}$:0-PA was determined to be 13.3 μM (Fig. 3B). The extrapolated Kd for 16:$\frac{0}{16}$:0-PS was calculated to be approximately 100 μM (Fig. 3B). These results indicate that HSP27 has a markedly higher affinity for PA than PS and PC.Figure 3Affinity of 6×His-HSP27 to 16:$\frac{0}{16}$:0-PA.A, Purified 6×His-HSP27 (0.1 μM) was incubated with the indicated concentrations (0–25 μM) of PC (control), 16:$\frac{0}{16}$:0-PS, and 16:$\frac{0}{16}$:0-PA liposomes and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were detected with Western blotting using an anti-6×His antibody. The position of 6×His-HSP27 is indicated with a black arrowhead. B, The amounts of protein in the precipitate were quantified by densitometry using ImageJ software. Binding activity was calculated as the percentage of the precipitate band intensity compared to the total band intensities (input). Values are presented as the mean ± SD of three independent experiments. The dissociation constant Kd was determined using GraphPad Prism 8 (a one-phase exponential decay model). HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
The PA-binding activity of clathrin coat assembly protein AP180 was affected by liposome diameters [39]. Therefore, the lipid binding activities of HSP27 were determined using liposomes with different diameters (100 nm and 1000 nm). HSP27 intensely bound to both sizes (100 nm (Fig. 4, A and B) and 1000 nm (Fig. 4, C and D)) of liposomes containing 16:$\frac{0}{16}$:0-PA), and the binding activity was stronger than those of 16:$\frac{0}{16}$:0-PS- and PC-containing liposomes (Fig. 4, A–D). No substantial differences between 100 nm and 1000 nm liposomes were observed (Fig. 4, A–D). Therefore, it is likely that different membrane curvatures and shapes formed by various liposome diameters fail to substantially affect the interaction of HSP27 with PA.Figure 4Binding activity of 6×His-HSP27 to 16:$\frac{0}{16}$:0-PA in different liposome diameters. A and C, A liposome-binding assay of 6×His-HSP27 using 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS, and PC liposomes with different diameters (100 nm (A) and 1000 nm (C)) was conducted. The purified 6×His-HSP27 (0.5 μM) was incubated with PC, 16:$\frac{0}{16}$:0-PA or 16:$\frac{0}{16}$:0-PS liposomes (PA or PS: 200 μM) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were stained with Coomassie Brilliant Blue. The position of 6×His-HSP27 is indicated with a black arrowhead. B and D, The amounts of protein in the supernatant (s) and precipitate (p) were quantified by densitometry using ImageJ software. Binding activity was calculated as the percentage of the precipitate band intensity compared to the total band intensity (100 nm (B) and 1000 nm (D)). Values are presented as the mean ± SD of three independent experiments. ∗∗∗$p \leq 0.005$, one-way ANOVA followed by Tukey's post hoc test. ANOVA, analysis of variance; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
## HSP27 most strongly binds to PA among various lipids
To measure the lipid binding selectivity of HSP27 in more detail, we carried out a lipid overlay assay using a nitrocellulose membrane spotted with diverse lipids that included 16:0 as fatty acid chains. HSP27 exhibited an intense interaction with 16:$\frac{0}{16}$:0-PA (Fig. 5A). However, other acidic lipids, such as phosphatidylinositol (PI), PS, phosphatidylglycerol, and 3-sulfogalactosylceramide, and neutral lipids, such as triglyceride, DG, PC, phosphatidylethanolamine, cholesterol (Chol), and sphingomyelin, failed to interact with HSP27 (Fig. 5A). PI-4-monophosphate (PI[4]P), PI-4,5-bisphosphate (PI[4,5]P2), and cardiolipin (CL), which are acidic lipids, are also associated with HSP27 (Fig. 5A). However, their binding intensities were lower than that of PA (Fig. 5B). Therefore, these results indicate that HSP27 selectively and most intensely binds to PA.Figure 5Binding activity of 6×His-HSP27 to various lipids. A, Lipid overlay assay of 6×His-HSP27 using various lipids. Equimolar amounts (100 pmol) of various lipids were spotted onto nitrocellulose membranes (Lipid Strips, Echelon Biosciences) as indicated. The acyl chain(s) of these glycerolipids and sphingolipid are C16:0. The membrane was incubated with purified 6×His-HSP27 (20 nM). Lipid-bound proteins were detected with an anti-6×His antibody. The data shown are representative of three independent experiments that gave similar results. B, Spot intensities were quantified by densitometry using ImageJ software. The binding activity (spot intensity) of HSP27 to PA was set to $100\%$. Values are presented as the mean ± SD of three independent experiments. ∗$p \leq 0.05$, ∗∗∗$p \leq 0.005$ versus PA, one-way ANOVA followed by Tukey's post hoc test. ANOVA, analysis of variance; Chol, cholesterol; CL, cardiolipin; PA, phosphatidic acid; PC, phosphatidylcholine; PE, phosphatidylethanolamine; PG, phosphatidylglycerol; PI, phosphatidylinositol; PI[4]P, phosphatidylinositol-4-monophosphate; PI[4,5]P2, phosphatidylinositol-4,5-bisphosphate; PI[3,4,5]P3, phosphatidylinositol-3,4,5-trisphosphate; SGC, 3-sulfogalactosylceramide; SM, sphingomyelin; TG, triglyceride.
## HSP27 strongly interacts with SFA/MUFA-PA but not PUFA-PA
To assess the PA molecular species selectivity of HSP27, we performed a liposome precipitation assay using various PA species, including 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:1-, 18:$\frac{1}{18}$:1-, 18:$\frac{0}{18}$:1-, 18:$\frac{0}{18}$:0-, 18:$\frac{0}{20}$:4- and 18:$\frac{0}{22}$:6-PA. SFA- and/or MUFA-containing PAs, 16:$\frac{0}{18}$:1-, 18:$\frac{1}{18}$:1-, 18:$\frac{0}{18}$:1- and 18:$\frac{0}{18}$:0-PA, intensely interacted with HSP27 (Fig. 6A). The binding activities of these PA species were almost the same as that of 16:$\frac{0}{16}$:0-PA (Fig. 6B). However, PUFA-containing PAs, 18:$\frac{0}{20}$:4- and 18:$\frac{0}{22}$:6-PA, showed substantially lower binding activities to HSP27 (Fig. 6B). These results indicate that HSP27 preferably binds to SFA- and/or MUFA-containing PAs. Figure 6Binding activity of 6×His-HSP27 to various PA species. A, purified 6×His-HSP27 (0.5 μM) was incubated with PC, 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{18}$:1-PA, 18:$\frac{1}{18}$:1-PA, 18:$\frac{0}{18}$:1-PA, 18:$\frac{0}{18}$:0-PA, 18:$\frac{0}{20}$:4-PA or 18:$\frac{0}{22}$:6-PA liposomes (PA: 200 μM) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were stained with Coomassie Brilliant Blue. The position of 6×His-HSP27 is indicated with a black arrowhead. B, the amounts of protein in the supernatant (s) and precipitate (p) were quantified by densitometry using ImageJ software. Binding activity was calculated as the percentage of the precipitate band intensity compared to the total band intensity. Values are presented as the mean ± SD of three independent experiments. ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, one-way ANOVA followed by Tukey's post hoc test. ANOVA, analysis of variance; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine.
## 16:0/16:0-PA-binding activities of N- and C-terminal regions of HSP27
To determine the 16:$\frac{0}{16}$:0-PA-binding region in HSP27, we divided HSP27 into two parts, the N-terminal region (HSP27-NT, aa 1–80) and the C-terminal region (HSP27-CT, aa 81–205) of HSP27 including an α-crystallin domain (Fig. 7A), and expressed in E. coli and highly purified them (Fig. 7B). HSP27-NT interacted moderately more strongly with 16:$\frac{0}{16}$:0-PA than HSP27-CT (Fig. 7, C and D), indicating that while the N-terminal region more strongly contributes to the 16:$\frac{0}{16}$:0-PA binding, both the N- and C-terminal regions of HSP27 are important for 16:$\frac{0}{16}$:0-PA binding. Figure 7Binding activities of 6×His-HSP27 and its mutants to 16:$\frac{0}{16}$:0-PA.A and B, The 6×His-HSP27-N (1–80 aa), 6×His-HSP27-C (81–205 aa), and 6×His-HSP27-R27E proteins expressed in E. coli cells (A) were purified, separated by SDS-PAGE ($15\%$ acrylamide), stained with Coomassie Brilliant Blue, and detected by Western blotting with anti-6×His antibody (B). C, E, and G, Liposome binding assay of 6×His-HSP27-N (C), 6×His-HSP27-C (E), and 6×His-HSP27-R27E using 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS and PC liposomes (X/PC/chol = $\frac{20}{50}$/30 mol% (X = PC, PA or PS) (1 mM total lipid). The purified (C) 6×His-HSP27-N (1.1 μM), (E) 6×His-HSP27-C (1.1 μM) or (G) 6×His-HSP27-R27E (0.5 μM) was incubated with each liposome (1 mM total lipids) and then separated by ultracentrifugation. SDS-PAGE ($15\%$ acrylamide) was performed, and separated proteins were detected by Western blotting with anti-6×His antibody (C) or stained with Coomassie Brilliant Blue (E and G). D, F, and H, the amounts of protein in the supernatant (s) and precipitate (p) were quantified by densitometry using ImageJ software. Binding activity was calculated as the percentage of the precipitate band intensity compared to the total band intensity. Values are presented as the mean ± SD of three independent experiments. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, one-way ANOVA followed by Tukey's post hoc test. ANOVA, analysis of variance; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
We next attempted to determine residues in HSP27 critical for 16:$\frac{0}{16}$:0-PA binding. Some reports have indicated that α-synuclein has α-helices containing several basic residues, which bind to acidic phospholipids [40, 41, 42]. We searched predicted α-helices in HSP27 using Chou and Fasman secondary structure prediction (https://www.biogem.org/tool/chou-fasman/) and found a sole Arg-containing α-helix (aa 26–32) in HSP27-NT (aa 1–80). Therefore, Arg27 was replaced with Glu (HSP27-R27E) (Fig. 7A), expressed in E. coli, and highly purified (Fig. 7B). However, the 16:$\frac{0}{16}$:0-PA-binding activity of HSP27-R27E was not considerably reduced (Fig. 7, G and H). These results indicate that Arg27 is not a critical residue for the 16:$\frac{0}{16}$:0-PA binding of HSP27.
## HSP27 colocalizes with constitutively active DGKα in cells
We next investigated whether HSP27 can associate with PA in cells. When EGFP-DGKα-CA (a constitutively active mutant lacking EF-hand motifs (Δ1–196)), which produces SFA- and/or MUFA-containing PAs (16:$\frac{0}{18}$:0-, 16:$\frac{0}{18}$:1- and 18:$\frac{0}{18}$:1-PA) [29], was expressed in COS-7 cells, the constitutively active mutant was located at the plasma membrane (Fig. 8, A–C). Moreover, mCherry-HSP27 coexpressed with EGFP-DGKα-CA showed a higher plasma membrane/cytosol ratio of HSP27 at the membrane region where DGKα-CA is colocalized with HSP27 (∼3.1) than mCherry-HSP27 coexpressed with EGFP alone (∼1.6) (Fig. 8, A–C). Notably, the plasma membrane/cytosol ratio (∼2.4) of HSP27 was significantly attenuated by CU-3 (10 μM), a DGKα-selective inhibitor [29], (Fig. 8, A and C), although DGKα-CA was located at the plasma membrane even in the presence of CU-3 (Fig. 8, A and B). These results indicate that the colocalization of EGFP-DGKα-CA with HSP27 was markedly reduced by CU-3. In addition, although EGFP-DGKα-CA-KD, a kinase-dead inactive mutant of DGKα-Δ1–196 in which Gly-435 is substituted with Asp [43], was located at the plasma membrane (plasma membrane/cytosol ratio: ∼4.0) (Fig. 8, D and E), plasma membrane/cytosol ratio (∼3.0) of mCherry-HSP27 coexpressed with the inactive mutant was lower than that (∼4.0) with EGFP-DGKα-CA (Fig. 8, D and F). These results indicate that the inactive mutant was less strongly colocalized with mCherry-HSP27 than EGFP-DGKα-CA. Therefore, these results indicate that the colocalization between HSP27 and DGKα occurs in a DGK activity (PA production)-dependent manner, suggesting that HSP27 can interact with PA produced by DGKα in cells. Figure 8Translocation of HSP27 from the cytoplasm to the plasma membrane depending on DGKα-DGKα-CA. A, EGFP alone or EGFP-DGKα-CA (a constitutively active mutant) was co-expressed with mCherry-HSP27 in COS-7 cells as indicated. After 20 h of transfection, 10 μM CU-3 (a DGKα-selective inhibitor) or DMSO was added and incubated for 30 min, and then the cells were fixed. The localization of EGFP-DGKα-CA (green) and mCherry-HSP27 (red) in the absence or presence of CU-3 was quantified using ImageJ software. Bars, 25 μm. B, quantitative image analysis of EGFP-DGKα-CA localization at the plasma membrane in COS-7 cells in the presence of DMSO ($$n = 31$$ from three independent experiments, 10–11 cells/experiment) or CU-3 ($$n = 30$$ from three independent experiments, 10 cells/experiment). Each dot shows the plasma membrane: cytosol intensity ratio for EGFP-DGKα-CA in COS-7 cells. Values are presented as the mean ± SD. Two-tailed t test. C, quantitative image analysis of mCherry-HSP27 localization cotransfected with pEGFP DGKα-CA or pEGFP alone at the plasma membrane in COS-7 cells in the presence of DMSO ($$n = 31$$ from three independent experiments, 10–11 cells/experiment (cotransfected with pEGFP-DGKα-CA or pEGFP-alone)) or CU-3 ($$n = 30$$ from three independent experiments, 10 cells/experiment). Each dot shows the plasma membrane: cytosol intensity ratio mCherry-HSP27 in COS-7 cells. Values are presented as the mean ± SD. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, ∗∗∗$p \leq 0.005$, one-way ANOVA followed by Tukey’s post hoc test. D, EGFP-DGKα-CA or EGFP-DGKα-CA-KD (a kinase-dead mutant) was co-expressed with mCherry-HSP27 in COS-7 cells as indicated. After 20 h of transfection, the cells were fixed. The localization of EGFP-DGKα-CA (green), EGFP-DGKα-CA-KD (green), and mCherry-HSP27 (red) was quantified using ImageJ software. Bars, 25 μm. E, Quantitative image analysis of EGFP-DGKα-CA ($$n = 28$$ from three independent experiments, 9–10 cells/experiment) and EGFP-DGKα-CA-KD ($$n = 29$$ from three independent experiments, 9–10 cells/experiment) localization at the plasma membrane in COS-7 cells. Each dot shows the plasma membrane:cytosol intensity ratio for EGFP-DGKα-CA and EGFP-DGKα-CA-KD in COS-7 cells. Values are presented as the mean ± SD. Two-tailed t test. F, quantitative image analysis of mCherry-HSP27 (cotransfected with pEGFP DGKα-CA ($$n = 28$$ from three independent experiments, 9–10 cells/experiment) or pEGFP-DGKα-CA-KD ($$n = 29$$ from three independent experiments, 9–10 cells/experiment) localization at the plasma membrane in COS-7 cells. Each dot shows the plasma membrane: cytosol intensity ratio mCherry-HSP27 in COS-7 cells. Values are presented as the mean ± SD. ∗$p \leq 0.05$, two-tailed t test. ANOVA, analysis of variance; DGK, diacylglycerol kinase; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
## 16:0/16:0-PA induces dissociation of the HSP27 oligomer
It was reported that HSP27 forms a large oligomer (12-mer–35-mer) [44] and that the oligomer dissociation of HSP27 enhances its chaperone activity [44, 45]. To assess oligomer dissociation, we performed blue native polyacrylamide gel electrophoresis (BN-PAGE). HSP27 was detected as a large oligomer (more than octamer) in the absence of phospholipids (Fig. 9A). In the presence of PC and 16:$\frac{0}{16}$:0-PS, HSP27 was electrophoresed to the same position as that in the absence of phospholipids (Fig. 9). However, in the presence of 16:$\frac{0}{16}$:0-PA, HSP27 was partly detected as less than octamer (tetramer and hexamer) (Fig. 9), suggesting that 16:$\frac{0}{16}$:0-PA selectively induces oligomer dissociation of HSP27.Figure 9Oligomer dissociation of HSP27 depending on 16:$\frac{0}{16}$:0-PA. A, purified 6×His-HSP27 (2.3 μM) was incubated with 16:$\frac{0}{16}$:0-PA, 16:$\frac{0}{16}$:0-PS, and PC liposomes (PA or PS: 200 μM). BN-PAGE ($4\%$ stacking gel, $6\%$ separation gel) was conducted, and separated proteins were detected with Western blotting using an anti-6×His antibody. The positions of tetramer, hexamer, and octamer of 6×His-HSP27 are indicated with black arrowheads. B, the amounts of less than 180 kDa protein were quantified by densitometry using ImageJ software. The band intensity of less than 180 kDa protein of mock was set to $100\%$. Values are presented as the mean ± SD of four independent experiments. ∗$p \leq 0.05$, ∗∗$p \leq 0.01$, one-way ANOVA followed by Tukey’s post hoc test. ANOVA, analysis of variance; BN-PAGE, blue native sulfate polyacrylamide gel electrophoresis; HSP, heat shock protein; PA, phosphatidic acid; PC, phosphatidylcholine; PS, phosphatidylserine.
## HSP27 is highly expressed in AKI melanoma cells but not Jurkat T cells
To analyze whether HSP27 is substantially expressed in T cells, where DGKα plays roles different from cancer cells [6, 34], we next examined the expression levels of HSP27 in Jurkat T cells by Western blotting. We found that the HSP27 protein was barely detectable in Jurkat T cells (less than $10\%$ of AKI melanoma cells) (Fig. 10, A and B), although the protein band was strongly detected in AKI melanoma cells (Fig. 10, A and B), suggesting that SFA/MUFA-PAs function via HSP27 only in melanoma cells but not in T cells. Figure 10HSP27 expression in AKI and Jurkat cells. A, AKI cells and Jurkat cells were washed two times with phosphate-buffered saline and lysed in HEPES buffer containing 25 mM HEPES (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol by sonication. After sonication, cell lysates were separated into soluble (supernatant (s)) and membrane (precipitate (p)) fractions by ultracentrifugation (200,000g for 30 min at 4 °C). The precipitate (p) was dissolved in HEPES buffer. SDS-PAGE ($15\%$ acrylamide) was conducted and separated proteins were detected by Western blotting using an anti-HSP27 antibody. B, the amounts of protein in AKI cells and Jurkat cells were quantified by densitometry using ImageJ software. HSP27 expression was calculated as the percentage of the band intensity of AKI lysates or Jurkat lysates compared to the band intensity of AKI lysates. Values are presented as the mean ± SD of three independent experiments. ∗∗∗$p \leq .005$, two-tailed t test. ANOVA, analysis of variance; HSP, heat shock protein; SDS-PAGE, sodium dodecyl sulfate polyacrylamide gel electrophoresis.
## Discussion
In the present study, we demonstrated for the first time that a chaperone, HSP27, is a novel PABP that prefers SFA- and/or MUFA-containing PA species. Several PABPs have been reported to recognize different PA species [11, 12, 13, 14, 15]. HSP27 was newly added to the list, which is still growing. Active DGKα recruited and colocalized with HSP27 at the plasma membrane in a DGK activity (PA)-dependent manner. Notably, 16:$\frac{0}{16}$:0-PA induced oligomer dissociation of HSP27, which enhances its chaperone activity.
HSP27 most strongly interacted with PA among the lipids examined (Fig. 5). However, HSP27 interacted with CL, PI[4]P, and PI[4,5]P2 with lower affinity than PA (Fig. 5). HSP27, which is a cytosolic protein, can interact with PA, PI[4]P, and PI[4,5]P2 but not with CL because CL is primarily localized in the inner membranes of mitochondria [46]. The amounts of PI[4]P and PI[4,5]P2 are substantially low in cells (0.02–0.2 mol% of total cellular phospholipids) [47] compared with PA (1–4 mol% of total cellular phospholipids) [48]. Therefore, it is likely that HSP27 acts as a PABP, but not a CL- or phosphoinositide-binding protein, in cells.
Essentially the same results (PA, PC, and PS binding) were obtained by the liposome sedimentation (Figs. 2 and 4) and lipid overlay (Fig. 5) assays. However, high background activities, probably due to hydrophobic interaction, were observed in the liposome sedimentation assay. It is likely that, in the lipid overlay assay, HSP27 mainly recognizes the hydrophilic polar head of PA. However, in the liposome sedimentation assay, HSP27 probably distinguishes fatty acid moieties (hydrophobic region) of PA in liposomes and/or different circumstances on liposome surfaces including different density of lipids generated by distinct fatty acid composition of PAs in addition to the polar head of PA.
The amounts of PS are higher than those of PA in cellular membranes. PS liposomes cosedimented HSP27 (Fig. 2, G and H). However, the sedimentation activity is almost the same as PC-binding activity (Fig. 2, G and H). Moreover, the PS-binding activity was not detected in the lipid overlay assay (Fig. 5). Indeed, HSP27 is not localized to the plasma membrane where PS is enriched (Fig. 8). Moreover, PS did not induce oligomer dissociation of HSP27 (Fig. 9). These results allow us to speculate that PS-binding activity of HSP27 is substantially lower than its PA-binding activity and does not activate HSP27 in cells.
The Kd of HSP27 for 16:$\frac{0}{16}$:0-PA was determined to be 13.3 μM (Fig. 3). The Kd values of PDE4A1, Opi1p, and sporulation-specific protein 20p for PA are 6.8, 4.5, and 2.2 μM, respectively [49]. In addition, the values of α-synuclein [50], creatine kinase muscle type [51], L-lactate dehydrogenase A [52], synaptojanin-1 [53] and neurofibromatosis type-1 (Ras GTPase-activating protein) [54] were 6.6, 2.0, 3.8, 0.5, and 12.0 μM, respectively. Therefore, the value of HSP27 is comparable to those of the PABPs described earlier.
HSP27 binds to SFA/MUFA-PA more strongly than to PUFA-PA (Fig. 6). There is only one report indicating that a PABP, creatine kinase muscle type, prefers SFA/MUFA-PAs [51]. In contrast to these PABPs, PDE4A1, Opi1p, and sporulation-specific protein 20p failed to exhibit substantial preference among PA molecular species [49]. α-Synuclein prefers MUFA-containing PA (18:$\frac{1}{18}$:1-PA) over SFA-containing PAs (16:$\frac{0}{16}$:0- and 18:$\frac{0}{18}$:0-PA) and PUFA-containing PAs (18:$\frac{0}{20}$:4-PA) [55]. L-lactate dehydrogenase A interacted with SFA-containing and PUFA-containing PAs more strongly than MUFA-containing PAs [52]. Praja-1 [56], synaptojanin-1 [53], and clathrin coat assembly protein AP180 [39] preferentially bound to PUFA-PA. DGKγ more intensely associates with 18:$\frac{0}{20}$:4-PA and 18:$\frac{1}{18}$:1-PA than 18:$\frac{0}{18}$:0-PA [57]. These results indicate that different PABPs display selectivities for different molecular species of PA.
It is possible that, in addition to DGKα, other PA-producing enzymes such as lysoPA acyltransferase and phospholipase D (PLD) also affect HSP27 function. Indeed, lysoPA acyltransferase isoform expression has been shown to enhance the proliferation of cancer cells and correlates with an increased risk of tumor development and aggressiveness of tumors [58]. Moreover, increased expression of PLD enzymes (PLD1 and PLD2) has been implicated as contributing factors in several types of human cancer, and the role of PLD in pathways involved in cancer progression and tumorigenesis has been reported [59].
Although HSP27 exists as a large oligomer (12-mer–35-mer) [44], Ser/Thr phosphorylation destabilizes HSP27 oligomeric assembly and leads to a dimer form, which is active [44, 45]. Moreover, oligomer decomposition of purified HSP27 by reduction, even without phosphorylation, also results in activation [60], indicating that deoligomerization itself induces HSP27 activation. Notably, 16:$\frac{0}{16}$:0-PA, but not PC or 16:$\frac{0}{16}$:0-PS, also selectively induced oligomer dissociation of HSP27 (Fig. 9). Therefore, it is likely that SFA-/MUFA-PA-dependent deoligomerization causes activation of HSP27.
PA binding may have effects similar to Ser/Thr phosphorylation because both PA binding and phosphorylation introduce negative charge(s) of a phosphate group to the protein. It was reported that HSP27 is phosphorylated by mitogen-activated protein kinase (MAPK)-activated protein kinase (MAPKAPK) 2 and 3, MAPKAPK5, PKC, cGMP-dependent kinase, Akt/protein kinase B, and protein kinase D [61]. The activities of PKCδ [62] and ε [63, 64] are enhanced by PA in addition to DG. Akt/protein kinase B [65, 66] and C-Raf upstream of MAPK (extracellular signal-regulated kinase (ERK)) [67, 68, 69] are also activated by PA. Therefore, it is possible that PA synergistically induces the dissociation of HSP27 oligomers via direct binding to HSP27 and activation of HSP27 phosphorylation pathways.
Overexpressed DGKα recruited HSP27 to the plasma membrane in COS7 cells in a DGK activity (PA)-dependent manner (Fig. 8), suggesting that SFA- and/or MUFA-containing PA species generated by DGKα bind to and recruit HSP27 to the plasma membrane and activate the protein. DGKα is highly expressed in cancer cells, such as melanoma [23] and hepatocellular carcinoma [24] cells, but not in normal melanocytes or hepatocytes. Therefore, it is possible that SFA- and/or MUFA-containing PAs produced by abundant DGKα can interact with and activate HSP27 in cancer cells as well and, consequently, induce cancer cell proliferation and cancer progression. DGKα commonly generates SFA- and/or MUFA-containing PA species, for example, 16:$\frac{0}{16}$:0-PA in melanoma and T cells [30, 35]. However, HSP27 is barely expressed in Jurkat T cells (Fig. 10), while HSP27 is also enriched in cancer cells [37, 38]. Moreover, the human protein atlas showed that HSP27 is not expressed in lymph node and spleen (https://www.proteinatlas.org/ENSG00000106211-HSPB1/tissue). Therefore, this expression pattern may explain at least in part how DGKα plays reverse roles in T cells (attenuator) and cancer cells (enhancer) [6, 34]. However, further studies are required to elucidate signal transduction through the DGKα–16:$\frac{0}{16}$:0-PA–HSP27 pathway during cancer cell proliferation and cancer progression more in detail and how DGKα has cancer- and T-cell-selective functions.
Recently, it has been revealed that DGK isozymes utilize different DG species and thus produce distinct PA species in different cells [12, 70]. DGKα generates SFA- and/or MUFA-containing PAs, such as 16:$\frac{0}{16}$:0-, 16:$\frac{0}{18}$:0-, and 16:$\frac{0}{16}$:1-PA, in serum-starved AKI melanoma cells [30]. In the present study, we demonstrated that SFA/MUFA-PAs selectively interacted with HSP27 (Fig. 6) and enhanced its oligomer dissociation (Fig. 7), which is closely linked to its activation [44, 45]. Indeed, colocalization of DGKα and HSP27 at the plasma membrane was observed (Fig. 8). Moreover, a DGKα-selective inhibitor attenuated the colocalization (Fig. 8), indicating that the colocalization occurred in a DGK activity (PA)-dependent manner. Therefore, there is the possibility that SFA/MUFA-PAs produced by DGKα activate HSP27 in cancer cells.
Elevated levels of DGKα and PA are related to cancer initiation and progression [23, 34, 71, 72]. DGKα prevents apoptosis through the PKCζ–NF-κB pathway in melanoma cells [23]. Moreover, DGKα promotes hepatocellular carcinoma proliferation via activation of the Ras–Raf–MAPK/ERK kinase–ERK pathway [24]. Furthermore, this isozyme inhibits apoptosis of glioblastoma and melanoma cells through the PDE4A1–cAMP–mTOR pathway [32]. PKCζ [73], C-Raf [67, 68, 69], and PDE4A1 [49, 74] are activated by PA. Therefore, it is likely that PA regulates the activities of these enzymes in the signaling pathways. In addition to these enzymes, our results showed that PA targets HSP27, which prevents apoptosis and promotes proliferation in cancer cells. Therefore, it is possible that DGKα utilizes multiple pathways to promote the aggressiveness of cancer cells.
In summary, in the present study, we demonstrated that SFA/MUFA-PAs, which are produced by DGKα in cancer cells [30], strongly bind to HSP27, which is highly expressed in melanoma cells but not in T cells and attenuates its oligomer formation. Our results shed light on a novel function of SFA/MUFA-PA and allow us to speculate about the functional linkage between the pro-cancer proteins, HSP27 [37, 38] and DGKα [33, 34].
## Materials
L-α-PC from egg yolk, 1,2-dipalmitoyl-sn-glycero-3-phosphoserine (16:$\frac{0}{16}$:0-PS), 1,2-dipalmitoyl-sn-glycero-3-phosphate (16:$\frac{0}{16}$:0-PA), 1,2-distearoyl-sn-glycero-3-phosphate (18:$\frac{0}{18}$:0-PA), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphate (16:$\frac{0}{18}$:1-PA), 1-stealoyl-2-oleoyl-sn-glycero-3-phosphate (18:$\frac{0}{18}$:1-PA), 1,2-dioleoyl-sn-glycero-3-phosphate (18:$\frac{1}{18}$:1-PA), 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphate (18:$\frac{0}{20}$:4-PA), and 1-stearoyl-2-docosahexaenoyl-sn-glycero-3-phosphate (18:$\frac{0}{22}$:6-PA) were purchased from Avanti Polar Lipids (Alabaster, AL, USA). Cholesterol (Chol) was purchased from Wako Pure Chemical Industries (Tokyo, Japan). Membrane Lipid Strips were purchased from Echelon Biosciences (Salt Lake City, UT, USA).
## Cell culture
AKI cells (a human melanoma-derived cell line) and COS-7 cells (fibroblast-like cell lines derived from monkey kidney tissue) were grown in Dulbecco’s modified Eagle’s medium (DMEM; Wako Pure Chemical Industries) supplemented with $10\%$ fetal bovine serum (Thermo Fisher Scientific, Waltham, MA), 100 units/ml penicillin, and 100 μg/ml streptomycin (Wako Pure Chemical Industries). The cells were maintained at 37 °C in an environment containing $5\%$ CO2.
## Preparation of liposomes
The following lipid mixtures were used to identify 16:$\frac{0}{16}$:0-PA-specific binding proteins from AKI melanoma cells: 16:$\frac{0}{16}$:0-PS liposomes [Chol (30 mol%) and PC Mix (from egg yolk) (60 mol%) and 16:$\frac{0}{16}$:0-PS (10 mol%)] and 16:$\frac{0}{16}$:0-PA liposomes [Chol (30 mol%) and PC Mix (from egg yolk) (60 mol%) and 16:$\frac{0}{16}$:0-PA (10 mol%)]. The combined dried lipid mixture was hydrated at 95 °C in HEPES buffer (25 mM HEPES, pH 7.4, 100 mM NaCl, and 1 mM dithiothreitol) for 45 min and vortexed for 1 min once every 15 min during hydration. The liposomes were then subjected to five freeze–thaw cycles (−196 °C for 3 min, 95 °C for 3 min) [75]. Liposomes were formed by sonication at 90 °C using a Branson Sonifier 450 [76].
The following lipid mixtures were used to determine the properties of the HSP27 protein: the control liposome [Chol (30 mol%) and PC Mix (from egg yolk) (70 mol%)], PS liposome [Chol (30 mol%), PC Mix (from egg yolk) (50 mol%) and 16:$\frac{0}{16}$:0-PS (20 mol%)], and the PA liposome [Chol (30 mol%), PC Mix (from egg yolk) (50 mol%) and each PA species (20 mol%)]. For the lipid-binding assay, the combined dried lipid mixture was hydrated at 95 °C in HEPES buffer for 45 min and vortexed for 1 min once every 15 min during hydration. The liposomes were then subjected to five freeze–thaw cycles (−196 °C for 3 min, 95 °C for 3 min). Liposomes were formed by sonication at 90 °C using a Branson Sonifier 450, or the liposomes were further extruded 11 times through a 100 nm or 1000 nm polycarbonate membrane using a Mini Extruder (Avanti Polar Lipids) [77]. The extruder was brought to 95 °C prior to extrusion. Since the lipid forms a bilayer, half of the actual concentration was considered [78].
## Identification of HSP27 as a PA-binding protein
AKI cells were washed two times with phosphate-buffered saline and lysed in HEPES buffer containing 25 mM HEPES (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol. After sonication, insoluble materials were removed by ultracentrifugation (200,000g for 30 min at 4 °C). AKI cell lysates were incubated with the PC liposomes at 4 °C for 30 min, and nonspecific protein bound to the vesicles were removed by centrifugation at 200,000g for 1 h at 4 °C. The resultant supernatant was incubated with the 16:$\frac{0}{16}$:0-PS or 16:$\frac{0}{16}$:0-PA liposomes at 4 °C for 30 min and then centrifuged at 200,000g at 4 °C for 1 h. The precipitates were dissolved in HEPES buffer containing 25 mM HEPES, pH 7.4, 100 mM NaCl, and 1 mM dithiothreitol. The 16:$\frac{0}{16}$:0-PA-binding proteins were separated by SDS-PAGE and visualized by silver staining (Fig. 1). In-gel digestion and LC-MS/MS identification of proteins in the ∼28 kDa bands were carried out as previously described [79]. Desalted tryptic peptides were analyzed by an Ultimate 3000 RSLCnano system (Thermo Fisher Scientific, Waltham, MA, USA) coupled to a Q Exactive hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) equipped with a nano ESI source. The protein identification was performed using PEAKS XPro (PEAKS Studio 10.6 build 20201015; Bioinformatics Solutions Inc. Waterloo, Ontario, CA). The analytical parameters were set as follows: search engine, sequest HT; protein database, Swissprot (Homo Sapiens); enzyme name, trypsin; parent mass tolerance, 10.0 ppm; false discovery rate <0.01; unique peptides ≥ 2. Proteins belonging to keratin were excluded from the identification results as contaminants. Based on triplicate experiments, we targeted the protein of average mass, ∼28 kDa, which was reproducibly identified in the PA-liposome-binding fraction.
## Reverse transcription PCR, protein expression, and purification of HSP27
AKI cells were washed twice with phosphate-buffered saline (pH 7.4) and collected by centrifugation (500g, 4 °C, 3 min), and total RNA was isolated as previously described [80]. cDNA was generated using Transcriptor reverse transcriptase (Roche Diagnostics, Mannheim, Germany).
Human HSP27 cDNA (Accession number: AB020027) was amplified using the primers 5′-GGTGGTGGATCCATGACCGAGCGCCGC-3′ (forward) and 5′-GGTGGTCTCGAGTTACTTGGCGGCAGTCTC-3′ (reverse) from prepared cDNA (human AKI cells) by PCR, ligated with pET-28a vector (Novagen–Merck, Darmstadt, Germany), which carries an N-terminal 6 ×His tag, and transfected into Rosetta 2 (DE3) E. coli cells (Novagen). The expression and purification of the 6× His-tagged HSP27 protein using nickel-nitrilotriacetic acid agarose (Qiagen, Hilden, Germany) were performed as previously described [81].
## Western blotting
Western blotting was conducted as previously described [23]. AKI and COS-7 cells were homogenized in ice-cold HEPES buffer (25 mM HEPES, pH 7.4, 100 mM NaCl, and 1 mM dithiothreitol). A protein-transferred polyvinylidene fluoride membrane (Pall Corporation, Port Washington, NY) was incubated with anti-HSP27 antibody (ab2790, Abcam, Cambridge, UK) and an anti-6× His antibody (D291-3S, Medical & Biological Laboratories, Nagoya, Japan).
## Lipid overlay assay
One hundred picomoles of various lipids was spotted in a nitrocellulose membrane (Lipid Strips; Echelon Biosciences). The membranes were blocked with $2\%$ skim milk in phosphate-buffered saline (pH 7.4) for 1 h at 4 °C. After blocking, 10 ml of $3\%$ fatty acid–free bovine serum albumin and $0.1\%$ Tween 20 in phosphate-buffered saline (pH 7.4) containing 6× His-tagged HSP27 (final concentration: 20 nM) was added to the membranes. The membrane was incubated for 20 min at 4 °C and was then incubated with an anti-6× His antibody for 1 h at 4 °C, followed by incubation with anti-mouse IgG conjugated with horseradish peroxidase (Bethyl Laboratories, Montgomery, TX, USA) antibody. Finally, lipid-bound 6× His-HSP27 was visualized using an enhanced chemiluminescence Western blotting detection system (GE Healthcare, Little Chalfont, UK).
## Liposome-binding assay
The purified 6× His-tagged HSP27 protein (final concentration: 0.5 μM) was dissolved in HEPES buffer and incubated with the PA-containing or control liposomes at 4 °C for 30 min. Samples were ultracentrifuged at 200,000g at 4 °C for 1 h. The precipitate was dissolved in HEPES buffer containing 25 mM HEPES (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were stained with Coomassie Brilliant Blue or detected by Western blotting using an anti-6× His antibody.
To measure the binding activity between PA-containing liposomes and endogenous HSP27, AKI cells were washed two times with phosphate-buffered saline and lysed in HEPES buffer containing 25 mM HEPES (pH 7.4), 100 mM NaCl, and 1 mM dithiothreitol by sonication. After sonication, insoluble materials were removed by ultracentrifugation (200,000g for 30 min at 4 °C). The cell lysates were incubated with the PA-containing or control liposomes at 4 °C for 30 min. Samples were ultracentrifuged at 200,000g for 1 h at 4 °C. The precipitate was dissolved in HEPES buffer. SDS-PAGE ($15\%$ acrylamide) was conducted, and separated proteins were detected by Western blotting using an anti-HSP27 antibody.
## Plasmid constructs for COS-7 cell transfection
pEGFP-DGKα-Δ1–196, which is a constitutively active mutant (DGKα-CA) [29], and pEGFP-DGKα-CA-KD, a kinase-dead inactive mutant of DGKα-Δ1–196 in which Gly-435 is substituted with Asp [43], were generated previously. pmCherry-HSP27 was constructed by inserting a PCR fragment encoding HSP27 amplified from pET-28a-HSP27 into the EcoRI/SalI sites of the pmCherry-C1 vector (Sigma-Aldrich, St Louis, MO, USA).
## Confocal laser scanning microscopy
COS-7 cells seeded on coverslips were transiently transfected with plasmids using PolyFect reagent (Qiagen) as described by the manufacturer. After 20 h of transfection, the cells were incubated with the DGKα selective inhibitor CU-3 [29] (or DMSO alone as a control) in DMEM (final concentration: 10 μM) for 30 min to inhibit PA generation by DGKα-CA. The cells were then fixed in $4\%$ paraformaldehyde. The coverslips were mounted with Vectashield (Vector Laboratories, Burlingame, CA, USA). Fluorescence images were obtained with an Olympus FV1000-D (IX81) confocal laser scanning microscope (Olympus, Tokyo, Japan) equipped with a UPLSAPO 60 × 1.35 NA oil at room temperature. EGFP fluorescence was excited at 488 nm, and mCherry fluorescence was excited at 543 nm. Images were obtained using FV-10 ASW software (Olympus).
## Blue native polyacrylamide gel electrophoresis
The purified 6× His-tagged HSP27 protein (final concentration: 2.3 μM) was dissolved in HEPES buffer and incubated with the PA-containing or control liposomes at 4 °C for 30 min. BN-PAGE ($4\%$ stacking gel and $6\%$ separation gel) was carried out as previously described [82]. The gel was incubated in denature buffer A (20 mM Tris-HCl (pH 7.4), 150 mM glycine, and $0.1\%$ SDS) for 10 min at room temperature. After transfer, polyvinylidene fluoride membranes were washed with methanol, followed by incubation in denature buffer B (50 mM Tris-HCl (pH 7.4), $2\%$ SDS, $0.8\%$ β-mercaptoethanol) for 30 min at 50 °C. The proteins were detected using an anti-6× His antibody.
## Densitometry
Band intensities were quantified by densitometry using ImageJ software (https://imagej.nih.gov/ij/index.html) as described (https://lukemiller.org/index.php/$\frac{2010}{11}$/analyzing-gels-and-western-blots-with-image-j/)
## Statistical analysis
Data are represented as the means ± SD and were analyzed using one-way analysis of variance followed by Tukey's or Dunnett's post hoc test for multiple comparisons or two-tailed t test for the comparison of two groups using Prism 8 (GraphPad Software, San Diego, CA, USA) to determine any significant differences. $p \leq 0.05$ was considered significant.
## Data availability
The data that support the findings of this study are available from the corresponding author [sakane@faculty.chiba-u.jp] upon reasonable request.
## Conflict of interest
The authors declare that they have no conflicts of interest with the contents of this article.
## Author contributions
N. Y., F. H., C. M., M. E., and Y. M. methodology, investigation, and formal analysis. F. S. and N. Y. writing - original draft. F. S. conceptualization. N. Y., F. H., C. M., M. E., Y. M., and F. S. writing - review and editing.
## Funding and additional information
This work was supported in part by grants from $\frac{10.13039}{501100001700}$MEXT/$\frac{10.13039}{501100001691}$JSPS (KAKENHI Grant Numbers: JP17H03650 (Grant-in-Aid for Scientific Research (B)), and JP20H03205 (Grant-in-Aid for Scientific Research (B)) (F. S.), the $\frac{10.13039}{501100013038}$Japan Food Chemical Research Foundation (F. S.), and the Senshin Medical Research Foundation (F. S.), the Uehara Memorial Foundation (F. S.), the Tojuro Iijima Foundation for Food Science and Technology (F. S.), the Sugiyama Chemical and Industrial Laboratory (F. S.), the Mishima Kaiun Memorial Foundation (F. S.), and the Toyo Suisan Foundation (F. S.).
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|
---
title: 'Prevalence and Predictors of Carpal Tunnel Syndrome Symptoms Among Teachers
in Riyadh: A Cross-Sectional Study'
journal: Cureus
year: 2023
pmcid: PMC10023996
doi: 10.7759/cureus.35040
license: CC BY 3.0
---
# Prevalence and Predictors of Carpal Tunnel Syndrome Symptoms Among Teachers in Riyadh: A Cross-Sectional Study
## Abstract
Background Carpal tunnel syndrome (CTS) is a musculoskeletal disorder (MSD) afflicting the upper limbs with a prevalence of approximately $14.4\%$ in the general population. Previous studies have noted the increasing prevalence of MSDs among teachers but have not investigated in depth the prevalence and predictors of CTS symptoms in this population. The aim of this study was to help fill this gap in the literature by investigating teachers working in Riyadh, Saudi Arabia.
Methods We conducted this cross-sectional study in Riyadh using an online survey. We distributed the Boston carpal tunnel questionnaire (BCTQ) to schoolteachers in the city through the social media applications Twitter, WhatsApp, and Telegram. We assessed the respondents’ symptoms using Univariate association analyses with a Wilcoxon rank sum test for the continuous variables and Fisher’s exact test and Pearson’s chi-squared test for the categorical variables. We assessed the independent risk factors for CTS by constructing multivariate binary logistic regression models and expressed the results using the odds ratio (OR) and $95\%$ confidence intervals ($95\%$ CIs), with $p \leq 0.05$ indicating statistical significance.
Results The sample for this study included 490 teachers. Among them, the prevalence of moderate to severe CTS symptoms was $40.0\%$, and self-reported CTS was $9.1\%$. The teachers who were female, relatively old, left-handed, retired, and spent significant time using a pen, keyboard, and/or blackboard were more likely than those who were male, relatively young, right-handed, and did not spend significant time using a pen, keyboard, and/or blackboard to self-report CTS and exhibit moderate to severe symptoms.
Conclusions We found a relatively high percentage ($40.0\%$) of CTS symptoms among teachers working in Riyadh. This finding suggests that any sign of CTS symptoms should be checked to ensure early diagnosis and treatment, which contribute to positive outcomes, particularly given the recent increase in such risk factors for CTS as diabetes, hypothyroidism, and high BMI in populations worldwide.
## Introduction
Carpal tunnel syndrome (CTS) is a musculoskeletal disorder (MSD) that afflicts the upper limbs with a prevalence of $14.4\%$ in the general population [1]. The etiologies of the disorder include the work involved in certain occupations [2]. Teaching is among the occupations associated with MSDs, which afflict an estimated $35\%$ to $95\%$ of educators [3]. The numerous ergonomic and work-related factors that predispose this population to CTS include repeated flexion and extension of the wrist and other wrist movements associated with long hours spent grading assignments, preparing lessons, performing nonteaching clerical duties, attending developmental courses, and participating in extracurricular activities with students [4]. Furthermore, in recent years, the increasing rates of metabolic disorders such as diabetes, hyperthyroidism, and obesity have added to the burden on healthcare systems worldwide, and these disorders are considered risk factors for CTS, especially in the context of stressful work [5-7]. Unsurprisingly, the early diagnosis of CTS is associated with more favorable post-surgical outcomes than is the case when treatment begins long after symptoms have started [8]. Therefore, when teachers present with clinical symptoms of CTS, caregivers should take measures to facilitate early diagnosis and, thereby, improve treatment outcomes.
Studies in several countries have reported on the prevalence of MSD in teachers, with reports of wrist/hand pain specifically occurring at a prevalence of $13.4\%$ in Turkey, $26\%$ in Bolivia, $40.7\%$ in Malaysia, and $44\%$ in the Philippines [9-12]. Reports in other studies of the prevalence of wrist pain in Saudi Arabia range from $7.4\%$ to $22.1\%$ [13-16]. However, these studies address wrist/hand pain as a symptom without exploring its characteristics or relationship to CTS in teachers. To our knowledge, only Stevens et al. [ 17] and Younis et al. [ 18] have reported the prevalence of CTS among teachers, offering widely divergent estimates of $2.9\%$ and $62\%$, respectively. In view of the increase in MSDs among teachers and of hand and wrist pain in particular, we reasoned that CTS, a common MSD, would be similarly prevalent among teachers.
Accordingly, the aim of this study was to estimate the prevalence of CTS symptoms and assess the factors associated with it among schoolteachers working in Riyadh, Saudi Arabia.
## Materials and methods
For this cross-sectional study, we distributed an online-based survey through the social media applications Twitter, WhatsApp, and Telegram to the members of teachers’ groups in Riyadh and visited schools to encourage participation. The data of this study were collected during the months of January and February 2022.
Eligibility criteria Male and female teachers who had been working for at least a year were included in the study. Any teachers with a history of orthopedic trauma or congenital disorders of the wrist were excluded.
Measures The survey included two sets of questions. Those in the first set were designed to collect the demographic data, as explained below. The second set of questions asked about the participants’ CTS symptoms, for which purpose we used a validated Arabic version of the Boston carpal tunnel questionnaire (BCTQ-A). The original BCTQ developed by Levine et al. [ 19] has demonstrated excellent reliability and reproducibility and has been shown to be a valid instrument for screening the symptoms of CTS in previous studies [20-25]. The BCTQ-A used in this study has an intraclass correlation coefficient of 0.8 [21].
Demographic questions The demographic questions on the survey asked about age and gender in order to determine the relationship, if any, between these characteristics and CTS symptoms. Thus, the women were asked whether they were pregnant to account for the increased prevalence of symptoms among pregnant women [26], and height and weight were obtained to calculate the BMI. We gathered additional data about social status, tobacco use, and exercise to assess the significance of protective and harmful factors and about hand dominance to assess whether right- or left-handedness is more associated with CTS symptoms. We also asked the participants whether they were currently working or retired, their level of education, the subjects that they taught, and whether they worked on-site or virtually to determine whether these factors were associated with CTS symptoms. The survey also collected information on the years that the participants had spent teaching and the amount of time that they had spent writing using a pen, keyboard, and/or blackboard to assess any association between these factors and CTS symptoms. Lastly, we asked whether the participants had been clinically diagnosed with CTS by a doctor to estimate their self-reporting of CTS and about other medical co-morbidities that could correlate with the symptoms of CTS.
Measurement of CTS symptoms The participants responded to the BCTQ-A scale using a five-point Likert scale according to the severity of the participants’ symptoms, with 1 indicating no symptoms and 5 indicating the greatest severity. Table S1 provides further details about the responses as well as their coding.
We sought to compare teachers who had clinically confirmed CTS with clinically uncertain subjects as well as symptomatic with asymptomatic teachers. Symptomatic teachers were defined as those with at least one moderate to severe symptom in the hand and/or wrist identified on the BCTQ-A’s symptom severity and functional status scales (response codes 3 to 5 in).
Sample size calculation Given that more than 100,000 teachers work in Riyadh, the estimated sample size was 385 participants at a $95\%$ confidence interval (CI) [27]. Our initial sample included 763 potential participants. After excluding those who did not meet the eligibility criteria (i.e., were not teachers and did not work in Riyadh), our final sample size was 490 participants.
Statistical analysis We used RStudio (R version 4.1.1) for the statistical analysis. The descriptive statistics included the frequencies and percentages for the categorical data and means and standard deviations (SDs) for the numerical data. We assessed the prevalence of CTS using a one-sample proportions test with continuity correction, presenting the prevalence along with the respective $95\%$ CI. To conduct the univariate association analyses, we used a Wilcoxon rank sum test for the continuous variables and Fisher’s exact test or Pearson’s Chi-squared test for the categorical variables. We assessed the independent risk factors for CTS by constructing multivariate binary logistic regression models. The variables that showed significant association with the clinically confirmed diagnosis of CTS served as the independent variables in the first model. In the second model, we incorporated the factors that were significantly associated with having at least one moderate-to-severe symptom in the hands and/or wrists based on the BCTQ scale. We checked the assumptions of both models, and the results showed no significant multicollinearity, with the variance inflation factors being generally < 5. Additionally, the numerical variables were linearly associated with the dependent outcome variables in both models.
The results of the regression analysis are expressed using the OR and $95\%$ CIs, with $p \leq 0.05$ indicating statistical significance.
Ethics approval and consent to participate We obtained ethical approval for this study from King Abdullah International Medical Research Center, Ministry of National Guards Health Affairs, Riyadh, Saudi Arabia (protocol number NRC21R/$\frac{539}{12}$). The center’s IRB committee approved the informed consent form given to each participant before the study. Participation in this study was entirely voluntary, and the participants’ anonymity was secured in a manner consistent with the ethical considerations in the Declaration of Helsinki.
## Results
Sociodemographic and occupational characteristics The present study includes data from the 490 teachers in the sample. Overall, 304 ($62.0\%$) were women, most were married ($84.1\%$), and most were right-handed ($91.8\%$). There were significant gender differences between the male and female participants in terms of the proportions of those who were single ($14.5\%$ and $8.9\%$, respectively; $p \leq 0.0001$), active smokers ($21.0\%$ and $1.0\%$; $p \leq 0.0001$), exercised ($48.4\%$ and $33.2\%$; $p \leq 0.0001$) and were right-handed ($88.2\%$ and $94.1\%$; $$p \leq 0.020$$, Table 1).
Concerning the participants’ specific duties, the most common subjects that they taught were math ($17.8\%$), Arabic ($15.3\%$), and religious education ($9.8\%$). Table 1 also shows their active teaching, years of experience, and amount of time spent using a pen, keyboard, and/or blackboard. Significantly higher proportions of the male participants than the female participants were teaching onsite ($90.3\%$ and $75.0\%$, respectively; $p \leq 0.0001$) and in a middle school ($40.3\%$ and $27.0\%$; $$p \leq 0.001$$), while the female participants had spent on average more time using a pen, keyboard, and/or blackboard (3.5 ± 2.0 and 3.1 ± 1.7 hours daily; $$p \leq 0.028$$, Table 1).
**Table 1**
| Parameter | Category | Overall, N = 490 | Male, N = 186 | Female, N = 304 | p |
| --- | --- | --- | --- | --- | --- |
| Age | Mean ± SD | 40.8 ± 8.0 | 41.2 ± 8.6 | 40.6 ± 7.6 | 0.594 |
| BMI | Underweight | 4 (0.8%) | 1 (0.5%) | 3 (1.0%) | 0.218 |
| | Normal | 134 (27.3%) | 42 (22.6%) | 92 (30.3%) | |
| | Overweight | 207 (42.2%) | 87 (46.8%) | 120 (39.5%) | |
| | Obese | 145 (29.6%) | 56 (30.1%) | 89 (29.3%) | |
| Marital status | Single | 54 (11.0%) | 27 (14.5%) | 27 (8.9%) | <0.0001 |
| | Married | 412 (84.1%) | 159 (85.5%) | 253 (83.2%) | |
| | Divorced / widowed | 24 (4.9%) | 0 (0.0%) | 24 (7.9%) | |
| Pregnant | Yes | | | 15 (5.1%) | |
| Active smoker | Yes | 42 (8.6%) | 39 (21.0%) | 3 (1.0%) | <0.0001 |
| Exercise | Yes | 191 (39.0%) | 90 (48.4%) | 101 (33.2%) | <0.0001 |
| Hand dominance | Right-handed | 450 (91.8%) | 164 (88.2%) | 286 (94.1%) | 0.020 |
| | Left-handed | 40 (8.2%) | 22 (11.8%) | 18 (5.9%) | |
| Teach onsite or virtually | Onsite | 396 (80.8%) | 168 (90.3%) | 228 (75.0%) | <0.0001 |
| Teach onsite or virtually | Virtual | 94 (19.2%) | 18 (9.7%) | 76 (25.0%) | |
| Which level do you teach | Kindergarten | 15 (3.1%) | 1 (0.5%) | 14 (4.6%) | 0.001 |
| Which level do you teach | Elementary school | 136 (27.8%) | 42 (22.6%) | 94 (30.9%) | |
| Which level do you teach | Middle school | 157 (32.0%) | 75 (40.3%) | 82 (27.0%) | |
| Which level do you teach | High school | 182 (37.1%) | 68 (36.6%) | 114 (37.5%) | |
| Currently working or retired | Currently working | 462 (94.3%) | 178 (95.7%) | 284 (93.4%) | 0.292 |
| Currently working or retired | Retired | 28 (5.7%) | 8 (4.3%) | 20 (6.6%) | |
| If retired, how long since you retired* | Mean ± SD | 6.1 ± 3.8 | 3.0 ± 0.1 | 6.3 ± 3.9 | 0.500 |
| How long have you been teaching | Mean ± SD | 14.8 ± 8.2 | 15.3 ± 8.2 | 14.6 ± 8.3 | 0.316 |
| Duration of using a pen/keyboard/board (hours) | Mean ± SD | 3.4 ± 1.9 | 3.1 ± 1.7 | 3.5 ± 2.0 | 0.028 |
Medical history and characteristics of CTS One hundred and seventy-nine of the participants reported a positive history of a chronic condition ($36.5\%$), most often hypothyroidism ($26.8\%$), hypertension ($22.3\%$), and diabetes ($16.8\%$, Figure 1). Notably, 45 of the participants indicated that they had been diagnosed with CTS, for a prevalence of $9.2\%$ ($95\%$ CI = 6.8 to 12.2). There was no statistically significant difference between the male and female participants in terms of the prevalence of CTS ($10.2\%$ and $8.6\%$, respectively; $$p \leq 0.536$$).
**Figure 1:** *The distribution of chronic conditions among teachers.*
Factors associated with self-reported CTS We found self-reported CTS to be significantly associated with being left-handed rather than right-handed ($22.2\%$ and $6.7\%$, respectively; $$p \leq 0.002$$) and retired rather than actively working ($28.9\%$ and $3.4\%$; $p \leq 0.0001$). Additionally, the teachers who self-reported CTS were significantly older than those who did not (44.4 ± 8.3 and 40.5 ± 7.9 years; $$p \leq 0.003$$), and they had been teaching for longer periods (17.7 ± 8.4 and 14.5 ± 8.2 years; $$p \leq 0.023$$). Furthermore, CTS was significantly associated with longer durations of using a pen, keyboard, and/or blackboard (4.0 ± 1.7 and 3.3 ± 1.9 hours per day; $$p \leq 0.003$$, Table 2). The multivariate analysis, then, indicated that the independent risk factors for CTS included left-hand dominance (OR = 4.10, $95\%$ CI, 1.66 to 9.56, $$p \leq 0.001$$), being retired (OR = 9.67, $95\%$ CI, 3.62 to 26.4, $p \leq 0.0001$), and having used a pen, keyboard, and/or blackboard for long periods of time (OR = 1.21, $95\%$ CI, 1.03 to 1.42, $$p \leq 0.018$$; Table 3).
**Table 2**
| Parameter | Category | Self-reported CTS | Self-reported CTS.1 | Self-reported CTS.2 | Moderate to severe symptoms (BCTQ) | Moderate to severe symptoms (BCTQ).1 | Moderate to severe symptoms (BCTQ).2 | Unnamed: 8 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Parameter | Category | No, N = 445 | Yes, N = 45 | p | No, N = 294 | Yes, N = 196 | p | p |
| Age | Mean ± SD | 40.5 ± 7.9 | 44.4 ± 8.3 | 0.003 | 39.8 ± 7.7 | 42.4 ± 8.2 | <0.0001 | <0.0001 |
| Gender | Male | 167 (37.5%) | 19 (42.2%) | 0.536 | 136 (46.3%) | 50 (25.5%) | <0.0001 | <0.0001 |
| | Female | 278 (62.5%) | 26 (57.8%) | | 158 (53.7%) | 146 (74.5%) | | |
| Pregnant | Yes | 13 (4.8%) | 2 (7.7%) | 0.629 | 5 (3.3%) | 10 (6.9%) | 0.148 | 0.148 |
| BMI | Underweight | 4 (0.9%) | 0 (0.0%) | 0.395 | 1 (0.3%) | 3 (1.5%) | 0.054 | 0.054 |
| | Normal | 125 (28.1%) | 9 (20.0%) | | 85 (28.9%) | 49 (25.0%) | | |
| | Overweight | 189 (42.5%) | 18 (40.0%) | | 132 (44.9%) | 75 (38.3%) | | |
| | Obese | 127 (28.5%) | 18 (40.0%) | | 76 (25.9%) | 69 (35.2%) | | |
| Marital status | Single | 52 (11.7%) | 2 (4.4%) | 0.283 | 37 (12.6%) | 17 (8.7%) | 0.354 | 0.354 |
| | Married | 372 (83.6%) | 40 (88.9%) | | 244 (83.0%) | 168 (85.7%) | | |
| | Divorced / widowed | 21 (4.7%) | 3 (6.7%) | | 13 (4.4%) | 11 (5.6%) | | |
| Active smoker | Yes | 39 (8.8%) | 3 (6.7%) | 0.786 | 31 (10.5%) | 11 (5.6%) | 0.056 | 0.056 |
| Exercise | Yes | 169 (38.0%) | 22 (48.9%) | 0.153 | 118 (40.1%) | 73 (37.2%) | 0.520 | 0.520 |
| Hand dominance | Right-handed | 415 (93.3%) | 35 (77.8%) | 0.002 | 276 (93.9%) | 174 (88.8%) | 0.043 | 0.043 |
| | Left-handed | 30 (6.7%) | 10 (22.2%) | | 18 (6.1%) | 22 (11.2%) | | |
| Teach onsite or virtually | Onsite | 360 (80.9%) | 36 (80.0%) | 0.884 | 240 (81.6%) | 156 (79.6%) | 0.574 | 0.574 |
| | Virtual | 85 (19.1%) | 9 (20.0%) | | 54 (18.4%) | 40 (20.4%) | | |
| Which level do you teach | Kindergarten | 14 (3.1%) | 1 (2.2%) | 0.438 | 6 (2.0%) | 9 (4.6%) | 0.404 | 0.404 |
| Which level do you teach | Elementary school | 126 (28.3%) | 10 (22.2%) | | 82 (27.9%) | 54 (27.6%) | | |
| Which level do you teach | Middle school | 145 (32.6%) | 12 (26.7%) | | 98 (33.3%) | 59 (30.1%) | | |
| Which level do you teach | High school | 160 (36.0%) | 22 (48.9%) | | 108 (36.7%) | 74 (37.8%) | | |
| Currently working or retired | Currently working | 430 (96.6%) | 32 (71.1%) | <0.0001 | 288 (98.0%) | 174 (88.8%) | <0.0001 | <0.0001 |
| Currently working or retired | Retired | 15 (3.4%) | 13 (28.9%) | | 6 (2.0%) | 22 (11.2%) | | |
| How long have you been teaching | Mean ± SD | 14.5 ± 8.2 | 17.7 ± 8.4 | 0.023 | 14.3 ± 8.2 | 15.7 ± 8.2 | 0.048 | 0.048 |
| Duration of using a pen/keyboard/board (hours) | Mean ± SD | 3.3 ± 1.9 | 4.0 ± 1.7 | 0.003 | 3.0 ± 1.6 | 3.9 ± 2.1 | <0.0001 | <0.0001 |
Factors associated with moderate to severe symptoms on the BCTQ The participants who reported having moderate to severe symptoms (i.e., with Likert scores from 3 to 5) for any of the 19 items on the BCTQ-A were considered to have positive symptoms. The teachers with moderate to severe symptoms were significantly older than those without such symptoms (42.4 ± 8.2 and 39.8 ± 7.7 years, respectively; $p \leq 0.0001$), had taught for longer periods (14.3 ± 8.2 and 15.7 ± 8.2 years; $$p \leq 0.048$$), and had used a pen, keyboard, and/or blackboard for longer periods (3.0 ± 1.6 and 3.9 ± 2.1 hours per day; $p \leq 0.0001$). These symptoms were also significantly associated with the female gender ($74.5\%$ and $53.7\%$; $p \leq 0.0001$), left-handedness ($11.2\%$ and $6.1\%$, $$p \leq 0.043$$), and being retired ($11.2\%$ and $2.0\%$; $p \leq 0.0001$; Table 2). The multivariate analysis showed that experiencing moderate to severe symptoms was independently associated with the female gender (OR = 2.62, $95\%$ CI, 1.72 to 4.06, $p \leq 0.0001$), being retired (OR = 3.62, $95\%$ CI, 1.41 to 10.6, $$p \leq 0.011$$), and left-hand dominance (OR = 2.23, $95\%$ CI, 1.09 to 4.61, $$p \leq 0.018$$). Additionally, older teachers (OR = 1.04, $95\%$ CI, 1.00 to 1.09, $$p \leq 0.034$$) and those who had been using a pen, keyboard, and/or blackboard for relatively longer times (OR = 1.30, $95\%$ CI, 1.17 to 1.46, $p \leq 0.0001$) were more likely to report moderate to severe symptoms (Table 3).
**Table 3**
| Parameter | Category | Self-reported CTS | Self-reported CTS.1 | Self-reported CTS.2 | Moderate to severe symptoms (BCTQ) | Moderate to severe symptoms (BCTQ).1 | Moderate to severe symptoms (BCTQ).2 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Parameter | Category | OR | 95% CI | p-value | OR | 95% CI | p-value |
| Gender | Male | NS | NS | NS | Ref | Ref | Ref |
| | Female | NS | NS | NS | 2.62 | 1.72, 4.06 | <0.0001 |
| Age | Numeric | 1.00 | 0.93, 1.06 | 0.886 | 1.04 | 1.00, 1.09 | 0.034 |
| Hand dominance | Right-handed | Ref | Ref | Ref | Ref | Ref | Ref |
| | Left-handed | 4.10 | 1.66, 9.56 | 0.001 | 2.23 | 1.09, 4.61 | 0.028 |
| Currently working or retired | Currently working | Ref | Ref | Ref | Ref | Ref | Ref |
| | Retired | 9.67 | 3.62, 26.4 | <0.0001 | 3.62 | 1.41, 10.6 | 0.011 |
| How long have you been teaching | Numeric | 1.04 | 0.98, 1.10 | 0.185 | 0.99 | 0.96, 1.03 | 0.712 |
| Duration of using a pen/keyboard/board | Numeric | 1.21 | 1.03, 1.42 | 0.018 | 1.30 | 1.17, 1.46 | <0.0001 |
## Discussion
Our analysis showed that teachers who were female, older, left-handed, retired, and had spent long hours using a pen, keyboard, and/or blackboard were more likely to self-report CTS and to exhibit moderate to severe symptoms. The time spent teaching may also play a role in developing CTS, for teachers who had spent relatively long periods teaching experienced the symptoms of CTS more than those who spent less.
The participants’ responses to the BCTQ-A indicated that the prevalence of moderate to severe symptoms was $40.0\%$. We found the prevalence of self-reported CTS among school teachers to be $9.1\%$, which is, surprisingly, higher than that reported in the general population [1,28]. Moreover, previous researchers estimated the percentage of wrist and/or hand symptoms in Saudi teachers from $7.4\%$ to $22.1\%$ [13-16]. Younis et al. [ 18] found that $62\%$ of the symptomatic school teachers whom they studied had clinically confirmed CTS, but this relatively high percentage may be attributable to the fact that they included only symptomatic teachers in their sample. Despite the large teaching workforce in Saudi Arabia, there is a gap in the literature regarding the occurrence of MSDs among the country’s educators. Thus, whereas the prevalence of hand and/or wrist symptoms reported in previous studies was lower than the prevalence of back and neck pain, we found that $40.0\%$ of the teachers in our sample (196 of 490) reported moderate to severe symptoms that could negatively impact their work productivity.
Gender was significantly associated with the prevalence of CTS symptoms in the current study, with female teachers being more affected by symptoms than male teachers. Likewise, Younis et al. [ 18], Atroshi et al. [ 1] and De Krom et al. [ 28] all found the prevalence of CTS to be higher among women than men in the general population. So also, Erick et al. [ 3], in a systematic review, found that women were more prone to severe wrist/hand pain than men. A possible explanation for this gender difference is that females generally have smaller wrists than men and/or a lower pain threshold for reporting these symptoms and/or that men may hesitate to report such symptoms out of embarrassment [29].
We also found the number of hours that teachers spent writing to be associated with the prevalence of CTS symptoms. Thus, the teachers with symptoms had spent an average of 3.9 ± 2.1 hours per day using a pen, keyboard, and/or blackboard. Similarly, Younis et al. [ 18] reported the average working time for teachers with CTS in the study to be 6.8 hours per day. Additionally, Cardoso et al. demonstrated that the prevalence of MSDs increased with the amount of time that the teachers in their study spent teaching [30]. Erick et al. showed further that both the time spent by teachers on teaching and awkward positioning of the arm while working can contribute significantly to the development of MSDs, and that teachers who work with their arms in awkward positions were 1.59 times more likely to develop wrist and/or hand pain than those who did not [31]. Other researchers have likewise hypothesized that an occupational causative factor of CTS among teachers is the position of the hand and head while writing on blackboards [32]. The odds ratio of 1.30 for writing using a pen, keyboard, and/or blackboard that we found, however, indicates that the association was not significant. Accordingly, not only the time that teachers spend teaching and writing but also the way in which they perform their tasks may be indicators of the development of CTS.
More than a third ($36.5\%$) of the teachers in the current study had had previous medical problems. Of these comorbidities, diabetes, hypothyroidism, and a relatively high BMI are the most commonly known risk factors of CTS [33-35]. Another study linked metabolic disorders such as diabetes specifically to CTS [36]. Therefore, such risk factors, which have become widespread in populations worldwide in recent years, may be contributing to the status of CTS as an emerging workplace health issue.
Limitations The limitations of this study include, in the first place, the design. Specifically, since we conducted a cross-sectional study, the results may not be generalizable across Saudi Arabia let alone to other populations. Furthermore, Because the study was survey-based, it was subject to recall bias and subjective differences in the reporting of the items in the BCTQ-A, particularly those related to pain. Another limitation is the convenience sampling method that we employed: because of the COVID-19 pandemic, we could only reach the intended population through social media platforms, thus introducing a possible source of sampling bias. The use of the BCTQ, a validated tool for reporting CTS symptoms, increased the rigor of this study, but reinforcing these findings with a clinical approach would have provided more precise and valuable results. Thus, future research along these lines could include a clinical dimension and broader regional coverage and make use of a larger sample size and a different design, such as prospective studies.
## Conclusions
We found the prevalence of CTS symptoms to be $40.0\%$ among the teachers working in Riyadh whom we surveyed. This proportion is greater than that reported in the general population. Further assessment of the risk factors discussed here is needed. Additionally, we recommend that teachers consult medical advice when they suspect that they are developing CTS symptoms since early diagnosis and treatment are associated with favorable outcomes. There is a particular need for such research given the increase in the general population of such CTS risk factors as diabetes, hypothyroidism, and high BMI.
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|
---
title: 'Moderate-intensity stepping in older adults: insights from treadmill walking
and daily living'
authors:
- T. Yates
- J Henson
- P. McBride
- B Maylor
- L. Y. Herring
- J. A. Sargeant
- M. J. Davies
- P. C. Dempsey
- A. V. Rowlands
- C. L. Edwardson
journal: The International Journal of Behavioral Nutrition and Physical Activity
year: 2023
pmcid: PMC10024004
doi: 10.1186/s12966-023-01429-x
license: CC BY 4.0
---
# Moderate-intensity stepping in older adults: insights from treadmill walking and daily living
## Body
Brisk walking is a powerful predictor of all-cause mortality and cardiovascular disease [1–5], with recent data also showing associations with a lower risk of severe COVID-19 [6]. Research to date has predominately utilised self-reported walking pace or functional walking tests. However, the widespread use of accelerometers within research and commercial activity trackers has enabled measures of walking pace applicable to daily living. Specifically, step cadence has been used as a measure of walking intensity with a threshold of 100 steps/minute used to identify moderate-intensity stepping [7], including in associations with health outcomes [8–11]. The recent CADENCE study, which directly counted steps during treadmill walking with oxygen consumption measured by indirect calorimetry, went on to systematically confirm that 100 steps/minute acts as an evidence-based heuristic threshold in all adults [12, 13], including older adults [14].
Generalisability of the 100 steps/minute threshold needs confirming in older adults, particularly as it has been shown that the metabolic cost of walking is higher than in younger populations, with low walking speeds of < 3 km/h achieving > 3 metabolic equivalents (METS) [15–17]. This supports a hypothesis that step cadence thresholds for moderate-intensity stepping may be lower in older adults. The CADENCE study did not support this hypothesis as the walking pace required to achieve 3 METS was consistent to that reported for the general population (~ 4 km/h) [14, 18], which may explain why the moderate-intensity step cadence threshold of 100 steps/minute was also consistent with younger populations [12, 13]. Therefore, it is important to further investigate the generalisability of the 100 steps/minute threshold to a wider population of older adults.
The aim of this study was to investigate moderate-intensity step cadence values during treadmill walking in older adults and to use data collected during daily living to quantify stepping cadence behaviour in relation to derived thresholds.
## Abstract
### Background
A step cadence of 100 steps/minute is widely used to define moderate-intensity walking. However, the generalizability of this threshold to different populations needs further research. We investigate moderate-intensity step cadence values during treadmill walking and daily living in older adults.
### Methods
Older adults (≥ 60 years) were recruited from urban community venues. Data collection included 7 days of physical activity measured by an activPAL3™ thigh worn device, followed by a laboratory visit involving a 60-min assessment of resting metabolic rate, then a treadmill assessment with expired gas measured using a breath-by-breath analyser and steps measured by an activPAL3™. Treadmill stages were undertaken in a random order and lasted 5 min each at speeds of 1, 2, 3, 4 and 5 km/h. Metabolic equivalent values were determined for each stage as standardised values (METSstandard) and as multiples of resting metabolic rate (METSrelative). A value of 3 METSstandard defined moderate-intensity stepping. *Segmented* generalised estimating equations modelled the association between step cadence and MET values.
### Results
The study included 53 participants (median age = 75, years, BMI = 28.0 kg/m2, $45.3\%$ women). At 2 km/h, the median METSstandard and METSrelative values were above 3 with a median cadence of 81.00 (IQR 72.00, 88.67) steps/minute. The predicted cadence at 3 METSstandard was 70.3 ($95\%$ CI 61.4, 75.8) steps/minute. During free-living, participants undertook median (IQR) of 6988 [5933, 9211] steps/day, of which 2554 [1297, 4456] steps/day were undertaken in continuous stepping bouts lasting ≥ 1 min. For bouted daily steps, $96.4\%$ ($90.7\%$, $98.9\%$) were undertaken at ≥ 70 steps/minute.
### Conclusion
A threshold as low as 70 steps/minute may be reflective of moderate-intensity stepping in older adults, with the vast majority of all bouted free-living stepping occurring above this threshold.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12966-023-01429-x.
## STAND-UP study
The analysis included data from the ‘Sedentary behaviour in older adults: investigating a new therapeutic paradigm (STAND UP) study’ [19]. One of the work packages consisted of a cross-sectional study collecting accelerometer data during free-living conditions followed by a lab-based assessment of different physical activities. The aim was to develop age-appropriate cut-points for sedentary behaviour and physical activity definitions in older adults. Participants were recruited from the community by working with a network of social clubs, faith centres and other community venues from 2014 to 2016. Participants were eligible to take part if they were ≥ 60 years of age, able to walk without assistance from support devices or other persons, and able to communicate in verbal and written English. Those with self-reported chronic disease likely to affect participation in or outcomes of the study were excluded (e.g. asthma, diabetes, heart disease, memory problems).
## Visit 1
The first visit was organised to collect baseline data, confirm eligibility, and undertake familiarisation with the study procedures including treadmill walking. Data collection included height and weight (to the nearest 0.5 cm and 0.1 kg respectively) and a 60-s sit-to-stand test of physical function, where participants were instructed to stand from a standardised sitting position as many times as possible within 60 s. At the end of this visit participants were fitted with an activPAL3™ device (PAL Technologies, Ltd., Glasgow, UK) on the midline anterior aspect of the right thigh and asked to wear the device continuously for 7 full days. Devices were waterproofed using a nitrile sleeve and affixed to the thigh using Hypafix Transparent (BSN Medical, Hull, UK) dressing.
## Visit 2
Following collection of baseline data, participants attended an exercise and metabolic laboratory (Diabetes Research Centre, Leicester General Hospital, United Kingdom) in a fasted condition. Participants first completed a 60-min resting metabolic rate (RMR) protocol whilst supine. Expired gas was collected from a calibrated open circuit breath-by-breath Cortex Metalyzer (Leipzig, Germany) using the low flow rate setting. The initial 30 min acclimatised participant to the laboratory and equipment and the final 30 min was used for the REE measurement. The first 5-min period where the average per minute VO2 and VCO2 changed by less than $10\%$ was used to calculate REE. Following REE measurement, participants consumed a standardised breakfast (men 500 kcal, women 400 kcal) and rested for a further 30 min. Participants then undertook a series of activities typical of daily living. This included treadmill walking at 1, 2, 3, 4, and 5 km/h, with each stage lasting 5 min, performed in a randomised order. During each phase of the test, breath-by-breath expired gas was collected with data between minutes 2 to 4 averaged for analysis (Cortex Metalyzer, Leipzig, Germany). An activPAL3™ device was worn throughout.
## Device-assessed physical activity and sedentary behaviour
activPAL3™ devices were initialised using default settings (Professional Research Edition; PAL Technologies Ltd., Glasgow, UK). For the free-living data, event files were processed using Processing PAL (Version, 1.3, University of Leicester, UK), which uses a validated algorithm to determine the waking wear time within each valid day [20]. A valid waking day was defined as a day with < $95\%$ of time spent in any one behaviour (e.g., standing or sitting), ≥ 500 step events (1000 steps/day) and ≥ 10 h of valid waking hours data [20]. At least 3 valid days of data were included for the free-living assessment. Cadence (steps/minute) was calculated per step event as ‘(number of steps × 2/interval length) × 60’. Cadence for continuous uninterrupted bouts of stepping, as a marker of purposeful walking, were calculated as bouts lasting a minute or longer and calculated as ‘(number of steps × 2/interval length) × 60’. Bouted steps above different step cadence thresholds anticipated to cover the threshold for moderate-intensity within this population (50–100 steps/minute in 10 unit increments) were extracted for analysis. For the laboratory visit, 1 s epoch files were created from the event files in order to extract stepping data for minutes 2 to 4 of each walking stage to match the METS data. ActivPAL devices have previously been shown to accurately count steps during treadmill and continuous walking within controlled conditions, including at slower speeds in older adults, with an absolute percentage error of < $1\%$ [21, 22], with another study reporting that over $90\%$ of steps above 69 steps/minute are counted [23].
## Metabolic equivalent (METS) values
METS were calculated using data from minutes 2 to 4 of each treadmill stage. METSstandard were calculated using the standardized formula: VO2 (mL/kg/min)/3.5. METSrelative were calculated by dividing VO2 (mL/kg/min) during each treadmill stage by the resting metabolic rate VO2; unlike METSstandard, METSrelative accounts for individual differences in VO2 during rest and thus provides a multiple of resting energy expenditure. A value of 3 METSstandard defined the threshold for moderate-intensity stepping.
## Data inclusion
Sixty-two individuals completed the study, of which 58 undertook at least one stage of the treadmill test, with 4 individuals excluded for safety reasons related to balance and proprioception. activPAL malfunction or difficulties with monitor placement on frail skin excluded a further 5 participants, with 53 providing valid activPAL data included in the analysis.
## Statistical analysis
*Segmented* generalised estimating equations modelled the association between step cadence and MET values, taking into account repeated measures using an exchangable correlation matrix. A breakpoint at 100 steps/min was selected as it provided the best model fit, consistent with the previous literature [14]. As the focus of this paper is on the threshold for moderate-intensity stepping, the regression line up to 100 steps/minute was used for this analysis (see Supplementary Figure S1). Equations were used to predict step cadence at 3 METstandard and 3 METrelative along with METSstandard and METrelative values at 100 steps/minute. In order to assess whether results were consistent across different characteristics, models were stratified by sex (women, men), age (categorised at the median), BMI (< 30, ≥ 30 kg/m2), height (categorised at the sex stratified median), physical function status (categorised at the median) and physical activity status (< 7500 steps/day, ≥ 7500 steps/day) for all outcomes apart from step cadence at 3 METSrelative which were not investigated further due to predicted values falling outside the range of plausible purposeful stepping (< 50 steps/minute). Receiver Operating Characteristic (ROC) curve analyses were performed to further test the strength of association between step cadence and moderate-intensity classification. Sensitivity, specificity, positive predictive values (PPV) and negative predictive values (NPV) were generated for the derived step cadence threshold for moderate-intensity and compared to those observed for a threshold of 100 steps/minute; values were corrected to account for repeated measures using generalised estimating equations [24].
Data are presented as median (IQR) for descriptive data or estimate ($95\%$ CI) for modelled data. Data were analysed using IBM SPSS Statistics (version 24.0). A p-value of < 0.05 was considered statistically significant.
## Results
The study included 53 participants (median age = 75, years, BMI = 28.0 kg/m2, $45.3\%$ women), with characteristics displayed in Table 1.Table 1Participant characteristicsCategorical characteristicsNumberColumn % Sex Men2954.7 Women2445.3 Ethnicity White European$4584.9\%$ Other$815.1\%$Continuous characteristicsMedian25th percentile75th percentile Age (years)756878 Body Mass Index (kg/m2)28.026.629.9 Lower limb function (sit-to-stand repetitions)191623 Resting VO2 (ml/kg/min)2.772.523.02 Total ambulatory activity (steps/day)698859339211 Ambulatory activity at ≥ 1 min bouts (steps/day)255412974456
## Lab data
MET and step cadence value for each stage of the treadmill test are displayed in Table 2. At 2 km/h, the median (IQR) METSstandard and METSrelative values were 3.08 (2.73, 3.43) and 3.62 (3.27, 4.30) respectively at a cadence of 81.0 (72.0, 88.7) steps/min. Table 2Metabolic equivalent and stepping cadence values during treadmill walkingTreadmill speedMETSstandardMETSrelativeCadence (steps/minute)Median25th Percentile75th percentileMedian25th Percentile75th percentileMedian25th Percentile75th percentile1 km/h2.292.123.072.882.473.5364.749.385.32 km/h3.082.733.433.623.274.3081.072.088.73 km/h3.553.154.244.473.935.0294.786.799.34 km/h4.153.694.855.284.565.85108.7100.7114.05 km/h4.524.085.325.565.106.43115.3105.3123.3 The predicted METSstandard and METSrelative value at 100 steps/minute, based on 200 data observations across the treadmill stages, were 3.77 ($95\%$ CI 3.54, 4.01) and 4.74 (4.43, 5.04) respectively. With a gradient of 0.026 METSstandard and 0.032 METSrelative per 1 step/minute difference in cadence, the predicted cadence value at 3 METSstandard or METSrelative were 70.3 (61.4, 75.8) steps/minute and 45.8 (27.7, 56.6) steps/minute, respectively (Supplementary Table S1, Supplementary Figure S1). Values were largely consistent across age, sex, BMI, height, physical function and physical activity status (Supplementary Table S1).
The ROC curve for the association between MET classification and stepping cadence is shown in Supplementary Figure S2, with an AUC value of 0.814. A threshold of 70 steps/minute correctly classified (true positive and true negative values) $80\%$ of all observations, with a high sensitivity and PPV ($93.4\%$ and $82.6\%$, respectively) (Supplementary Table S2). However, the NPV ($64.4\%$) and specificity ($37.6\%$) were lower. Conversely, a threshold of 100 steps/minute had lower accuracy ($62.4\%$ of all observations correctly classified), low sensitivity ($54.6\%$), but reasonable specificity ($87.1\%$) (Supplementary Table S2).
## Free-living data
Participants undertook a median (IQR) of 6988 [5933, 9211] steps/day, of which 2554 [1297, 4456] steps/day were undertaken in continuous stepping bouts of 1 min or more (Table 1). Figure 1 shows the amount and proportion bouted daily steps undertaken at or above incremental thresholds from 50 to 100 steps/minute. In total, $96.4\%$ (90.7, 98.9) of all bouted steps were undertaken at or above a cadence of 70 steps/minute, with $67.0\%$ ($38.9\%$, $82.3\%$) undertaken at or above 100 steps/minute. Fig. 1Number and percentage of bouted steps taken at or above incremental cadence thresholds during daily living. Data as median (IQR)
## Discussion
In this analysis of older adults, the threshold for moderate-intensity physical activity was reached at a stepping cadence of 70 steps/minute which was substantially below the widely used 100 steps/minute, with the lower threshold providing higher levels of accuracy and sensitivity, but lower levels of specificity. These findings have application to free-living conditions as the vast majority of bouted stepping during daily living occurred above 70 steps/minute.
Our threshold is lower than that reported in the CADENCE study [12–14], which concluded that 100 steps/minute acts as a heuristic threshold for all adults, including older adults aged 60–85 years [14]. However, the prediction interval for the CADENCE study did include 70 steps/minute, suggesting some overlap in the range of predicted threshold between studies. There are some differences in population characteristics that may help explain the difference the mean predicted thresholds. Most notably, the walking speed needed to elicit METSstandard varied substantially between our studies. In the CADENCE study, average values above 3 METSstandard were only seen at walking speeds of around 4 km/h. However in our study, 3 METSstandard was exceeded at half this speed (2 km/h). Therefore whilst the gradient in the association between step cadence and METSstandard was similar between studies (0.026 vs 0.020), the difference in walking speed needed to elicit 3 METSstandard resulted in a substantially different intercept and step cadence thresholds. Previous literature supports the findings in our cohort, with slow walking speeds consistently shown to result in elevated METSstandard values in older adults [15–17, 25, 26], and slow walking speeds of < 3 km/h shown to cross the threshold into moderate-intensity activity [15–17]. The reasons for the difference in the metabolic cost of walking with age are not fully understood, although age-related adaptations in the recruitment and activation of leg muscles and excess body weight have been proposed [27, 28]. Indeed, median BMI values in our cohort were higher than the CADENCE study (28.0 vs 26.7 kg/m2) and more reflective of older adults within the general population [29].
The importance of defining the step cadence threshold for moderate-intensity physical activity in older adults is highlighted by the inclusion of free-living purposeful (bouted) stepping behaviour within this study. Over $90\%$ of bouted steps occurred above 70 steps/minute. Therefore, if the threshold for moderate-intensity stepping exists at around 70 steps/minute in older adults, a focus on increasing continuous stepping activity is likely to a priori also promote moderate-intensity physical activity. This has important implications for public health campaigns and physical activity promotion interventions and tools where a focus on simply promoting increased walking time or distance may be optimal. This is supported by observational studies showing that associations between step cadence and health outcomes are largely attenuated after adjustment for overall stepping volume [9, 11], including in older adults [8, 10]. However, if the threshold sits at closer to 100 step/minute, only around two thirds of continuous daily stepping is undertaken at a moderate-intensity, which may support a focus on targeting both the volume and intensity of walking for optimal health.
Within this older population, the RMR VO2 was 2.77 ml/kg/minute, which is consistent with other studies and substantially below the value used to calculate METSstandard (3.5 ml/kg/minute) [30], which was derived in younger populations. Consequently, METSrelative values for walking were higher than those for METSstandard with virtually all treadmill walking occurring above 3 METSrelative, further supporting the conclusion that any continuous stepping occurs at a moderate-intensity in older adults.
Our analysis has some strengths and limitations. Strengths include the use of activPAL devices to collect both free-living and treadmill walking activity, allowing for interpretation and application of generated cadence thresholds into routine daily activity. However, accelerometer devices in general may not capture all activity during light or infrequent stepping [23], and the use of activPAL may also present some limitations. Previous research, including in older adults, has shown good validity between observed and activPAL counted steps during treadmill or controlled slow walking (within the range used in this study [21–23]), providing reassurance that misclassification of steps is unlikely to account for the magnitude of difference between our study and the CADENCE study, which amounted to approximately 30 steps/minute. Moreover, a walking pace of 2 km/h elicited over 3 METs within this study compared to a walking pace of 4 km/h within the CADENCE study, therefore our findings are also consistent with a lower stepping cadence simply reflecting the slower pace required to achieve the moderate-intensity threshold. However, it is also possible that conducting treadmill walking in the postprandial state may have acted to elevate derived MET values, thus lowering the step cadence needed to achieve 3 METS. We did not capture metabolic data during free-living conditions, and are therefore unable to verify whether MET values are consistent between treadmill and free-living stepping at derived cadence thresholds. The relatively small sample size may not be generalizable to all older adults, or those with greater mobility limitations.
In conclusion, this study suggests that a threshold as low as 70 steps/minute may on average be reflective of moderate-intensity walking in older adults, with the vast majority of bouted (≥ 1 min) stepping occurring above this threshold. However, the low specificity at a threshold of 70 steps/minute highlights a higher risk of misclassifying low-intensity steps as moderate-intensity steps compared to 100 steps/minute, suggesting important trade-offs in the selection of the threshold used whilst also highlighting the need for further research to refine this threshold for moderate-intensity stepping in older adults and within other populations associated with accelerated aging, such as those living with long-term conditions.
## Supplementary Information
Additional file 1: Supplementary Table S1. Predicted values across participant characteristics. Supplementary Table S2. Levels of agreement between predicted stepping intensity and actual stepping intensity across 200 observations for a METSstandard definition of moderate-intensity. Supplementary Figure S1. Distribution of data points and regression lines. Supplementary Figure 2. Receiver Operating Characteristic (ROC) curve characteristics.
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|
---
title: Antiviral and ROS scavenging potential of Carica papaya Linn and Psidium guajava
leaves extract against HIV-1 infection
authors:
- Pratiksha Jadaun
- Prachibahen Shah
- R. Harshithkumar
- Madhukar S. Said
- Shubhangi P. Bhoite
- Sowmya Bokuri
- Selvan Ravindran
- Neetu Mishra
- Anupam Mukherjee
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10024014
doi: 10.1186/s12906-023-03916-x
license: CC BY 4.0
---
# Antiviral and ROS scavenging potential of Carica papaya Linn and Psidium guajava leaves extract against HIV-1 infection
## Abstract
Antiretroviral therapy is the only treatment option for HIV-infected patients; however, it has certain drawbacks in terms of developing multiple toxic side effects. Thus, there is a continuous need to explore safe and efficacious anti-retroviral agents. Carica papaya Linn and *Psidium guajava* are known for their various biological activities. In this study, we characterized the bioactive fractions of methanolic leaves extract from both plants using the High-resolution electrospray ionization mass spectrometry (HR-ESI–MS) technique, followed by the investigation of their potential as anti-HIV-1 and antioxidant agents through in vitro mechanistic assays. The anti-HIV-1 activity was examined in TZM-bl cells through luciferase gene assay against two different clades of HIV-1 strains, whereas the intracellular ROS generation was analyzed by Fluorescence-Activated Cell Sorting. Additionally, the mechanisms of action of these phyto-extracts were determined through the Time-of-addition assay. The characterization of *Carica papaya* Linn and *Psidium guajava* leaves extract through HR-ESI–MS fragmentation showed high enrichment of various alkaloids, glycosides, lipids, phenolic compounds, terpenes, and fatty acids like bioactive constituents. Both the phyto-extracts were found to be less toxic and exhibited potent antiviral activity against HIV-1 strains. Furthermore, the phyto-extracts also showed a decreased intracellular ROS in HIV-1 infected cells due to their high antioxidant potential. Overall, our study suggests the anti-HIV-1 potential of *Carica papaya* Linn and *Psidium guajava* leaves extract due to the synergistic action of multiple bioactive constituents.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12906-023-03916-x.
## Background
Human immunodeficiency virus (HIV), a member of the Retroviridae family in the genus of Lentivirus, is the main causing agent for severe immune suppression [1, 2]. The disease severity is responsible for the development of a chronic, deadly condition, called acquired immunodeficiency syndrome or AIDS, which commits lifelong threats to the infected individuals [3].
The employed existing antiretroviral therapies are showing promising results, but not enough for the complete eradication of HIV/AIDS [4]. Although ARTs have reduced the viral load, transmission, and morbidity/mortality ratio to a great extent, their mechanism of action showed deleterious effects related to mtDNA, impaired glucose metabolism, hepatotoxicity, multidrug-resistant strains after prolonged periods of infection and upsurge of the reactive oxygen species (ROS) within the cell [5–10]. Testing ethnomedicines and phytoconstituents for the development of anti-HIV potency like pharmaceuticals to overcome multidrug resistance, while at the same time, lacking in antioxidants is the need of the hour to meet the necessity of generating novel alternative treatments.
Studies have shown that plants are rich in secondary metabolites and phytochemicals like flavonoids, alkaloids, polyphenolics, sulphated polysaccharides, triterpenes, phenolics and coumarins. Moreover, phytoconstituent-based alternative medicines are cheaper, easily accessible, fewer side effects with better tolerance [11, 12]. Natural bioactive compounds that could possess a distinct mode of action are not usually found in synthetic drugs [13]. Various investigation has revealed the medicinal properties of phytotherapeutic plant *Carica papaya* Linn. ( C. papaya) and *Psidium guajava* (P. guajava) as anti-virals, immunomodulatory, anti-inflammatory, antimicrobial, and antioxidant activity [14, 15]. The polyphenol compound of these plants such as tocopherol, ascorbic acid, carotenoids, folic acid and flavonoids are the strong biochemical antioxidant components [16]. Toxicological studies on mice and other animal models also showed no significant adverse effects, mutagenic, or aberrant effects of C. papaya and P. guajava [17–20].
Notably, there is very limited research available on the distinctive constituents of C. papaya and P. guajava that have shown any antiviral effects. Therefore, the present study was designed to examine the chemical compositions, antiviral properties, and antioxidant activities of these two methanolic phyto-extracts. It is known that the interaction between ROS and HIV infection may represent a new approach to both prevention and treatment, however, the effect of the methanolic extract of C. papaya and P. guajava has not been investigated extensively in the anti-HIV-1 research. Therefore, the present study was undertaken to characterize the leaves extract of C. papaya and P. guajava to identify the presence of bioactive components and assess the effectiveness of these phyto-extracts against HIV-1 infection and their antioxidant properties at the cellular level. We conducted cellular assays to examine the effects of these two plants extracts upon the pathogenicity of HIV-1 strains, unveiled the underlying mechanisms, and determined its role as a free radical scavenger during HIV-1 infection. Hence, this study might divulge the role of leaves extract of C. papaya and P. guajava as dual antiviral and cytoprotective anti-oxidant agents that might be helpful in the development of an novel anti-HIV-1 therapy or in conjunction with the present ART regime.
## Plant collection for phyto-extract preparation
The collection of plant material was done after obtaining the necessary permission, purely for research purposes and it complies with international and institutional guidelines and legislation. Briefly, fresh and young leaves of C. papaya and P. guajava were collected from the local region of the city, near Sinhagad Road, Pune, India. The voucher specimens were formally identified and deposited at the Western Regional Centre, Botanical Survey of India (BSI), Pune, India (authentication no. DP01 for C. papaya and NMPG-1 for P. guajava). The leaves were primarily washed under tap water followed by distilled water to remove the dust particles. Further, the clean leaves were shaded, dried and pulverized followed by the methanol extract preparation as described earlier [21]. Methanol is one of the effective solvents resulting in the highest extraction yield, and therefore used for examining various biological activities including anti-viral and anti-oxidant activity of different plant extracts as reported earlier [22–25]. In this study, the 50 g of dried leaves powder of both plants was dissolved in 250 ml of $100\%$ methanol, followed by incubation for 24 h at 150 rpm and 25 °C on a rotary shaker. After incubation, the mixture was allowed to stand for 10 min for the sedimentation and the supernatant was filtered through Whatman filter paper no. 1. The extracted samples were collected and placed on a rotary evaporator at 60 °C for methanol evaporation. The leaves extracts were then collected by protecting them from direct light and kept at 4 °C for further characterization and phytochemical analysis through High-resolution electrospray ionization mass spectrometry (HR-ESI–MS).
## Phytochemical characterization and Identification of bioactive components from C. papaya and P. guajava leaves extract
Qualitative phytochemical tests were performed to check the presence of carbohydrates, flavonoids, saponins, tannins, terpenoids, cardiac glycosides, steroids, quinones and coumarins using standard protocols as described earlier by Shaikh and Patil, 2020 [26]. The high throughput HR-ESI–MS technique was used in this study to identify the chemical constituent and bioactive compounds present in the leaves extract of C. papaya and P. guajava. Briefly, 1 mg extract was dissolved in 1.5 ml LC–MS grade methanol and clarified through a 0.2 μm filter membrane. Further 10μ/L was injected into the HR-MS column. The HR-ESI–MS analysis was carried out on an Agilent 6530 Q-TOF (Agilent, USA) mass spectrometer connected to an HPLC Prime Infinity II 1260 system (800 bar). A dual electrospray ionization (ESI) source was used for the ionization process. For LC-based metabolite separation, a Hypersil GOLD C18 (2.1 × 150 mm, 1.9 μm particle size, Thermo Scientific, USA) column was used at 40 °C with a flow rate of 0.3 ml/min. Silica gel (60 − 100 mesh and 100 − 200 mesh, Hi-media, India) was used for the chromatography column.
## Cell lines and HIV-1 stock
TZM-bl cells (HeLa modified cell line; initially called JC53-bl; clone 13) were procured from the National Institute of Health (NIH)—HIV Reagent Program, and maintained in DMEM (Gibco, USA) containing $10\%$ FBS (Moregate, Australia) and supplemented with HEPES (Gibco, USA), antibiotics (Sigma, USA) at 37 °C in a $5\%$ CO2 humidified chamber.
The primary isolates of HIV-1UG070 (X4, Subtype D) and HIV-1VB028 (R5, Subtype C) were obtained from the virus bank repository maintained at the Division of Virology, ICMR-National AIDS Research Institute, Pune.
## Cytotoxicity assay by MTT
Cytotoxic effect of both the plant extract C. papaya and P. guajava was performed in the TZM-bl cell line following the methods described earlier [27, 28]. Briefly, 1 × 105 adherent TZM-bl cells/well were seeded on 96 well plate and incubated for 24 h supplied with $5\%$ CO2 at 37 °C. The purified phyto-extract dilutions were treated on the cell-seeded plates in dose dependent manner by taking an initial concentration of 6 mg/ml for C. papaya and 8 mg/ml for P. guajava, and incubated for 48 h. After the incubation period, the phyto-extracts were evaluated by adding 20 µl (5 mg/ml) MTT to all wells and further incubated for 3 h, which allows the MTT to get metabolized; the supernatant was replaced with 150 µl dimethyl sulfoxide (DMSO) to dissolve the formazan crystals. After the final incubation of an hour, the O.D. value was recorded at 550 nm and 630 nm using a multimode plate reader. The viability was observed based on a comparison with the absorbance of untreated and treated cells. The mean absorbance (O.D.) of duplicate wells was used to calculate the percentage of cell viability as follows: Percentage of cell viability = (Absorbance of Extract Treated Cells – Absorbance of Blank) / (Absorbance of Control – Absorbance of Blank) × $100\%$. The CC50 was obtained at the concentration where $50\%$ of the cells remain viable in presence of the phyto-extracts from three independent assays.
## Cell associated assay (CA)
Based on the CC50 value, a range of non-cytotoxic concentrations of the C. papaya and P. guajava extracts were used, and the anti-HIV-1 activity was evaluated as described previously [28–31]. Briefly, the TZM-bl cells (1 × 104 cells/well) were first infected with the virus HIV-1VB028 and HIV-1UG070 for 2 h at 37 °C in $5\%$ CO2 incubator followed by the treatment with different dilutions of the extracts, along with the addition and incubation of 25 µg/ml DEAE-dextran for viral internalization. After 48 h post incubation, the luciferase activity was measured using the Britelite™ plus reagent on a luminometer (Perkin Elmer, USA) [31, 32]. Standard nucleoside reverse transcriptase inhibitor drug Azidothymidine or AZT was used at the known concentration of 0.45 µM/ml for both the phyto-extracts, as a positive control.
## Cell free assay (CF)
While in cell free assay (CF), the viral stocks were first treated with the serial dilutions of the C. papaya and P. guajava methanolic extracts and incubated for 1 h, at 37 °C in $5\%$ CO2 atmosphere preceding its addition on the TZM-bl cells (1 × 104 cells/well) [28–30]. At 48 h post-infection, the luciferase activity was measured as described above. Dextran sulphate (DS) was used as a positive control for the cell-free assays at the known concentration of 15 µg/ml for both the phyto-extracts.
The percentage of HIV-1 inhibition and EC50 value for both CA and CF assays were calculated based on the activity of the respective phyto-extracts. The results were compared with the positive controls after carrying out all the experiments in triplicate.
## Time-of-addition assay (TOA)
The Time-of-addition assay or TOA test was carried out as previously described with some modifications [33]. The designed assay, which included the positive and negative controls, was quite similar to that utilized for measuring the inhibitory potencies through anti-HIV-1 assays. In 96-well plates, TZM-bl cells (1 × 104 cells/well) were seeded and after overnight incubation, the cells were infected with the HIV-1VB028 strain at 400 TCID50/ml. The inhibitors were either added to the wells concurrently (0hpi) or at different hours of post-infection as indicated (0.25-24hpi). At 48hpi the luciferase activity was assessed as described previously. The well-known anti-retrovirals Dextran sulphate (DS: Viral adsorption to the host cell inhibitor—15 µg/ml), Azidothymidine (AZT: An Nucleotide Reverse Transcriptase Inhibitors or NRTI—0.9 µM), Raltegravir (RAL: Integrase inhibitor—0.48 µM), Ritonavir (RTV: Protease inhibitor—45 µM), along with the C. papaya (1.25 mg/ml) and P. guajava (0.085 mg/ml) methanolic extract were employed in the experiment.
## HIV-1 protease (PR) inhibition activity assay
The C. papaya and P. guajava extracts were tested for HIV-1 protease inhibitory activity using an HIV-1 PR inhibitor screening Fluorometric assay kit following the manufacturer's instructions (Abcam, Cambridge, UK). Briefly, each sample was incubated with the HIV-1 PR enzyme for 15 min at room temperature. The fluorescent substrate was then added, and the PerkinElmer EnSpire plate reader was used to measure the fluorescence (excitation/emission = $\frac{330}{450}$ nm) in a kinetic mode for 120 min at 37 °C. The kit-supplied Inhibitor Control (IC) Pepstatin (1 mM) and known protease inhibitor RTV (45 µM) were used as the positive controls, whereas, DMSO ($1\%$, v/v) and kit-supplied Enzyme Control (EC) were used as the vehicle and negative controls, respectively, to normalize the background noise.
## Assessment of antioxidant activity
TZM-bl cells were seeded in 60 mm dishes at a density of 1 × 105 cells per plate and placed in an incubator for 24 h at 37 °C with $5\%$ CO2. After incubation, the cells were infected with HIV-1VB028 and were treated with the respective phyto-extracts having concentration determined by their CC50 and EC50 values. At 24hpi, the cells were trypsinized, collected and centrifuged at 2000 rpm for 5 min and incubated with 5 µM DCF-DA green molecular probe at 37 °C incubator. At 30 min post-incubation, the cells were centrifuged again, washed and resuspended in PBS, followed by filtration through nylon mesh, and the acquisition was done using FACS Aria flow cytometer (BD Bioscience, USA). Flow Jo™ software was utilized to analyze the data. A total of 50,000 cells from each sample set were examined during the assay.
## Free radical scavenging assay by DPPH method
DPPH (2,2-diphenyl-1-picryl-hydrazyl-hydrate) free radical method is an antioxidant assay based on electron transfer that produces a violet solution in ethanol [34]. 50 µl of each phyto-extract (C. papaya and P. guajava), based on their respective EC50, was added to a 96-well plate in triplicates. 100 µl of 0.2 mM of freshly prepared methanolic DPPH solution was added to the extracts, mixed, vortexed, and incubated on the 96-well plate at room temperature in a dark environment for 30 min. The absorbance was measured at 517 nm. Ascorbic acid was used as a standard and DPPH and methanol were taken as control. The DPPH scavenging effects were measured using the following formula: DPPH scavenging effect (%) = (A0 – A1/A0) × 100; where, A0 = Absorbance of Control; A1 = Absorbance of Sample.
## Results
In this study, we aimed to characterize the bioactive compounds of C. papaya L and P. guajava methanolic leaves extract, and identified the presence of multiple phytoconstituents through the High-resolution electrospray ionization mass spectrometry analysis. Further, we evaluated the anti-HIV-1 and antioxidant activity of these two methanolic phyto-extracts against the HIV-1 primary isolates for anti-retroviral drug screening.
## Phytochemical screening and structural elucidations of isolated compounds
Qualitative phytochemical screening was done using standard phytochemical tests for different bioactive compounds of methanolic leaves extract of C. papaya and P guajava. In our phytochemical analysis, we found the absence of saponins and tannins in both phyto-extracts, but the presence of reducing carbohydrates, terpenoids, cardiac glycosides, steroids, coumarins and quinones. However, flavonoids are detected only in the papaya leaf extract (Table S1).
Further, the compounds were identified using the chromatogram fragmentation patterns and compared with highly advanced metabolites searched against the METLIN database (https://metlin.scripps.edu), filtered with a score > $80\%$ in the computer library with their retention time for HR-ESI–MS extract of C. papaya and P. guajava, respectively (Figs. 1 and 2). The detailed list of identified compounds for C. papaya and P. guajava leaves extract are recorded in the supporting information (Table S2 and Table S3). The HR-ESI–MS fragmentation shows that both the phyto-extracts are enriched in various steroids, terpenoids, phenolic compounds, and fatty acids. The characterization of C. papaya leaves extract shows $25\%$ of alkaloids (peptides, amino acids), $5\%$ of glycoside, $10\%$ lipids, $20\%$ of phenolic compounds (aromatic phenol, quinone, flavonoids), $20\%$ of terpenes, $15\%$ of aliphatic compounds (fatty acids, alcohol, saturated and unsaturated alkenes) as well as $5\%$ of other bioactive compounds (Table S2). Similarly, P. guajava extract shows $40\%$ of alkaloids (peptide, amino acids), $10\%$ of glycoside, $10\%$ lipids, $10\%$ of phenolic compounds (aromatic phenol, quinone), $10\%$ of terpenes, $15\%$ of aliphatic compounds (fatty acids, alcohol, saturated and unsaturated alkenes) and $5\%$ of other compounds (Table S3). It was observed that both the phyto-extracts have a higher percentage of alkaloids terpenes. Fig. 1Chromatogram fragmentation of High-resolution electrospray ionization mass spectrometry analysis of *Carica papaya* Linn methanolic leaves extractFig. 2Chromatogram fragmentation of High-resolution electrospray ionization mass spectrometry analysis of *Psidium guajava* methanolic leaves extract
## In vitro cytotoxicity of C. papaya L and P. guajava leaves extract on TZM-bl cells
Initially, the phyto-extracts of C. papaya and P. guajava were screened to assess their effects on the cellular viability of TZM-bl cells by MTT quantitative colorimetric assay. The dose-dependent effect of C. papaya (0.375–6.0 mg/mL) and P. guajava (0.125–8.0 mg/mL) on cell viability was represented for the concentrations of the phyto-extracts against the percentage of viable cells (Fig. 3A and B). The concentration that allows the $50\%$ cells viable or the CC50 values for C. papaya and P. guajava were calculated to be 2.07 and 1.84 mg/ml, respectively, from three independent replicates (Fig. 3C).Fig. 3Determination of cytotoxic concentration of phyto-extracts. The effect of different concentrations of (A) *Carica papaya* Linn and (B) *Psidium guajava* extract on TZM-bl cell viability. C The comparative graphical illustration of CC50 concentration of C. papaya and P. guajava
## Anti-HIV-1 activity of C. papaya and P. guajava leaves extract
The TZM-bl cells were used for the screening of anti-HIV-1 activity. Based on the CC50 values, the concentration of 1.5 mg/ml was selected for both the phyto-extracts, as the percentage cell viability at this concentration is $69.68\%$ and $65.17\%$ for C. papaya and P. guajava, respectively. The ability of the C. papaya and P. guajava to inhibit its replication in the cell-associated (CA) and cell free (CF) virus was assessed using two different clades of HIV-1.
## Phyto-extracts mediated inhibition of HIV-1 replication
In the cell associated assay, the half maximal effective concentration EC50 values of the C. papaya extract against HIV-1VB028 (R5, Subtype C) and HIV-1UG070 (X4, subtype D) were 1.03 mg/ml and 1.25 mg/ml, whereas 0.070 mg/ml and 0.085 mg/ml for P. guajava, respectively. It was observed that the C. papaya and P. guajava showed a dose-dependent anti-HIV-1 activity in the TZM-bl cells (Fig. 4 and Figure S1). C. papaya extract exhibited significant inhibition of the replicating CA virus at the minimum concentration of 1–1.5 mg/ml (Fig. 4A and B; Figure S1A and B), whereas, P. guajava showed a consistent inhibition across the different concentrations (0.03125–1.5 mg/ml (Fig. 4C and D; Figure S1C and D).Fig. 4Anti-HIV-1 activity of C. papaya and P. guajava in cell associated study. Dose-dependent inhibition of (A) HIV-1VB028 and (B) HIV-1UG070 replication in presence of C. papaya extract (0.125–1.500 mg/ml). The effect of different concentrations of P. guajava extract (0.03125–1.500 mg/ml) on (C) HIV-1VB028 and (D) HIV-1UG070 replication. Standard drug Azidothymidine (AZT) at (0.45 µM/ml) was used as the positive control of HIV-1 inhibition
## Suppression of HIV-1 transmission through cell-free assays
Apart from the viral replication within infected cells and the cell-to-cell transmission, the HIV-1 can also disseminate between CD4+ T lymphocytes by cell-free diffusion. Therefore, we examined the anti-HIV-1 potency of the C. papaya and P. guajava extracts in the cell-free system (Fig. 5 and Figure S2). The EC50 values of the C. papaya extract against HIV-1VB028 (R5, Subtype C) and HIV-1UG070 (X4, subtype D) were 1.075 mg/ml and 1.176 mg/ml whereas 0.073 mg/ml and 0.054 mg/ml for P. guajava, respectively. Likewise the cell associated assay, we observed significant inhibition of HIV-1 infection at the concentration of 1–1.5 mg/ml for C. papaya (Fig. 5A and B; Figure S2A and B) and 0.0625–1.5 mg/ml for P. guajava (Fig. 5C and D; Figure S2C and D) leaves extract in both X4 and R5 subtypes. Fig. 5Effects of C. papaya and P. guajava on HIV-1 suppression through cell-free assays. Percentage of inhibition observed in dose dependent manner for (A) HIV-1VB028 and (B) HIV-1UG070 in presence of C. papaya extract (0.125-1.500 mg/ml). The effect of different concentrations of P. guajava extract (0.03125–1.500 mg/ml) on (C) HIV-1VB028 and (D) HIV-1UG070 isolates. The results of cell-free assays were compared with Dextran sulphate (DS) at 15 µg/ml as the positive control of HIV-1 inhibition
## Determining the phyto-extracts targets of action against HIV-1
To identify the possible targets of interaction of the C. papaya and P. guajava leaves extract, and to provide the basis for further investigations, the time-of-addition assay was conducted with the phyto-extracts and known antiretrovirals (Fig. 6). The resulting C. papaya extract profile shows the loss-of-inhibition at 16hpi, a profile similar to Ritonavir (RTV), which is a known protease inhibitor (Fig. 6A and B). Whereas, the loss-of-inhibition profile of P. guajava extract exhibited even earlier than it was observed for the viral entry and adsorption to the host cell inhibitor Dextran Sulphate (DS) (Fig. 6A and C). This TOA analysis indicates the inhibitory effect of C. papaya extract through the suppression of HIV-1 protease, while the mode-of-action of P. guajava extract by blocking the viral entry to the cell, overall inhibiting the HIV-1 infection. Fig. 6Mode-of-action of C. papaya and P. guajava through TOA assay. A The target of methanolic extract of C. papaya and P. guajava was compared to known antiretroviral drugs. Final concentrations of drugs and extracts were introduced at various time intervals concurrently and/or after HIV-1VB028 infection as indicated. Comparative analysis of loss-of-inhibition profile of (B) C. papaya and (C) P. guajava with known HIV-1 PR inhibitor (RTV) and HIV-1 entry inhibitor (DS), respectively. The loss-of-inhibition profile was calculated in terms of Relative Luciferase Unit (RLU). The data represented are the mean and standard deviation of at least three independent experimental replicates Furthermore, we confirmed the protease activity of C. papaya extract against HIV-1 PR through the kit based in vitro HIV-1 protease inhibition assay. The result revealed $74.29\%$ inhibition of HIV-1 protease activity at the given concentration of 1.25 mg/ml of C. papaya leaves extract (Fig. 7). The result was compared with known HIV-1 protease inhibitor Ritonavir (10 μM) as a positive control and the assay was validated with the kit provided Inhibitor control, Pepstatin (1 mM) and Enzyme control (EC).Fig. 7Protease Inhibition assay of *Carica papaya* Linn leaves extract. The percentage inhibition of HIV-1 protease enzyme activity in the presence of C. papaya (1.25 mg/ml) compared to the known HIV-1 PR inhibitor RTV (10 μM) and kit provided Inhibitor Control (IC), Pepestatin (1 mM). Enzyme Control (EC) represents the negative control to normalize the background fluorescence
## ROS scavenging activity of phyto-extracts in HIV-1 infected cells
Reactive oxygen species or ROS are short lived and highly reactive molecules, and high doses of ROS activate the cell death signaling pathways, i.e. apoptosis and necroptosis. During the pathological condition, ROS elevation was detected by using fluorescent-based molecular probe 2’,7’ Dichlorodihydroflurescin diacetate (DCFH2-DA). This dye is known to assess the activity of hydroxyl, peroxyl and other mitochondrial ROS within the cells. In the presence of hydrogen peroxide, DCFH2-DA oxidized into fluorescent Dichloroflurescin DCF while emits fluorescence, which was detected by FACS analyzer. The unstained untreated cells was used to nullify the background noise (Fig. 8A). The DCF fluorescence, observed under the normal homeostasis, in control cells was $42.8\%$, whereas, the virus control showed $87.2\%$ (Fig. 8B and C). After 24 h post infection, cells showed increase in fluorescence accumulation as compared to the control cells. However, C. papaya treated cells resulted a noteworthy decrease in intracellular ROS production as evident with the DCF fluorescence value of $58.4\%$ (Fig. 8D), while P. guajava treatment resulted decrease in fluorescence to $67.1\%$ (Fig. 8E). The mean of three independent assays was analyzed statistically to examine the significance of ROS production in the cells treated with C. papaya and P. guajava extracts (Fig. 8F). An unrelated ROS generator (H2O2) and its scavenger (Catalase) were also used as additional controls (Figure S3). Together these results clearly showed that the phyto-extracts treatment eventually decreased the level of intracellular ROS in HIV-1 infected cells suggesting that the presence of C. papaya and P. guajava extracts ameliorated the production of ROS and having significant ROS scavenging potential. Fig. 8ROS Scavenging effects of C. papaya and P. guajava in HIV-1 infected cells. A Unstained untreated cells. B ROS generation in cell control (CC). C ROS generation in virus control (VC). D ROS inhibition in HIV-1 infected cells treated with C. papaya leaves extract. E Inhibition of intracellular ROS production in HIV-1 infected but P. guajava leaves extract treated cells. In each FACS image acquisition, fifty thousand cells were examined. The image group is representative of the three independent replicates. F *Comparative analysis* from the mean of three independent assays for intracellular ROS generation in HIV-1 infected and/or C. papaya and P. guajava leaves extract treated cells. * $p \leq 0.05$ and ** $p \leq 0.01$ The free radical scavenging activity of C. papaya and P. guajava leaves extract was further elucidated by DPPH assay, and was found to be $12.5\%$ and $10.5\%$ respectively (Table 1). Overall, C. papaya was found to have higher antioxidant capacity as compared to P. guajava extracts. Table 1Spectrophotometrically recorded DPPH scavenging activity of *Carica papaya* L and *Psidium guajava* leaf extractsPlant ExtractsAbsorbance (517 nm)% DPPH scavenging activityCarica papaya Linn0.322 ± 0.040 a$12.5\%$Psidium guajava0.33 ± 0.078 a$10.3\%$a ± SD of 3 replicates
## Discussion
In this study, the biological activities of methanolic leaves extract from *Carica papaya* Linn and *Psidium guajava* were evaluated. The phytochemical profile of these two extracts were characterized by high throughput HR-ESI–MS analysis and revealed the presence of different bioactive constituents (Figs. 1 and 2; Table S2 and S3). The detailed in vitro studies indicated that both the leaves extract are less cytotoxic in nature (Fig. 3), with pronounced anti-HIV-1 activity (Figs. 4 and 5); while the TOA assay revealed the targets of action against HIV-1 (Fig. 6). In vitro enzymatic validation also confirmed the inhibitory role of C. papaya extract against HIV-1 protease (Fig. 7). Additionally, the ROS scavenging activity of these phyto-extracts unveiled their antioxidant potential during HIV-1 infection (Fig. 8 and Table 1). Overall, the results indicate synergistic action of bioconstituents of C. papaya and P. guajava leaves extract lead to antiviral and antioxidant activity in HIV-1 infected cells.
In absence of non-toxic antiretroviral drug therapies, lack of vaccine, diversity in viral strains, and emergence of resistant viruses often led the researchers for continuous search of alternative antivirals and new lead molecules to prevent the deleterious effect of HIV-1 infection. Investigation on bioactive molecules from natural plant sources has been one of the best strategies for the treatment of various infectious diseases, including HIV-1. Natural products are therefore regaining appeal in drug development as they circumvent the limitations of synthetic libraries, such as their lack of chemical variety [35]. Additionally, natural products have historically shown to be excellent starting points for the development of pharmaceuticals, with $34\%$ of medications authorized by the FDA between 1981 and 2010 were from natural sources [13]. Numerous active ingredients of natural products and their derivatives, including alkaloids, quinones, flavonoids, terpenoids, glycans, organic acids, and others, have antiviral action [36]. According to a previous report, of all small-molecule drugs created in the last 28 years, $63.1\%$ were natural product-based therapies [13]. This number indicates the enormous potential for new medicine discovery offered by natural product and their derivatives.
C. papaya and P. guajava represent an important source of phenolic compounds, some of which have been shown to have inhibitory effects against a number of viral infections [37, 38]. In particular, terpene compositions were found to be effective against SARS-CoV-2 and alkaloids isolated in I. indigotica roots were reported to reduce the influenza infection as well as the viral neuraminidase activities in vitro [39–41]. Furthermore, alkaloids, phenolic compounds, and terpenes are known bioactive components that have been shown to have antioxidant properties and to have an influence on oxidative stress and related signalling pathways [42–45].
In vitro viability testing has become an essential step in contemporary drug discovery, as it characterizes a compound's hazardous potential and gives proof of its safety index [46, 47]. Hence, crude methanolic extracts of the fruit plant *Carica papaya* Linn and *Psidium guajava* were tested for cytotoxicity in the TZM-bl cell line using MTT-based cell viability assay. The crude extracts were found to be less cytotoxic to the cells, as exhibited by the CC50 values, which were recorded as 2.07 and 1.84 mg/ml respectively for C. papaya and P. guajava in this study (Fig. 3 and Table 2), in accordance with the previous reports on other methanolic plant extracts those are already known to be low cytotoxic in nature [48]. Effects of C. papaya and P. guajava have already been documented with no detectable side effects that could be considered harmful, mutagenic, or otherwise aberrant [17–20].Table 2Summary of CC50, EC50 and SI of *Carica papaya* L and *Psidium guajava* leaf extractsExtract(s)Cell Cytotoxicity (aCC50, mg/ml)Anti-HIV-1 Activity Cell—Associated AssayAnti-HIV-1 Activity Cell—Free AssayHIV-1VB028HIV-1UG070HIV-1VB028HIV-1UG070bEC50cS.IEC50S.IEC50S.IEC50S.IC. papaya L2.071.032.0091.251.661.0751.9261.1761.760P. guajava1.840.0726.280.08521.650.07325.210.05434.07aCC50: The cytotoxic concentration of the extracts that caused the reduction of viable cells by $50\%$bEC50: The effective concentration of the extracts that resulted in $50\%$ inhibition in HIV-1 infectioncS.I.: Selective *Index is* the ratio of cell cytotoxicity to its biological activity i.e. CC50/EC50All data presented are averages of three independent experiments Many studies have shown the potential of phyto-extracts against the HIV/AIDS. In a recent investigation it was observed that the methanolic extract of *Curcuma aeruginosa* Roxb. plant suppressed the HIV-1 PR [49]. Similar to this, another group of researchers reported the inhibition of HIV-1 encoded viral proteins by the bioactive from of Alnus firma's methanol extract [50]. Literature has revealed the medicinal properties of phytotherapic plants like *Carica papaya* Linn and *Psidium guajava* for their antiviral and antioxidant activity [15]. In this brief study, we focused only on the anti-HIV-1 property and the antioxidant potential of C. papaya and P. guajava phyto-extract. The in vitro screening assays showed that both the extracts significantly inhibited the cell associated viral replication and cell free transmission of HIV-1 using two clinical isolates HIV-1VB028 and HIV-1UG070 from two different subtype of the virus (Figs. 4 and 5; Table 2). Additionally, Selectivity Index (SI) revealed that both the extracts mentioned above had variable activities. The C. papaya extract demonstrated anti-HIV-1 activity against two different HIV-1 strains with SI values of 2.0 and 1.66 in cell associated assays, while cell free assays revealed SI values of 1.92 and 1.76 respectively. It's interesting to note that the SI index of P. guajava revealed as 26.28 and 21.65 in cell associated assays and 25.21 to 34.07 in cells free assays (Table 2). These results suggest that the P. guajava extract might act as more effective anti-HIV-1 agent over C. papaya extract. Further we also carried out time-of-addition (TOA) experiment (Fig. 6). This TOA method determines how long a compound may be added to a cell culture without losing its antiviral properties. An antiviral compound's relative location in the time scale can be used to determine the target comparing to a reference drug. If the unknown drug's profile resembles with the existing known anti-HIV drug, it is highly likely that the unknown drug is targeting through the same route, or at the very least one that is active at the same time [51]. P. guajava inhibition profile resembles dextran sulphate, which is an known inhibitor of viral entry and/or adsorption to the host cells. Earlier, *Cistus incanus* extract was also demonstrated to inhibit a very early step in the HIV-1 replication cycle comparable to a fusion inhibitor [33, 52]. In our experimental set up, P. guajava showed pattern similar to Dextran Sulphate, while C. papaya showed loss-of-inhibition profile similar to the HIV-1 protease inhibitor Ritonavir. Studies have revealed that any one molecule might have dual target, i.e., targeting two or more distinct phases of viral lifecycle by one such inhibitor, however the last target that can be blocked by the inhibitor during the viral replication will always be revealed by this experiment [51]. As in this study, the resulting P. guajava profile shows loss-of-inhibition at time points as observed for the entry and/or adsorption inhibitor DS, whereas, the loss of inhibition pattern and enzymatic validation confirmed the inhibitory role of C. papaya extract against HIV-1 protease (Fig. 7).
The loss of immunological cells, especially CD4+ TH cells, is the defining feature of HIV-1 infection. According to the earlier study, the HIV-1 envelope glycoproteins are the prime cause for declined CD4+ cells in the infected patients, which in turn trigger the accumulation of oxidative stress within the cells and leads towards the cell death [53]. In this study, the DCFH2-DA method was used to determine the HIV-1 induced ROS formation in the living cells, where the intensity of the fluorescence was associated with the measurement of generated ROS within the cells [54]. The phyto-extracts treatment of C. papaya and P. guajava in the HIV-1 infected cells reduced the virus mediated ROS production significantly (Fig. 8). The reduction of ROS accumulation within the cells signifies the therapeutic effectiveness of these extracts as potential agents for the treatment of HIV-1 infection.
Recent studies have highlighted the manipulation of oxidative stress and antioxidant-dependent pathways to facilitate the novel strategies for HIV cure through preclinical in vitro studies and clinical trials [31, 55–58]. There are ample evidences on improved status of HIV-1 infected patients with the treatment of antioxidants that enhances the glutathione levels while lowering the lipid peroxidation [58]. The redox alterations is one of the crucial factors of HIV-1 pathogenicity, such as neurotoxicity and dementia, exhaustion of CD4+/CD8+ T-cells, predisposition to lung infections, and certain side effects of the antiretroviral therapy [56]. Thus, anti-HIV-1 activity of any compound with antioxidant effects may offer a new strategy for prevention and treatment. Hence, maintaining the antioxidant level is an important parameter of HIV-1 patient management. According to the literature and the high throughput characterization carried out during this study revealed that both the plant extracts are the enriched source of antioxidants (Table S2 and Table S3), and nutraceutical value with multiple health benefits [14, 15, 59, 60]. Although the involvement of the entire phyto-complex cannot be ruled out and perhaps the major limitation of this present study, our data indicates that the prime bioactive components, such as phenolic compounds, alkaloids, and terpenes, might be the key regulator of the antiviral effects of the C. papaya and P. guajava extracts. Together, these substances have the potential to affect both the virion life cycle and the host cells’ defense mechanism, primarily by reversing the redox imbalance, which is necessary for the viral infection to establish. However, the role of individual phytoconstituents on HIV-1 inhibition remains to be elucidated, in-depth mechanistic study would be the isolation of these phytoconstituents to unveil the plausible modus operandi. Overall, based on this study, it can be stated that the *Carica papaya* Linn and *Psidium guajava* plant extracts have anti-viral activity against HIV-1 with the anti-oxidant potential, hence need to be explored further.
## Conclusions
This study has characterized the presence of several bioconstitutents of enriched antioxidant properties in the leaves extract of the fruit plant *Carica papaya* Linn and Psidium guajava, and demonstrated that C. papaya and P. guajava extracts exhibit a dose-dependent inhibition against both the primary isolates HIV-1UG070 and HIV-1VB028 in cell associated, as well as in cell free assays. Furthermore, these extracts exhibited the radical scavenging activity against the HIV-1 induced ROS production within the cells, which further extenuates the viral replication. Thus, the future prospective work of isolation of the identified bioactive compounds and investigating their impact on the activity of viral encoded proteins those are crucial to the HIV-1 life cycle will be explored for additional antiviral strategies.
## Supplementary Information
Additional file 1: Table S1. Phytochemical constituents of methanolic leaves extract of *Carica papaya* and Psidium guajava. Table S2. Identified Compound from *Carica papaya* extract using HR-ESI-MS.*Carica papaya* extract shows $25\%$ of alkaloids (Peptide, Amino acids), $5\%$ of Glycoside, $10\%$ lipids, $20\%$ of Phenolic compounds (Aromatic Phenol, Quinone, Flavonoids), and $20\%$ of Terpenes, $15\%$ of Aliphatic Compounds (Fatty acids, alcohol and saturated, Unsaturated Alkenes) as well as $5\%$ of other. We observed that both extracts have a higher percentage of alkaloids, Terpenes. Table S3. Identified Compound from *Psidium guajava* extract using HR-ESI-MS. The HR-ESI-MS data of *Psidium guajava* extract shows $40\%$ of alkaloids (Peptide, Amino acids), $10\%$ of Glycoside, $10\%$ lipids, $10\%$ of Phenolic compound (Aromatic Phenol, Quinone), and $10\%$ of Terpenes, $15\%$ of aliphatic Compounds (Fatty acids, alcohol and saturated, Unsaturated Alkenes) as well as $5\%$ of other compounds were observed. Figure S1. Anti-HIV-1 activity of C. papaya and P. guajava in cell associated study. Dose-dependent inhibition of (A) HIV-1VB028 and (B) HIV-1UG070 replication in presence of C. papaya extract (0.125-1.500mg/ml). The effect of different concentrations of P. guajava extract (0.03125-1.500mg/ml) on (C) HIV-1VB028 and (D) HIV-1UG070 replication. Figure S2. Effects of C. papaya and P. guajava on HIV-1 suppression through cell-free assays. Percentage of inhibition observed in dose dependent manner for (A) HIV-1VB028 and (B) HIV-1UG070 in presence of C. papaya extract (0.125-1.500mg/ml). The effect of different concentrations of P. guajava extract (0.03125-1.500mg/ml) on (C) HIV-1VB028 and (D) HIV-1UG070 isolates. Figure S3. ( A) ROS generator 15μM H2O2 (H2O2 generator) for 6 h served as positive control. ( B) 15μM H2O2 (H2O2 generator) and 250U Catalase served as scavenger/inhibitor of ROS generation in the experiment.
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|
---
title: A clinical trial on anti-diabetic efficacy of submerged culture medium of Ceriporia
lacerata mycelium
authors:
- Bo-Hyung Kim
- Sung-Vin Yim
- Seong Deok Hwang
- Yoon Soo Kim
- Jeong-Hwan Kim
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10024018
doi: 10.1186/s12906-023-03895-z
license: CC BY 4.0
---
# A clinical trial on anti-diabetic efficacy of submerged culture medium of Ceriporia lacerata mycelium
## Abstract
### Background
Increased glucose level and insulin resistance are major factors in Type 2 diabetes mellitus (T2M), which is chronic and debilitating disease worldwide. Submerged culture medium of *Ceriporia lacerata* mycelium (CLM) is known to have glucose lowering effects and improving insulin resistance in a mouse model in our previous studies. The main purpose of this clinical trial was to evaluate the functional efficacy and safety of CLM in enrolled participants with impaired fasting blood sugar or mild T2D for 12 weeks.
### Methods
A total of 72 participants with impaired fasting blood sugar or mild T2D were participated in a randomized, double-blind, placebo-controlled clinical trial. All participants were randomly assigned into the CLM group or placebo group. Fasting blood glucose (FBG), HbA1c, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide were used to assess the anti-diabetic efficacy of CLM for 12 weeks.
### Results
In this study, the effectiveness of CLM on lowering the anti-diabetic indicators (C-peptide levels, insulin, and FBG) was confirmed. CLM significantly elicited anti-diabetic effects after 12 weeks of ingestion without showing any side effects in both groups of participants. After the CLM treatment, FBG levels were effectively dropped by $63.9\%$ (ITT), while HOMA-IR level in the CLM group with FBG > 110 mg/dL showed a marked decrease by $34\%$ up to 12 weeks. Remarkably, the effect of improving insulin resistance was significantly increased in the subgroup of participants with insulin resistance, exhibiting effective reduction at 6 weeks ($42.5\%$) and 12 weeks ($61\%$), without observing a recurrence or hypoglycemia. HbA1c levels were also decreased by $50\%$ in the participants with reduced indicators (FBG, insulin, C-peptide, HOMA-IR, and HOMA-IR). Additionally, it is noteworthy that the levels of insulin and C-peptide were significantly reduced despite the CLM group with FBG > 110 mg/dL. No significant differences were detected in the other parameters (lipids, blood tests, and blood pressure) after 12 weeks.
### Conclusion
The submerged culture medium of CLM showed clinical efficacy in the improvement of FBG, insulin, C-peptide, HbAc1, and HOMA-index. The microbiome-based medium could benefit patients with T2D, FBG disorders, or pre-diabetes, which could guide a new therapeutic pathway in surging the global diabetes epidemic.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12906-023-03895-z.
## Background
As the average life expectancy has been extended due to the increase in medical benefits, multifactorial chronic metabolic disorder such as diabetes is significantly growing nowadays. There are currently about 0.5 billion people with diabetes worldwide, reaching ~ 0.7 billion by 2045, confirming the most predominated form of the leading causes of death and disabilities worldwide [1, 2]. Diabetes is characterized by insulin resistance due to chronic hyperglycemia, which is still a serious and worsening problem related to the development of diabetic complications, despite many pharmaceutical developments and global intensive efforts to control blood sugar [3]. Moreover, in the pandemic era of COVID-19, metabolic syndrome such as diabetes is rapidly increasing more than usual because of the abnormal daily living environment where exercise and eating habits are limited by non-face-to-face or quarantine measures. Recently, the long-term pandemic has increased the chances of developing diabetes: it has been reported that people with mild COVID-19 infections and no previous risk factors for diabetes also have an increased chance of developing the chronic disease [4]: People with a high body mass index (BMI), a measure of obesity, and significant risk factors for T2D more than doubled their risk of developing diabetes after infection with COVID-19.
Meanwhile, there are no clear symptoms of prediabetes, which means one may have it and not be aware until they receive testing. Over 1 billion people worldwide live with prediabetes, which is defined by increased fasting blood glucose (FBG), impaired glucose tolerance (IGT), and/or higher HbA1c. Prediabetes has an expected prevalence as that of type 2 diabetes (T2D), both of which have considered as global epidemic of the twenty-first century [5, 6]. According to the American Diabetes Association, an estimated 10 ~ $23\%$ of people with prediabetes will move on to develop T2D within 5 years [5]. Therefore, anyone at risk of prediabetes should undergo regular checkups, including high BMI and waist circumference, age over 45, or other cardiovascular disease [5].
There is currently no fundamental cure for diabetes: although many commercial hypoglycemic drugs have been developed to control high blood sugar, they are costly with potential serious complications such as hypoglycemia, insulin resistance, and severe cardiovascular and cancer-related risks [7–10]. As a direction for finding active antidiabetic agents in natural sources, the potential of treating diabetes with edible and medicinal mushrooms combining traditional medicine and various research studies has been well demonstrated [11–13].
Ceriporia lacerata (Phanerochaetaceae, Basidiomycota) is a type of white rot fungus that decomposes cellulose and lignin of trees in the natural state to play a key role in biological reduction [14, 15] (Fig. 1a, i). The cultured C. lacerate is composed of microscopic polypores, so it looks like a white moss (Fig. 1a, ii). During the liquid culture process, various secondary metabolites, e.g., exo-metabolites, along with its mycelial growth, are generated depending on the environmental conditions (Fig. 1a, iii). Recently, FugenCelltech Co. Ltd has successfully developed an exclusive green mass production method of tableted culture medium of C. lacerata mycelium (CLM) as an anti-diabetic nutraceutical product (Cepona™) (Fig. 1b, (i)-(v)).Fig. 1Culture procedure of submerged culture media of C. lacerata mycelium (a). The green manufacture processes of CLM tablets (b) including solid culture (i), pre-culture (ii), main culture (iii), lyophilization (iv), and tableting (v). A schematic anti-diabetic effect of CLM at the cellular level (c): normal blood glucose state (i), high blood glucose state (ii), glucose control by CLM (iii) The submerged culture medium of CLM has been studied to control high blood glucose levels [16, 17], insulin secretion via cell protective effect [18], antihyperglycemic efficacy by lowering insulin resistance [19], and insulin signal transduction via activation of AMPK and GLUT4 [19, 20]. Therefore, it is considered that CLM has an exceptional potential as a novel functional ingredient that can help control blood sugar for people with pre-diabetes, by lowering impaired fasting blood sugar/insulin resistance, as proposed in Fig. 1c (i-iii).
In the present study, we have examined whether CLM tablet has anti-diabetic effects in Korean participants who have glucose intolerance or mild T2D. A randomized, double-blind, and placebo-controlled clinical trial was thoroughly conducted.
## Preparation and characterization of test sample and placebo
The microbial organism used in this work was cultured after inoculation of C. lacerata, originally owned by FugenCellTech, Co. Ltd., Korea, in potato dextrose agar (Difco. Co., Maryland, USA) medium at 25 °C for 9 days. As a pre-culture process, 4 g/L of starch, 20 g/L of glucose, and 600 mL of purified water were mixed in the CLM liquid medium and agitated for 10 days at 300 rpm, 25 °C, pH 5, according to the prior literature [16]. After the pre-culture was completed, the mycelium culture medium was transferred to a liquid medium prepared by mixing 12.5 g/L of sucrose, 2.5 g/L of skim soybean meal, 2.5 g/L of starch, 0.125 g/L of antifoam agent, and 400 L of purified water. The culture was further incubated continuously for 9 days by injecting air at a rotational speed of 100 rpm. The submerged culture medium of CLM was freeze-dried and pulverized, and then used according to the capacity of each experimental group based on the dry weight. The raw material was manufactured without using any toxic or unauthorized chemicals and solvents at a highly controlled GMP-certified plant (acquired permission for functional nutraceutical from the Korean Ministry of Food and Drug Safety on Dec. 2019). CLM tablets were prepared using a freeze-dried culture medium of CLM. As the excipient, it is composed of crystalline cellulose, hydroxypropyl methylcellulose, silicon dioxide, and magnesium stearate. The formulation was stored at room temperature, which is not only easy to supply, but also secured its biological safety through GLP-toxicity tests such as single-dose toxicity tests (rodents), repeated-dose toxicity tests (rodents), and genotoxicity tests (return mutations, micronucleus tests, and chromosomal abnormalities tests). The placebo tablet was composed of lactose, crystalline cellulose, hydroxypropyl methylcellulose, magnesium stearate, and caramel pigments. The formulation was stored at room temperature.
## Clinical trial design and process
This study is a single-center, randomized, double-blind, placebo-controlled clinical trial. All processes were conducted at a single center (Kyung Hee University Hospital, Seoul, South Korea) from Feb. 15. 2015 (1st patients screened) to May. 5. 2015 (last patients completed). A total of 113 volunteers with impaired fasting blood sugar and mild T2D participated in the screening procedure, of which 72 participants met the inclusion and exclusion criteria and were enrolled in this study. To calculate the effective number of participants, the following assumptions were employed. The statistical hypothesis test of the evaluation variable is a one-sided test. Level of significance is $5\%$Type 2 error (β) is set to 0.2 and the power of the test is maintained at $80\%$.The ratio of the number of test samples between the test group and the control group, e.g., (Jesus of the test group) = (Jesus of the control group), 1:1.After ingestion of test food, functional evaluation variables of the test group and the control group were compared.
To estimate the number of participants, the criteria for evaluating the blood glucose lowering function was used as in the human application test [21], as below: *The hypothesis* is as follows:µt = µc (After the test, the measured value of the endpoint of the test group is the same as that of the control group)H1: µt < µc (After the test, the measured value of the endpoint of the test group is less than that of the control group).
Assuming the above [1]-[5], the number of test samples required for the clinical trial is as follows (one-sided test): Formula for calculating the number of participants is expressed by the difference in the resulting variable as below,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Number}\;\mathrm{of}\;\mathrm{participants}\;(\mathrm N)=\frac{\left(\mathrm{Z}\alpha+\mathrm{Z}\beta\right)^2\ast\sigma^2\ast2}{\mathrm{E}^{2}}$$\end{document}Numberofparticipants(N)=Zα+Zβ2*σ2*2E2 (σ: standard deviation of the post-treatment change value in the previous trial, E: judged to be clinically significant, α = 0.05 (Zα = 1.645), β = 0.2 (Zβ = 0.840), σ = 5.4).
When the number of participants was calculated as follows by the above equation, the minimum number of participants is about 23 per group.
The effective number of samples is 23 x ($\frac{120}{100}$) x ($\frac{130}{100}$) = 35.88, when calculated by considering the $20\%$ dropout rate and $30\%$ compliance rate in the obtained sample number. Thus, the number of participants to be enrolled in each group is 36, and the total number of participants is 72.
The 72 selected participants were randomly assigned to the test group or control group at a ratio of 1:1 based on the following steps: [1] Patients selected as study participants were assigned to each group using a randomization method based on probability. [ 2] Random allocation table using a function that generates a random number of the SPSS program. [ 3] One of the two groups was assigned from the lowest number in the order of date of visit (Day 0). The random number (RN) was assigned according to the randomization table in the order of participation in the group to be assigned to the test group or the control group. [ 4] The administering pharmacist applied the medication to be administered to the participants according to the randomization plan, which was provided according to the code given to the participants. [ 5] The randomization of blocks using random code program of statistical program was designed to have the same number of participants.
All physicians and outcome assessors were blinded to randomization allocation. During the experiment, neither the investigator nor the participants were aware of each participant was assigned to, and they did not remove their blindfolds until a medical emergency occurred to protect the privacy of the assigned group. To ensure this, research activities, including screening, enrollment, informed consent, baseline data collection, randomization, and medication administration, were conducted solely by research personnel.
All participants were visited at the initial screening (visit 1), baseline (visit 2), 6 weeks (visit 3), 12 weeks (visit 4), and follow up period. After randomly assigning the participants to the CLM group ($$n = 36$$) or placebo group ($$n = 36$$) at a ratio of 1:1, the participants of each group were administered 2 tablets of CLM (550 mg/tablet) or 2 tablets of placebo (550 mg/tablet) before meal 3 times a day for 12 weeks. The medication compliance of the test product was measured by counting the dose or number of remaining medications at each visit. If the medication compliance was less than $80\%$ for two consecutive times, the participants were excluded from the PP analysis.
## Clinical trial participants
Participants corresponding to all the following criteria were recruited. After the investigator fully explained the purpose and method of this test, possible risks, and rewards to the participants who wished to participate, a person who agreed to participate in this test in writing was selected as the final participant. The detail test schedule was described in Table S3. There was no change after the start of the trial designating the outcome as primary or secondary.
## Inclusion criteria
Participants with 20–75 years of age and non-lactating women of no childbearing potential, excluding illiterate person. Participants with 100–140 mg/dL of FBG, who do not take diabetic drugs. Participants Participant with less than $7.0\%$ of HbA1c. Participants with less than 110 mg/dL of FBG even if the HbA1c is $6.5\%$-$7.0\%$.Participants who voluntarily decided to participate in the clinical trial after fully understanding the detailed explanation of the trial and agreed in writing to abide by these precautions.
## Exclusion criteria
Participants who experienced adverse reactions such as allergies when taking medicines, health functional foods, etc. Participants with hypersensitivity to mushrooms or a history of the same reaction. Participants with gastrointestinal diseases that may affect the absorption of the test product for human application (e. g., Crohn's disease) or a person with a history of gastrointestinal surgery (except for simple appendectomy or hernia surgery).Participants who show the following results in a diagnostic medical examination. AST, ALT > 2 times the upper limit of the normal range. Other significant diagnostic medical examination findings. Those who have the following clinically significant diseases; diabetes patients taking hypoglycemic drugs or insulin, patients with uncontrolled hypertension (over $\frac{140}{90}$ mmHg), patients with blood LDL-cholesterol over 160 mg/dL, patients with thyroid dysfunction, depression, schizophrenia, alcoholism, drug addiction, heart failure, angina pectoris, cardiovascular disease, or acute and chronic liver disease (chronic hepatitis B, chronic hepatitis C, various cirrhosis, liver cancer, etc.).Participants who have taken anti-obesity, anti-depressants, contraceptives, oral steroids, or female hormones. Participants who have taken merchant hormones, or who have taken drugs that affect the absorption, metabolism, and excretion of the test food, or drugs that may affect blood sugar reduction. Pregnant women and lactating women. Those who participated in other clinical trial within 1 month before the first intake date. Participants who cannot follow the requirements of clinical trial by investigator. Participants who proved to be inappropriate by other doctors.
## Evaluation of efficacy and safety
For evaluation of anti-diabetic activity of CLM, laboratory test for FBG, HbA1c, insulin, and C-peptide were performed at each visit. Oral glucose tolerance test (OGTT) (2-h postprandial glucose test) was performed at the baseline (visits 2) and after 12 weeks (visit 4).
HOMA-IR index is the most used method for estimating insulin resistance and was calculated as follows. The product of basal glucose (mg/dL) and fasting insulin (ulU/mL) divided by 22.5. HOMA-IR by C-peptide was analyzed by replacing insulin with C-peptide in HOMA-IR formula. For safety evaluation, vital sign (systolic and diastolic blood pressure, pulse rate), electrocardiogram, laboratory test (complete blood cell count, chemistry laboratory test, urinalysis) and adverse events were thoroughly checked for participants.
## Statistical analysis
All data were statistically processed using SPSS Statistics (ver. 21.0) for Windows. Data processing for efficacy was based on an ITT analysis of participants taking the test product for which at least one primary endpoint was measured. In addition, PP analysis, which analyzes data obtained from participants who completed the study according to the human application test plan, was used as an auxiliary data. To evaluate the efficacy of the dropout participants, the analysis was performed by applying the LOCF (Last Observation Carried Forward) method, which took the final record evaluated after administration of the test product or the control product. In all tests, a statistical treatment result of $p \leq 0.05$ was considered significant. Less than three decimal places were not displayed.
## Primary efficacy evaluation method
The change in FBG at the end point (6 weeks, 12 weeks (LOCF)) after administration of the test group and the control group compared to the baseline value (visit 2) was analyzed by repeated ANOVA measurement. Student's t-test was used to analyze the amount of change after 12 weeks compared to the baseline. In addition, ANCOVA was used to compare the mean values of study variables between the groups, after correcting the baseline value by adjusting differences in total caloric intake from differences in baseline values, at the end of the study. A p-value < 0.05 was considered statistically significant.
## Secondary efficacy evaluation method
The change in the parameters (postprandial blood glucose, glycated hemoglobin (HbAlc), insulin, C-peptide, lipids) at the end of dosing compared to the baseline value was analyzed using student's t-test, and the change amount for 12 weeks in each group was analyzed using the paired t-test. In addition, ANCOVA was used to compare the mean values of study variables between the groups, after correcting the baseline value by adjusting differences in total caloric intake from differences in baseline values, at the end of the study, at the end of the study. A p-value < 0.05 was considered statistically significant.
Furthermore, a stratified analysis was performed based on fasting hyperglycemic participants with a FBG > 110 mg/dL on the basis that the median FBG at the baseline was 110 mg/dL. The median HOMA-IR at baseline was 1.66, which was clinically consistent with the value determined by the Japanese Diabetes Association for normal insulin resistance [22], thus sub-group analysis was performed based on HOMA-IR > 1.66 as well as HOMA-IR < 1.66. Descriptive statistics on the changes in glucose, insulin, C-peptide, HOMA-IR, and HOMA IR by C-peptide after ingestion of samples at 12 weeks compared to the baseline were achieved for each group, and the degree of change before and after the sample intake within the group was measured by paired t-test. Intergroup comparisons of changes at each time point were also presented to evaluate statistically significant difference by performing two-sample t-test or Wilcoxon rank-sum test depending on whether normality was satisfied. Analysis of HbA1c was performed by Chi-square test.
## Safety endpoint and analysis method
For safety analysis, chi-square test was performed by identifying the number of participants with adverse reactions by group based on laboratory test items. For 12 weeks, we analyzed whether there were significant differences between the two groups in adverse reactions, laboratory test results, and vital signs between the test group and the control group.
## Patient characteristics
Between Nov. 2013 and Jan. 2015, a total of 113 patients were screened, of which 72 were enrolled and randomized, 36 to CLM and 36 to placebo intake (Fig.2). Of the 72 participants, nine (7 in the CLM group and 2 in the placebo group) were dropped out due to failure to follow-up (tracking failure), and thus a total of 63 (29 in the test group and 34 in the placebo group) participants completed this clinical trial. Fig. 2The CONSORT Diagram All participants' baseline and demographic characteristics (Table S1), progress of clinical trial protocol changes (Table S2), and the evaluation timeline (Table S3) were summarized in the Supplementary Information. Total 63 participants completed the measurements for 12 weeks without losses and exclusions. There were no significant differences between CLM and placebo group in the baseline characteristics, including age, sex, weight, height, vital sign, smoking, drinking, drugs, meals, and exercise.
## Primary efficacy analysis of CLM on fasting blood glucose (FBG)
The level of FBG as a primary endpoint was significantly decreased in CLM group ($$n = 36$$) after 6 weeks and 12 weeks, while that of placebo group ($$n = 36$$) showed no differences (Table 1).Table 1Analysis of FBG at the baseline, 6 weeks, and 12 weeksEvaluation variableGroupsBaseline6 weeks12 weeksp valueGlucose(ITT)Placebo110.4 ± 8.9111.9 ± 14.6110.7 ± 15.50.504CLM113.6 ± 9.9106.3 ± 12.8105.9 ± 14.0Glucose(PP)Placebo110.7 ± 9.1111.8 ± 14.8110.8 ± 16.00.492CLM113.4 ± 10.3106.8 ± 13.1107.2 ± 15.1p values were calculated by repeated measure of ANOVA
## Effect of CLM on fasting blood glucose (FBG)
On the ITT analysis, the level of FBG as a primary endpoint was significantly decreased in CLM group after 12 weeks (7.7 ± 4.1 mg/dL, $$p \leq 0.034$$) (Table 2).Table 2Analysis of FBG from baseline to 12 weeks (ITT)Evaluation variablePlaceboCLMp valueap valuebMean ± SDGlucoseBaseline110.4 ± 8.9113.6 ± 9.90.149after 12 weeks110.7 ± 15.5105.9 ± 14.00.177Differencec0.3 ± 6.6-7.7 ± 4.10.028*p valued0.8830.002*0.034*aCompared between groups: p-value by independent t-testbCompared between groups: p-value by ANCOVA (adjustment with baseline and calorie)cDifference between the values of baseline and after 12 weeksdCompared within groups: p-value by paired t-test*$p \leq 0.05$ On the PP analysis, the level of FBG as a primary endpoint was significantly decreased in CLM group after 12 weeks (6.2 ± 4.8 mg/dL, $$p \leq 0.045$$) (Table 3).Table 3Analysis of FBG from baseline to 12 weeks (PP)Evaluation variablePlaceboCLMp valueap valuebMean ± SDGlucoseBaseline110.7 ± 9.1113.4 ± 10.30.268after 12 weeks110.8 ± 16.0107.2 ± 15.10.357Differencec0.1 ± 6.9-6.2 ± 4.80.089p valued0.9470.024*0.045*aCompared between groups: p-value by independent t-testbCompared between groups: p-value by ANCOVA (adjustment with baseline and calorie)cDifference between the values of baseline and after 12 weeksdCompared within groups: p-value by paired t-test*$p \leq 0.05$
## Effect of CLM on biomarkers (blood glucose, HbA1c, insulin, and C-peptide) 2 h after meal (OGTT) for 12 weeks
Two hours after intake of CLM, blood glucose, HbA1c, insulin, and C-peptide were measured (Table 4 (ITT) and Table 5 (PP)). Compared with the placebo group, the level of insulin was decreased in CLM group from 7.3 ± 5.5 to 5.0 ± 4.9 uIU/mL in ITT analysis ($$p \leq 0.062$$); from 7.0 ± 5.5 to 5.0 ± 4.9 uIU/mL in PP analysis ($$p \leq 0.226$$), although the change in insulin did not show statistical significance. Particularly, the level of C-peptide was noticeably reduced in CLM group ($$p \leq 0.015$$ (ITT), $$p \leq 0.025$$ (PP)).Table 4Analysis of OGTT from baseline to 12 weeks (ITT)PlaceboCLMp valueap valuebMean ± SDBlood glucoseBaseline148.0 ± 47.3150.4 ± 58.60.848after 12 weeks138.5 ± 57.3140.8 ± 51.60.862Differencec-9.5 ± 10.0-9.6 ± 7.00.990p valued0.3900.1860.948HbA1cBaseline5.7 ± 0.65.8 ± 0.50.519after 12 weeks5.8 ± 0.55.8 ± 0.50.987Differencec-0.1 ± 0.10.0 ± 0.00.274p valued0.8910.1260.324InsulinBaseline6.6 ± 4.87.3 ± 5.50.599after 12 weeks7.2 ± 7.85.0 ± 4.90.149Differencec-0.6 ± 3.0-2.3 ± 0.60.090p valued0.6040.0620.137C-peptideBaseline2.2 ± 1.02.4 ± 1.40.542after 12 weeks2.2 ± 1.41.7 ± 0.70.048Differencec0.0 ± 0.4-0.7 ± 0.70.014*0.015*p valued0.7290.002*aCompared between groups: p-value by independent t-testbCompared between groups: p-value by ANCOVA (adjustment with baseline and calorie)cDifference between the values of baseline and after 12 weeksdCompared within groups: p-value by paired t-test*$p \leq 0.05$Table 5Analysis of OGTT from baseline to 12 weeks (PP)PlaceboCLMp valueap valuebMean ± SDBlood glucoseBaseline147.4 ± 48.4148.3 ± 56.60.945after 12 weeks138.4 ± 59.0143.4 ± 56.80.737Differencec-9.0 ± 67.1-4.9 ± 0.20.775p valued0.4440.4840.751HbA1cBaseline5.7 ± 0.65.8 ± 0.50.638after 12 weeks5.8 ± 0.55.8 ± 0.60.976Differencec0.1 ± 0.10.0 ± 0.10.417p valued0.7110.3580.502InsulinBaseline6.8 ± 4.87.0 ± 5.50.870after 12 weeks7.3 ± 8.05.3 ± 5.30.269Differencec0.5 ± 3.2-1.7 ± 0.20.243p valued0.6860.2260.172C-peptideBaseline2.2 ± 1.02.2 ± 1.00.839after 12 weeks2.2 ± 1.41.8 ± 0.70.104Differencec0.0 ± 0.4-0.4 ± 0.30.0740.025*p valued0.8980.010*aCompared between groups: p-value by independent t-testbCompared between groups: p-value by ANCOVA (adjustment with baseline and calorie)cDifference between the values of baseline and after 12 weeksdCompared within groups: p-value by paired t-test*$p \leq 0.05$
## Stratification analysis of FBG, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide from baseline to 12 weeks (FBG ≥ 110 mg/dl)
To clarify the antidiabetic efficacy of CLM in participants with hyperglycemia, stratified analyses (ITT and PP) were performed based on a median FBG value of 110 mg/dl, as CLM was supposed to be more effective in the group with hyperglycemia (FBG ≥ 110 ($$n = 36$$)). The results of setting the FBG level of 110 mg/dl as the stratified value were dramatic: all the parameters (FBG, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide) showed significant changes in both ITT and PP analyses, providing a better window for the index changes, compared with the OGTT results (Tables 6 and 7). Moreover, these results implied that CLM is more efficacious in hyperglycemic participants. Table 6Analysis of changes in glucose, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide from baseline to 12 weeks (ITT)Placebo ($$n = 18$$)CLM ($$n = 20$$)p valueMean ± SDBlood glucoseBaseline117.00 ± 7.85120.55 ± 7.830.1680&after 6 weeks116.78 ± 16.18111.75 ± 19.11Difference-0.22 ± 16.30-8.80 ± 18.250.1369*p-value**0.95460.0441after 12 weeks117.83 ± 18.29109.90 ± 21.02Difference0.83 ± 16.19-10.65 ± 18.940.0158&p-value**0.82970.0211InsulinBaseline8.37 ± 3.809.72 ± 5.120.3656*after 6 weeks7.92 ± 5.176.75 ± 5.56Difference-0.45 ± 6.21-2.97 ± 5.200.1818*p-value**0.76220.0193after 12 weeks9.75 ± 10.084.65 ± 3.68Difference1.38 ± 8.86-5.08 ± 4.940.0114&p-value**0.51660.0002C-peptideBaseline2.53 ± 1.012.91 ± 1.580.5484&after 6 weeks2.31 ± 1.032.16 ± 0.93Difference-0.22 ± 0.94-0.75 ± 1.390.3883&p-value**0.34280.0264after 12 weeks2.58 ± 1.781.87 ± 0.77Difference0.05 ± 1.13-1.04 ± 1.630.0178&p-value**0.83660.0099HOMA-IRBaseline2.44 ± 1.192.87 ± 1.500.3423*after 6 weeks2.32 ± 1.841.89 ± 1.60Difference-0.13 ± 2.18-0.98 ± 1.590.1715*p-value**0.80750.0121after 12 weeks3.14 ± 3.771.29 ± 1.06Difference0.69 ± 3.36-1.59 ± 1.620.0089&p-value**0.39400.0003HOMA-IR by C-peptideBaseline4.00 ± 1.824.64 ± 2.460.3420&after 6 weeks3.72 ± 2.13.29 ± 1.63Difference-0.28 ± 2.01-1.35 ± 2.380.2364&p-value**0.56470.0201after 12 weeks4.35 ± 3.762.83 ± 1.55Difference0.35 ± 2.41-1.81 ± 2.850.0105&p-value**0.54250.0103*Compared between groups; p-value by two sample t-test&Compared between groups; p-value by Wilcoxon rank sum test**Compared within groups; p-value by paired t-testTable 7Analysis of changes in glucose, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide from baseline to 12 weeks (PP)Placebo ($$n = 18$$)CLM ($$n = 14$$)p valueMean ± SDBlood glucoseBaseline117.00 ± 7.85122.14 ± 7.940.0702&after 6 weeks116.78 ± 16.1110.07 ± 16.85Difference-0.22 ± 16.30-12.07 ± 15.000.0432*p-value**0.95460.0100after 12 weeks117.83 ± 18.29108.86 ± 19.02Difference0.83 ± 16.19-13.29 ± 15.740.0191*p-value**0.82970.0076InsulinBaseline8.37 ± 3.8010.16 ± 5.350.2768*after 6 weeks7.92 ± 5.178.05 ± 6.17Difference-0.45 ± 6.21-2.11 ± 5.270.4032&p-value**0.76220.1582after 12 weeks9.75 ± 10.085.41 ± 3.980.0238&Difference1.38 ± 8.86-4.74 ± 4.87p-value**0.51660.0030C-peptideBaseline2.53 ± 1.012.61 ± 1.050.8150*after 6 weeks2.31 ± 1.032.24 ± 1.01Difference-0.22 ± 0.94-0.37 ± 0.810.6273*p-value**0.34280.1080after 12 weeks2.58 ± 1.781.98 ± 0.72Difference0.05 ± 1.13-0.63 ± 0.950.0752*p-value**0.83660.0263HOMA- IRBaseline2.44 ± 1.193.03 ± 1.570.2375*after 6 weeks2.32 ± 1.842.25 ± 1.79Difference-0.13 ± 2.18-0.78 ± 1.650.3568*p-value**0.80750.0983after 12 weeks3.14 ± 3.771.49 ± 1.17Difference0.69 ± 3.36-1.54 ± 1.660.0159&p-value**0.39400.0042HOMA-IR by C-peptideBaseline4.00 ± 1.824.22 ± 1.590.5062&after 6 weeks3.72 ± 2.163.33 ± 1.61Difference-0.28 ± 2.01-0.89 ± 1.560.3543*p-value**0.56470.0520after 12 weeks4.35 ± 3.762.92 ± 1.32Difference0.35 ± 2.41-1.31 ± 1.900.0289&p-value**0.54250.0232*Compared between groups; p-value by two sample t-test&Compared between groups; p-value by Wilcoxon rank sum test**Compared within groups; p-value by paired t-test Subsequently, the effectiveness of CLM on lowering all the anti-diabetic indicators (insulin, C-peptide, FBG levels) was validated (Fig. 3, Tables 6 and 7). Interestingly, after 6 weeks, the FBG level in the CLM-ingested group was significantly reduced ($7.3\%$ (ITT); $9.9\%$ (PP)) even though the levels of fasting blood insulin ($30.6\%$ (ITT); $20.77\%$ (PP)) and C-peptide ($25.8\%$ (ITT); $17.2\%$ (PP)) were significantly decreased. After 12 weeks, the levels of all the parameters were further dropped: the FBG level in the CLM-ingested group was significantly reduced ($8.8\%$ (ITT); $10.9\%$ (PP)) even though the levels of fasting blood insulin ($52.2\%$ (ITT); $46.8\%$ (PP)) and C-peptide ($35.7\%$ (ITT); $28.4\%$ (PP)) were decreased. On the other hand, there was no change in all parameters in the placebo group. Moreover, it can be inferred that the result of decreased FBG despite dropped level of insulin (Fig. 3) is due to decreased insulin resistance by CLM.Fig. 3Effect of CLM on FBG, insulin, and C-peptide († $p \leq 0.05$) HOMA-IR and HOMA-IR by C-peptide analyses were performed to confirm the effect of CLM on insulin resistance and glucose utilization efficacy as presented in Fig. 4. While the HOMA-IR level showed an increasing trend in the placebo group, the CLM group showed a marked decrease by $34.2\%$ at 6 weeks, $55.1\%$ at 12 weeks (ITT) ($25.8\%$ at 6 weeks, $50.8\%$ at 12 weeks (PP). For the HOMA-IR by C-peptide, that of the CLM group showed a marked decrease by $29.1\%$ at 6 weeks, $39.0\%$ at 12 weeks (ITT) ($21.1\%$ at 6 weeks, $30.8\%$ at 12 weeks (PP)), whereas it was unchanged in the placebo group. Fig. 4Effect of CLM on HOMA-IR and HOMA-IR by C-peptide († $p \leq 0.05$)
## Stratification analysis of HOMA-IR in participant with insulin resistance and normal participant
The effectiveness of CLM on improving insulin resistance against participant with insulin resistance (HOMA-IR > 1.66) was validated compared with normal participant (HOMA-IR < 1.66) as shown in Fig. 5. While the HOMA-IR level showed an increasing trend in the placebo group, the CLM group in the entire participant group showed a marked decrease by $34\%$ up to 12 weeks (Fig. 5a). In the normal group with low insulin resistance, it was revealed that HOMA-IR level remained within the normal range except for a sudden increase in HOMA-IR level in the placebo group after 6 weeks (Fig. 5b). Remarkably, in the group showing insulin resistance, it was dropped up to 6 weeks in the placebo group and then gradually increased again at 12 weeks, whereas in the CML group, it significantly reduced at both 6 weeks ($42.5\%$) and 12 weeks ($61\%$), without showing a recurrence (Fig. 5c).Fig. 5Effect of CLM against insulin resistance (a-c), exhibiting that the total HOMA-IR in the CLM group was substantially reduced at 6 weeks (*$$p \leq 0.046$$) and 12 weeks (**$$p \leq 0.018$$), while the placebo group was slightly increased (a). While maintaining normal HOMA-IR range in the participants whose insulin resistance was normal (initial HOMA < 1.66) through 6 weeks (*$$p \leq 0.5344$$) and 12 weeks (**$$p \leq 0.438$$) (b), a dramatic reduction was observed in the CLM group (initial HOMA-IR > 1.66) at 6 weeks (*$$p \leq 0.026$$) and 12 weeks (**$$p \leq 0.007$$) (c) Further statistical analysis revealed that participants with decreased HbA1c with reduced markers (FBG, insulin, C-peptide, HOMA-IR, HOMA-IR) were more distributed in the CLM intake group ($50\%$) than the placebo group ($36.1\%$) (Table S4). In particular, the participants with lowered FBG and HbA1c were dominated in the CLM group ($47.6\%$) compared to the placebo group ($12.5\%$). Therefore, it is presumed that CLM lowered FBG and relatively decreased glycated hemoglobin.
## Effect of CLM on lipids
In lipid analysis, there was no significant differences in each group before and after 12 weeks of testing, and there was no difference in the change value (Table S5).
## Symptoms of adverse reaction
Of the 72 participants receiving the human test product, a total of 7 adverse reactions occurred in 6 participants during the test period. The adverse reactions that occurred were pruritus in 1 patient, diarrhea and digestive disorder in 1 patient, digestive disorder in 1 patient, *Helicobacter pyrori* infection in 1 patient, tailbone pain in 1 patient, and digestive disorder in 1 patient. These adverse events were not related with the CLM or placebo samples. During the whole human trial period, there were no abnormal findings on the physical examination of any participants, and no serious adverse events occurred. Therefore, CLM is considered a safe ingredient/nutraceutical with no clinically significant adverse reactions even after 12 weeks of use. Consequently, in patients with impaired fasting glucose or mild T2D, 12 weeks of CLM intake could reduce fasting glucose and C-peptide without clinically significant or adverse side effects.
## Changes in laboratory test results
Changes in laboratory test results were analyzed using a paired t-test for change at the end point compared to the baseline value. There were no statistically significant changes before and after the test in both groups in vital signs (Table S6), general blood test (Table S7), and blood biochemical test (Table S8).
## Discussion
In our 12 weeks trial carried out on participants with impaired FBG or mild T2D, we observed that CLM was able to effectively improve the serum levels of FBG, insulin, C-peptide, and HOMA index, with an optimal tolerability profile that is important to guarantee long-term compliance of the treatment on prediabetes or T2D.
In OGTT analyses (ITT), the levels of FBG, HbA1c, insulin, and C-peptide were decreased by $6.38\%$, $0\%$, $31.5\%$, and $29.2\%$ ($p \leq 0.05$), respectively (in PP, $3.31\%$, $0\%$, $24.3\%$, and $18.2\%$ ($p \leq 0.05$)). In stratification analyses at FBG ≥ 110 mg/dl (ITT), the levels of FBG, insulin, C-peptide, HOMA-IR, and HOMA-IR by C-peptide were significantly reduced ($p \leq 0.05$) by $8.8\%$, $52.2\%$, $35.7\%$, $55.1\%$, and $39\%$, respectively (in PP, $10.9\%$, $46.8\%$, $28.4\%$, $50.8\%$, and $30.8\%$). This is presumed to be the result of reduced FBG despite lowered insulin levels due to reduced insulin resistance by CLM. Based on the HOMA-IR index > 1.66, the effect of improving insulin resistance was remarkably increased in the subgroup of participants with insulin resistance, exhibiting effective reduction at 6 weeks ($42.5\%$) and 12 weeks ($61\%$), without observing a recurrence or hypoglycemia. HbA1c levels were also decreased by $50\%$ in the participants with reduced indicators.
Usually, it is known that insulin level increases due to the rise of c-peptide when administering functional foods or hypoglycemic agents, thus there is a periodic mechanism in which blood sugar decreases. Therefore, it was found that the hypoglycemic effect of CLM decreased blood sugar, leading to reduce insulin demand along with decrease of C-peptide, a precursor of insulin, accordingly.
Therefore, the effect of CLM on improving insulin resistance was statistically proved through the stratified analysis, which confirmed that the hypoglycemic effect in all the CLM-ingested groups was possibly due to the decrease in insulin resistance. It is expected that oral administration of CLM would benefit patients with mild T2D, pre-diabetes, glucose tolerance, impaired FBG, or insulin resistance, since it has been demonstrated that there is an obvious improvement of all parameters in participants with insulin resistance.
Impaired FBG (IFG) and impaired glucose tolerance (IGT) are pre-diabetes stage associated with insulin resistance [23]. Recent studies including intensive lifestyle interventions [24] and meta-analyses [23, 25] have shown that physical activity and diet can reduce FBG levels and prevent or delay the transition between IFG, IGT and T2DM. CLM showed efficient anti-diabetic effect without clinically significant adverse side effects or recurrence for 12 months in patients with IFG, IGT, or T2DM, providing evidence for the health benefits of 12 weeks of CLM treatment in patients who do not reach their goal glycemia despite continuous medications such as metformin, sulfonylurea, DPP-4 inhibitors, and so on.
The anti-diabetic efficacy was consistent with our previous study using high fat diet diabetic mouse model [18]: it was observed that levels of FBG, blood insulin, and C-peptide were significantly decreased in the CLM group and Metformin compared to a control group, and the effect of improving insulin resistance and reducing FBG was equivalent to Metformin. Combining both human and animal data, it becomes noteworthy that CLM visibly reduces FBG despite a decrease in blood insulin, due to improved insulin resistance.
When CLM was immersed in culture for 10 days, the submerged culture medium contained mycelium (5.2 g/L) and EPS (1.62 g/L). In particular, the β-glucan content was about $15.69\%$ (w/w) [16]. It has been studied that the major bioactive components of CLM included EPS composed of mannose ($83.36\%$), galactose ($12.54\%$), and glucose ($4.10\%$) [26], as well as some flavonoids and sesquiterpenoids [27, 28]. Furthermore, the CLM manufacturing process is not an extraction process that mainly uses chemical organic solvents, but a bio-/eco-friendly, green microbial culture process, which can be well fit to the latest green manufacturing trends in the market.
In most diabetes-related clinical trials, it is difficult to manage the response to drugs or test samples due to differences in blood glucose levels of individual diabetic patients. In other words, if functional foods/supplements tailored to individual pharmacological characteristics and improved blood sugar control management methods for clinical participants can be combined, an era in which so-called antidiabetics can be personalized to everyone can be expected. In this context, despite the numerous antidiabetic drugs currently on the market and the introduction of various new treatments in the future, exercise, and diet control along with a steady intake of appropriate functional supplements tailored to each individual are eventually fundamental in the prevention and treatment of diabetes. In this regard, although this study is a clinical study limited to Korea, the use of CLM as a promising solution for prediabetes and diabetes could be widely applied worldwide to address the global diabetes epidemic.
Further studies are needed to elucidate the pharmacological potential and molecular mechanisms of cellular properties of active compounds derived from C. lacerata. In the future, if the biological activity and stability could be secured by identifying the active compound of the CLM component and conducting additional clinical trials, it would be developed as a new anti-diabetic therapeutic agent in the future. Still, because of the potential that microbial-derived natural culture medium components can appreciably improve or protect glycemic status against T2D, they can be considered as a niche opening their application as antidiabetic nutraceuticals/medical foods.
## Conclusions
CLM as a novel microbial ingredient elicited substantially positive anti-diabetic effects during the intake period (12 weeks), involving especially ameliorated glycemic control of FBG, peripheral insulin action, and reduced C-peptide and HbA1c, via improving insulin resistance. CLM is also considered a safe ingredient with no adverse reactions after 12 weeks of use. To the best of our knowledge, this is a well-defined human randomized controlled trial to examine the anti-diabetic effect of novel microbial mycelium on hypoglycemia and insulin resistance. The results of this study proposed that the microbial medium of CLM might reduce blood sugar, especially FBG and insulin tolerance, and its effect could be helpful for patients with impaired FBG or pre-diabetes. The efficacy of CLM against T2D could be beneficial as a functional supplementation considering that the size of the population with metabolic disorder (T2D, prediabetes, obesity) has been increasing, while anti-diabetic treatment is limited.
## Supplementary Information
Additional file 1: Table S1. Demographic baseline characteristics of subjects. Table S2. Progress of changes to the clinical trial protocol. Table S3. Schedule of clinical study assessments. Table S4. Effectof CLM on lowering HbA1c over subjects with decreased levels of FBG, insulin, C-peptide, insulin, HOMA-IR, and HOMA-IR by C-peptide. Table S5. Lipid changes at the baseline and after 12 weeks. Table S6 Changes in vital signs. Table S7. Changes in stability evaluation index (blood test). Table S8. Changes in stability evaluation index (biochemical test).
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17. Shin EJ, Kim JE, Kim JH, Park YM, Yoon SK, Jang BC, Lee SP, Kim BC. **Hypoglycemic effect of submerged culture of**. *Kor J Food Preserv* (2015.0) **22** 145-153. DOI: 10.11002/kjfp.2015.22.1.145
18. Kim JH, Park YK, Kim JE, Lee SP, Kim BC, Jang BC. **Crude solution of**. *Int J Mol Med* (2013.0) **32** 179-186. DOI: 10.3892/ijmm.2013.1364
19. Choi JW, Shin EJ, Lee SJ, Kim YH, Kim SR, Ji YM, Kim NY, An CH, Lee IH, Kim YS. **Effects of submerged culture of**. *J Korean Soc Food Sci* (2017.0) **46** 1419-1426. DOI: 10.3746/jkfn.2017.46.12.1419
20. Shin EJ, Kim JE, Kim JH, Park YM, Yoon SK, Jang BC, Lee SP, Kim BC. **Effect of submerged culture of**. *Korean J Food Preserve* (2015.0) **22** 893-900. DOI: 10.11002/kjfp.2015.22.6.893
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---
title: Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment
and follow-up of patients with endometrial cancer
authors:
- S. Koppikar
- A. Oaknin
- K. Govind Babu
- D. Lorusso
- S. Gupta
- L.-Y. Wu
- W. Rajabto
- K. Harano
- S.-H. Hong
- R.A. Malik
- H. Strebel
- I.M. Aggarwal
- C.-H. Lai
- T. Dejthevaporn
- S. Tangjitgamol
- W.F. Cheng
- W.Y. Chay
- D. Benavides
- N.M. Hashim
- Y.W. Moon
- M. Yunokawa
- T.D. Anggraeni
- W. Wei
- G. Curigliano
- A. Maheshwari
- U. Mahantshetty
- S. Sheshadri
- S. Peters
- T. Yoshino
- G. Pentheroudakis
journal: ESMO Open
year: 2023
pmcid: PMC10024150
doi: 10.1016/j.esmoop.2022.100774
license: CC BY 4.0
---
# Pan-Asian adapted ESMO Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with endometrial cancer
## Abstract
The most recent version of the European Society for Medical Oncology (ESMO) Clinical Practice Guidelines for the diagnosis, treatment and follow-up of patients with endometrial cancer was published in 2022. It was therefore decided, by both the ESMO and the Indian Society of Medical and Paediatric Oncology (ISMPO), to convene a virtual meeting in July 2022 to adapt the ESMO 2022 guidelines to take into account the variations in the management of endometrial cancer in Asia. These guidelines represent the consensus opinion of a panel of Asian experts representing the oncological societies of China (CSCO), India (ISMPO), Indonesia (ISHMO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), the Philippines (PSMO), Singapore (SSO), Taiwan (TOS) and Thailand (TSCO). Voting was based on scientific evidence and was conducted independently of the current treatment practices and treatment access constraints in the different Asian countries, which were discussed when appropriate. The aim of this guideline manuscript is to provide guidance for the optimisation and harmonisation of the management of patients with endometrial cancer across the different regions of Asia, drawing on the evidence provided by Western and Asian trials whilst respecting the variations in clinical presentation, diagnostic practices including molecular profiling and disparities in access to therapeutic options, including drug approvals and reimbursement strategies.
## Highlights
•This article provides the ESMO recommendations for the treatment of endometrial cancer adapted for patients in Asian countries.•The lack of availability of certain tests and treatments in some Asian countries is discussed for few recommendations.•The aim is to encourage evidence-based practices in the management of endometrial cancer in the different regions of Asia.
## Introduction
Cancer of the corpus uteri (endometrial cancer) is the most common gynaecological malignancy in high- and intermediate-income countries.1,2 In 2020, endometrial cancer was the sixth most commonly diagnosed cancer in women, with 417 367 new cases recorded, accounting for $2.2\%$ of the new cancers diagnosed worldwide. Approximately $40\%$ of these new cases occurred in Asia, with China, where endometrial cancer is the third most common female malignancy, accounting for nearly half [81 964] of the cases.3 Endometrial cancer was in turn responsible for 97 370 cancer deaths representing $1\%$ of all cancer deaths worldwide.4 Although endometrial cancer has a higher incidence in Western countries than in Asia, the incidence is increasing worldwide. Risk factors that are associated with sporadic endometrial cancer include obesity (high body mass index), diabetes, polycystic ovary syndrome, early age at menarche, late menopause, infertility, menopausal estrogen therapy and the use of tamoxifen,5,6 whilst inherited endometrial cancer is linked to Lynch and Cowden syndromes.7 A rising trend in endometrial cancer is being observed in several Asian countries. The number of new cases of endometrial cancer in 2020 was 16 413 cases in India, 4524 cases in Thailand, 4374 cases in the Philippines, 3425 cases in South Korea, 1401 cases in Malaysia and 775 cases in Singapore.8 The increasing incidence is attributed to evolving lifestyle, younger age at menarche, late age at menopause and fewer children, especially in women living in urban areas.9,10 Although endometrial cancer occurs most frequently in postmenopausal women, there is a higher proportion of younger women being diagnosed with endometrial cancer in China,11,12 with ∼$40\%$ of patients diagnosed before their menopause compared with <$25\%$ of Western women.13 In Hong Kong, $65\%$ of 1165 new cases of endometrial cancer diagnosed in 2018 occurred in women aged between 45 and 64 years (www3.ha.org.hk/cancereg).
The majority of endometrial cancers are diagnosed at an early stage and the 5-year overall survival rate for patients with localised disease is high ($95\%$), However, endometrial cancers with high-risk factors such as high-grade serous pathology and TP53 mutation have a tendency to recur.1,14 Patients with recurrent endometrial cancer have a poor prognosis, with a 5-year overall survival of <$20\%$, particularly in patients with metastatic disease.15 Guidelines and recommendations for the treatment and management of patients with endometrial cancer in Asia have been published for the Asia-Pacific region, India [National Cancer Grid (NCG) guidelines for endometrial cancer (tmc.gov.in)], Japan,16 Korea,17 Singapore,18 Taiwan,19 China, Thailand, the Philippines and Indonesia, and are important for the standardisation of diagnostic and treatment approaches. These guidelines aim to optimise clinical outcomes for what is a growing health care problem in each Asian country. The European Society for Medical Oncology (ESMO) guidelines for the diagnosis, treatment and follow-up of patients with endometrial cancer were published in 2022,20 and a decision was taken by ESMO and the Indian Society of Medical and Paediatric Oncology (ISMPO) that these guidelines should be adapted for the management and treatment of patients in Asian countries.
Consequently, representatives of ISMPO, ESMO, the Chinese Society of Clinical Oncology (CSCO), the Indonesian Society of Hematology and Medical Oncology (ISHMO), the Japanese Society of Medical Oncology (JSMO), the Korean Society of Medical Oncology (KSMO), the Malaysian Oncological Society (MOS), the Philippine Society of Medical Oncology (PSMO), the Singapore Society of Oncology (SSO), the Taiwan Oncology Society (TOS) and the Thai Society of Clinical Oncology (TSCO) convened for a virtual, ‘face-to-face’ working meeting on 9 July 2022, hosted by ISMPO, to adapt the recent ESMO Clinical Practice Guidelines20 for use in the clinical management of Asian patients with endometrial cancer. This manuscript summarises the Pan-Asian adapted guidelines developed at the meeting accompanied by the level of evidence (LoE), grade of recommendation (GoR) and percentage consensus reached for each recommendation.
## Methodology
This Pan-Asian adaptation of the current ESMO Clinical Practice Guidelines20 was prepared in accordance with the principles of ESMO standard operating procedures (http://www.esmo.org/Guidelines/ESMO-Guidelines-Methodology) and was an ISMPO–ESMO initiative endorsed by CSCO, ISHMO, JSMO, KSMO MOS, PSMO, SSO, TOS and TSCO.
An international panel of experts was selected from the ISPMO ($$n = 6$$), the ESMO ($$n = 6$$) and two experts representing each of the oncological societies of China (CSCO), Indonesia (ISHMO), Japan (JSMO), Korea (KSMO), Malaysia (MOS), the Philippines (PSMO), Singapore (SSO), Taiwan (TOS) and Thailand (TSCO). One expert from Thailand (ST) was member of the Thai Gynecologic Cancer Society endorsed by TSCO. Only two of the six expert members from the ISMPO (SG and KGB) were allowed to vote on the recommendations together with the experts from each of the nine other Asian oncology societies ($$n = 20$$). Among the six experts from ISMPO, three were medical oncologists and one a gynaecological oncologist, one a radiation oncologist and one a pathologist. The majority of experts from the other Asian societies were medical oncologists or gynaecological oncologists. None of the additional ISMPO members present and none of the ESMO experts were allowed to vote and were present only in an advisory role.
A modified Delphi process was used to review, accept or adapt each of the individual recommendations in the latest ESMO Clinical Practice Guidelines.20 The 20 voting Asian experts were asked to vote YES or NO (one vote per society) on the ‘acceptability’ (agreement with the scientific content of the recommendation) and ‘applicability’ (availability, reimbursement and practical challenges) of each of the ESMO recommendations in a pre-meeting survey (see Methodology in Supplementary Material S1, available at https://doi.org/10.1016/j.esmoop.2022.100744). For recommendations, where a consensus was not reached, the Asian experts were invited to modify the wording of the recommendation(s) at the virtual ‘face-to-face’ meeting using further rounds of voting, if necessary, in order to determine the definitive acceptance or rejection of an adapted recommendation and discuss the applicability challenges. The ‘Infectious Diseases Society of America-United States Public Health Service Grading System’ (Supplementary Material S4, available at https://doi.org/10.1016/j.esmoop.2022.100744)21 was used to define the LoE and strength (grade) of each recommendation. Any modifications to the initial recommendations were highlighted in bold text in a summary table of the final Asian recommendations and in the main text, if applicable. A consensus was considered to have been achieved when ≥$80\%$ of experts voted that a recommendation was acceptable.
## Results
In the initial pre-meeting survey, the 20 voting Asian experts reported on the ‘acceptability’ and ‘applicability’ of the 51 recommendations for the diagnosis, treatment and follow-up of patients with endometrial cancer from the 2022 ESMO Clinical Practice Guidelines.20 These recommendations were made in the five categories outlined in the text below and in Table 1.Table 1Summary of Asian recommendations for the treatment of patients with endometrial cancerRecommendationsAcceptability consensusRecommendation 1: Diagnosis, pathology and molecular biology1a. Histological type, FIGO grade, myometrial invasion and LVSI (focal/substantial) should be described for all endometrial cancer pathology specimens [V, A].1b. Molecular classification through well-established IHC staining for p53 and MMR proteins (MLH1, PMS2, MSH2, MSH6) in combination with targeted tumour sequencing (POLE hotspot analysis) should be carried out for all endometrial cancer pathology specimens regardless of histological type [IV, A]$.100\%$$100\%$Recommendation 2: Staging and risk assessment2a. Obtaining endometrial sampling by biopsy or dilatation and curettage (D & C) are acceptable initial approaches to the histological diagnosis of endometrial cancer [IV, A].2b. The preoperative work-up should include clinical and gynaecological examination, transvaginal ultrasound, pelvic MRI, a full blood count and liver and renal function profiles [IV, B].2c. Additional imaging tests (e.g. abdominal CT and thoracic scan and/or FDG–PET–CT) may be considered in those patients at high risk of extra-pelvic disease [IV, C]$.100\%$$100\%$$100\%$Recommendation 3: Management of local and locoregional diseaseSurgery3a. Hysterectomy with bilateral salpingo-oophorectomy is the standard surgical procedure in early-stage endometrial cancer [I, A].3b. Minimally invasive surgery is the recommended approach in stage I G1-G2 endometrial cancer [I, A].3c. Minimally invasive surgery may also be the preferred surgical approach in stage I G3 [II, A].3d. Ovarian preservation can be considered in premenopausal women with stage IA G1 endometrioid-type endometrial cancer [IV, A].3e. Sentinel lymph node excision (SLNE) can be considered as a strategy for nodal assessment in cases of low-risk/intermediate-risk endometrial cancer (e.g. stage IA, G1-G3 and stage IB, G1-G2) in experienced centres [II, A]. It can be omitted in cases without myometrial invasion. When SLNE is not available, LNE can be carried out in patients with stage 1A G3 and stage 1B disease [II, B].3f. Surgical lymph node staging should be carried out in patients with high-intermediate-risk/high-risk disease. Sentinel lymph node biopsy is an acceptable alternative to systematic LNE for lymph node staging in high-intermediate/high-risk stage I-II endometrial cancer when available and in centres with experience [III, B].3g. Full surgical staging including omentectomy, peritoneal biopsies and lymph node staging should be considered in serous endometrial cancers and carcinosarcomas [IV, B].3h. When feasible, and with acceptable morbidity, cytoreductive surgery to the maximal surgical extent should be considered in patients with stage III and IV disease [IV, B]$.100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$Low-risk endometrial cancer3i. For patients with stage IA (G1 and G2) endometrioid (dMMR and NSMP) type endometrial cancer with no or focal LVSI, adjuvant treatment is not recommended [I, E].3j. For patients with stage IA non-endometrioid type (and/or p53-abn), without myometrial invasion and no or focal LVSI, there are not enough data to make a definitive recommendation regarding adjuvant treatment. Adjuvant therapy (chemotherapy and/or brachytherapy) or no adjuvant treatment may be discussed on a case-by-case basis in a multidisciplinary team approach [IV, C].3k. For patients with stage I-II POLEmut cancers, omission of adjuvant treatment should be considered [III, D].3l. For patients with stage III POLEmut cancers, there is insufficient evidence on need for adjuvant treatment. Enrolment in clinical trials, adjuvant therapy or no adjuvant therapy are reasonable options [III, C]$.100\%$$100\%$$100\%$$100\%$Intermediate-risk endometrial cancer3m. For patients with stage IA G3 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI, adjuvant VBT is recommended to decrease vaginal recurrence [I, A].3n. For patients with stage IB G1-G2 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI, adjuvant VBT is recommended to decrease vaginal recurrence [I, A].3o. For patients with stage II G1 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI adjuvant VBT is recommended to decrease vaginal recurrence [II, B].3p. Omission of adjuvant VBT can be considered (especially for patients aged <60 years) for all above stages, after patient counselling and with appropriate follow-up [III, C]$.100\%$$100\%$$100\%$$100\%$High-intermediate-risk endometrial cancer with lymph node staging (pN0)3q. For patients with stage IA and IB with substantial LVSI, stage IB G3, stage II G1 with substantial LVSI and stage II G2-G3 (dMMR and NSMP):3q1. Adjuvant EBRT is recommended [I, A].3q2. Adding (concomitant and/or sequential) chemotherapy to EBRT could be considered, especially for G3 and/or substantial LVSI [II, C].3q3. Adjuvant VBT (instead of EBRT) could be considered to decrease vaginal recurrence, especially for those without substantial LVSI [II, B].3q4. Despite evidence of a benefit from adjuvant treatment, its omission is an option, when close follow-up can be ensured, following shared decision making with the patient [IV, C]$.100\%$$100\%$$100\%$$100\%$$100\%$High-intermediate-risk endometrial cancer without lymph node staging3r. For patients with stage IA and IB with substantial LVSI, stage IB G3, stage II G1 with substantial LVSI and stage II G2-G3 (dMMR and NSMP):3r1. Adjuvant EBRT is recommended [I, A].3r2. Adding (concomitant and/or sequential) chemotherapy to EBRT could be considered especially for patients with substantial LVSI and G3 disease [II, C].3r3. Adjuvant VBT followed by chemotherapy could be considered for patients with stage IB G3 disease without substantial LVSI, if EBRT is not feasible [III, C]$.100\%$$100\%$$100\%$$100\%$High-risk endometrial cancer3s. Adjuvant EBRT with concurrent and adjuvant chemotherapy is recommended [I, A].3t. Sequential chemotherapy and RT can be used [I, B].3u. Chemotherapy alone is an alternative option [I, B]$.100\%$$100\%$$100\%$Recommendation 4: Recurrent/metastatic disease4a. For patients with locoregional recurrence following primary surgery alone, the preferred primary therapy should be EBRT with or without VBT, depending on the site of recurrence [IV, A].4b. Adding systemic therapy to salvage RT could be considered [IV, C].4c. For patients with recurrent disease following RT, surgery should be considered only if a complete debulking with acceptable morbidity is anticipated [IV, C].4d. Complementary systemic therapy after surgery could be considered [IV, C].4e. The standard first-line chemotherapy treatment is carboplatin AUC 5-6 plus paclitaxel 175 mg/m2 every 21 days for six cycles [I, A].4f. Hormone therapy could be considered as an option for front-line systemic therapy for patients with low-grade carcinomas endometrioid histology with low-volume disease [III, A].4g. Progestins are the recommended agents [II, A].4h. Other options for hormonal therapies include AIs, tamoxifen and fulvestrant [III, C].4i. There is no standard of care for second-line chemotherapy. Doxorubicin and weekly paclitaxel are considered the most active therapies [IV, C].4j. Immune checkpoint blockade monotherapy should be considered after platinum-based therapy failure in patients with MSI-H/dMMR endometrial cancer [III, A].4k. Dostarlimab can be considered in patients with dMMR or MSI-H recurrent or advanced endometrial cancer after failure of prior platinum-based chemotherapy and has recently been approved by both the EMA and the FDA for this indication [III, B; ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) v1.1 score: 3].4l. Pembrolizumab is FDA approved for the treatment of TMB-H solid tumours (as determined by the FoundationOne CDx assay) that have progressed following prior therapy for endometrial cancer [III, B; ESMO-MCBS v1.1 score: 3; not EMA approved].4m. Pembrolizumab with lenvatinib is approved by the EMA for endometrial cancer patients who have failed a previous platinum-based therapy, and who are not candidates for curative surgery or RT. FDA approval is for endometrial cancer patients whose tumours are not dMMR/MSI-H [I, A; ESMO-MCBS v1.1 score: 4]$.100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$$100\%$Recommendation 5: Follow-up, long-term implications and survivorship5a. For low-risk endometrial cancer, the proposed surveillance is at least every 6 months, with physical and gynaecological examination for the first 2 years and then yearly until 5 years [V, C].5b. In the low-risk group, remote follow-up can be integrated into hospital-based follow-up [II, B].5c. For the high-risk groups, physical and gynaecological examinations are recommended every 3 months for the first 3 years, and then every 6 months until 5 years [V, C].5d. A CT scan or PET–CT could be considered in the high-risk group, particularly if node extension was present [V, D].5e. Regular exercise, healthy diet and weight management should be promoted with all endometrial cancer survivors [II, B]$.100\%$$100\%$$100\%$$100\%$$100\%$Bold text represents changes to the original recommendations adapted to the Asian context. AI, aromatase inhibitor; AUC, area under the curve; CT, computed tomography; D & C, dilation and curettage; EBRT, external beam radiotherapy; EMA, European Medicines Agency; ESMO-MCBS, European Society for Medical Oncology-Magnitude of Clinical Benefit Scale; FDA, Food and Drug Administration; FDG–PET, [18F]2-fluoro-2-deoxy-D-glucose–positron emission tomography; FIGO, International Federation of Gynaecology and Obstetrics; G 1, 2, 3, grade 1, 2, 3; IHC, immunohistochemistry; LNE, lymphadenectomy; LVSI, lymphovascular space invasion; MMR, mismatch repair; MRI, magnetic resonance imaging; MSI-H, microsatellite instability-high; NSMP, no specific molecular profile, POLE, DNA polymerase-epsilon; RT, radiotherapy; SLNE, sentinel lymph node excision; TMB-H, tumour mutation burden-high; VBT, vaginal brachytherapy.
During the pre-meeting survey there were 32 voting discrepancies in relation to scientific ‘acceptability’ (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744; ‘recommendations 3a, 3e, 3f, 3j, 3k, 3l, 3m, 3n, 3o, 3p, 3q2, 3q3, 3q4, 3r1, 3r2, 3r3, 3s, 3t, 3u, 4a, 4b, 4c, 4e, 4f, 4g, 4h, 4i, 4j,4k, 5a, 5b and 5c’), and 37 voting discrepancies in relation to the ‘applicability’ (Supplementary Table S3, available at https://doi.org/10.1016/j.esmoop.2022.100744) across the 10 different Asian societies.
## Diagnosis, pathology and molecular biology—recommendations 1a-b
Endometrial cancer is clinically a very heterogeneous malignancy for which the assignment of histological subtype, grade, disease extension and lymphovascular space invasion (LVSI) has been highly subjective,20,22 impacting on the accurate assessment of an individual patient’s risk of recurrence and metastasis, and therefore management. Furthermore, it has reduced the ability to accurately compare different clinical studies in terms of outcome due to uncertainty over the classification of patient risk.
The traditional histopathological classification of Bokhman identified two types of endometrial cancer, type I [endometrioid, grade 1-2 (G1-2) with a favourable prognosis], ∼$70\%$ of cases, and type II (G3 endometrioid and non-endometrioid histologies with a poor prognosis), ∼$30\%$ of cases.23 *There is* general agreement, however, that endometrioid tumours should now be classified according to the International Federation of Gynecology and Obstetrics (FIGO) defined criteria,20,24 providing a two-tier grading system with G1 and G2 endometrioid tumours grouped together as low grade, and G3 tumours classified as high grade. Factors traditionally associated with a high risk of recurrent disease include histologic subtype, FIGO G3 histology, myometrial invasion ≥$50\%$, LVSI,25, 26, 27 L1 cell adhesion molecule expression,28,29 lymph node metastases and tumour diameter >2 cm.
However, the heterogeneity of endometrial cancer is due to an array of underlying molecular alterations. The results of The Cancer Genome Atlas (TCGA) analysis30 showed that the molecular diversity of endometrial cancer could be stratified into four distinct molecular subgroups (Supplementary Table S4, available at https://doi.org/10.1016/j.esmoop.2022.100744). The four molecular subgroups are: (i) patients with copy number stable, ultra-mutated endometrial cancers characterised by pathogenic variants in the exonuclease domain of DNA polymerase-epsilon (POLE), (ii) patients with hyper-mutated endometrial cancer characterised by microsatellite instability (MSI) due to dysfunctional/deficient mismatch repair genes (dMMR), (iii) an MMR-proficient, low somatic copy number aberration (SCNA) subgroup with a low mutational burden and (iv) a high SCNA subgroup with frequent TP53 mutations. Therefore, well-established immunohistochemical (IHC) staining techniques for the detection of p53 and MMR proteins (MLH1, PMS2, MSH2, MSH6) are now recommended as standard practice for all endometrial cancer pathology specimens, regardless of histological type, together with sequencing of the exonuclease domain of POLE if available.17 Patients presenting with either newly diagnosed or recurrent/metastatic endometrial cancer should have a biopsy to confirm histology and assess tumour molecular biology.
These molecular classes are identified across all of the histological subtypes,31,32 and correlate with endometrial cancer prognosis.33 Thus, molecular classification could facilitate more accurate comparison of clinical outcomes between different groups of patients. Furthermore, it could impact treatment considerations. Firstly, testing for MMR/MSI status serves not only as a screening test for Lynch syndrome, but also identifies patients with metastatic disease who could benefit from immune checkpoint blockade agent. Secondly, the benefit of adjuvant chemotherapy is observed in patients with p53mut endometrial cancer,34 whilst the de-escalation of therapy in patients with POLE mutated (POLEmut) endometrial cancer, which has a favourable outcome, is being investigated. Thirdly, the overexpression/gene amplification of human epidermal growth factor receptor 2 (HER2), which has been demonstrated in $20\%$-$40\%$ of type II non-endometrioid endometrial cancers, supports the use of HER2-targeted therapy in combination with chemotherapy. This combined treatment has also recently been shown to be an effective treatment approach for patients with advanced and recurrent serous endometrial cancer.35, 36, 37, 38, 39 *As a* consequence, HER2 testing is now being proposed to guide the management of these patients.40,41 Endometrial cancers that have not been completely molecularly classified should be designated as endometrial cancers not-otherwise-specified and use the histology-based classification system.42 With improved tumour characterisation facilitated by more sophisticated diagnostic testing and molecular profiling, the diagnosis and management of patients with endometrial cancer is evolving towards a more objective, reproducible, personalised medicine approach. The algorithm for the diagnostic work-up of endometrial cancer proposed by ESMO20 and adapted from Vermij et al. 202042 is presented in Figure 1.The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO recommendations on diagnosis, pathology and molecular biology ‘recommendations 1a-b’ below and in Table 1. However, they mentioned that POLE hotspot mutation analysis was not available as part of the standard molecular evaluation in many centres in Asia.1a. Histological type, FIGO grade, myometrial invasion and LVSI (focal/substantial) should be described for all endometrial cancer pathology specimens20 [V, A].1b. Molecular classification through well-established IHC staining for p53 and MMR proteins (MLH1, PMS2, MSH2, MSH6) in combination with targeted tumour sequencing (POLE hotspot analysis)43,44 should be carried out for all endometrial cancer pathology specimens regardless of histological type20 [IV, A].See Supplementary Material S2, available at https://doi.org/10.1016/j.esmoop.2022.100744, for hereditary endometrial cancer testing and surveillance. Figure 1Diagnostic algorithm for the integrated molecular endometrial cancer classification.dMMR, mismatch repair deficient; EC, endometrial cancer; MMR, mismatch repair; NSMP, no specific molecular profile; p53mut, p53 mutant; pMMR, mismatch repair proficient; POLE, DNA polymerase epsilon; POLEmut, DNA polymerase epsilon-ultramutated.aPathogenic POLE variants include p.Pro286Arg, p.Val411Leu, p.Ser297Phe, p.Ala456Pro and p.Ser459Phe.25. bMMR deficiency is defined by the loss of one or more MMR proteins (MLH1, PMS2, MSH2 and MSH6). cp53 immunohistochemistry is an acceptable surrogate marker for TP53 mutation status in MMR-proficient, POLE wild-type EC. Permission to use figure under a Creative Commons CC BY License, Wiley obtained by ESMO.
## Staging and risk assessment—recommendations 2a-c
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO recommendations on diagnosis, pathology and molecular biology ‘recommendations 2a-c’ below and in Table 1.20 2a. Obtaining endometrial sampling by biopsy or dilation and curettage (D & C) are acceptable initial approaches to the histological diagnosis of endometrial cancer20 [IV, A].
2b. The preoperative work-up should include clinical and gynaecological examination, transvaginal ultrasound, pelvic magnetic resonance imaging (MRI),45 a full blood count and liver and renal function profiles20 [IV, B].
2c. Additional imaging tests [e.g. abdominal and thoracic computed tomography (CT) scan and/or [18F]2-fluoro-2-deoxy-D-glucose–positron emission tomography (18FDG–PET)–CT may be considered in those patients at high risk of extra-pelvic disease46 [IV, C].
## Surgery
Early endometrial cancer is typically treated with surgery to remove the macroscopic disease and stage the tumour for planning with regard to adjuvant therapy.
Traditionally, surgery for endometrial cancer was carried out via laparotomy until the results of two large, randomised trials showed minimally invasive laparoscopic techniques to have no negative impact on either staging or clinical outcomes.47,48 An algorithm for the surgical treatment and management of patients with stage I endometrial cancer is presented in Figure 2. Preservation of fertility in younger patients with endometrial carcinoma should be considered when appropriate49 (Supplementary Material S3, available at https://doi.org/10.1016/j.esmoop.2022.100744).Figure 2Stage I endometrial cancer: surgery. Burgundy box: general category or stratification; orange boxes: surgery; white box: other aspect of management. EC, endometrial cancer; EEC, endometrioid-type endometrial cancer; LNE, lymphadenectomy.aExcept in those restricted to polyps.
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO recommendations 3a-d below and in Table 1, without change.
3a. Hysterectomy with bilateral salpingo-oophorectomy is the standard surgical procedure in early-stage endometrial cancer [I, A].
3b. Minimally invasive surgery is the recommended approach in stage I (G1-G2) endometrial cancer [I, A] (Figure 2).
3c. Minimally invasive surgery may also be the preferred surgical approach in stage I G3 [II, A] (Figure 2).
3d. Ovarian preservation can be considered in premenopausal women with stage IA, G1 endometrioid-type endometrial cancer [IV, A] (Figure 2).
The comment of the Taiwanese experts with respect to inclusion of sentinel lymph node sampling as part of surgical procedure (recommendation 3a) is covered in recommendation 3e.
However, some Asian experts did not accept ESMO ‘recommendations 3e and 3f’ because they did not reflect real-life clinical practice in their countries with respect to sentinel lymph node excision (SLNE), which is not available in many centres in Asia.
Therefore, the original ‘recommendations 3e and 3f’ were modified, as per the bold text below and in Table 1. However, the consensus was that SLNE should be encouraged wherever possible, based on the evidence available from two studies,50,51 including in patients with deeply invasive endometrioid endometrial cancer,52 but not in patients with the more aggressive type II histology53,54 (see ‘recommendation 3g’ below). SLNE can be used for staging in patients with low- or intermediate-risk endometrial cancer and may represent an alternative to systematic lymphadenectomy (LNE) in high-intermediate- or high-risk stage I-II disease.20 The randomised Endometrial Cancer Lymphadenectomy Trial (ECLAT) is ongoing in patients with FIGO stage I and II disease with a high risk of recurrence, and should provide more evidence.55 3e. SLNE can be considered as a strategy for nodal assessment in cases of low-risk/intermediate-risk endometrial cancer (e.g. stage IA, G1-G3 and stage IB, G1-G2) in experienced centres [II, A]. It can be omitted in cases without myometrial invasion. When SLNE is not available, lymphadenectomy (LNE) can be carried out in patients with stage IA G3 and stage IB disease [II, B; consensus = $100\%$].
3f. Surgical lymph node staging should be carried out in patients with high-intermediate-risk/high-risk disease. Sentinel lymph node biopsy is an acceptable alternative to systematic LNE for lymph node staging in patients with high-intermediate/high-risk stage I-II endometrial cancer, when available and in centres with experience [III, B; consensus = $100\%$].
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO ‘recommendations 3g and 3h’ below.
3g. Full surgical staging including omentectomy, peritoneal biopsies and lymph node staging should be considered in serous endometrial cancers and carcinosarcomas [IV, B] (Figure 2).
3h. When feasible, and with acceptable morbidity, cytoreductive surgery to the maximal surgical extent should be considered in patients with stage III and IV disease20 [IV, B].
The risk groups for endometrial cancer are summarised in Supplementary Table S5, available at https://doi.org/10.1016/j.esmoop.2022.100744.
## Low-risk endometrial cancer
There is no indication for the use of adjuvant therapy for the treatment of patients with low-risk endometrial cancer,56, 57, 58 due to a low risk of recurrence. Also, in the few patients in whom local recurrence does occur, it can be treated effectively with radiotherapy (RT). Combined analysis of cohorts from the PORTEC-1 and PORTEC-2 studies59 and other studies33,60,61 has shown the presence of a POLE mutation (POLEmut) to be a favourable indicator of prognosis, independently of other clinicopathological characteristics. As a consequence, patients with stage I-II endometrial cancer with POLEmut tumours are now classified as low risk and unlikely to benefit from adjuvant therapy. Omitting adjuvant therapy in patients with G3 POLEmut endometrial cancer may also be an option, although currently there are no robust data available. Higher-level evidence from a prospective registry study is likely to be available shortly together with data from a cohort of the RAINBO trial (NCT05255653). The planned cohorts for the Trans PORTEC RAINBO programme of clinical trials aim to refine the adjuvant treatment of patients with endometrial cancer based on molecular profile including POLEmut status, dMMR, no specific molecular profile (NSMP) and abnormal p53 (p53abn).
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO ‘recommendation 3i’ below.
3i. For patients with stage IA (G1 and G2) endometrioid (dMMR and NSMP) type endometrial cancer with no or focal LVSI, adjuvant treatment is not recommended [I, E].
However, some of the Asian experts did not accept the ESMO ‘recommendations 3j, 3k and 3l’, which suggest the omission of adjuvant treatment, because there are little supporting data on the safety of omitting therapy. However, in relation to ‘recommendation 3k’ for patients with stage I-II POLEmut disease, there is encouraging, although limited, evidence regarding the omission of adjuvant therapy.34,43 When the POLEmut status of a tumour is unavailable, patients should be treated on the basis of the other available risk information. The current focus is on de-escalation of therapy in these patients, whenever possible. Thus, the wording of the original ‘recommendations 3j, 3k and 3l’ (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744) was revised, as per the bold text below and in Table 1 to reflect the concerns of the Asian experts, with $100\%$ consensus.
3j. For patients with stage IA non-endometrioid-type endometrial cancer (and/or p53abn), without myometrial invasion and no or focal LVSI, there are not enough data to make a definitive recommendation regarding adjuvant treatment. Adjuvant therapy (chemotherapy and/or brachytherapy) or no adjuvant treatment may be discussed on a case-by-case basis in a multidisciplinary team environment [IV, C; consensus = $100\%$].
3k. For patients with stage I-II POLEmut cancers, omission of adjuvant treatment should be considered [III, D; consensus = $100\%$].
3l. For patients with stage III POLEmut cancers, there is insufficient evidence on need for adjuvant treatment. Enrolment in clinical trials, adjuvant therapy or no adjuvant therapy are reasonable options [III, C; consensus = $100\%$].
The adjuvant therapy options for low-risk disease are outlined in Figure 3.Figure 3Stage I-IVA endometrial cancer: adjuvant therapy for low- and intermediate-risk patients. Burgundy boxes: general categories or stratification; green box: radiotherapy; white box: other aspects of management.dMMR, mismatch repair deficient; EC, endometrial cancer; EEC, endometrioid-type endometrial cancer; LVSI, lymphovascular space invasion; NSMP, no specific molecular profile; p53abn, p53 abnormal; POLEmut, polymerase epsilon-ultramutated; VBT, vaginal brachytherapy.aIf completely resected without residual disease.
## Intermediate-risk endometrial cancer
The PORTEC-156 and Gynaecology Oncology Group (GOG)-9957 trials demonstrated the benefit of pelvic external beam RT (EBRT) after surgery in reducing locoregional recurrence in patients with intermediate-risk endometrial cancer. However, a Norwegian trial62 and an ASTEC study group trial58 showed that EBRT and vaginal brachytherapy (VBT) achieve similar results. The long-term results of the PORTEC-2 study showed VBT to result in excellent vaginal control in women with high-intermediate-risk endometrial cancer, with 10-year vaginal control above $96\%$ in both arms. Although the risk of pelvic recurrence was significantly higher in the VBT group ($6\%$ versus $1\%$), no differences were found in 10-year rates for distant metastasis and overall survival. There were lower toxicity rates and better health-related quality of life among women who received VBT compared with EBRT.63 The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO ‘recommendations 3m, 3n and 3o’ below without change, after much discussion over the use of adjuvant RT. Adjuvant RT is not commonly used in Japan (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744), with chemotherapy being used as an alternative based on a study by the Japanese Gynecologic Oncology Group.64 The experts from China and Taiwan favoured EBRT ± VBT or EBRT alone, respectively, over VBT for stage II G1 endometrial cancer ‘recommendation 3o’.
3m. For patients with stage IA G3 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI, adjuvant VBT is recommended to decrease vaginal recurrence [1, A; consensus = $100\%$].
3n. For patients with stage IB G1-G2 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI, adjuvant VBT is recommended to decrease vaginal recurrence [I, A; consensus = $100\%$].
3o. For patients with stage II G1 endometrioid (dMMR or NSMP)-type endometrial cancer and no or focal LVSI adjuvant VBT is recommended to decrease vaginal recurrence [II, B; consensus = $100\%$].
It was mentioned by the experts that molecular profiling was not available in certain regions of Asia. In such situations, patients should be treated according to their assessed risk of recurrence.
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) ‘recommendation 3p’ below without any change.
3p. Omission of adjuvant VBT can be considered (especially for patients aged <60 years) for all above stages, after patient counselling and with appropriate follow-up [III, C].
## High-intermediate-risk endometrial cancer with lymph node staging (pN0)
There was much discussion over the adjuvant treatment of this group of patients which includes those with stage IA and IB disease with substantial LVSI, stage IB G3 and stage II G1 disease with substantial LVSI and stage II G2-G3 (dMMR or NSMP) disease.
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO ‘recommendation 3q.1’ below, with the proposal from Taiwan that chemotherapy might be considered as an alternative.
3q.1. Adjuvant EBRT is recommended [I, A].
However, some of the Asian experts did not accept the ESMO ‘recommendations 3q.2, 3q.3 and 3q.4’, regarding adjuvant treatment.
With regard to ‘recommendation 3q.2’, some of the experts considered that stronger evidence was needed for the benefit of the addition of chemotherapy, but accepted the recommendation without change based on the data from the PORTEC-3 trial.65 However, it was felt that the high incidence of short- and long-term side-effects associated with the addition of chemotherapy to EBRT, whilst conferring minimal benefit, needed to be discussed with these patients.
3q.2. Adding (concomitant and/or sequential) chemotherapy to EBRT could be considered, especially for G3 and/or substantial LVSI [II, C; consensus = $100\%$].
With regard to ‘recommendation 3q.3’, some of the experts considered that there was insufficient evidence to use the presence or absence of LVSI to decide the type of RT (VBT versus EBRT). In Korea EBRT is used for G3 disease, except in those without LVSI. ‘ Recommendation 3q.3’ was accepted completely by replacing ‘could be recommended’ with ‘could be considered’ as per the bold text below.
3q.3. Adjuvant VBT (instead of EBRT) could be considered to decrease vaginal recurrence, especially for those without substantial LVSI [II, B; consensus = $100\%$].
With regard to ‘recommendation 3q.4’, experts from 6 of the 10 Asian countries considered that adjuvant treatment should be recommended. Thus, the consensus was that the standard treatment for most patients should include adjuvant treatment. However, in highly selected patients (stage IA G1-G2), when close follow-up (every 3 months) is possible, adjuvant treatment may be withheld in consultation with the patient.
Thus, the original ‘recommendation 3q.4’ was revised from: 3q.4. With close follow-up, omission of any adjuvant treatment is an option following shared decision making with the patient [IV, C], to read as the ‘recommendation 3q.4’ below with the new text highlighted in bold.
3q.4. Despite evidence of a benefit from adjuvant treatment, its omission is an option, when close follow-up can be ensured, following shared decision making with the patient [IV, C].
An algorithm for the treatment of these patients is presented in Figure 4.Figure 4Stage I-IVA endometrial cancer: adjuvant therapy for high-intermediate-risk and high-risk patients. Burgundy boxes: general categories or stratification; olive green boxes: combination of treatments or other systemic treatments. ChT, chemotherapy; dMMR, mismatch repair deficient; EBRT, external beam radiotherapy; EC, endometrial cancer; LVSI, lymphovascular space invasion; NSMP, no specific molecular profile; p53abn, p53 abnormal; RT, radiotherapy.aIf completely resected without residual disease.
## High-intermediate-risk endometrial cancer without lymph node staging
Again, there was much discussion over the adjuvant treatment of this group of patients which includes those with stage IA and IB disease with substantial LVSI, stage IB G3 and stage II G1 disease with substantial LVSI and stage II G2-G3 (dMMR or NSMP) disease.
The Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the ESMO ‘recommendations 3r.1’ below without change.
3r.1. Adjuvant EBRT is recommended [I, A].
With regard to ‘recommendation 3r.2’, experts from some Asian countries, despite the evidence from the PORTEC-1 trial56 in patients who had undergone primary surgery (without node dissection) and the PORTEC-3 trial,66 were of the opinion that concomitant treatment should be reserved for medically fit patients, but was the preferred option for patients with substantial LVSI. For patients with no initial lymph node dissection, carrying out a lymph node dissection is also an option, followed by tailored adjuvant treatment. ‘ Recommendation 3r.2’ below was accepted without change with consideration to be given to the observations cited above.
3r.2. Adding (concomitant and/or sequential) chemotherapy to EBRT could be considered especially for patients with substantial LVSI and G3 disease [II, C; consensus = $100\%$].
With regard to ‘recommendation 3r.3’, five of the Asian countries did not agree with the original recommendation, and it was generally accepted that in the absence of lymph node staging, EBRT should be considered. Thus the original ‘recommendation 3r.3’ was revised from: 3r.3. Adjuvant VBT could be considered for IB G3 disease without substantial LVSI to decrease vaginal recurrence [II, B], to read as the ‘recommendation 3r.3’ below with the new text highlighted in bold text and the LoE and GoR changed from II, B to III, C.
3r.3. Adjuvant VBT followed by chemotherapy could be considered for patients with IB G3 disease without substantial LVSI, if EBRT is not feasible [III, C; consensus = $100\%$].
This recommendation is based on evidence from a subgroup analysis of the phase III GOG-249 trial of adjuvant pelvic RT versus VBT plus paclitaxel/carboplatin in high-intermediate- and high-risk early-stage endometrial cancer.67 Radiological evaluation, if not already carried out, should be done before using this option.
An algorithm for the treatment of these patients is presented in Figure 4.
## High-risk endometrial cancer
There were differences amongst the Asian experts in terms of ‘acceptability’ with regard to ‘recommendations 3s, 3t and 3u’ (see Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744).
There was much discussion over the adjuvant treatment of this group of patients with some of the experts considering the therapy proposed in ‘recommendation 3s’ below too toxic for patients with endometrial cancer due to their age and comorbidities although there are supporting data from the PORTEC-3 trial65,66 and GOG trial68 for the benefits of combining chemotherapy with RT in this patient group. High-risk endometrial cancer patients include those with stage III-IVA cancers without residual disease regardless of histology and regardless of molecular subtype, or stage I-IVA p53abn with myometrial invasion, or non-endometrioid cancers without residual disease with myometrial invasion (see Supplementary Table S5, available at https://doi.org/10.1016/j.esmoop.2022.100744). Carcinosarcomas (metaplastic dedifferentiated endometrial cancers) are also regarded as high risk and are commonly classified as p53abn.
However, the Asian experts decided to accept completely the original ESMO ‘recommendation 3s’ below, without change, provided that patients are properly evaluated based on individual factors for this treatment. For patients with major comorbidities or for whom there is an unambiguous contraindication for chemotherapy, RT alone can be considered.
3s. Adjuvant EBRT with concurrent and adjuvant chemotherapy is recommended [I, A; consensus = $100\%$].
After discussion, the Asian experts also accepted ‘recommendations 3t and 3u’ without change. Extended field RT can be considered along with EBRT and chemotherapy for patients with para-aortic node disease.
3t. Sequential chemotherapy and RT can be used [I, B; consensus = $100\%$].
3u. Chemotherapy alone is an alternative option [I, B; consensus = $100\%$].
However, concern was expressed over the use of chemotherapy alone (‘recommendation 3u’), due to the fact that the data regarding comparable efficacy were inconsistent. Certainly, data from the PORTEC-3 trial34 showed the treatment effect to differ between the different molecular subgroups. Poor prognosis patients with p53abn endometrial cancer benefitted significantly from chemoradiotherapy (CRT) regardless of stage and histological subtype, whilst patients with POLEmut cancers achieved an excellent benefit with either RT or CRT. No benefit was observed for CRT over RT for patients with dMMR endometrial cancer, whilst a trend for benefit was observed in the NSMP subgroup. An algorithm for the treatment of these patients is presented in Figure 4.
For any patients with endometrial cancer who are medically unfit for surgery, by virtue of severe comorbidities, definitive RT is an option (see Supplementary Material S4, available at https://doi.org/10.1016/j.esmoop.2022.100744).
## Recurrent/metastatic disease—recommendations 4a-m
As stated previously, the outcomes in patients with recurrent and/or metastatic endometrial cancer are poor.15 The management of these patients should, wherever possible, involve a multidisciplinary team approach, treatment in specialised centres and the development of individualised treatment plans. Algorithms for the treatment of recurrent locoregional and metastatic disease are presented in Figures 5 and 6, respectively. Several factors influence the outcomes (local control and survival) in patients with recurrent and/or metastatic disease, including its site and extent (isolated vaginal or peritoneal involvement), size (<2 cm or ≥2 cm), histology and relapse-free survival (RFS). Isolated vaginal recurrence, lower grade, endometrioid histology and longer RFS are associated with a better prognosis.69,70 Additionally, prior treatment (surgery and/or RT) and patient’s general condition also influence outcome. Figure 5Locoregional recurrent endometrial cancer. Burgundy box: general category; orange box: surgery; green boxes: radiotherapy; blue box: systemic anticancer therapy. Dotted arrow denotes optional follow-up therapy. EBRT, external beam radiotherapy; EC, endometrial cancer; RT radiotherapy; VBT, vaginal brachytherapy. Figure 6Metastatic endometrial cancer. Burgundy box: general category; blue boxes: systemic anticancer therapy. AI, aromatase inhibitor; AUC, area under the curve; ChT, chemotherapy; dMMR, mismatch repair deficient; EC, endometrial cancer; ICI, immune checkpoint inhibitor; ESMO-MCBS, European Society for Medical Oncology-Magnitude of Clinical Benefit Scale; MSI-H, microsatellite instability-high; MSS, microsatellite stable; pMMR, mismatch repair proficient.aIn patients eligible for further treatment after failure of platinum-based therapy.bESMO-MCBS v1.1 was used to calculate scores for new therapies/indications approved by the European Medicines Agency or Food and Drug Administration (FDA). The scores have been calculated by the ESMO-MCBS Working Group and validated by the ESMO Guidelines Committee.cFDA approval is restricted to patients whose tumours are not MSI-H or dMMR.
The Asian experts expressed concern over the omission of surgery from the ESMO ‘recommendation 4a’, and the recommendation of only VBT, which should be considered if there is isolated vaginal recurrence. Thus, ‘recommendation 4a’ was revised by inclusion of the text in bold below.
4a. For patients with locoregional recurrence following primary surgery alone, the preferred primary therapy should be EBRT with or without VBT, depending on the site of recurrence [IV, A; consensus = $100\%$].
It was discussed that surgery could be considered in selected patients in whom it is possible to achieve complete surgical resection in the absence of excessive morbidity, and that the use of VBT alone can be considered in the subgroup of patients with a small vaginal recurrence.
‘Recommendations 4b-e’ were accepted without change with the caveat that they may not be applicable in all cases, depending on extent of disease.
4b. Adding systemic therapy to salvage RT could be considered [IV, C; consensus = $100\%$].
4c. For patients with recurrent disease following RT, surgery should be considered only if a complete debulking with acceptable morbidity is anticipated71 [IV, C; consensus = $100\%$].
4d. Complementary systemic therapy after surgery could be considered71, 72, 73 [IV, C; consensus = $100\%$] (see Figure 5).
4e. The standard first-line chemotherapy treatment is carboplatin AUC 5-6 plus paclitaxel 175 mg/m2 every 21 days for six cycles [I, A; consensus = $100\%$].
In relation to ‘recommendation 4e’ there is no evidence of an increased benefit for >6 cycles of chemotherapy, but it was agreed that this could be considered on an individual basis.
Some Asian experts did not agree with the original ‘recommendation 4f’ because hormone therapy is rarely offered as first-line systemic therapy in these patients. The experts agreed that chemotherapy is the first choice of treatment. Hormone therapy can be considered for patients with low-grade, low-volume disease who are not suitable for chemotherapy, dependent on knowledge of the hormone receptor status [estrogen receptor (ER) and progesterone receptor (PgR)] of the tumour at the time of treatment. However, the predictive value of hormone receptor expression in endometrial cancer is not as strong as it is for patients with breast cancer due to the limitations associated with a lack of standardisation of tissue processing and factors such as a well-defined cut-off limit in relation to receptor levels.20 Furthermore, responses to hormone therapy have been reported in ER-/PgR-negative disease.74 Thus, due to these concerns, the text of the original recommendation ‘recommendation 4f’ below was modified by the inclusion of the bold text.
4f. Hormone therapy could be considered as an option for front-line systemic therapy in patients with low-grade carcinomas of endometrioid histology with low-volume disease [III, A; consensus = $100\%$].
The Asian experts accepted without change ‘recommendations 4g, 4h and 4i’ below, despite some discussion and the removal of the dosing details for medroxyprogesterone acetate and megestrol acetate (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744) in ‘recommendation 4g’. Aromatase inhibitors and fulvestrant are alternative options with limited benefits.75 A phase II study of anastrozole in recurrent ER-/PgR-positive endometrial cancer (the PARAGON trial) showed a low objective response but a meaningful clinical benefit in $44\%$ of patients.76 4g. Progestins are the recommended agents [II, A; consensus = $100\%$].
4h. Other options for hormonal therapies include aromatase inhibitors (AIs), tamoxifen and fulvestrant [III, C; consensus = $100\%$].
4i. There is no standard of care for second-line chemotherapy. Doxorubicin and weekly paclitaxel are considered the most active therapies77, 78, 79 [IV, C; consensus = $100\%$].
The Asian experts queried ‘recommendation 4j’, but eventually accepted it without change with the provision that for patients with a long disease-free interval after prior chemotherapy, retreatment with further platinum-based treatment can also be considered, based on a retrospective analysis,80 when immune checkpoint inhibitor therapy is not available.
After discussion, the GoR of ‘recommendation 4j’ was revised from B to A [ESCAT IA, ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) 3], as per the bold text below.
4j. Immune checkpoint blockade monotherapy should be considered after platinum-based therapy failure in patients with MSI-H/dMMR81,82 [III, A; consensus =$100\%$].
Immune checkpoint blockade alone or in combination with targeted therapies has emerged as a promising intervention in patients with recurrent endometrial cancer in view of a high mutational burden (dMMR/POLEmut subtypes), tumour-infiltrating lymphocytes and programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) expression. Pembrolizumab, which targets PD-1, has been investigated in the endometrial cohorts of the KEYNOTE-158 trial in patients pre-treated with chemotherapy, and a short progression-free survival (PFS), and showed PD-1 blockade to be highly effective.81 Data from the GARNET trial with the anti-PD-1 monoclonal antibody dostarlimab, which blocks interaction with the programmed death ligands PD-L1 and -L2, have led to the approval of dostarlimab monotherapy by the Food and Drug Administration (FDA) in the United States to treat dMMR recurrent or advanced endometrial cancer that has progressed on platinum-containing regimens82 (Figure 6). Agents that target PD-L1 such as avelumab83 and durvalumab84 have also shown promising activity in patients with dMMR endometrial cancer, as well as atezolizumab and nivolumab (anti-PD-1).85 The phase Ib/II KEYNOTE 146 trial86 showed encouraging response, PFS and overall survival rates with the combination of pembrolizumab and the multi-kinase inhibitor lenvatinib, and the phase III KEYNOTE-775 trial87 demonstrated the statistically significant PFS ($P \leq 0.0001$) and overall survival ($P \leq 0.0001$) benefits of this combination compared with standard chemotherapy. As a consequence, pembrolizumab in combination with lenvatinib has been approved by the FDA for patients with advanced endometrial cancer, that is not MSI-high (MSI-H) or dMMR, who have disease progression following prior systemic therapy in any setting and are not candidates for curative surgery or RT. The European Medicines Agency (EMA) approved pembrolizumab in combination with lenvatinib for the treatment of advanced or recurrent endometrial cancer in patients who have disease progression on or following prior treatment with a platinum-containing regimen in any setting regardless of MMR status and who are not candidates for curative surgery or RT (Figure 6).
However, due to the lack of availability of dostarlimab in 6 of the 10 Asian countries, the wording of the original ‘recommendation 4k’ was reworded from the original ESMO recommendation below, 4k. Dostarlimab has recently been approved by both the EMA and the FDA for this indication82 [III, B; ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) v1.1 score: 3], to read as follows: 4k. Dostarlimab can be considered in patients with dMMR or MSI-H recurrent or advanced endometrial cancer after failure of prior platinum-based chemotherapy and has recently been approved by both the EMA and the FDA for this indication [III, B; consensus = $100\%$; ESMO-Magnitude of Clinical Benefit Scale (ESMO-MCBS) v1.1 score: 3].
The Asian experts accepted completely without change ($100\%$ consensus) the original ESMO recommendations ‘recommendations 4l and 4m’ below and in Table 1.
4l. Pembrolizumab is FDA approved for the treatment of TMB-H solid tumours (as determined by the FoundationOne CDx assay) that have progressed following prior therapy for endometrial cancer88 [III, B; ESMO-MCBS v1.1 score: 3; not EMA approved].
4m. Pembrolizumab with lenvatinib is approved by the EMA for endometrial cancer patients who have failed a previous platinum-based therapy, and who are not candidates for curative surgery or RT. FDA approval is for endometrial cancer patients whose tumours are not dMMR/MSI-H [I, A; ESMO-MCBS v1.1 score: 4].
Targeted therapy approaches are also being investigated in patients with endometrial cancer. Uterine serous carcinoma (USC) is an aggressive endometrial cancer subtype associated with a poor outcome.89 One-third of USCs overexpress HER2/Neu,35 a target for trastuzumab in breast cancer. A small randomised phase II trial for the addition of trastuzumab to paclitaxel/carboplatin compared with paclitaxel/carboplatin alone in stage III-IV or recurrent USC demonstrated a meaningful benefit for PFS [hazard ratio (HR) 0.46, $$P \leq 0.005$$] and overall survival (HR 0.58).The benefit for stage III-IV was greater than in recurrent disease.37 The cyclin-dependent kinase inhibitor palbociclib has shown superiority in combination with letrozole in previously treated patients with ER-positive disease in the phase II ENGOT EN3 PALEO trial,90 and the WEE1 inhibitor adavosertib has been investigated in heavily pre-treated patients with serous tumours.91 Future directions include immune checkpoint blockade strategies in combination with other targeted therapies, immunotherapeutic agents, chemotherapy and RT.20
## Follow-up, long-term implications and survivorship—recommendations 5a-e
There is no evidence from randomised studies to support intensive, clinician-led, hospital-based, follow-up evaluations for patients with endometrial cancer and no consensus on what surveillance tests should be carried out.20,92 Thus, clinical monitoring can be adjusted according to the risk factors of the patient.
There was considerable discussion amongst the Asian experts about the frequency of follow-up appointments with no evidence of a survival benefit from intensive versus minimalist follow-up, even in high-risk patients, as demonstrated by the results of the European multicentre phase III TOTEM trial.93 Furthermore, the evidence showed that there was no need to add routine vaginal cytology, laboratory investigations or imaging to the minimalist follow-up strategies.
Thus, ‘recommendation 5a’ was modified very slightly as per the bold text below.
5a. For low-risk endometrial cancer, the proposed surveillance is at least every 6 months for the first 2 years and then yearly until 5 years. A physical and gynaecological examination should be performed at each follow-up [V, C; consensus = $100\%$].
With regard to ‘recommendation 5b’ the experts were concerned that access to phone follow-up would be difficult in certain regions. Therefore, ‘recommendation 5b’ (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744) was reworded to: 5b. In the low-risk group, remote follow-up can be integrated into hospital-based follow-up [II, B; consensus = $100\%$].
The Asian experts accepted ‘recommendations 5c, d and e’ below without change despite concern over the frequency/timing of follow-up in ‘recommendation 5c’.
5c. For the high-risk groups, physical and gynaecological examinations are recommended every 3 months for the first 3 years, and then every 6 months until 5 years [V, C].
5d. A CT scan or PET–CT could be considered in the high-risk group, particularly if node extension was present [V, D].
5e. Regular exercise, healthy diet and weight management should be promoted with all endometrial cancer survivors [II, B].
## Availability of diagnostic tests, drugs and equipment
Following the virtual face-to-face meeting hosted by ISMPO, the Pan-Asian panel of experts agreed with and accepted completely ($100\%$ consensus) the adapted ESMO guidelines listed in Table 1.
The drug and treatment availability for each of the 10 Asian countries is summarised in Supplementary Table S6, available at https://doi.org/10.1016/j.esmoop.2022.100744, and the ESMO-MCBSs for the different systemic therapy options and new therapy combinations for the treatment of endometrial cancer are presented in Supplementary Table S7, available at https://doi.org/10.1016/j.esmoop.2022.100744, and %%=%%=+%%=+https://www.esmo.org/guidelines/esmo-mcbs/esmo-mcbs-scorecards?mcbs_score_cards_form5BsearchText5D%mcbs_score_cards_form%5Btumour-type%5D=Gynaecological+Malignancies&mcbs_score_cards_form%5Btumour-sub-type%5D=Endometrial+Cancer. There was only one area of discrepancy in terms of diagnostic tests, drugs and equipment. This was POLE hotspot mutation analysis and the lack of/limited availability of such analysis in five of the Asian countries represented at the meeting.
## Conclusions
The results of voting by the Asian experts before (Supplementary Table S2, available at https://doi.org/10.1016/j.esmoop.2022.100744) and after the virtual/face-to-face working meeting showed >$80\%$ concordance (Table 1) with the ESMO recommendations for the treatment of patients with endometrial cancer. Following the virtual ‘face-to-face’ discussions, revisions were made to the wording of ‘recommendations 3e, 3f, 3j, 3l, 3q.4, 3r.3, 4a, 4k and 5b’ (Table 1), and resulted in the achievement of $100\%$ consensus for all the recommendations listed in Table 1.
Thus, the recommendations detailed in Table 1 can be considered the consensus clinical practice guidelines for the treatment of patients with endometrial cancer in Asia. As mentioned previously, the acceptance of each recommendation by each of the Asian experts was based on the available scientific evidence and was independent of the approval and reimbursement status of certain procedures and drugs in the individual Asian countries. A summary of the availability of the recommended treatment modalities and recommended drugs, as of July 2022, is presented for each participating Asian country in Supplementary Table S6, available at https://doi.org/10.1016/j.esmoop.2022.100744, and will impact on some management strategies that can be adopted by certain Asian countries.
## Supplementary data
Supplementary material 1 Supplementary material 2 Supplementary material 3 Supplementary material 4 Supplementary Table S1 Supplementary Table S2 Supplementary Table S3 Supplementary Table S4 Supplementary Table S5 Supplementary Table S6 Supplementary Table S7
## Funding
All costs relating to this consensus conference were covered by the ESMO and the ISMPO from central dedicated funds. There was no external funding of the event or the manuscript production.
## Disclosure
DB is a board member of the Society of Gynaecologic Oncologists of the Philippines; GC has received consulting fees from BMS, Roche, Pfizer, Eli Lilly, AstaZeneca, Daichii-Sankyo, Merck, Seagen and Ellipsis, received honoraria from Pfizer, Eli Lilly and Relay and support for attending meetings from Daichii-Sankyo, all outside the submitted work; TD declares fees for an advisory board from MSD, Pfizer, Eisai and Zuellig Pharma, as an invited speaker from AstraZeneca, Norvartis, Celtrion and Zeullig Pharma and role as a PI for Roche; SG declares honoraria from Lupin, Roche, Novartis, Eli Lilly, Eisai, Cipla, CADILA, Intas and AstraZeneca; honoraria for being on committees of the Indian Council of Medical Research (Government of India), Council of Scientific and Industrial Research (Government of India), Department of Biotechnology (Government of India), India Alliance and institutional; honoraria from Novartis and AstraZeneca for participation in steering committees; leadership roles include President of Indian Society of Medical and Paediatric Oncology and General Secretary of Women’s Cancer Initiative—Tata Memorial Hospital, both roles unpaid; KH declares fees for advisory boards from Chugai Astra Zeneca, and Takeda, invited speaker fees/honoraria from AstraZeneca, Takeda, MSD, Chugai and Daiichi-Sankyo and institutional research grants from Daiichi Sankyo and Merck; CHL declares fees from MSD Advisory board, DMC member for Novartis and Hygeia and local PI for ENGOT-en11and KEYNOTE 826 trials, President of TGOG; DL reports consultant honoraria from AstraZeneca, Clovis Oncology, GSK, MSD, ImmunoGen, Genmab, Amgen, Seagen and PharmaMar, invited member of advisory boards for Merck Serono, Seagen, Immunogen, Genmab, Oncoinvest, Corcept, Sutro; invited speaker and member of advisory boards and receives direct research funding from Clovis Oncology, GSK, MSD and PharmaMar, institutional funding for clinical trials/contracted research from AstraZeneca, Clovis Oncology, GSK, MSD, Genmab, Seagen, ImmunoGen, Incyte, Novartis and Roche and participates in non-remunerated activities as principal investigator and non-remunerated leadership roles for GCIC; RAM declares fees for Esisai advisory board, MSD expert testimony expert input forum Specialised Therapeutics and acting as local PI for AstraZenca and Novartis; AO has served on advisory boards for AstraZeneca Farmaceutica Spain, SA, AstraZeneca KK, Clovis Oncology, Corcept Therapeutics, Deciphera Pharmaceutical, Eisai Europe Limited, EMD Serono, Inc., Roche, GlaxoSmithKline (GSK), Got It Consulting SL, Immunogen, Merck Sharp & Dohme (MSD) de España, Mersana Therapeutics, Novocure GmbH, PharmaMar, PRIME Oncology, Roche, Shattuck Labs, Inc., Sutro Biopharma, Agenus and Tesaro and received support for travel or accommodation from Roche, AstraZeneca and PharmaMar, research funding paid directly to institution from AbbVie Deutschland, Ability Pharma, Advaxis Inc., Aeterna Zentaris, Amgen SA, Aprea Therapeutics AB, Clovis Oncology, Eisai, F. Hoffmann-La Roche, Regeneron Pharmaceuticals, ImmunoGen, MSD de España SA, Millennium Pharmaceuticals, PharmaMar SA, Tesaro and Bristol Myers Squibb (BMS), participation to non-remunerated activities and non-remunerated leadership roles for GCIC and GEICO; SP declares fees for consultancy/advisory roles from Abbvie, Amgen, Arcus, AstraZeneca, Bayer, Beigene, Bio Invent, Biocartis, Blueprint Medicines, Boehringer Ingelheim, BMS, Daiichi Sankyo, Debiopharm, ecancer, Eli Lilly, Elsevier, F-star, Foundation Medicine, Genzyme, Gilhead, GSK, Illumina, Incyte, IQIVIA, iTHeos, Janssen, Medscape, MSD, Merck Serono, Mirati, Novartis, Novocure, Pharma Mar, Phosplatin Therapeutics, Pfizer, Regeneron, Roche/Genentech, Sanofi, SeaGen, Takeda, Vaccibody, speaker roles for AstraZeneca, Boehringer Ingelheim, BMS, ecancer, Eli Lilly, Fishawack, Illumina, Imedex, Medscape, Mirati MSD, Novartis, Oncology Education, PER, Pfizer, PRIME, RMEI, Roche/Genentech, RTP, Sanofi, Takeda and steering committee and trial chair roles as follows: AstraZeneca, coordinating PI, institutional, no financial interest, MERMAID-1; AstraZeneca, Steering Committee Member, institutional, no financial interest, MERMAID-2, POSEIDON, MYSTIC; Beigene, Steering Committee Member, institutional, no financial interest, BGB-A317-A1217-301/AdvanTIG-301; BMS, Steering Committee Member, institutional, no financial interest, Clinical Trial Steering committee CheckMate 743, CheckMate 73L, CheckMate 331 and 451; BMS, Steering Committee Member, institutional, no financial interest, RELATIVITY 095; GSK, Trial Chair, institutional, no financial interest, Clinical Trial Chair ZEAL-1; iTeos, Steering Committee Member, institutional, no financial interest, phase 2 Inupadenant with chemo; Mirati, Steering Committee Member, institutional, no financial interest, Clinical Trial Steering Committee SAPPHIRE; MSD, Steering Committee Member, institutional, no financial interest, Clinical Trial Steering Committee PEARLS, MK-7684A; Pharma Mar, Steering Committee Member, institutional, no financial interest, LAGOON; Phosplatin Therapeutics, Steering Committee Member, institutional, no financial interest, phase $\frac{1}{2}$ trials; Roche/Genentech, Trial Chair, institutional, no financial interest, Clinical Trial Chair Skyscraper-01; chair ALEX; steering committee BFAST; steering committee BEAT-Meso; steering committee ImPower-030, IMforte. Also role as ESMO president, ETOP/EORTC/SAKK PI, involved in academic trials, ETOP/IBCSG partners’ officer. Council member and scientific chair, SAKK, vice-President Lung Group, SAMO, Vice President. SK declares honoraria from Novartis and Eli Lilly; HS declares honoraria from Sun Pharma, the Philippines and support for attending an investigator meeting from DebioPharm; WW declares invited speaker for AstraZeneca and Innovent (Suzhou) Co. MY declares fees as an invited speaker for AstraZeneca, Eisai, Merck and Co Inc., MOCHIDA pharmaceuticals, MSD, Chugai Taiho and Takeda and for expert testimony from MSD, Chugai, Takeda and Eisai; TY declares institutional grants from Amgen, Boehringer Ingelheim, Chugai, Daiichi Sankyo, Genomedia, MSD, Ono, Pfizer, Sanofi, Sysmex and Taiho, and honoraria Bayer, Chugai, Merck biopharma, MSD and Ono. All other authors have declared no conflicts of interest.
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|
---
title: Differences in the Association Between Alcoholic Beverage Type and Serum Urate
Levels Using Standardized Ethanol Content
authors:
- Sho Fukui
- Masato Okada
- Mahbubur Rahman
- Hiroki Matsui
- Atsushi Shiraishi
- Takehiro Nakai
- Hiromichi Tamaki
- Mitsumasa Kishimoto
- Hiroshi Hasegawa
- Takeaki Matsuda
- Kazuki Yoshida
journal: JAMA Network Open
year: 2023
pmcid: PMC10024203
doi: 10.1001/jamanetworkopen.2023.3398
license: CC BY 4.0
---
# Differences in the Association Between Alcoholic Beverage Type and Serum Urate Levels Using Standardized Ethanol Content
## Key Points
### Question
Are there differences in the association of serum urate levels with consumption of alcohol, including Japanese traditional beverages, when intake is standardized for ethanol content?
### Findings
In this cross-sectional study using medical checkup data from 78 153 Japanese participants, differences were observed in the association of serum urate levels with alcohol consumption even after ethanol content was standardized. Consumption of beer and wine was associated with high and moderate increases in serum urate levels, respectively; in contrast, sake was associated with a modest increase in serum urate levels.
### Meaning
The results of this study suggest that alcoholic beverage type, in addition to ethanol content, should be considered as a contributing factor to high serum urate levels.
## Abstract
This cross-sectional study examines differences in the association of serum urate levels with consumption of alcohol, including Japanese traditional beverages such as sake and shochu, using intake standardized for ethanol content.
### Importance
Differences have been observed in the association of serum urate levels with consumption of different types of alcoholic beverages. However, previous studies have not standardized the unit of intake for ethanol content, and only limited types of alcoholic beverages have been evaluated.
### Objective
To examine differences in the association of serum urate levels with various types of alcoholic beverages when their intakes are standardized for ethanol content.
### Design, Setting, and Participants
This retrospective cross-sectional study was conducted using data from participants aged 20 years or older who completed a medical checkup at St Luke’s International University in Japan between October 1, 2012, and October 31, 2021. Participant demographics, blood test results, and lifestyle questionnaire data were used as covariates. Analysis was performed in December 2021.
### Exposures
Consumption of alcoholic beverages, including beer, sake (rice wine), shochu (Japanese spirit), wine, and whiskey.
### Main Outcomes and Measures
Serum urate levels were measured during the medical checkup. The beverage unit was standardized to 1 standard drink, which contained 20 g of ethanol. Multivariable linear regression including interaction terms of alcohol consumption and dominant alcoholic beverage was performed.
### Results
This study included 78 153 participants. Their mean (SD) age was 47.6 (12.8) years; 36 463 ($46.7\%$) were men and 41 690 were women ($53.3\%$). A total of 45 755 participants ($58.5\%$) were regular alcohol drinkers. Consistent associations of serum urate levels with alcohol consumption were observed in the beer-dominant group, with β coefficients (for 1 standard drink per day) of 0.14 mg/dL ($95\%$ CI, 0.11-0.17 mg/dL; $P \leq .001$) for men and 0.23 mg/dL ($95\%$ CI, 0.20-0.26 mg/dL; $P \leq .001$) for women. A moderate increase in serum urate levels was observed in the wine-dominant group compared with a modest and nonsignificant increase in the sake-dominant group, with β coefficients (for 1 standard drink per day) for the latter group of 0.05 mg/dL ($95\%$ CI, −0.01 to 0.10; $$P \leq .10$$) for men and 0.04 mg/dL ($95\%$ CI, −0.05 to 0.14 mg/dL; $$P \leq .38$$) for women. Restricted cubic splines showed different patterns in associations of serum urate levels with ethanol intake by dominant alcoholic beverages.
### Conclusions and Relevance
The results of this study suggest that the extent of the association of serum urate levels with alcohol intake was different for alcoholic beverages even after ethanol content was standardized. Higher beer consumption among men and women was consistently associated with higher serum urate levels, whereas sake was not associated with changes in serum urate levels. Therefore, alcoholic beverage type, in addition to ethanol content, should be considered as a factor contributing to hyperuricemia.
## Introduction
Uric acid is an end product of purine metabolism.1 *Elevated serum* urate levels are a well-known precursor of gout, which is caused by deposition of monosodium urate crystals. The prevalence of hyperuricemia has been increasing worldwide2,3 and has attracted increasing attention as an independent risk factor for cardiovascular disease4 and an associated increase in mortality.5 In addition, previous studies suggest that high serum urate levels are associated with hypertension,6 peripheral arterial disease,7 diabetes,8 and chronic kidney disease.9,10 Serum urate levels increase in association with intake of a broad range of foods and drinks11,12 and with medical conditions such as diabetes and kidney dysfunction.13 Alcohol consumption is the primary dietary risk factor contributing to high serum urate levels.11 The ethanol in alcoholic beverages plays an important role in changing serum urate levels by increasing uric acid production14,15 and decreasing the elimination of uric acid to urine by modulating kidney tubule function.16 Other ingredients in alcoholic beverages (eg, purines) can also affect serum urate levels.17 Therefore, the influence of serum urate levels differs by alcoholic beverage type (eg, beer, liquor, and wine).18 *In previous* studies, the ethanol content in 1 alcoholic beverage unit has not been standardized, and only limited types of alcoholic beverages have been evaluated. These limitations make it difficult to compare the influence of alcoholic beverage type regardless of ethanol content. Furthermore, the popularity of Japanese alcoholic beverages such as sake (Japanese rice wine) and shochu (Japanese spirit) is increasing worldwide. However, there are limited studies regarding the association of alcoholic beverage intake with serum urate levels.19,20 *For this* study, we used medical checkup data to examine differences in serum urate levels associated with alcoholic beverage types in Japan. We compared the association of serum urate levels with consumption of alcoholic beverages, including sake and shochu, by standardizing intake for ethanol content.
## Study Design and Setting
This cross-sectional study was approved by the St Luke’s International University Institutional Review Board. The study was conducted based on the Declaration of Helsinki and relevant ethical guidelines for medical research in Japan. Written informed consent was waived because of the retrospective design, with options for opting out. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.
We used data from the St Luke’s Health Check-up Database (SLHCD) for October 1, 2012, to October 31, 2021. The SLHCD collects data on participant demographics and results from medical checkups performed at the Center for Preventive Medicine at St Luke’s International Hospital Tertiary Care Center in Tokyo, Japan. In Japan, all employers are required to encourage employees to undergo medical checkups, including laboratory testing, at least annually.21 In our medical checkups, participants were routinely asked to answer a questionnaire regarding their medical history and lifestyle factors. They also underwent a physical examination, determination of height and weight, blood testing, other assessments (electrocardiogram and chest radiography), and subsequent medical interviews to obtain recommendations for lifestyle modification.
## Study Participants and Data Collection
We included consecutive participants aged 20 years or older who had undergone a medical checkup and completed the lifestyle questionnaire. We excluded individuals who refused to participate in the research, were receiving pharmacotherapy for hyperuricemia at assessment, answered that they drink “other types of alcoholic beverages,” or reported drinking more than 10 standard alcoholic drinks per day. Individuals who consumed more than 10 alcoholic drinks per day were considered very heavy drinkers, with an average amount of daily alcohol consumption equivalent to high-intensity drinking.22 Heavy drinking may potentially lower serum urate levels as a result of kidney tubular dysfunction.23,24 We used data from the first medical checkup during the study period for each participant. Data on participant demographics (eg, age, sex, body mass index [BMI], serum urate blood test results, and estimated glomerular filtration rate [eGFR]), prespecified medical conditions, and treatment status were retrieved from the SLHCD. We used data on medication use for angina, myocardial infarction, transient ischemic attack, cerebral infarction, hypertension, diabetes, dyslipidemia, chronic kidney disease, tuberculosis, and nontuberculosis mycobacteria infections because these conditions can be treated with medications that can potentially change serum urate levels.25 Examples of these medications include aspirin, diuretics, calcium blockers, angiotensin receptor blockers, sodium-glucose cotransporter 2 inhibitors, statins, pyrazinamide, and ethambutol. The lifestyle questionnaires included smoking status (never, previous, or current), daily physical activity (very low, low, moderate, or high), exercise level (<1, 1-2, 3-5, or >5 days per week with at least 20 minutes of light, sweaty exercise), and frequency of dietary intake of carbohydrate (eg, rice, bread, and noodles), meat and eggs, seafood, vegetables, fruits, milk and milk products, soy, fat (eg, fried foods, animal fat, and other fatty meals), and sweets.
## Measurement of Alcohol Consumption and Definition of Dominant Alcoholic Beverage Groups
The lifestyle questionnaire asked participants whether they were regular alcohol drinkers (≥1 alcoholic beverage per week). If they answered yes, they were then asked to report the frequency of alcohol consumption (days per week) and the average amount of each alcoholic beverage type consumed. The types and amounts of alcoholic beverages were recorded based on daily consumption as the number of the following: small cans (350 mL), large cans (500 mL), or large bottles (633 mL) of beer; gou (Japanese traditional alcohol units) of sake; glasses of shochu (Japanese spirit; usually 100 mL/glass); glasses of wine (usually 120 mL); a single shot of whiskey (30 mL); and glasses of other beverages.
We calculated the ethanol content in these beverages using the standardized alcohol content as follows: $5\%$ for beer, $15\%$ for sake, $25\%$ for shochu, $12\%$ for wine, and $40\%$ for whiskey. The ethanol content for a unit of each alcoholic beverage was converted to a standard drink, which included the same ethanol content to make the comparison easier. Because there is no universal standard drink unit in Japan, we defined a standard drink as a unit that includes 20 g of pure ethanol based on the recommended upper limit of daily alcohol consumption by the Japanese Ministry of Health, Labor, and Welfare. One standard drink is equivalent to 500 mL of beer, 167 mL (0.93 gou) of sake, 100 mL of shochu (approximately 1 glass), 208 mL of wine (approximately 1.7 glasses), or 62.5 mL of whiskey (approximately 2 shots).
A dominant alcoholic beverage was defined as an alcoholic beverage type that accounted for $75\%$ or more of total ethanol consumption. Participants were classified according to their dominant alcoholic beverage type. If participants had no dominant beverage type, they were classified as a mixed-alcohol group.
## Statistical Analysis
First, we described the characteristics of participants overall and by groups of dominant alcoholic beverages. Then we performed multivariable linear regression analyses to evaluate the association of serum urate levels (in milligrams per decaliter) with alcoholic beverage consumption. In these analyses, serum urate level was set as a dependent variable, which was normally distributed; independent variables of interest included total alcohol consumption and consumption of each alcoholic beverage (standard drinks per day). In addition, we evaluated the association of alcohol consumption with hyperuricemia (defined as serum urate levels ≥7 mg/dL for men and ≥6 mg/dL for women, based on previous studies26) using a multivariable logistic regression model. In these analyses, age, sex, BMI, eGFR, medication use, smoking status, daily physical activity, exercise level, and dietary questionnaire results were used as covariates for adjustments. Each lifestyle or dietary factor (described in eTables 1 and 2 in Supplement 1) was adjusted as a categorical variable, with the answer indicating the most infrequent factor as a reference. In addition, the amounts of other alcoholic beverages were also adjusted when we focused on a specific type of alcoholic beverage as an exposure of interest. All analyses were stratified by sex because a large difference in serum urate levels between men and women has been observed.3 Second, we directly compared the association of serum urate levels with alcohol intake in individual dominant alcoholic beverage groups. Considering that ethanol content is the most important factor for increased serum urate levels, we included interaction terms of total alcohol consumption and dominant alcoholic beverage in the multivariable linear model using the same covariates as in previous analyses. Serum urate levels were then estimated using daily alcohol consumption at the mean values of other covariates. The association of estimated serum urate level with daily alcohol consumption was visualized.
Thereafter, we developed more flexible models to assess the association of serum urate levels with alcohol consumption among each dominant group using a restricted cubic spline to evaluate the potential departure from a linear association. We used the mkspline function in Stata 17, version 17.1 (StataCorp LLC), with percentile knots (5th, 35th, 65th, and 95th percentiles). In addition, analyses with fixed knots of 1, 2, 3, and 4 standard drinks per day were performed as sensitivity analyses.
$P \leq .05$ in 2-tailed tests was considered significant for all analyses. Analysis was performed in December 2021.
## Participant Characteristics
A total of 78 153 participants were included in this study (eFigure 1 in Supplement 1). Their mean (SD) age was 47.6 (12.8) years; 36 463 ($46.7\%$) were men and 41 690 ($53.3\%$) were women. A total of 45 755 participants ($58.5\%$) were regular alcohol drinkers, and their mean (SD) consumption of total alcoholic beverages was 1.10 (1.13) standard drinks per day. Among men, there were 21 377 beer drinkers ($58.6\%$), 3090 sake drinkers ($8.5\%$), 5900 shochu drinkers ($16.2\%$), 4883 wine drinkers ($13.4\%$), and 2142 whiskey drinkers ($5.9\%$). Among women, there were 12 137 beer drinkers ($29.1\%$), 945 sake drinkers ($2.3\%$), 1515 shochu drinkers ($3.6\%$), 7722 wine drinkers ($18.5\%$), and 523 whiskey drinkers ($1.3\%$). These drinkers were not mutually exclusive, as 1 participant could drink several types of alcoholic beverages.
In contrast, the dominant alcoholic beverage groups were mutually exclusive. Beer-dominant drinking was most common among both men and women. Men more frequently consumed sake, shochu, and whiskey compared with women (Table 1), whereas wine was more common among women (Table 2). Results of the lifestyle and dietary questionnaires are summarized in eTables 1 and 2 in Supplement 1, respectively.
## Association of Total Alcohol Consumption and Alcoholic Beverage Consumption With Serum Urate Levels or Hyperuricemia
In a multivariable linear regression model, a 1-unit increase in total alcohol consumption was associated with serum urate levels, with β coefficients of 0.10 mg/dL ($95\%$ CI, 0.09-0.11 mg/dL; $P \leq .001$) for men and 0.14 mg/dL ($95\%$ CI, 0.12-0.15 mg/dL; $P \leq .001$) for women. In a model including all alcoholic beverages, consumption of most beverages showed associations with serum urate levels, whereas sake consumption among women was not significant (β = 0.04 mg/dL [$95\%$ CI, −0.01 to 0.09 mg/dL]; $$P \leq .14$$). Although ethanol content was standardized, the extent of serum urate variations per consumption of 1 standard drink of each beverage differed. Beer and whiskey were associated with the highest increases in serum urate levels, followed by wine and shochu. Sake was associated with modest increases in serum urate levels among men and women (Table 3). The extent of the association of alcohol consumption with hyperuricemia in multivariable logistic regression was also different among alcoholic beverages (eTable 3 in Supplement 1).
**Table 3.**
| Variable | Men | Men.1 | Women | Women.1 |
| --- | --- | --- | --- | --- |
| Variable | β (95% CI), mg/dL | P value | β (95% CI), mg/dL | P value |
| Total alcohol consumptiona | 0.10 (0.09 to 0.11) | <.001 | 0.14 (0.12 to 0.15) | <.001 |
| Alcoholic beverage typeb | | | | |
| Beer | 0.12 (0.10 to 0.14) | <.001 | 0.21 (0.19 to 0.23) | <.001 |
| Sake | 0.06 (0.03 to 0.09) | <.001 | 0.04 (−0.01 to 0.09) | .14 |
| Shochu | 0.08 (0.07 to 0.10) | <.001 | 0.09 (0.06 to 0.12) | <.001 |
| Wine | 0.10 (0.07 to 0.13) | <.001 | 0.10 (0.07 to 0.13) | <.001 |
| Whiskey | 0.19 (0.14 to 0.23) | <.001 | 0.16 (0.10 to 0.23) | <.001 |
## Association of Serum Urate Levels With Alcohol Consumption for Each Type of Dominant Alcoholic Beverage
We performed multivariable linear regression analyses including an interaction term between daily alcohol consumption and dominant alcoholic beverage type. We observed differences in the association of serum urate levels with ethanol intake among the groups. Whereas alcohol consumption of 1 standard drink in the beer-dominant group was consistently associated with higher serum urate levels for men (0.14 mg/dL [$95\%$ CI, 0.11-0.17 mg/dL]; $P \leq .001$) and women (0.23 mg/dL [$95\%$ CI, 0.20-0.26 mg/dL]; $P \leq .001$), the association in the sake-dominant group was not statistically significant for men (0.05 mg/dL [$95\%$ CI, −0.01 to 0.10 mg/dL]; $$P \leq .10$$) and women (0.04 mg/dL [$95\%$ CI, –0.05 to 0.14 mg/dL]; $$P \leq .38$$) (eTable 4 in Supplement 1). Among men, the beer- and whiskey-dominant groups had the steepest slopes in estimated serum urate levels along with alcohol consumption (Figure); the wine-dominant group had an intermediate slope, whereas the other groups had less steep slopes. Among women, the beer-dominant group had the steepest slope in serum urate levels along with alcohol consumption; the wine- and shochu-dominant groups had moderate slopes, whereas the sake- and whiskey-dominant groups had less steep slopes.
**Figure.:** *Estimated Serum Urate Levels and Alcohol Consumption for Each Dominant Alcoholic Beverage GroupSerum urate levels were estimated using multivariable linear regression including an interaction term between daily alcohol consumption and dominant alcoholic beverage. Lines and shaded areas represent estimated values and 95% CIs, respectively. A standard drink indicates 500 mL of beer, 167 mL (0.93 gou) of sake, 100 mL of shochu, 208 mL of wine, or 62.5 mL of whiskey.*
We set a beer-dominant group as a reference for comparison because beer is the most common alcoholic beverage consumed worldwide. Compared with the beer-dominant groups, the model with interaction terms revealed significant differences in the association of serum urate levels with alcohol consumption (per 1 standard drink) among men in the sake-dominant group (−0.09 mg/dL [$95\%$ CI, −0.15 to −0.03 mg/dL]; $$P \leq .004$$), shochu-dominant group (−0.08 mg/dL [$95\%$ CI, −0.12 to −0.05 mg/dL]; $P \leq .001$), and mixed group (−0.08 mg/dL [$95\%$ CI, −0.12 to −0.05 mg/dL]; $P \leq .001$). For women, significant differences were also observed in all other beverage groups, including sake (−0.19 mg/dL [$95\%$ CI, −0.29 to −0.09 mg/dL]; $P \leq .001$), shochu (−0.12 mg/dL [$95\%$ CI, −0.17 to −0.06 mg/dL]; $P \leq .001$), wine (−0.11 mg/dL [$95\%$ CI, −0.16 to −0.06 mg/dL]; $P \leq .001$), whiskey (−0.17 mg/dL [$95\%$ CI, −0.28 to −0.06 mg/dL]; $$P \leq .002$$), and mixed (−0.15 mg/dL [$95\%$ CI, −0.20 to −0.11 mg/dL]; $P \leq .001$) (Table 4).
**Table 4.**
| Variable | Men | Men.1 | Women | Women.1 |
| --- | --- | --- | --- | --- |
| Variable | β Coefficient (95% CI), mg/dLa | P value | β Coefficient (95% CI), mg/dLa | P value |
| Alcohol consumption of 1 standard drink/d in beer-dominant groupb | 0.14 (0.11 to 0.17) | <.001 | 0.23 (0.20 to 0.26) | <.001 |
| Comparison with other beverage groups | | | | |
| Beer | 0 [Reference] | | 0 [Reference] | |
| Sake | −0.09 (−0.15 to −0.03) | .004 | −0.19 (−0.29 to −0.09) | <.001 |
| Shochu | −0.08 (−0.12 to −0.05) | <.001 | −0.12 (−0.17 to −0.06) | <.001 |
| Wine | −0.02 (−0.08 to 0.04) | .44 | −0.11 (−0.16 to −0.06) | <.001 |
| Whiskey | 0.04 (−0.05 to 0.14) | .34 | −0.17 (−0.28 to −0.06) | .002 |
| Mixed | −0.08 (−0.12 to −0.05) | <.001 | −0.15 (−0.20 to −0.11) | <.001 |
## Modeling of Association of Serum Urate Levels With Alcohol Consumption Using a Restricted Cubic Spline
To examine the degree of departure from the linear association, we developed more flexible models using a restricted cubic spline with 4 percentile knots (5th, 35th, 65th, and 95th percentiles)27 for each dominant alcoholic beverage group. The associations were expressed as almost linear. Among men and women, the beer-dominant groups showed an increase in serum urate levels with alcohol consumption. As for the whiskey-dominant group, serum urate levels were associated with alcohol consumption among men, but there was no such trend among women (with wide $95\%$ CIs). In the sake-dominant group, the slope was less steep, particularly in women (eFigure 2 in Supplement 1). We also used a restricted cubic spline with fixed knots (1, 2, 3, or 4 drinks per day) for each dominant alcoholic beverage group in sensitivity analyses. Trends in slopes were similar to those with percentile knots for men and women (eFigure 3 in Supplement 1).
## Discussion
In this cross-sectional study, we observed differences in the association of serum urate levels with consumption of various alcoholic beverages. Beer and whiskey consumption among men and beer consumption among women were consistently associated with larger increases in serum urate levels than other alcoholic beverages. In contrast, sake consistently showed a modest elevation of serum urate levels with increased intake. In this study, the association of serum urate levels with beer consumption was approximately 2 to 5 times that with sake.
Ethanol in alcoholic beverages is known as a major component that elevates serum urate levels by increasing uric acid production and decreasing urinary excretion of uric acid.14,16 *In previous* studies, the ethanol content in 1 unit of alcoholic beverage differed.18,28,29 *In this* study, we converted the unit of each alcoholic beverage to a standard drink containing the same ethanol content, allowing for comparison with a focus on alcoholic beverage type. Beer intake was associated with higher serum urate levels in this study, as also shown in previous studies.11,18,30 As for sake, only a few studies from Japan19,20 concluded that sake consumption was also associated with hyperuricemia. The outcome used in those studies was hyperuricemia (serum urate level ≥7 mg/dL), which was different from the outcome of serum urate levels as a continuous variable in this study. In addition, confounding by dietary factors was not adjusted in these studies. Although the association of serum urate levels with wine consumption is under debate,11,18,30 this study supports the idea that wine consumption is associated with a substantial but milder increase in serum urate levels. We observed that whiskey consumption was consistently associated with high serum urate levels in men; the same trend was observed in the analyses without dominant alcoholic beverage groups in women. These results were compatible with those from previous studies.11,18,30 However, the results from analyses including an interaction term in women were inconsistent, which might be explained by the decreased number of female whiskey-dominant drinkers when dominant beverage groups were created.
Considering that ethanol content was standardized in this study, the difference in serum urate change may be explained by variation in other factors among beverages. In addition to ethanol, other components such as purines, which are metabolized to uric acid, are known to influence serum urate levels.17 Beer contains the highest amount of purines, while other beverages include small but differing amounts.31 Moreover, total energy intake may be associated with serum urate levels via obesity, and beer usually has higher energy than other beverages.26,32,33 Other components—such as ingredients with antioxidant properties, like polyphenols—may have a potential role in ameliorating serum urate levels, since serum urate can increase in response to oxidative stress.18 Antioxidants such as polyphenols are included predominantly in wine34; sake also includes other antioxidants such as ferulic acid, which may be associated with sake intake and serum urate levels. Furthermore, the gut microbiota can potentially mediate between alcohol consumption and changes in serum urate levels. A previous study suggests that both acute and chronic alcohol consumption can modify microbiome composition.35 Beer was associated with changes in gut microbiome variety in a Japanese cohort.36 An association of gut microbiome variety with serum urate levels has also been observed.37 While our study showed an association between serum urate levels and whiskey consumption among men as reported in previous studies,11,18 whiskey contains only a small amount of purines. One study even suggested a urate-lowering property of whiskey in the short term.38 Further studies investigating physiologic mechanisms are needed. Because individuals who purchase different alcoholic beverages tend to buy different foods,39,40 we should also consider residual confounding, particularly for dietary habits, which might not be fully adjusted by the dietary questionnaire.
Previous studies revealed that genetic variance plays an important role in the development of hyperuricemia41 and its influence can be larger than that of dietary factors.11 Dietary factors are still important because they are modifiable.42 Dietary modification may be considered as a potential intervention, although the effects seem modest, in addition to pharmacotherapy with urate-lowering medications.32 Considering that genetic variance plays an important role in hyperuricemia, there may be a specific population for which alcohol consumption contributes to greater increases in serum urate levels. Approaches to the high-risk population with consideration of gene-diet interactions should be applied in the future.
## Strengths and Limitations
This study has several strengths. First, to our knowledge, this is the largest study to analyze the association of serum urate levels with alcohol consumption. This study also used flexible modeling with a spline, suggesting an almost linear association of serum urate levels with alcohol consumption. In addition, we included various types of alcoholic beverages, including sake (Japanese rice wine), which is becoming more popular worldwide. Finally, we directly compared serum urate levels in association with alcoholic beverages by establishing dominant alcoholic beverage groups and using interaction analyses.
This study also has several limitations. In this single-center study, our target population was participants who had received medical checkups, and most lived in urban areas in Japan. These aspects of the study can limit the generalizability of our findings. The questionnaires used in these medical checkups were not standardized instruments such as food frequency questionnaires.28 The self-report questionnaire might cause misclassification, which could differ by alcoholic beverage type. This is a cross-sectional study, and we did not directly examine how changing the amount of alcohol intake or switching the dominant drink may impact future serum urate levels. Additionally, there may be unadjusted confounders such as potential variations in dietary habits.
## Conclusions
In this cross-sectional study, there were differences in the association of serum urate levels with alcohol intake among various types of alcoholic beverages, even when ethanol intake was standardized. Beer was associated with increased serum urate levels among both men and women. In contrast, sake consistently had a modest influence. In addition to the amount of total alcohol consumption, the type of alcoholic beverage was associated with serum urate levels.
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|
---
title: Effects of Time-Restricted Eating on Nonalcoholic Fatty Liver Disease
authors:
- Xueyun Wei
- Bingquan Lin
- Yan Huang
- Shunyu Yang
- Chensihan Huang
- Linna Shi
- Deying Liu
- Peizhen Zhang
- Jiayang Lin
- Bingyan Xu
- Dan Guo
- Changwei Li
- Hua He
- Shiqun Liu
- Yaoming Xue
- Yikai Xu
- Huijie Zhang
journal: JAMA Network Open
year: 2023
pmcid: PMC10024204
doi: 10.1001/jamanetworkopen.2023.3513
license: CC BY 4.0
---
# Effects of Time-Restricted Eating on Nonalcoholic Fatty Liver Disease
## Key Points
### Question
Is time-restricted eating more effective in improving nonalcoholic fatty liver disease than daily calorie restriction?
### Findings
In this randomized clinical trial including 88 patients with obesity and nonalcoholic fatty liver disease, the intrahepatic triglyceride content was reduced by $6.9\%$ in the time-restricted eating group and $7.9\%$ in the daily calorie restriction group during 12 months, but with no significant between-group differences. Time-restricted eating also did not produce additional benefits for reducing body fat or major metabolic risk factors compared with daily calorie restriction.
### Meaning
The findings of this randomized clinical trial support the importance of caloric restriction with use of time-restricted eating among adults with obesity and nonalcoholic fatty liver disease.
## Abstract
This randomized clinical trial compares the use of time-restricted eating with daily calorie restriction in adults with obesity and nonalcoholic fatty liver disease.
### Importance
The efficacy and safety of time-restricted eating (TRE) on nonalcoholic fatty liver disease (NAFLD) remain uncertain.
### Objective
To compare the effects of TRE vs daily calorie restriction (DCR) on intrahepatic triglyceride (IHTG) content and metabolic risk factors among patients with obesity and NAFLD.
### Design, Setting, and Participants
This 12-month randomized clinical trial including participants with obesity and NAFLD was conducted at the Nanfang Hospital in Guangzhou, China, between April 9, 2019, and August 28, 2021.
### Interventions
Participants with obesity and NAFLD were randomly assigned to TRE (eating only between 8:00 am and 4:00 pm) or DCR (habitual meal timing). All participants were instructed to maintain a diet of 1500 to 1800 kcal/d for men and 1200 to 1500 kcal/d for women for 12 months.
### Main Outcomes and Measures
The primary outcome was change in IHTG content measured by magnetic resonance imaging; secondary outcomes were changes in body weight, waist circumference, body fat, and metabolic risk factors. Intention-to-treat analysis was used.
### Results
A total of 88 eligible patients with obesity and NAFLD (mean [SD] age, 32.0 [9.5] years; 49 men [$56\%$]; and mean [SD] body mass index, 32.2 [3.3]) were randomly assigned to the TRE ($$n = 45$$) or DCR ($$n = 43$$) group. The IHTG content was reduced by $8.3\%$ ($95\%$ CI, −$10.0\%$ to −$6.6\%$) in the TRE group and $8.1\%$ ($95\%$ CI, −$9.8\%$ to −$6.4\%$) in the DCR group at the 6-month assessment. The IHTG content was reduced by $6.9\%$ ($95\%$ CI, −$8.8\%$ to −$5.1\%$) in the TRE group and $7.9\%$ ($95\%$ CI, −$9.7\%$ to −$6.2\%$) in the DCR group at the 12-month assessment. Changes in IHTG content were comparable between the 2 groups at 6 months (percentage point difference: −0.2; $95\%$ CI, −2.7 to 2.2; $$P \leq .86$$) and 12 months (percentage point difference: 1.0; $95\%$ CI, −1.6 to 3.5; $$P \leq .45$$). In addition, liver stiffness, body weight, and metabolic risk factors were significantly and comparably reduced in both groups.
### Conclusions and Relevance
Among adults with obesity and NAFLD, TRE did not produce additional benefits for reducing IHTG content, body fat, and metabolic risk factors compared with DCR. These findings support the importance of caloric intake restriction when adhering to a regimen of TRE for the management of NAFLD.
### Trial Registration
ClinicalTrials.gov Identifiers: NCT03786523 and NCT04988230
## Introduction
Nonalcoholic fatty liver disease (NAFLD) has become a major worldwide public health challenge.1 It affects approximately $20\%$ to $30\%$ of adults in the general population, and more than $70\%$ of patients with obesity and diabetes have NAFLD.2,3,4,5 Approximately $29.2\%$ of adults in the general population have NAFLD in China.6 *It is* closely related to obesity, type 2 diabetes, hyperlipidemia, and hypertension and has been associated with an increased risk of cardiovascular diseases.1,7 Weight loss via lifestyle modifications has been documented to improve liver fat and metabolic disorders.8 Dietary calorie restriction has been proven to be effective in reducing weight and intrahepatic lipid levels among patients with NAFLD.9,10,11 Nevertheless, long-term adherence to lifestyle modification is difficult. Time-restricted eating (TRE) is one of the most popular intermittent fasting regimens involving a specific eating period within a 24-hour cycle. The TRE regimen has gained attention because it reduces weight and enhances adherence.12,13 Studies in rodents suggest that food timing rather than calorie intake underlies the beneficial effects of TRE regimen.14,15 Evidence indicates that fat storage increases during the day and is the greatest after an evening meal.16 Observational studies suggest that eating meals later in the day may be associated with the success of weight loss therapy in humans.17,18 Several pilot clinical trials reported that TRE can result in reduced calorie intake and is associated with a decrease in body weight and fat mass in individuals with obesity.19,20,21,22 However, most of the reported benefits of TRE are either untested or undertested in humans and cannot isolate the effects of TRE itself. A small clinical trial reported that the regimen of eating 2 meals (eating periods from 6:00 am to 4:00 pm) reduced intrahepatic lipids measured by proton magnetic resonance spectroscopy compared with the control regimen (eating 6 smaller meals) among 54 patients with type 2 diabetes during 12 weeks’ intervention.23 To date, the efficacy of TRE on NAFLD is uncertain. Furthermore, to our knowledge, no studies compared the effects of TRE and daily calorie restriction (DCR) on intrahepatic lipid levels in patients with NAFLD.
The Time Restricted Feeding on Nonalcoholic Fatty Liver Disease (TREATY-FLD) randomized clinical trial aimed to compare the effects of TRE vs DCR on intrahepatic triglyceride (IHTG) content and metabolic risk factors among patients with obesity and NAFLD. We hypothesized that 8-hour TRE would be more effective than DCR in improving NAFLD and metabolic risk factors.
## Study Design
This randomized, parallel-group, observer-blinded clinical trial was designed to compare the effects of 8-hour TRE vs DCR on the IHTG content and metabolic risk factors among patients with NAFLD. Eligible trial participants were randomly assigned to the TRE or DCR program for 12 months. The duration of intervention of the original study design was 6 months (registered as NCT03786523); at the beginning of the study, we revised the design and prolonged the intervention to 12 months to compare the long-term effects of TRE vs DCR on NAFLD (registered separately as NCT04988230). The duration of the intervention included the original designed 6 months and the next 6 months follow-up visits. The trial protocol and statistical analysis plan are available in Supplement 1. Patient recruitment and intervention were conducted from April 9, 2019, through August 28, 2021, at the Nanfang Hospital in Guangzhou, China. The trial was overseen by a steering committee affiliated with the Southern Medical University Institutional Review Board. The study protocol and informed consent form were approved by institutional review boards of the Nanfang Hospital of Southern Medical University. All patients provided written informed consent before enrollment; no financial compensation was provided. The study follows the Consolidated Standards of Reporting Trials (CONSORT) reporting guideline for randomized clinical trials.
## Participants
All study participants were recruited from the public via promotional leaflets, posters, internet, and community screenings. All interested persons were prescreened to identify potential individuals aged 18 to 75 years with obesity (body mass index [BMI] between 28.0 and 45.0 [calculated as weight in kilograms divided by height in meters squared]) and ultrasonography-diagnosed NAFLD. After the prescreening, potential participants were invited to attend a screening magnetic resonance imaging examination at the study clinic. Those who had NAFLD confirmed by magnetic resonance imaging (IHTG content ≥$5\%$) were enrolled in this study. Among the criteria for exclusion were acute or chronic viral hepatitis, drug-induced liver disease, autoimmune hepatitis, diabetes, serious liver dysfunction, chronic kidney disease, excessive alcohol consumption (>20 g/d for women or >30 g/d for men), serious cardiovascular or cerebrovascular disease within 6 months, severe gastrointestinal diseases or gastrointestinal surgery in the past 12 months, active participation in a weight loss program, use of medications that affect weight or energy balance, and current or planned pregnancy.
## Randomization and Blinding
Eligible participants were randomly assigned to the TRE or DCR group with an allocation ratio of 1:1. Randomization was conducted in a block size of 6. The computer-generated randomization sequence was prepared by an independent researcher who was not involved in the study. Investigators who assessed the study outcomes and analyzed the data were blinded to the group assignment.
## Intervention Programs
All participants were instructed to follow a diet of 1500 to 1800 kcal/d for men and 1200 to 1500 kcal/d for women. The diets were composed of $40\%$ to $55\%$ carbohydrate, $15\%$ to $20\%$ protein, and $20\%$ to $30\%$ fat.24 All participants were provided with 1 protein shake (Nutriease; Zhejiang Nutriease Co) per day for the first 6 months and received dietary counseling for the duration of the study. Participants assigned to the TRE group were instructed to consume the prescribed calories from 8:00 am to 4:00 pm every day, and only noncaloric beverages were permitted outside of the daily eating window. Participants in the DCR group had no eating time restriction during the 12-month study period.
Dietary counseling was conducted by trained nutritionists. Participants received written dietary information booklets, which had food portion advice and sample menus of similar dietary energy restrictions in accordance with the Dietary Guidelines for macronutrient intake.24,25 Participants were encouraged to weigh foods to ensure accuracy of intake. All participants were required to write a dietary log and record daily food pictures and mealtimes on a custom mobile study application. All participants received follow-up telephone calls or a text message through the study app about their energy intake twice per week. The trained nutritionists also met with study participants individually every 2 weeks to assess their adherence to the program and provide suggestions for improvements and personalized energy targets during the first 6 months of the trial. Participants were instructed to maintain their diet regimens during the next 6-month follow-up visit and write in their dietary log and record food pictures and mealtimes 3 times per week. In this phase, participants received follow-up telephone calls or a text message through the study app once per week and met with the nutritionist monthly. Dietary intake and mealtimes were assessed daily using each participant’s log and timely recorded food photographs based on the nutrient content listed in the China Food Composition Tables.26 All participants attended health education sessions monthly over 12 months and were instructed not to change their physical activity habits throughout the trial.
## Adherence to the Intervention Programs
Adherence to the diet program was evaluated as days that participants met the requirements of the diet program. In the TRE group, participants were required to both eat within the prescribed eating period and meet the daily caloric intake goal. In the DCR group, participants were required to consume the prescribed daily energy amount.
## Outcomes
The primary outcome was change in the IHTG content from baseline to 6 and 12 months. The IHTG content was measured using magnetic resonance imaging (Ingenia 3.0T mDIXON Quant; Philips Healthcare)27,28,29 at baseline, 6 months, and 12 months. The secondary outcomes were changes in body weight, BMI, waist circumference, body fat mass, lean mass, liver stiffness, liver enzyme levels, and other metabolic risk factors, including plasma glucose levels, serum lipid levels, and blood pressure. Body fat mass and lean mass were quantified using a whole-body dual x-ray system (Lunar iDXA; GE Healthcare). Abdominal visceral fat and subcutaneous fat areas were measured by computed tomography (Revolution; GE Healthcare) at the level of the lumbar vertebrae.30 Liver stiffness was assessed by transient elastography (FibroScan 502 Touch; Echosens). Metabolic risk factors and liver enzyme levels were measured using standard methods at baseline and the 6- and 12-month follow-up visits.
Nutrient intake was estimated by 3 consecutive 24-hour dietary recalls (2 weekdays and 1 weekend day) at baseline and 6 months. Nutrient intake was calculated based on the China Food Composition Tables. Physical activity was assessed using the International Physical Activity Questionnaire at baseline, 6 months, and 12 months.31 Additional outcomes included quality of life as measured according to the 12-item Short-Form Health Survey Questionnaire (SF-12),32 depressive symptoms as measured by the Patient Health Questionnaire-9,33 and sleep quality as measured by the Pittsburgh Sleep Quality Index.34
## Statistical Analysis
We estimated that with a sample size of 68 individuals, the trial would provide greater than $90\%$ statistical power to detect a significant difference of $0.8\%$ (unit value) in the reduction of IHTG content (SD, $1.0\%$) between the TRE group and the DCR group at a significance level of.05 using a 2-tailed test. The expected group difference and SD of reduction in IHTG content were based on preliminary data for comparison between the TRE regimen with caloric intake restriction and regular caloric intake (no time restriction).23,35 Accounting for an $80\%$ follow-up rate, a total of 88 participants were enrolled in this trial.
Data were analyzed according to participants’ randomization assignment (intention-to-treat). PROC MIXED of SAS statistical software, version 9.4 (SAS Institute Inc) was used to obtain point estimates and SEs of the treatment effects and to test for differences between treatments. Group differences in the study outcomes were evaluated using the general linear model for continuous variables and the χ2 test for categorical variables. We also used a linear mixed-effects model to compare the effects of the 2 diet programs on the IHTG content and main outcomes. In the linear mixed model, an autoregressive correlation matrix was used to correct within-participant correlation for repeated measurements, participants were treated as a random effect, and intervention group, follow-up time, and their 2-factor interactions were assumed to be estimable fixed effects. Missing data were handled by multiple imputations ($$n = 20$$) at random using the Markov chain Monte Carlo method. Data are presented as least-squares means with $95\%$ CIs for continuous variables and risk ratios for categorical outcomes. $P \leq .05$ was considered statistically significant.
## Results
A total of 88 eligible patients with obesity and NAFLD (mean [SD] age, 32.0 [9.5] years; 49 men [$56\%$]; 39 women [$44\%$]; and mean [SD] BMI, 32.2 [3.3]) were randomly assigned to the TRE ($$n = 45$$) or DCR ($$n = 43$$) group (Figure 1). Of those participants, 81 ($92\%$) completed the 6-month intervention and 74 ($84\%$) completed the entire 12-month intervention. Baseline characteristics had comparable distribution between the TRE and DCR groups (Table 1).
**Figure 1.:** *Flowchart of Trial Participants* TABLE_PLACEHOLDER:Table 1.
The mean (SD) percentage of days that participants adhered to both the prescribed calories and eating period was $85.0\%$ ($10.7\%$) in the TRE group and $85.7\%$ ($9.4\%$) in the DCR group during 12 months (eTable 1 in Supplement 2). The average daily energy deficit and percentage of energy intake from carbohydrates, fat, and protein were similar in the 2 groups during 12 months. By design, the mean daily eating duration in the TRE group was significantly shorter than that of the DCR group. Physical activity was also similar between the 2 diet groups and was stable during 12 months. Scores on the SF-12 physical and mental components, Patient Health Questionnaire-9 depression module, and Pittsburgh Sleep Quality Index were similar in the 2 groups.
## Primary Outcome
The IHTG content was reduced by $8.3\%$ ($95\%$ CI, −$10.0\%$ to −$6.6\%$) at 6 months and $6.9\%$ ($95\%$ CI, −$8.8\%$ to −$5.1\%$) at 12 months in the TRE group. Likewise, it was reduced by $8.1\%$ ($95\%$ CI, −$9.8\%$ to −$6.4\%$) at the 6-month assessment and $7.9\%$ ($95\%$ CI, −$9.7\%$ to −$6.2\%$) at 12 months in the DCR group. However, the net change in IHTG content was not significantly different between the groups at the 6-month (percentage point difference: −0.2; $95\%$ CI, −2.7 to 2.2; $$P \leq .86$$) or 12-month (percentage point difference: 1.0; $95\%$ CI, −1.6 to 3.5; $$P \leq .45$$) assessments (Figure 2). Liver stiffness was reduced by 2.1 kPa ($95\%$ CI, −2.7 to −1.6 kPa) in the TRE group and 1.7 kPa ($95\%$ CI, −2.3 to −1.2 kPa) in the DCR group at 12 months, with no significant difference between the 2 groups ($$P \leq .33$$). The percentages of participants with resolution of NAFLD (defined as IHTG content <$5\%$) at month 12 were similar in the 2 groups (TRE group, $33\%$ vs DCR group, $49\%$; $$P \leq .31$$). Sensitivity analysis using multiple imputed data showed similar results for the primary outcomes (eTable 2 in Supplement 2). Furthermore, the IHTG content reductions were similar for the 2 regimens when assessed according to adherence to the prescribed diet (eFigure 1 in Supplement 2).
**Figure 2.:** *Effect of Time-Restricted Eating (TRE) vs Daily Calorie Restriction (DCR) on the Intrahepatic Triglyceride (IHTG) ContentA, Change in IHTG content. Data are presented as estimated absolute change of IHTG content. Error bars represent 95% CIs. B, Percentage of IHTG content change for each participant. C, Change in liver stiffness. Data are presented as estimated absolute change of liver stiffness. Error bars represent 95% CIs. D, Percentage of patients with resolution of nonalcoholic fatty liver disease (NAFLD) at 6-month (P = .40) and 12-month (P = .31) assessment. Resolution of NAFLD is defined as IHTG content less than 5%.*
## Weight Loss and Body Fat
During the 12-month intervention, body weight was significantly reduced by 8.4 kg ($95\%$ CI, −10.3 to −6.4 kg) in the TRE group and 7.8 kg ($95\%$ CI, −9.7 to −5.9 kg) in the DCR group, with no significant between-group differences (−0.6 kg; $95\%$ CI, −3.3 to 2.2 kg; $$P \leq .69$$) (Table 2; eFigure 2 in Supplement 2). Likewise, waist circumference, body fat percentage, fat mass, lean mass, total abdominal fat, subcutaneous fat, visceral fat, and visceral to subcutaneous fat ratio were all significantly reduced in the 2 groups, with no significant between-group differences.
**Table 2.**
| Outcome | Change (95% CI) | Change (95% CI).1 | Difference between groups (95% CI) | P valuea |
| --- | --- | --- | --- | --- |
| Outcome | TRE (n = 45) | DCR (n = 43) | Difference between groups (95% CI) | P valuea |
| Weight, kg | Weight, kg | Weight, kg | Weight, kg | Weight, kg |
| Month 6 | −9.8 (−11.7 to −7.9) | −9.7 (−11.6 to −7.9) | −0.1 (−2.8 to 2.6) | .94 |
| Month 12 | −8.4 (−10.3 to −6.4) | −7.8 (−9.7 to −5.9) | −0.6 (−3.3 to 2.2) | .69 |
| BMI | BMI | BMI | BMI | BMI |
| Month 6 | −3.6 (−4.3 to −2.9) | −3.4 (−4.1 to −2.8) | −0.2 (−1.1 to 0.8) | .70 |
| Month 12 | −3.1 (−3.8 to −2.4) | −2.8 (−3.5 to −2.1) | −0.3 (−1.3 to 0.6) | .51 |
| Waist circumference, cm | Waist circumference, cm | Waist circumference, cm | Waist circumference, cm | Waist circumference, cm |
| Month 6 | −10.0 (−12.1 to −8.0) | −9.1 (−11.1 to −7.1) | −0.9 (−3.8 to 2.0) | .53 |
| Month 12 | −9.3 (−11.4 to −7.2) | −8.2 (−10.2 to −6.2) | −1.1 (−4.0 to 1.9) | .47 |
| Body fat percentage, % | Body fat percentage, % | Body fat percentage, % | Body fat percentage, % | Body fat percentage, % |
| Month 6 | −4.8 (−6.1 to −3.6) | −4.6 (−5.8 to −3.3) | −0.3 (−2.0 to 1.5) | .77 |
| Month 12 | −4.6 (−5.9 to −3.3) | −3.7 (−5.0 to −2.5) | −0.9 (−2.7 to 0.9) | .34 |
| Fat mass, kg | Fat mass, kg | Fat mass, kg | Fat mass, kg | Fat mass, kg |
| Month 6 | −7.1 (−8.5 to −5.6) | −7.0 (−8.4 to −5.6) | −0.1 (−2.1 to 2.0) | .96 |
| Month 12 | −6.1 (−7.6 to −4.6) | −5.8 (−7.2 to −4.4) | −0.3 (−2.4 to 1.8) | .77 |
| Lean mass, kg | Lean mass, kg | Lean mass, kg | Lean mass, kg | Lean mass, kg |
| Month 6 | −2.3 (−3.0 to −1.7) | −2.1 (−2.7 to −1.5) | −0.2 (−1.1 to 0.7) | .61 |
| Month 12 | −2.1 (−2.8 to −1.4) | −1.8 (−2.4 to −1.2) | −0.3 (−1.2 to 0.6) | .54 |
| Total abdominal fat, cm2 | Total abdominal fat, cm2 | Total abdominal fat, cm2 | Total abdominal fat, cm2 | Total abdominal fat, cm2 |
| Month 6 | −118.5 (−146.7 to 90.3) | −101.9 (−129.7 to −74.1) | −16.6 (−56.2 to 23.0) | .41 |
| Month 12 | −94.0 (−123.5 to 64.6) | −86.6 (−114.5 to 58.7) | −7.4 (−48.0 to 33.2) | .72 |
| Subcutaneous fat, cm2 | Subcutaneous fat, cm2 | Subcutaneous fat, cm2 | Subcutaneous fat, cm2 | Subcutaneous fat, cm2 |
| Month 6 | −78.9 (−97.9 to −59.9) | −59.4 (−78.2 to −40.7) | −19.5 (−46.2 to 7.2) | .15 |
| Month 12 | −60.0 (−79.9 to −40.2) | −51.8 (−70.6 to −33.0) | −8.2 (−35.6 to 19.1) | .55 |
| Visceral fat, cm2 | Visceral fat, cm2 | Visceral fat, cm2 | Visceral fat, cm2 | Visceral fat, cm2 |
| Month 6 | −41.6 (−52.8 to −30.4) | −38.6 (−49.6 to −27.6) | −2.9 (−18.7 to 12.8) | .71 |
| Month 12 | −36.6 (−48.5 to −24.8) | −33.6 (−44.7 to −22.5) | −3.0 (−19.3 to 13.3) | .71 |
| Visceral to subcutaneous fat ratio, % | Visceral to subcutaneous fat ratio, % | Visceral to subcutaneous fat ratio, % | Visceral to subcutaneous fat ratio, % | Visceral to subcutaneous fat ratio, % |
| Month 6 | −2.8 (−5.7 to 0.1) | −4.4 (−7.3 to −1.6) | 1.7 (−2.4 to 5.8) | .42 |
| Month 12 | −4.9 (−8.1 to −1.7) | −4.7 (−7.6 to −1.8) | −0.2 (−4.5 to 4.1) | .92 |
| Liver stiffness, kPa | Liver stiffness, kPa | Liver stiffness, kPa | Liver stiffness, kPa | Liver stiffness, kPa |
| Month 6 | −1.9 (−2.5 to −1.4) | −1.7 (−2.2 to −1.2) | −0.2 (−0.9 to 0.5) | .57 |
| Month 12 | −2.1 (−2.7 to −1.6) | −1.7 (−2.3 to −1.2) | −0.4 (−1.1 to 0.4) | .33 |
## Metabolic Risk Factors and Liver Enzymes
Metabolic risk factors, including systolic and diastolic blood pressure, pulse rate, and total cholesterol, triglyceride, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol levels were all significantly improved in the 2 groups over 12 months, with no significant between-group differences (Table 3). Both diets significantly reduced fasting plasma glucose level, hemoglobin A1c, and homeostasis model assessment of insulin resistance (HOMA-IR) at 6 months, and TRE significantly reduced HOMA-IR compared with DCR at 12 months. Similarly, both diets significantly reduced levels of liver enzymes, including serum alanine aminotransferase, aspartate aminotransferase, and γ-glutamyltransferase, with no significant between-group differences.
**Table 3.**
| Outcome | Change (95% CI) | Change (95% CI).1 | Difference between groups (95% CI) | P valuea |
| --- | --- | --- | --- | --- |
| Outcome | TRE (n = 45) | DCR (n = 43) | Difference between groups (95% CI) | P valuea |
| Systolic blood pressure, mm Hg | Systolic blood pressure, mm Hg | Systolic blood pressure, mm Hg | Systolic blood pressure, mm Hg | Systolic blood pressure, mm Hg |
| Month 6 | −12.1 (−14.8 to −9.4) | −9.3 (−12.0 to −6.7) | −2.8 (−6.6 to 1.0) | .15 |
| Month 12 | −11.0 (−13.9 to −8.1) | −8.5 (−11.2 to −5.9) | −2.5 (−6.4 to 1.4) | .21 |
| Diastolic blood pressure, mm Hg | Diastolic blood pressure, mm Hg | Diastolic blood pressure, mm Hg | Diastolic blood pressure, mm Hg | Diastolic blood pressure, mm Hg |
| Month 6 | −7.4 (−9.5 to −5.2) | −5.9 (−8.1 to −3.8) | −1.4 (−4.5 to 1.7) | .36 |
| Month 12 | −7.4 (−9.7 to −5.1) | −5.5 (−7.6 to −3.3) | −1.9 (−5.1 to 1.3) | .23 |
| Pulse, beats/min | Pulse, beats/min | Pulse, beats/min | Pulse, beats/min | Pulse, beats/min |
| Month 6 | −4.9 (−7.7 to −2.1) | −3.4 (−6.2 to −0.6) | −1.4 (−5.4 to 2.5) | .47 |
| Month 12 | −5.3 (−8.3 to −2.3) | −3.1 (−5.9 to −0.3) | −2.2 (−6.3 to 1.9) | .29 |
| Triglycerides, mg/dL | Triglycerides, mg/dL | Triglycerides, mg/dL | Triglycerides, mg/dL | Triglycerides, mg/dL |
| Month 6 | −62.4 (−78.7 to −46.0) | −55.3 (−71.5 to −39.1) | −7.1 (−30.1 to 15.9) | .54 |
| Month 12 | −39.0 (−56.7 to −21.2) | −38.0 (−54.3 to −21.6) | −1.0 (−25.1 to 23.1) | .93 |
| Total cholesterol, mg/dL | Total cholesterol, mg/dL | Total cholesterol, mg/dL | Total cholesterol, mg/dL | Total cholesterol, mg/dL |
| Month 6 | −12.5 (−20.5 to −4.6) | −12.4 (−20.2 to −4.6) | −0.2 (−11.3 to 11.0) | .98 |
| Month 12 | −10.2 (−18.5 to −1.9) | −7.9 (−15.8 to −0.0) | −2.3 (−13.8 to 9.2) | .69 |
| HDL-C, mg/dL | HDL-C, mg/dL | HDL-C, mg/dL | HDL-C, mg/dL | HDL-C, mg/dL |
| Month 6 | 5.1 (2.9 to 7.3) | 3.9 (1.7 to 6.0) | 1.2 (−1.8 to 4.3) | .42 |
| Month 12 | 6.5 (4.2 to 8.9) | 3.7 (1.6 to 5.9) | 2.8 (−0.4 to 6.0) | .08 |
| LDL-C, mg/dL | LDL-C, mg/dL | LDL-C, mg/dL | LDL-C, mg/dL | LDL-C, mg/dL |
| Month 6 | −8.4 (−15.5 to −1.3) | −8.2 (−15.2 to −1.2) | −0.2 (−10.2 to −9.8) | .97 |
| Month 12 | −12.5 (−20.0 to −5.0) | −8.2 (−15.2 to −1.1) | −4.3 (−14.6 to 6.0) | .41 |
| Plasma glucose, mg/dL | Plasma glucose, mg/dL | Plasma glucose, mg/dL | Plasma glucose, mg/dL | Plasma glucose, mg/dL |
| Month 6 | −6.2 (−11.2 to −1.2) | −5.1 (−10.0 to −0.2) | −1.1 (−8.1 to 5.9) | .76 |
| Month 12 | −5.9 (−11.3 to −0.5) | −0.8 (−5.8 to 4.2) | −5.1 (−12.4 to 2.3) | .17 |
| HOMA-IR | HOMA-IR | HOMA-IR | HOMA-IR | HOMA-IR |
| Month 6 | −1.7 (−2.6 to −0.9) | −1.3 (−2.2 to −0.5) | −0.4 (−1.6 to 0.8) | .50 |
| Month 12 | −1.6 (−2.5 to −0.6) | −0.0 (−0.9 to 0.8) | −1.6 (−2.8 to −0.3) | .18 |
| Hemoglobin A1c, % | Hemoglobin A1c, % | Hemoglobin A1c, % | Hemoglobin A1c, % | Hemoglobin A1c, % |
| Month 6 | −0.2 (−0.3 to −0.1) | −0.2 (−0.3 to −0.1) | 0.0 (−0.1 to 0.2) | .59 |
| Month 12 | −0.2 (−0.3 to −0.1) | −0.1 (−0.2 to 0.0) | −0.1 (−0.3 to 0.1) | .38 |
| Alanine aminotransferase, U/L | Alanine aminotransferase, U/L | Alanine aminotransferase, U/L | Alanine aminotransferase, U/L | Alanine aminotransferase, U/L |
| Month 6 | −14.4 (−20.3 to −8.5) | −17.1 (−22.9 to −11.3) | 2.7 (−5.6 to 11.1) | .52 |
| Month 12 | −14.2 (−20.6 to −7.7) | −11.6 (−17.5 to −5.7) | −2.6 (−11.4 to 6.2) | .56 |
| Aspartate aminotransferase, U/L | Aspartate aminotransferase, U/L | Aspartate aminotransferase, U/L | Aspartate aminotransferase, U/L | Aspartate aminotransferase, U/L |
| Month 6 | −5.3 (−8.6 to −2.0) | −7.2 (−10.4 to −4.0) | 1.9 (−2.6 to 6.5) | .40 |
| Month 12 | −5.4 (−9.0 to −1.8) | −6.1 (−9.4 to −2.9) | 0.7 (−4.1 to 5.6) | .76 |
| γ-Glutamyltransferase, U/L | γ-Glutamyltransferase, U/L | γ-Glutamyltransferase, U/L | γ-Glutamyltransferase, U/L | γ-Glutamyltransferase, U/L |
| Month 6 | −11.7 (−16.9 to −6.4) | −14.9 (−20.1 to −9.8) | 3.3 (−4.1 to 10.6) | .38 |
| Month 12 | −11.3 (−16.8 to −5.7) | −13.5 (−18.7 to −8.3) | 2.2 (−5.4 to 9.9) | .56 |
## Adverse Events
No deaths or serious adverse events occurred throughout the study. Occurrence of mild adverse events, including appetite change, discomfort in the stomach, constipation, dyspepsia, hunger, decreased appetite, dizziness, and fatigue, were not significantly different in the 2 groups (eTable 3 in Supplement 2).
## Discussion
This randomized clinical trial contributes novel findings on the effects of TRE vs DCR on NAFLD. First, this study indicated that the 8-hour TRE diet (eating period from 8:00 am to 4:00 pm) was no more effective in reducing the IHTG content and in achieving resolution of NAFLD among patients with NAFLD than DCR (habitual meal timing) with the same caloric intake restriction. Second, TRE and DCR diets produced comparable effects in reducing body weight, waist circumference, body fat, and visceral fat. Furthermore, both diets were equally effective in reducing blood pressure, plasma glucose level, HOMA-IR, liver enzyme levels, and lipid levels during 12 months. Third, caloric intake restriction seems to explain most of the beneficial effects of the TRE regimen.
Time-restricted eating has been promoted as a potential alternative weight loss strategy to DCR.13,36 However, the benefits of the TRE regimen on NAFLD are still untested or undertested in humans. Time-restricted eating regimens have either imposed a shortening window of eating while maintaining participants’ usual caloric intake22,37 or hypoenergetic intake.38 Cai et al39 reported that TRE with ad libitum intake did not improve liver stiffness compared with the control during a 12-week diet program among 176 patients with NAFLD. Kahleova and colleagues23 reported that a regimen of eating 2 meals (between 6:00 am and 4:00 pm) reduced IHTG content more than a regimen of eating 6 meals with the same caloric intake restriction in a 12-week clinical trial among 54 patients with obesity and type 2 diabetes. So far, the long-term effect of TRE on NAFLD remains uncertain.
To our knowledge, this study is the first randomized clinical trial to compare the long-term effect of TRE vs DCR on NAFLD. This trial showed that the 2 diet regimens had similar effects on reducing IHTG content and improving liver stiffness and that it was feasible for participants to adhere to their assigned calorie intake restrictions. Both diets with an energy intake of 1200 to 1800 kcal/d resulted in nearly $40\%$ resolution of NAFLD. Furthermore, the results suggest that caloric intake restriction explained most of the beneficial effects of a TRE regimen. These findings support a strategy of TRE combined with caloric intake restriction (prescribed according to current dietary guidelines) as a viable and sustainable approach for NAFLD management.
Several small clinical trials assessed the effects of short-term TRE on weight and waist circumference in obese populations and reported inconsistent findings.19,21,38,39,40,41 Lowe and colleagues41 reported that short-term TRE had no favorable benefits on reducing body weight and waist circumference reduction among 116 adults with obesity. In contrast, Cai et al39 found that 12-week TRE significantly reduced body weight in 97 patients with NAFLD compared with the controls. Evidence suggests that the effect of TRE with ad libitum intake on weight loss appeared to be likely associated with a decrease in energy intake.19,42,43 Nevertheless, small clinical trials reported that the TRE regimens with isoenergetic intake improved body weight in healthy adults and select metabolic parameters in men with prediabetes.22,37,40 By contrast, another study reported no differences in body weight and waist circumference during a 12-month TRE diet program with caloric intake restriction in 58 low-income women with obesity compared with the controls.40 *Our data* indicate that both diet regimens equally reduced body weight and waist circumference and were feasible for participants to adhere to their assigned intervention in terms of energy intake restriction. However, there were no substantial differences in weight and waist circumference between TRE and DCR during the 12-month intervention. Our study suggests that long-term TRE and DCR might be equally effective and could be recommended for weight loss in individuals with obesity.
In this trial, TRE and DCR significantly reduced body fat and visceral fat with no significant between-group differences. Several small, short-term studies reported that the TRE regimen significantly reduced body fat mass.19,40,44,45 In contrast, de Oliveira Maranhão Pureza and colleagues40 compared the effect of a 12-month TRE program vs hypoenergetic diet and reported no differences in body fat in 58 women with obesity. A meta-analysis of clinical trials also suggested that TRE seems to have no favorable effect on body fat reduction compared with the controls.36 Our study suggests that TRE is no more effective than DCR in body fat and visceral fat reduction among individuals with obesity.
In addition, our study indicated that there were no significant differences between TRE and DCR on cardiovascular risk factors, including blood pressure, fasting glucose levels, and lipid levels. Other studies found that short-term TRE improved glycemic control, insulin sensitivity, and blood pressure in individuals with prediabetes or adults with obesity.19,22 By contrast, Haganes et al45 reported no statistically significant effect of TRE on glycemic control in women with obesity. However, these trials did not compare the effects of TRE vs DCR on metabolic risk factors in individuals with obesity. Our data showed that TRE was more effective for improving insulin sensitivity than DCR.
## Limitations
This study has limitations. First, the primary outcome was the IHTG content instead of biopsy-proven steatosis or fibrosis. However, the IHTG content measured by magnetic resonance imaging and liver stiffness measured by transient elastography are highly correlated with the histologic features of steatosis and fibrosis.46,47 Furthermore, physical activity was not controlled in this study because we aimed to examine isolated effects of diet intake on NAFLD. However, physical activity was assessed using the International Physical Activity Questionnaire.
## Conclusions
In this randomized clinical trial of adults with obesity and NAFLD, a TRE regimen did not achieve additional benefits for reducing IHTG content, weight, body fat, and metabolic risk factors compared with DCR, whereas TRE might be more effective in improving insulin sensitivity than DCR. In addition, both diets produced a comparable effect on liver stiffness and resolution of NAFLD. These data support the importance of caloric intake restriction when adhering to a regimen of TRE for the management of NAFLD.
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|
---
title: Global Prevalence of Diabetic Retinopathy in Pediatric Type 2 Diabetes
authors:
- Milena Cioana
- Jiawen Deng
- Ajantha Nadarajah
- Maggie Hou
- Yuan Qiu
- Sondra Song Jie Chen
- Angelica Rivas
- Parm Pal Toor
- Laura Banfield
- Lehana Thabane
- Varun Chaudhary
- M. Constantine Samaan
journal: JAMA Network Open
year: 2023
pmcid: PMC10024209
doi: 10.1001/jamanetworkopen.2023.1887
license: CC BY 4.0
---
# Global Prevalence of Diabetic Retinopathy in Pediatric Type 2 Diabetes
## Key Points
### Question
What is the prevalence of diabetic retinopathy (DR) in children with type 2 diabetes (T2D)?
### Findings
In this systematic review and meta-analysis of 27 observational studies including 5924 unique patients with pediatric T2D, $6.99\%$ of participants with T2D had DR; the prevalence increased significantly more than 5 years after T2D diagnosis. The heterogeneity was high across studies.
### Meaning
These results suggest that the increasing risk of DR in children with T2D warrants the implementation of global screening programs at diagnosis and annually to ensure early detection and treatment to preserve vision in this population.
## Abstract
This systematic review and meta-analysis uses data from observational studies to estimate the global prevalence of diabetic retinopathy among patients with pediatric type 2 diabetes.
### Importance
Type 2 diabetes (T2D) is increasing globally. Diabetic retinopathy (DR) is a leading cause of blindness in adults with T2D; however, the global burden of DR in pediatric T2D is unknown. This knowledge can inform retinopathy screening and treatments to preserve vision in this population.
### Objective
To estimate the global prevalence of DR in pediatric T2D.
### Data Sources
MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Library, the Web of Science, and the gray literature (ie, literature containing information that is not available through traditional publishing and distribution channels) were searched for relevant records from the date of database inception to April 4, 2021, with updated searches conducted on May 17, 2022. Searches were limited to human studies. No language restrictions were applied. Search terms included diabetic retinopathy; diabetes mellitus, type 2; prevalence studies; and child, adolescent, teenage, youth, and pediatric.
### Study Selection
Three teams, each with 2 reviewers, independently screened for observational studies with 10 or more participants that reported the prevalence of DR. Among 1989 screened articles, 27 studies met the inclusion criteria for the pooled analysis.
### Data Extraction and Synthesis
This systematic review and meta-analysis followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guidelines for systematic reviews and meta-analyses. Two independent reviewers performed the risk of bias and level of evidence analyses. The results were pooled using a random-effects model, and heterogeneity was reported using χ2 and I2 statistics.
### Main Outcomes and Measures
The main outcome was the estimated pooled global prevalence of DR in pediatric T2D. Other outcomes included DR severity and current DR assessment methods. The association of diabetes duration, sex, race, age, and obesity with DR prevalence was also assessed.
### Results
Among the 27 studies included in the pooled analysis (5924 unique patients; age range at T2D diagnosis, 6.5-21.0 years), the global prevalence of DR in pediatric T2D was $6.99\%$ ($95\%$ CI, $3.75\%$-$11.00\%$; I2 = $95\%$; 615 patients). Fundoscopy was less sensitive than 7-field stereoscopic fundus photography in detecting retinopathy ($0.47\%$ [$95\%$ CI, $0\%$-$3.30\%$; I2 = $0\%$] vs $13.55\%$ [$95\%$ CI, $5.43\%$-$24.29\%$; I2 = $92\%$]). The prevalence of DR increased over time and was $1.11\%$ ($95\%$ CI, $0.04\%$-$3.06\%$; I2 = $5\%$) at less than 2.5 years after T2D diagnosis, $9.04\%$ ($95\%$ CI, $2.24\%$-$19.55\%$; I2 = $88\%$) at 2.5 to 5.0 years after T2D diagnosis, and $28.14\%$ ($95\%$ CI, $12.84\%$-$46.45\%$; I2 = $96\%$) at more than 5 years after T2D diagnosis. The prevalence of DR increased with age, and no differences were noted based on sex, race, or obesity. Heterogeneity was high among studies.
### Conclusions and Relevance
In this study, DR prevalence in pediatric T2D increased significantly at more than 5 years after diagnosis. These findings suggest that retinal microvasculature is an early target of T2D in children and adolescents, and annual screening with fundus photography beginning at diagnosis offers the best assessment method for early detection of DR in pediatric patients.
## Introduction
The obesity epidemic has been the primary factor in the increase in pediatric type 2 diabetes (T2D) case numbers globally.1,2,3,4,5,6,7,8 Type 2 diabetes is a more aggressive disorder in youths than it is in adults, with early comorbidities and complications including hypertension, nephropathy, polycystic ovary syndrome, and dyslipidemia.9,10,11,12,13,14,15 Diabetic retinopathy (DR) is the leading cause of blindness in adults with T2D and has several subtypes.16,17 Hyperglycemia increases vascular permeability and can lead to capillary occlusion, which results in nonproliferative DR (NPDR). This phase may be followed by a proliferative phase of DR with the formation of new blood vessels. Macular edema with fluid accumulation can also develop and may impact central vision.18 Children are developing T2D early in life and will live with their diabetes for several decades, which may increase their lifetime risk of developing DR and progress to blindness if undetected and untreated.19,20 While current guidelines recommend screening for DR in youths with T2D at diagnosis and annually thereafter, the global burden of DR is still not fully quantified.21,22 Understanding the scale of DR will help define its natural history and support the development of personalized clinical practice guidelines dedicated to children with T2D.
The main goal of this systematic review and meta-analysis was to assess the global prevalence of DR in pediatric patients with T2D. We also aimed to assess the severity profile of DR and the current diagnostic assessment methods. Other outcomes included the association of diabetes duration, sex, race, age, obesity, hypertension, and hemoglobin A1c (HbA1c) level with DR prevalence.
## Systematic Review Protocol and Registration
This systematic review and meta-analysis has been registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD42018091127).23 The study was exempt from the need for review and approval by an ethics review board because we used only aggregated deanonymized data that were already published. This study followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE)24 and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)25 reporting guidelines for systematic reviews and meta-analyses (eTable 1 in Supplement 1).
## Search Strategies
A senior health sciences librarian (L.B.) developed the search strategies in MEDLINE, Embase, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Central Register of Controlled Trials, the Cochrane Database of Systematic Reviews, and the Web of Science: Conference Proceedings Citation Index–Science. We searched the gray literature (ie, literature containing information produced by government agencies, academic institutions, and for-profit organizations that is not available through traditional publishing and distribution channels) through access to ClinicalTrials.gov, the Cochrane Central Registry of Controlled Trials, the Web of Science: Conference Proceedings Citation Index–Science, and article references screened at the full-text stage to retrieve potentially eligible articles (eTables 2-6 in Supplement 1). Concepts of pediatrics and T2D were combined with terms referencing observational study design and DR. Search terms included diabetic retinopathy; diabetes mellitus, type 2; prevalence studies; and child, adolescent, teenage, youth, and pediatric. No language restrictions were applied, and the searches were limited to human studies. If a conference abstract was suitable for inclusion, we searched for full-text articles in the included databases. We also contacted the principal investigators to determine whether the studies were published and to obtain data as needed. The initial database searches were from the date of database inception to April 4, 2021, with updated searches conducted on May 17, 2022.
## Eligibility Criteria
The T2D diagnostic criteria used by different studies are reported in eTable 7 in Supplement 1. In our original protocol, eligible studies included patients with T2D who were diagnosed at 18 years or younger, reported findings for 10 or more patients, had an observational study design (including cross-sectional and cohort studies), and reported the prevalence of DR. During screening, we identified a substantial number of studies that defined pediatric populations as 21 years or younger, so this age cutoff was adopted for study inclusion. We excluded articles reporting on patients with gestational diabetes or other types of diabetes. We planned to include the largest reported sample size for large studies with serial data reporting.
## Study Selection, Data Abstraction, and Quality Appraisal
Three teams of 2 independent reviewers (including M.C., J.D., A.N., M.H., Y.Q., S.S.J.C., A.R., and P.P.T.) screened titles, abstracts, and full-text articles, completed data abstraction, and assessed the risk of bias and level of evidence. Disagreements were resolved through discussion, and a third reviewer (M.C.S.) was available for consultation and resolution of ongoing disputes. The extracted data included age at diagnosis and study inclusion, sex, race, diabetes duration, sample size, obesity rates, method of DR diagnosis, DR classification, total prevalence of DR, and sex- and race-specific prevalence proportions (if reported). Race-based data were collected because race-based differences in T2D prevalence and DR rates were previously reported.26,27 Race-specific data from the studies were obtained either from medical records or self-reported information from participants. If a longitudinal study reported the prevalence of DR at multiple time points, we extracted the values closest to the time of diabetes diagnosis. We also contacted the principal investigators of the studies to retrieve missing data when needed.
The risk of bias was assessed using a validated instrument developed for prevalence studies that evaluates the internal and external validity of studies.28 The level of evidence was evaluated using the Oxford Centre for Evidence-Based Medicine criteria.29 Local and current random sample surveys were given a level of 1 and nonrandom surveys a level of 3 (corresponding to the highest and lowest levels of evidence used in this systematic review and meta-analysis); studies were also rated lower based on imprecision, indirectness, and inconsistency.29
## Statistical Analysis
We performed a random-effects meta-analysis if 2 or more studies reported on the prevalence of DR in similar populations using identical study designs, methods, and outcomes.30,31 Otherwise, we tabulated the results and presented a narrative review of the studies. The primary outcome was the global pooled prevalence (with $95\%$ CI) of DR. We conducted the meta-analysis with prevalence estimates transformed using the Freeman-Tukey double arcsine method31 to prevent the need to stabilize variances because some studies reported prevalence rates of $0\%$, and we transformed the results back to prevalence estimates for interpretation.32,33 To verify the results of the Freeman-Tukey double arcsine analysis and to control for sampling error and bias, an exploratory analysis was also conducted using the random intercept mixed-effects logistic regression model, recognizing that the model does not account for study weights.34 In addition to $95\%$ CIs, we also estimated $95\%$ prediction intervals (PIs) to assess the possible range of new values in the present study.35 Both inconsistency index (I2 statistic) and χ2 P values were used to quantify heterogeneity. An I2 greater than $75\%$ and $P \leq .10$ were indicators of heterogeneity.36 Subgroup analyses, meta-regression analysis, sensitivity analysis, and publication bias evaluations were performed only if more than 10 studies were identified for a given outcome. Subgroup meta-analyses were performed when 2 or more studies reported the prevalence of DR by sex or race, with the latter classified using National Institutes of Health definitions.37 We also performed subgroup analyses by DR severity classification (minimal to moderate NPDR, severe NPDR, proliferative DR, and macular edema), assessment method (fundoscopy or fundus photography), and diabetes duration (<2.5 years, 2.5-5.0 years, or >5.0 years). We also added a random-effects meta-regression analysis to assess the separate associations of obesity, hypertension, HbA1c level, age at diagnosis, age at study inclusion, and diabetes duration with DR prevalence.36 We reported the statistical significance of the regression coefficient for the association between each variable and DR prevalence. We calculated the mean difference in HbA1c level for patients with vs without DR. Sensitivity analysis was performed by removing studies reported only in conference abstracts, studies with a sample size of 50 patients or less, studies that included some participants older than 18 years, or studies with a high risk of bias. Publication bias assessment was conducted with a contour-enhanced funnel plot and Egger test, and visual inspection was used to assess asymmetry.38 The meta-analysis of prevalence was performed using the metafor package in RStudio software, version 1.1.383, using R language version 3.4.3 (R Foundation for Statistical Computing).39,40,41
## Study Selection and Characteristics
We screened 1989 deduplicated titles and abstracts, and 190 abstracts were chosen for full-text screening. A total of 29 studies12,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69 met the inclusion criteria (eFigure 1 in Supplement 1). Of those, 6 studies42,43,44,45,46,47 ($20.7\%$) had a cross-sectional design, 13 studies48,49,50,51,52,53,54,55,56,57,58,59,60 ($44.8\%$) had a retrospective cohort design, and 10 studies12,61,62,63,64,65,66,67,68,69 ($34.5\%$) had a prospective cohort design. Additional details about the included studies are reported in the Table and eTable 8 in Supplement 1.
**Table.**
| Source | Study location | Study design | Age, y | Age, y.1 | Duration of diabetes, y | Prevalence of DR, No. (%) | Sample size | Diabetic retinopathy classification: No. (%) of participants | Method of assessment | Definition |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Source | Study location | Study design | Diagnosis of T2D | Study enrollment | Duration of diabetes, y | Prevalence of DR, No. (%) | Sample size | Diabetic retinopathy classification: No. (%) of participants | Method of assessment | Definition |
| Eppens et al,42 2006 | Western Pacific | Cross-sectional | Median (IQR), 12.0 (10.7 to 13.5)a | Median (IQR), 14.9 (13.2 to 16.4)a | Median (IQR), 2.3 (1.4 to 3.6)a | 2 (0.6) | 284 | NR | NR | NR |
| Farah et al,43 2006 | US | Cross-sectional | <21.0 | Range, 10.0 to 21.0a | Median (range), 1.8 (<2.0 to 15.0)a | 1 (2.5) | 40 | Minimal NPDR: 1 (2.5) | Fundoscopy | Modified Airlie House |
| Unnikrishnan et al,44 2008 | India | Cross-sectional | Mean (SD), 16.2 (2.9) | Mean (SD), 18.9 (4.9) | NR | 0 | 36 | NR | Fundoscopy | Background or proliferative retinopathy |
| Aulich et al,45 2019 | Australia | Cross-sectional | NR | Mean (SD), 15.1 (1.9)a | Median (IQR), 1.8 (0.3 to 3.3)a | 2 (6.7) | 30 | NR | 7-Field stereoscopic fundus photography | ETDRS |
| Khalil et al,46 2019 | Egypt | Cross-sectional | Mean (SD), 18.0 (2.0) | Mean (SD), 19.8 (1.1) | Mean (SD), 2.5 (2.0) | 0 | 13 | NR | Fundoscopy | Normal fundus, nonproliferative retinopathy, or proliferative retinopathy |
| Ferm et al,47 2021 | US | Cross-sectional | NR | NR | NR | 13 (3.1) | 416 | NR | Nonmydriatic fundus photography | NR |
| Scott et al,48 2006 | New Zealand | Retrospective cohort | NR | Mean (SD), 20.0 (0.4) | Mean (SD), 3.0 (0.3) | 8 (7.6) | 105 | Mild to moderate NPDR: 4 (3.8); severe NPDR, PDR, or macular edema: 4 (3.8) | Either fundoscopy or fundus photography (type NR) | NR |
| Lee et al,49 2007 | Japan | Retrospective cohort | NR | NR | NR | 10 (27.8) | 36 | NR | NR | NR |
| Osman et al,50 2013 | Sudan | Retrospective cohort | <10 y: 3 participants; 11 to 18 y: 35 participants | NR | NR | 0 | 38 | NR | NR | Medical records |
| Dart et al,51 2014 | Canada | Retrospective cohort | Mean (SD), 13.5 (2.2) | Mean (SD), 16.5 (2.3) | Median (range), 4.4 (0 to 27.4) | 40 (11.7) | 342 | NR | NR | Medical records |
| Geloneck et al,52 2015 | US | Retrospective cohort | Mean (SD), 11.8 (2.7) | Mean (SD), 14.5 (2.1) | Mean (SD), 2.8 (2.3) | 0 | 32 | NR | Fundoscopy | Medical records |
| Newton et al,53 2015b | New Zealand | Retrospective cohort | Range, 6.5 to 17.0c | <17.0 | 0 | 1 (4.3) | 23 | NR | NR | Medical records |
| Wang et al,54 2017 | US | Retrospective cohort | Median (IQR), 18.0 (16.0 to 21.0) | NR | Median (IQR), 3.1 (1.9 to 4.9) | 127 (7.2) | 1768 | NPDR: 6 (0.3); PDR: 1 (0.1); unspecified: 120 (6.8) | NR | Medical records |
| Yeh and Bernardo,55 2017b | US | Retrospective cohort | Mean, 13.8 | NR | NR | 1 (7.1) | 14 | NR | Fundoscopy | Medical records |
| Koziol et al,56 2020 | Poland | Retrospective cohort | NR | <18.0 | NR | 79 (1.8) | 4291 | NR | NR | Medical records |
| Ek et al,57 2020 | Sweden | Retrospective cohort | Mean (SD), 15.0 (1.9)a | Mean (SD), 22.2 (3.7) | Mean (SD), 6.7 (2.8) | 32 (31.1) | 103 | NR | Fundus photography (type NR) | NR |
| Porter et al,58 2020 | US | Retrospective cohort | Mean (SD), 17.0 (3.0) | Mean (SD), 18.1 (2.6) | Mean (SD), 1.1 (1.3) | 3 (6.0) | 50 | Mild NPDR: 3 (6.0) | NR | ETDRS |
| Amutha et al,59 2021 | India | Retrospective cohort | Mean (SD), 16.6 ( 2.5)a | Mean (SD), 23.2 ( 9.7)a | Median (IQR), 5.7 (NR to NR)a | 118 (27.5) | 429 | NR | Both fundoscopy (for initial screening) and 7-field stereoscopic fundus photography (for confirmation) | ETDRS |
| Bai et al,60 2022 | US | Retrospective cohort | Mean (SD), 17.3 (3.4) | <22.0 | Range, 0 to 15.0 | 17 (26.6) | 64 | NPDR: 11 (64.7); PDR: 4 (6.3); macular edema: 2 (3.2) | NR | Medical records |
| Eppens et al,12 2006 | Australia | Prospective cohort | Median (IQR), 13.2 (11.6 to 15.0)a | Median (IQR), 15.3 (13.6 to 16.4)a | Median (IQR), 1.3 (0.6 to 3.1)a | 1 (4.0) | 25 | NR | 7-Field stereoscopic fundus photography | Modified Airlie House |
| Shield et al,61 2009 | Ireland; UK | Prospective cohort | Median (IQR), 13.6 (9.9 to 16.8)a | Median (IQR), 14.5 (10.8 to 17.8)a | Median, 1.0a | 0 | 55 | NR | NR | NR |
| Ruhayel et al,62 2010 | Australia | Prospective cohort | Mean (SD), 11.6 (1.9)a | Mean (SD), 16.8 (1.7)a | Mean (SD), 5.2 (2.0)a | 4 (25.0) | 16 | NPDR: 4 (25.0); 3 with unilateral hard exudates and 1 with dot hemorrhages | NR | Medical records |
| Jefferies et al,63 2012 | New Zealand | Prospective cohort | Median (IQR), 12.9 (7.1 to 15.5) | NR | NR | 0 | 52 | NR | NR | NR |
| Schmidt et al,64 2012 | Austria; Germany | Prospective cohort | Mean (SD), 13.5 (3.4) | Mean (SD), 15.3 (3.0) | NR | 12 (1.8) | 684 | NR | NR | NR |
| Jensen et al,66 2021b | US | Prospective cohort | <20.0 | NR | Mean (SD), 7.5 (2.1) | 140 (31.3) | 447 | NR | Fundus photography (type NR) | NR |
| Jensen et al,66 2021b | US | Prospective cohort | <20.0 | NR | Mean (SD), 12.4 (2.1) | 126 (55.0) | 229 | Mild NPDR: 91 (39.7); moderate NPDR: 26 (11.4); PDR: 9 (4.0) | Fundus photography (type NR) | NR |
| Preechasuk et al,67 2022 | Thailand | Prospective cohort | Mean (SD), 16.9 (6.4) | Mean (SD), 23.4 (8.5) | Median (IQR), 5.2 (1.6-9.4) | 8 (9.0) | 89 | Mild to moderate NPDR: 4 (4.5); severe NPDR: 4 (4.5) | NR | Presence of any severity of DR, macular edema, vitreous hemorrhage, or tractional retinal detachment |
| TODAY Study Group,68 2013 | US | Prospective cohort | NR | Mean (SD), 18.1 (2.5) | Mean (SD), 4.9 (1.5) | 71 (13.7) | 517 | Minimal NPDR: 64 (12.3); mild NPDR: 7 (1.4) | 7-Field stereoscopic fundus photography | ETDRS |
| TODAY Study Group,65 2021 | US | Prospective cohort | NR | Mean (SD), 25.4 (2.5) | Mean (SD), 12.0 (1.5) | 210 (50.0) | 420 | Minimal NPDR: 95 (22.6); mild NPDR: 68 (16.2); moderate NPDR: 16 (3.8); moderately severe NPDR: 3 (0.7); severe NPDR: 5 (1.2); early or stable, treated PDR: 10 (2.4); high-risk PDR: 5 (1.2); macular edema: 14 (3.3) | 7-Field stereoscopic fundus photography | ETDRS |
| Zuckerman Levin et al,69 2022 | Israel | Prospective cohort | Mean (SD), 14.7 (1.9) | Mean (SD), 14.7 (1.9) | At presentation: 0 | At presentation: 4 (1.9) | At presentation: 216 | NR | NR | Progressive retinal changes (nonproliferative or proliferative) |
| Zuckerman Levin et al,69 2022 | Israel | Prospective cohort | Mean (SD), 14.7 (1.9) | Mean (SD), 14.7 (1.9) | At follow-up: mean (SD), 2.9 (2.1) | At follow-up: 5 (4.6) | At follow-up: 108 | NR | NR | Progressive retinal changes (nonproliferative or proliferative) |
All patients were diagnosed with T2D between ages 6.5 years and 21.0 years, with 1 patient diagnosed at age 6.5 years and having a background diagnosis of Prader-Willi syndrome. The diabetes duration ranged from inclusion at diabetes diagnosis to 15.0 years after diagnosis.
## Pooled Global Prevalence of DR
The number of DR cases was small, and heterogeneity was high across studies. Among 29 eligible studies,12,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69 1 study65 was excluded from the pooled analysis because it provided follow-up data on the same patient set included in another study,68 and 1 study56 was excluded because it provided the number of observations of DR, but it was unclear whether the data included unique patients. In this article, we report the prevalence values without and with the inclusion of the latter study56 because the results did not change substantially. The pooled global prevalence of DR across 27 studies12,42,43,44,45,46,47,48,49,50,51,52,53,54,55,57,58,59,60,61,62,63,64,66,67,68,69 involving 5924 unique patients was $6.99\%$ ($95\%$ CI, $3.75\%$-$11.00\%$; I2 = $95\%$; $P \leq .001$; 615 patients) (Figure 1). The DR prevalence was $1.14\%$ ($95\%$ CI, $0.05\%$-$3.07\%$; I2 = $41\%$; $$P \leq .13$$; 18 of 819 patients) in cross-sectional studies,42,43,44,45,46,47 $11.29\%$ ($95\%$ CI, $5.82\%$-$18.10\%$; I2 = $94\%$; $P \leq .001$; 357 of 3004 patients) in retrospective cohort studies,48,49,50,51,52,53,54,55,57,58,59,60 and $6.52\%$ ($95\%$ CI, $0.95\%$-$15.66\%$; I2 = $97\%$; $P \leq .001$; 240 of 2101 patients) in prospective cohort studies.12,61,62,63,64,66,67,68,69 When including the study that reported the number of observations of patients with T2D and DR,56 the pooled global prevalence was $6.69\%$ ($95\%$ CI, $3.64\%$-$10.47\%$; I2 = $97\%$; $P \leq .001$; 4291 observations; 10 215 total unique patients plus observations).
**Figure 1.:** *Prevalence of Diabetic Retinopathy in Youths With Type 2 Diabetes by Study Design*
The exploratory analysis using the random intercept mixed-effects logistic regression model, in which the results were compared with the Freeman-Tukey double arcsine analysis, had consistent results with overlapping $95\%$ CIs. All outcomes, with the exception of DR prevalence by race, had results somewhat close to each other using the 2 methods; for example, DR prevalence was $6.99\%$ vs $5.03\%$ when comparing the generalized linear mixed-effects method vs the Freeman-Tukey double arcsine transformation method. However, the data were limited. The $95\%$ PIs were broad (including a range of $0.30\%$-$50.75\%$ in the generalized linear mixed-effects model vs $0\%$-$33.99\%$ in the Freeman-Tukey double arcsine transformation model), primarily due to the high heterogeneity among the studies (eTable 9 in Supplement 1).
## Prevalence of DR Based on Severity
Only 9 studies43,48,54,58,60,65,66,67,68 reported DR classification using Early Treatment for Diabetic Retinopathy *Study criteria* or modified Airlie House criteria, which both classify DR severity based on the presence and extent of retinal thickening, microaneurysms, cotton wool spots, dot blot hemorrhages, venous beading, intraretinal microvascular anomalies, and neovascularization. The prevalence of minimal to moderate NPDR was $11.16\%$ ($95\%$ CI, $1.52\%$-$27.21\%$; I2 = $97\%$; $P \leq .001$; 200 of 1030 patients),43,48,58,66,67,68 the prevalence of severe NPDR was $2.57\%$ ($95\%$ CI, $0.58\%$-$5.69\%$; I2 = $50\%$; $$P \leq .16$$; 12 of 509 patients),65,67 the prevalence of proliferative DR was $2.43\%$ ($95\%$ CI, $0.04\%$-$7.47\%$; I2 = $95\%$; $P \leq .001$; 28 of 2481 patients),54,60,65,66 and the prevalence of macular edema was $3.09\%$ ($95\%$ CI, $1.64\%$-$4.91\%$; I2 = $0\%$; $$P \leq .88$$; 16 of 484 patients) (Figure 2).60,65
**Figure 2.:** *Prevalence of Diabetic Retinopathy in Youths With Type 2 Diabetes by SeverityDR indicates diabetic retinopathy; NPDR, nonproliferative diabetic retinopathy; and PDR, proliferative diabetic retinopathy.*
## Prevalence of DR Based on Method of Retinopathy Assessment
The prevalence of DR in 5 studies43,44,46,52,55 using fundoscopy to diagnose DR was $0.47\%$ ($95\%$ CI, $0\%$-$3.30\%$; I2 = $0\%$; $$P \leq .55$$; 2 of 135 patients). The prevalence of DR in 4 studies12,45,59,68 using 7-field stereoscopic fundus photography to diagnose DR was $13.55\%$ ($95\%$ CI, $5.43\%$-$24.29\%$; I2 = $92\%$; $P \leq .001$; 192 of 1001 patients) (Figure 3). Other assessment methods included nonmydriatic fundus photography in 1 study,47 and an unspecified form of fundus photography in 3 studies.48,57,66 A total of 15 studies42,49,50,51,53,54,56,58,60,61,62,63,64,67,69 did not report the DR assessment method used.
**Figure 3.:** *Prevalence of Diabetic Retinopathy in Youths With Type 2 Diabetes by Method of Assessment*
## Global Prevalence of DR Based on Diabetes Duration
Analyzing only prospective cohort studies that reported mean T2D duration,61,62,65,66,67,68,69 the prevalence of DR with T2D duration of less than 2.5 years was $1.11\%$ ($95\%$ CI, $0.04\%$-$3.06\%$; I2 = $5\%$; $$P \leq .30$$; 4 of 271 patients)61,69 (eFigure 2 in Supplement 1). For T2D duration of 2.5 to 5.0 years, the prevalence was $9.04\%$ ($95\%$ CI, $2.24\%$-$19.55\%$; I2 = $88\%$; $P \leq .001$; 76 of 625 patients)68,69; for T2D duration of greater than 5.0 years, the prevalence was $28.14\%$ ($95\%$ CI, $12.84\%$-$46.45\%$; I2 = $96\%$; $P \leq .001$; 362 of 972 patients).62,65,66,67 When analyzing only retrospective cohort studies that reported mean T2D duration,48,52,53,58 DR prevalence was similar for T2D duration of less than 2.5 years ($5.30\%$; $95\%$ CI, $0.87\%$-$12.19\%$; I2 = $0\%$; $$P \leq .90$$; 4 of 73 patients)53,58 and T2D duration of 2.5 to 5.0 years ($3.09\%$; $95\%$ CI, $0\%$-$14.06\%$; I2 = $75\%$; $$P \leq .05$$; 8 of 137 patients)48,52 (eFigure 3 in Supplement 1). Analyzing only cross-sectional studies42,43,44,45,46,47 was not possible because these studies did not report all ranges of T2D duration. When all study designs12,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69 were included in the analysis, the prevalence of DR at less than 2.5 years after diagnosis of diabetes was $1.78\%$ ($95\%$ CI, $0.25\%$-$4.20\%$; I2 = $23\%$; $$P \leq .26$$; 9 of 384 patients).43,53,58,61,69 The prevalence increased sharply at 2.5 to 5.0 years after diagnosis to $5.08\%$ ($95\%$ CI, $1.04\%$-$11.22\%$; I2 = $80\%$; $P \leq .001$; 84 of 775 patients),46,48,52,68,69 with a further increase to $28.83\%$ ($95\%$ CI, $15.97\%$-$43.63\%$; I2 = $95\%$; $P \leq .001$; 394 of 1075 patients)57,62,65,66,67 at 5.0 years after diagnosis (eFigure 4 in Supplement 1).
## Global Prevalence of DR Based on Sex
The odds ratio of DR prevalence was lower in male vs female patients (0.40; $95\%$ CI, 0.02-7.21; I2 = $75\%$; $$P \leq .53$$; 45 of 88 male patients vs 91 of 177 female patients)49,66 (eFigure 5 in Supplement 1), with a wide $95\%$ CI that prevented reaching a definite conclusion on sex differences in DR prevalence. Two studies54,57 did not report a sex-specific prevalence value but found that male patients had a higher risk of DR than female patients when examining hazard ratios.
## Prevalence of DR Based on Race
Racial classifications were based on medical records48,50,52,53,60,69 or self-reporting60,63,66 by participants in the studies. The overall pooled prevalence of DR in Middle Eastern or White patients was $24.07\%$ ($95\%$ CI, $6.26\%$-$47.91\%$; I2 = $88\%$; $P \leq .001$; 57 of 171 patients).46,57,66 Asian patients had a prevalence of $13.31\%$ ($95\%$ CI, $2.49\%$-$30.05\%$; I2 = $93\%$; $P \leq .001$; 136 of 590 patients)44,49,59,67 (eFigure 6 in Supplement 1). There were insufficient data to assess the pooled prevalence in other racial groups.
## Association of Age, Diabetes Duration, Obesity, HbA1c Level, and Hypertension With DR Prevalence
Age ($P \leq .001$; 15 patients), diabetes duration ($$P \leq .02$$; 13 patients), and hypertension prevalence ($$P \leq .03$$; 17 patients) were positively associated with DR prevalence. Meta-regression analysis revealed no associations between obesity prevalence ($$P \leq .93$$; 13 patients) or mean age at diabetes diagnosis ($$P \leq .26$$; 14 patients) and DR prevalence. In addition, there was no association between glycemic control ($$P \leq .60$$; 18 patients) and DR prevalence after examining the data in aggregate and by study design. However, patients with T2D who developed DR had a higher HbA1c level when compared with patients without retinopathy (mean HbA1c difference, 1.37 ($95\%$ CI, 0.95-1.79; I2 = $0\%$; $P \leq .001$)47,68 (eFigure 7 in Supplement 1).
## Risk of Bias, Level of Evidence, and Publication Bias
Most studies had a low risk of bias (8 studies44,47,52,54,56,57,58,60 [$27.6\%$]) or a moderate risk of bias (20 studies12,42,43,45,46,48,50,51,53,55,59,61,62,63,64,65,66,67,68,69 [$69.0\%$]), with only 1 study49 ($3.4\%$) having a high risk of bias (eTable 10 in Supplement 1). The risk of bias was higher if the patients were from a single clinic or city and not a nationally representative sample; data from those studies12,43,45,46,48,49,50,51,52,53,55,58,59,60,62,63,67 may therefore have limited generalizability. Some studies42,43,49 did not use representative sampling frameworks, and others43,45,49,65,66,68 did not take a census or randomly select patients. Some studies12,46,53,55,59,62,64,65,66,68,69 also had missing data of greater than $25\%$, potentially leading to nonresponse bias.
The risk of bias was also present if the definition of DR or the assessment method was not described, which occurred in 7 studies.42,49,61,63,64,67,69 In some studies,44,48,54,56,61,64 it was unclear whether all participants were examined using the same methods. A total of 14 studies12,47,51,54,56,57,58,59,60,61,63,64,67,69 ($48.3\%$) had the highest level of evidence (level 1), while 9 studies42,44,46,48,50,52,53,55,62 ($31.0\%$) had level 2 evidence, and 6 studies43,45,49,65,66,68 ($20.7\%$) had level 3 evidence. No publication bias was identified for the prevalence of DR outcome (eFigure 8 in Supplement 1) based on the Egger test ($$P \leq .52$$).
## Sensitivity Analysis
Results of the sensitivity analyses are shown in eTable 11 in Supplement 1. Of note, excluding the studies that involved patients older than 18 years decreased the pooled estimate of DR prevalence to $3.03\%$ ($95\%$ CI, $1.02\%$-$5.81\%$) (Figure 1; eTable 11 in Supplement 1), suggesting that DR risk increased with age.
## Discussion
This systematic review and meta-analysis found that the global prevalence of DR was $6.99\%$ among children with T2D, and DR prevalence increased significantly at more than 5 years after T2D diagnosis. The current data suggest that approximately 1 in 14 children and adolescents with T2D will have DR within a few years after diabetes diagnosis. While most patients included in this review had minimal or mild NPDR, a substantial minority had more severe disease, such as proliferative DR or macular edema, that can lead to visual impairment and potentially irreversible vision loss. Notably, there was some evidence to suggest that the prevalence of DR rapidly increased with age and diabetes duration; almost 1 in 4 children with T2D for 5 years or more developed DR. The analysis of the associations of sex and race with DR prevalence was inconclusive due to limited data. The scale of DR prevalence in youths with T2D supports the recommendations for periodic patient screening.21,22 When the data from pediatric patients with T2D are compared with those of pediatric patients with type 1 diabetes (T1D), only $2\%$ of children with T1D develop mild NPDR, irrespective of their age at diabetes onset, and none develop proliferative DR or macular edema.70 However, DR prevalence increases sharply at 5 years after diagnosis, to approximately $25\%$.70 Clinical practice guidelines for T1D currently recommend screening for DR at puberty or beginning at age 11 years if the child has had diabetes for 2 to 5 years.71 The early years of transition from pediatric to adult care among patients aged 18 to 21 years are also associated with an increase in DR among patients with T1D.72 Surveillance for DR is important during this transitional stage, when many patients also have difficulty maintaining glycemic control.72 Among adults with T2D, $21\%$ to $39\%$ of patients have DR at diagnosis, and the rates increase thereafter.19,73 Our results suggested that diabetes duration was positively associated with DR in pediatric T2D. Notably, the findings of the current review suggest that this increase is emerging decades earlier among these children compared with adults with T2D. The long-term outcomes of DR are not yet known due to the relative novelty of the condition. Longitudinal studies are warranted to assess these outcomes.
Hyperglycemia can result in structural and functional retinal abnormalities in pediatric patients with T2D as early as 2 years after diagnosis.74 Adolescents with T2D have substantial multifocal electroretinographic implicit time delays compared with youths with T1D and youths without diabetes.74 In adults with T2D, similar findings have suggested impairment of neural retinal function, future vascular lesions, and increased risk of DR.74,75,76,77,78 In addition, adolescents with T2D had substantially lower retinal thickness and retinal venular dilation when compared with patients without diabetes.74 These findings suggest that retinal abnormalities are present early in pediatric T2D, so it is important that screening be undertaken to detect DR early in this population to prevent impaired vision and blindness.
While the current pediatric T2D clinical practice guidelines recommend regular screening of DR at baseline and annually thereafter, screening guidelines are not routinely followed.21,22 Only $22\%$ to $54\%$ of pediatric patients with T2D have had dilated eye examinations.79,80 Because the findings of this review suggest the prevalence of DR increases rapidly with diabetes duration, there is an immediate need for regular screening to be performed consistently. The benefits of early identification of DR include increased focus on improving glycemic control to minimize microvascular disease, maintaining blood pressure, and streamlining the monitoring of DR progression.19,54 Intensive glycemic control in adolescents with T1D reduced DR by $53\%$ in the Diabetes Control and Complications Trial.81,82 Similarly, the UK Prospective Diabetes Study in adults with T2D showed that strict glycemic and blood pressure control reduced DR progression by $34\%$.83,84 In our study, hypertension prevalence was associated with DR prevalence, suggesting that hypertension may also be associated with DR in pediatric T2D. However, although patients with higher HbA1c values had higher DR prevalence, this finding did not reach statistical significance. The analysis of the association of DR with HbA1c levels yielded variable results, with some studies47,49,54,57,62,65,68 reporting an association and other studies52,58,67 finding no association. When pooling studies that reported mean HbA1c values in patients with and without DR,47,68 the mean difference suggested significantly higher HbA1c levels in patients with vs without DR. It is possible that our analysis of mean HbA1c levels did not reveal an association with DR prevalence because it was based on mean HbA1c values extracted from cross-sectional data, which may not account for longitudinal fluctuations in glycemic control over time. In addition, there is some evidence to suggest that there are racial and ethnic differences in HbA1c levels, with Asian, Black, Hispanic, and Indigenous individuals having higher HbA1c values than White individuals.85 While maintaining adequate glycemic control is an important step in preventing microvascular complications, any association between glycemic control and DR is likely polygenic.19 The screening method had implications for DR prevalence and explained some of the heterogeneity among studies included in this review, and 7-field stereoscopic fundus photography identified more cases of DR than fundoscopy. Compared with the gold standard of 7-field stereoscopic photography, indirect fundoscopy has a sensitivity of $76\%$ and a specificity of $95\%$.86 In contrast, 4-field wide-angle stereoscopic photography has a sensitivity of $94\%$ and a specificity of $96\%$.86 Digital nonmydriatic imaging has a sensitivity of $98\%$ and a specificity of $86\%$ for detecting DR, and direct fundoscopy with pupil dilation has a sensitivity of $65\%$ and a specificity of $97\%$.86 *It is* important to note that all but 143 of the included studies12,42,43,44,45,46,47,48,49,50,51,52,53,54,55,57,58,59,60,61,62,63,64,65,66,67,68,69 did not specify whether direct or indirect fundoscopy was performed. Fundus photography is more sensitive than fundoscopy for diagnosing mild cases of DR, while fundoscopy is better at detecting retinal thickening from macular edema and early neovascularization.19,52,87 Although both methods are accepted in screening guidelines, it has been suggested that fundus photography be used in the pediatric population because fundoscopy is challenging to perform in children.52 Fundus photography requires specialized equipment and trained staff to acquire the images and perform the analysis.19,88 These technologies may not be accessible, especially in low- and middle-income countries where pediatric T2D rates are rapidly increasing.89 The disparity in global access to health care resources impacts adherence to and generalizability of screening guidelines, and advocacy for access to retinal imaging and training personnel in different health care settings is important.90 Furthermore, emerging technologies, such as low-cost cameras, automated grading of retinal images, and virtual access to specialist assessments and care, will likely offer more equitable access to care and improve outcomes.91,92 For example, autonomous artificial intelligence systems have been developed for the early detection of DR. These systems have been reported to have a sensitivity of $85.7\%$ and a specificity of $79.3\%$ in diagnosing more than mild severity of DR in youths when compared with consensus grading by retinal specialists.93,94 While these systems are not yet approved for use in children, they offer a promising screening solution because they do not require supervision by eye care professionals and can be used in settings in which specialists are not available.93 Data on DR prevalence in pediatric T2D by sex or race were scarce. No conclusions about DR prevalence by sex or race could be reached due to the limited data. National estimates of DR among US and UK adults suggest that Black, Hispanic, and South Asian individuals have a $5\%$ to $10\%$ higher prevalence of DR compared with White individuals.95,96,97 In the SEARCH for Diabetes in Youth Study,98 youths with T2D from non-White racial groups had a higher prevalence of DR (odds ratio, 2.05; $95\%$ CI, 0.97-4.33). Differences in HbA1c level or diabetes duration did not explain this finding.98 An extensive study of ophthalmic screening patterns in the US80 found that Black and Hispanic children, especially those with low socioeconomic status, had $11\%$ to $18\%$ lower rates of DR screening than White children. These findings highlight the need for the creation of equitable screening strategies for DR that can reach all pediatric patients with T2D.
## Limitations
This study has several limitations. The heterogeneity was high across studies. Some studies did not report the DR assessment method42,49,50,51,53,54,56,58,60,61,62,63,64,67,69 or the type of fundus photography48,57,66 used. Data on DR prevalence by sex and race were limited, so we could not reach any conclusions.
In addition, the small number of DR cases in the included studies can have implications for the reliability of the variance and pooled estimates.34 *For this* reason, we used the Freeman-Tukey double arcsine transformation99,100 to stabilize variance in our meta-analysis. We also conducted a generalized linear mixed analysis for each outcome to assess the robustness of the results99,100 (eTable 11 in Supplement 1). We ultimately presented the results of our Freeman-Tukey transformation analysis because generalized linear mixed models do not provide a weight for each study, which is important information for clinicians to have access to when making practice recommendations.99,100 We calculated $95\%$ PIs alongside $95\%$ CIs to provide information on the estimated range of true case rates in this study (eTable 11 in Supplement 1). Given the high heterogeneity across studies included in this review, the $95\%$ PIs were broad for some outcomes and suggested the need for more high-quality, adequately powered longitudinal studies.
## Conclusions
The findings of this systematic review and meta-analysis suggest that the retinal microvasculature is an early target of T2D in children and that the risk of DR continues to increase over time. Mechanistic insights into the pathogenesis of DR in children with T2D remain limited, and this area warrants prioritized investigation. Increasing the number of children with T2D who undergo regular DR screening is important to meet current clinical practice guideline standards. Fundus photography is more sensitive in diagnosing early DR than fundoscopy. These assessments will likely maintain vision and quality of life and improve long-term outcomes. Equitable access to health care resources to detect and treat DR is a global priority that needs increased attention.
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|
---
title: 'Dietary Temperature’s Influence on Energy Balance in Humans: Protocol for
a Randomized Controlled Trial and Crossover Design'
journal: JMIR Research Protocols
year: 2023
pmcid: PMC10024216
doi: 10.2196/42846
license: CC BY 4.0
---
# Dietary Temperature’s Influence on Energy Balance in Humans: Protocol for a Randomized Controlled Trial and Crossover Design
## Abstract
### Background
According to the first law of thermodynamics, energy cannot be created or destroyed in an isolated system. Water has a characteristically high heat capacity, indicating that the temperature of ingested fluids and meals could contribute to energy homeostasis. Citing the underlying molecular mechanisms, we present a novel hypothesis that states that the temperature of one’s food and drink contributes to energy balance and plays a role in the development of obesity. We provide strong associations with certain molecular mechanisms that are activated by heat and correlate them with obesity and a hypothetical trial that could test this hypothesis. We conclude that if meal or drink temperature proves to contribute to energy homeostasis, then depending on its contribution and scale, future clinical trials should attempt to adjust this effect when analyzing data. In addition, previous research and established relationships of disease states with dietary patterns, energy intake, and food component intakes should be revisited. We understand the common assumption that thermal energy in food is absorbed by the body during digestion and dissipated as heat into the environment, not contributing to the energy balance. We challenge this assumption herein, including a proposed study design that would test our hypothesis.
### Objective
This paper hypothesizes that the temperature of ingested foods or fluids influences energy homeostasis through the expression of heat shock proteins (HSPs), especially HSP-70 and HSP-90, which are expressed to a greater extent in obesity and are known to cause deficits in glucose metabolism.
### Methods
We provide preliminary evidence supporting our hypothesis that greater dietary temperatures disproportionally induce activation of both intracellular and extracellular HSPs and that these HSPs influence energy balance and contribute to obesity.
### Results
This trial protocol has not been initiated and funding has not been sought at the time of this publication.
### Conclusions
To date, no clinical trials are available regarding the potential effects of meal and fluid temperature on weight status or its confounding effects in data analysis. A potential mechanism is proposed as a basis by which higher temperatures of foods and beverages might influence energy balance via HSP expression. On the basis of the evidence supporting our hypothesis, we propose a clinical trial that will further elucidate these mechanisms.
### International Registered Report Identifier (IRRID)
PRR1-$\frac{10.2196}{42846}$
## Background
The first law of thermodynamics, also known as the law of conservation of energy, states that energy can neither be created nor destroyed in an isolated system [1]. The human body has its own mechanisms to adapt to changing temperatures; however, it is possible that higher or lower temperatures of food and beverages (referred to as dietary temperature) impose some implications on the energy demands of the body. On the basis of diet composition, chewing habits, and the amount of fluid (and its corresponding temperature) ingested, the human body is exposed to a wide range of temperatures [2,3]. This exposure affects the entire gastrointestinal tract, being greatest at the mouth, followed by the esophagus and stomach, with the latter exposed for a greater time [4-7]. Indeed, there have been several studies relating dietary temperature to gastric function [6], frequency of gastric myoelectrical activity [7], gastric emptying time, gastrin release, gastric acid secretion [8], esophageal cancer [9,10], and intraluminal upper gastrointestinal temperature and motility [5]. Although when it comes to energy metabolism, it has been assumed that higher dietary temperatures are dissipated during digestion and do not contribute to energy balance.
One of the most immediate responses of body cells to higher temperatures is the induction and increased synthesis of heat shock proteins (HSPs), especially HSP-70 [11,12] and HSP-90 [13,14]. HSP-70 [15] and HSP-90 [16] possess N-terminal adenosine triphosphatase fragments at their ends and tightly regulate adenosine triphosphate (ATP) activity and ATP hydrolysis [13,17-19], providing the heat energy to drive the adenosine diphosphate to ATP conversion. Indeed, cells exposed to higher temperatures increase HSP activity [11-14], and the time profile of peripheral blood mononuclear leukocytes HSP-70 response to in vitro heat shock is temperature dependent [11,20]. For instance, under in vitro hyperthermic conditions (40-41 °C), the time course was characterized by a sharp rise in HSP-70 concentration immediately after heat shock treatment ($P \leq .05$ for 40 °C at 0 hours), followed by a steady and progressive decline over time [11].
We have already shown that HSPs can extract energy from the environment (here, dietary temperatures), thereby contributing to the energy balance. The mathematical calculations are beyond the scope of this paper, and interested readers may read our previous paper [21]. The summary is graphically illustrated in Figure 1.
Importantly, obesity has been linked to HSP expression, specifically for HSP antibodies (HSP-27 [22], HSP-72 [23], HSP-60, HSP-65, and HSP-70 [24,25]). The table in the Multimedia Appendix 1 [22,24-26] lists the studies investigating the relationship between anti-HSP antibodies and indexes of body mass and body composition. Specifically, serum HSP-27, HSP-60, HSP-65, HSP-70, and HSP-72 antibody levels are significantly increased in people with obesity compared to people with a normal weight [22-26]. Furthermore, in the diet-induced obese mouse model of insulin resistance, HSP-90 inhibitors activate the heat shock factor 1 stress response pathway and improve glucose regulation [27]. A similar experiment on diet-induced obese mice supports this hypothesis in which HSP-90β knockdown reverses insulin resistance and improves glucose tolerance [28].
**Figure 1:** *Hypothetic mechanism by which hot meals or drinks may contribute to energy balance. ADP: adenosine diphosphate; ATP: adenosine triphosphate; ATPase: adenosine triphosphatase.*
## Intracellular Versus Extracellular HSPs
HSPs help to maintain cellular homeostasis through a mechanism called thermotolerance. Cells exposed to mild stress induce HSPs, which later protect them against subsequent stress. In cells exposed to severe stress, HSPs promote apoptosis. HSPs meanwhile exist in extracellular fluids and contribute to immunomodulation [29]. It is noteworthy that the intracellular HSP function may be different from that of extracellular HSPs, as the latter appears to manifest a function different from the well-known chaperone role [29]. For instance, intracellular HSP-70 exerts a profound anti-inflammatory effect, whereas extracellular HSP-70 activates proinflammatory pathways [30]. In the case of obesity, both intracellular [31] and extracellular [32] HSP-70 levels are significantly greater in participants with obesity than in lean controls. It has been proposed that the ratio of extracellular medium HSP-70 to intracellular HSP-70 contents may be a determining factor to trigger a chronic proinflammatory status, which leads to insulin resistance and the development of type 2 diabetes mellitus (T2DM) [30].
## Weight Regain and HSPs
In the only published randomized controlled trial (RCT) linking weight regain to HSPs, Roumans et al [33] explored the expression of stress proteins during weight loss and weight maintenance concerning weight regain. They compared the in vivo findings with the results from in vitro cultured human Simpson-Golabi-Behmel syndrome (SGBS) adipocytes. In total, 18 healthy participants participated in an 8-week dietary program with a 10-month follow-up period. They categorized the participants as weight maintainers or weight regainers based on their weight changes during the intervention. Abdominal subcutaneous adipose tissue biopsies were conducted before and after the diet and after the follow-up. In vitro–differentiated SGBS adipocytes were starved for 96 hours with low (0.55 mmol/L) glucose. Weight regainers showed increased expressions of calnexin, β-actin, HSP-27, HSP-60, and HSP-70. Changes in HSP-27 and HSP-70 and β-actin levels were linked to HSP-60, a novel key factor in weight regain after weight loss. SGBS adipocytes manifested increased concentrations of β-actin and HSP-60 after 96 hours of glucose restriction [33]. In another RCT, 20 young overweight men without metabolic syndrome participated in a 3-week residential program on a low-fat diet and moderate aerobic exercise. After 3 weeks of the diet program, significant reductions in BMI, serum lipids and lipid ratios, and oxidative markers were recorded ($P \leq .05$), along with the reduced expression of HSP-90 and HSP-27 [34].
There is ample literature on the effects of heat stress on fat deposition [35], insulin sensitivity [36], and the facilitating role of HSPs in adiposity [37-39]. The most direct evidence supporting our hypothesis comes from studies conducted on pigs. Exposure to low or high ambient temperatures directly impacts heat production and energy balance in pigs [40]. Heat stress causes enhanced adiposity [41] and growth performance through reduced energy and nitrogen use [42]. However, it is noteworthy that novel observations challenge this long-held traditional dogma, linking reduced productive output during heat stress to decreased nutrient intake. It is now clear that the heat stress response profoundly changes postabsorptive carbohydrate, lipid, and protein metabolism independently of decreased feed intake through harmonized alterations in fuel supply and use by various tissues [43]. One might argue whether heat stress in pigs is relevant to the effects of food temperature on energy metabolism in humans, especially when there is no direct evidence to show that ingesting hot food raises body temperature to such an extent. In addition, it may be argued that the association of HSPs with obesity does not automatically infer causation, and it is well known that these systems respond to metabolic stress. Therefore, to test this hypothesis, we prepared a short proposal to conduct a clinical trial, as described in the Methods section.
Altogether, these trials might provide preliminary evidence that HSPs contribute to obesity and weight regain. There is evidence that other HSPs, that is, HSP-A12A, regulate adipocyte differentiation and diet-induced obesity through positive feedback regulation with PPARγ (peroxisome proliferator-activated receptor gamma) [44].
To explore the mechanism of the effects of heat stress on porcine adipocytes, Qu et al [35] used an in vitro adipocyte differentiation model to determine the cellular responses that occur during adipocyte differentiation in pigs. Stromovascular cells (preadipocytes) were differentiated for 9 days at a normal (37 °C) or heat stress (41.5 °C) temperature under $5\%$ CO2. They measured the expression of a long list of heat stress genes, such as HSP-27, HSP-60, HSP-70, and HSP-90, as well as cellular triglyceride and ATP concentration. Expectedly, heat stress significantly enhanced the expression of HSP genes but surprisingly had no effect on the level of PPARγ, although C/EBPα was significantly induced. Heat stress leads to the enhanced expression of genes involved in fatty acid uptake and cellular triglyceride synthesis. With the progression of differentiation, the total cellular ATP significantly decreased. However, cells under heat stress had significantly greater cellular ATP than those under control temperature. They concluded that heat stress promoted increased adipocyte triglyceride storage, possibly through the upregulation of genes involved in fatty acid uptake and triglyceride synthesis [35]. It was later determined that the induction of phosphoenolpyruvate carboxykinase expression in adipose tissue by heat stress indicates that increased glyceroneogenesis might also be involved in increased fat storage in pigs under heat stress [38].
Although exposure to a higher dietary temperature is a different form of exposure to higher environmental heat in animals, these animal studies [35,40-42] have invaluable lessons that can be translated into a clinical trial. The temperature range of heat stress applied usually lies between 37 °C and 41.5 °C for in vitro studies [35], which can theoretically simulate physiological responses during eating and drinking of hot meals and drinks, and ranges between 12 °C and 29 °C for exposure to ambient temperature studies [40], which is an acceptable temperature difference range (Δ) to study higher or lower dietary temperatures in humans.
Participants with obesity showed increased expression of HSP-60, HSP-72, HSP-90, and GRP-94 levels, specifically influencing BMI and percent body fat [45], whereas insulin resistance resulted in a decrease in intracellular HSP-70 levels. It also appears that weight loss can reduce HSP activation, as demonstrated in a recent trial where acupuncture in combination with dietary restriction was effective in reducing all immunologic factors (anti-HSP antibodies including anti–HSP-27, anti–HSP-60, anti–HSP-65, and anti–HSP-70) and weight loss [46]. Altogether, these findings indirectly support the hypothesis that a network composed of different HSPs (mainly HSP-70 and HSP-90, but including other HSPs, possibly via modulation of insulin sensitivity or resistance) may alter energy balance and contribute to weight gain and loss, which may have important implications in obesity treatment and prevention.
## Thermic Response
Although the higher temperature of ingested meals and fluids may have some overlap with specific dynamic action (SDA), it should be emphasized that these are 2 different concepts; meal or fluid temperature is a subset of SDA, whereas SDA, or thermic response, is a physiological phenomenon, which comprises the energy expended on all activities of the body after the ingestion of a meal and leads to a rapid postprandial increase in the metabolic rate. SDA is influenced by the size, type, composition, and temperature of the meal as well as body size, body composition, and multiple environmental factors (such as gas concentration and ambient temperature). In humans, a commonly used estimate of the thermic effect is approximately $10\%$ of the caloric intake; however, the average maximum increase in metabolic rate originating from digestion could reach $25\%$ [47].
However, there is indirect evidence that states that meal temperature might influence energy balance, which is not accounted for by SDA. Meal temperature is one of the determining factors of gastric emptying and absorption of food compositions such as carbohydrates in the duodenum [48], which consequently affects heart rate [8] and thereby metabolic needs. In endotherms, including humans, if meals and fluids are ingested at a temperature lower than the core body temperature, extra heat should be generated to elevate the meal temperature to the body temperature. Thus, the cost of food warming is indicated by the SDA response [47]. The contribution of food warming to SDA should vary as a function of meal temperature and mass. In other words, more energy is expended to warm a large, cold meal compared with a smaller, warmer meal [49]. It might be conjectured that the reverse may hold true for warmer meals and fluids and thereby contribute to energy expenditure and thus energy balance.
The results of direct or indirect calorimetry studies can be criticized; these studies are usually short term and do not account for the influence of between-individual differences in dietary temperatures, neither on the day of calorimetry measurements nor the day before. It might be a good practice to advocate the measurement or control of meal temperature during the day of measurement and at least 1 week before indirect calorimetry. A careful appraisal of the methodology of most laboratory studies shows that, at best, participants are asked to refrain from caffeine and alcohol intake and any vigorous physical activity 1 day before the calorimetry study or a standard diet is prescribed [50-54]. It can also be argued that heat acclimation may partially promote thermoregulatory adaptations in people who consume hot foods and fluids. This might be true; however, this has not been experimentally tested. In addition, there might be an interaction between dietary temperature and some ingredients of food composition (for instance, capsinoids) or some dietary behaviors (such as the frequency of consuming ingredients like capsinoids) that might circumvent thermoregulatory pathways.
In a recent meta-analysis, increases in HSP-70 protein expression after heat acclimation were shown to be moderated by the number of heat acclimation days [12]. In other words, there is a strong possibility that between-individual differences in eating behaviors in terms of the temperature of foods and drinks they consume may influence HSP-70 expression and, if HSP-70 expression contributes to energy balance, then this will confound matching protocols in clinical trials, such as treatment-matching, subject-matching, and data-matching protocols.
## Evidence From the Effect of Dietary Temperature on Food and Liquid Intake
Although the current hypothesis is centered on the influence of dietary temperature on HSPs to cause changes in metabolic processes that favor energy storage, a potential confounding effect that may exist is the effect dietary temperature may have on eating behaviors. In an experiment conducted on rats, no interaction was reported between nurture temperature and meal temperature. The food temperature per se had no significant effect on food intake [55]. In contrast, food temperature had an impact on food intake in patients who were hospitalized. The percentage of hospitalized patients who rated the meal temperature as good was higher for those served with isothermal trolleys than those who were not. The amount of food consumed by patients served with isothermal trolleys was significantly greater than that consumed by patients served without the trolleys [56]. Similar results have been obtained in another hospital setting [57]. Furthermore, serving temperature may affect expected satiety and complementary food purchases. Consumers are more likely to choose complementary food items when they consume or intend to consume a food or beverage served cold rather than hot [58], and warm temperature attenuates the preference for savory meals [59]. The dietary temperature may additionally influence executive function and processes associated with obesity, whereas lower executive function acts as a risk factor for increases in specific eating behaviors that play a role in the development of weight management problems [60,61]. Therefore, the dietary temperature may have a complex interaction with total energy consumption, which deserves to be clinically investigated under controlled conditions.
The aforementioned literature justifies the need to conduct a well-controlled clinical trial to test whether higher dietary temperatures interfere with body and cellular energetic states and, thus, influence or confound the results of clinical trials that aim to compare or evaluate dietary interventions.
## Overview
To the best of our knowledge, no studies are available regarding the possible contribution of dietary temperatures to the HSP response. We provide preliminary evidence supporting our hypothesis that higher dietary temperatures disproportionally induce activation of both intracellular and extracellular HSPs and that these HSPs influence energy balance and obesity. In this paper, we outline a protocol to test these hypotheses and elucidate the potential roles dietary temperature plays in obesity development.
## A Proposal to Conduct an RCT
A 4-arm randomized design investigating diets served at different temperatures in 5 °C increments (with core body temperature as a reference) in healthy adults would be the first step to elucidate the potential mechanism linking higher meal temperature with energy contribution. The same protocol can be replicated in patients who are overweight and obese or in people with T2DM. In addition, a crossover design can be performed with necessary modifications.
## Participants: Inclusion and Exclusion Criteria
The participants will include 80 healthy (BMI 25-35 kg/m2) men and women aged 19-65 years. Entry criteria will include people who are not currently dieting to lose weight and have had no weight loss or gain >2 kg over the past 3 months; not taking any medications (including antifever) that affect energy expenditure or eating; not using tobacco; not pregnant or lactating or planning to become pregnant in the next 12 months; and have no limitations to eating specific foods or to swallowing (eat and drink) a normally estimated range of meal temperatures (37-52 °C). The participants must not have major health problems; known cardiovascular (cardiac, peripheral vascular, and cerebrovascular), pulmonary (chronic obstructive pulmonary disease, interstitial lung disease, and cystic fibrosis), or metabolic (diabetes, thyroid disorders, and renal or liver disease) disease; and severe fever.
## Procedures
The day before the experimental session, the participants will be instructed to avoid alcohol, caffeine, and strenuous exercise and to drink plenty of water for 24 hours. On the day of the experimental session, the participants will be asked to arrive at the laboratory adequately rested and will be provided with boluses of soups (each 100 ml) with a predefined temperature to be taken once (to overcome the problem of temperature dissipation).
The energy content of bolus diets (including the thermoneutral diet and temperature-graded bolus diets) will be matched to each of the participant’s objectively calculated energy demands [62] and will be provided in the following manner: $20\%$ energy for breakfast, $50\%$ for lunch, and $30\%$ for dinner. This procedure will facilitate the administration of boluses with graded temperatures (eg, +5 °C increments) with a fair precision from 37 °C to 52 °C, which is both ethical and within the optimal drinking temperature of 57.8 °C and <85 °C for the maximum temperature used to serve hot drinks such as coffee [63].
After assuring sufficient hydration (urine-specific gravity <1.025) [64], the participants will be instrumented in a thermoneutral room (approximately 25 °C) and then will be entered into a whole-body calorimeter regulated to 35 °C with approximately $20\%$ relative humidity, where they will rest in an upright seated position for a 30-minute habituation period while steady-state measurements will be obtained.
The dietary temperature will be measured using a noncontact thermometer. All drinks and a standardized diet will be served at room temperature to maximize gastric emptying and fluid uptake while minimizing diuresis. It is expected that by using this metered intake to control for intake pacing, ingestion will be completed between 3 minutes and 5 minutes and will control for heat dissipation of foods and fluids as well. To avoid thermal injury while providing a satisfactory sensation to the participants, the optimum temperature for serving hot fluids and meals will be observed [63].
The drinks and meals will be consumed at a speed that is as rapid as the participants can comfortably manage, which is described elsewhere in detail (mean 11 seconds for 200 mL and 38 seconds for 500 mL) [65,66]. The intraesophageal and intragastric temperatures will be monitored using thermocouples, which is a widely used technique [67]. As currently there is no specific dynamic model for real-time monitoring or measuring of temperature changes of digested meals or drinks along the alimentary tract, inner food and drink temperature will be estimated using the inverse heat conduction problem methodology on surface temperature measurements obtained using thermography [68]. For practical and physiological purposes, considering the applied speed of ingestion, the magnitude of temperature changes along the alimentary tract could be considered as negligible [65-67,69,70].
## Measures
All outcome measures will be measured at 0, 6, and 12 weeks. The participants will be asked to follow their assigned diet served at 37 °C, 42 °C, 47 °C, and 52 °C for 12 weeks.
Blood samples will be collected from each participant for analysis after 12-hour fasting, 3 times during the study (at the beginning and 6 and 12 weeks later).
Adipose tissue biopsies, protein isolation, and in vitro cell culture experiments will be performed as described elsewhere [33].
Cellular HSP-70 and HSP-90 levels will be measured to determine the thermic response to feeding, that is, evaluation of the causal relationship among higher dietary temperature, HSP-70/HSP-90 concentrations, and their time profile, as well as measures of obesity, aerobic fitness, resting metabolic rate, and diet-induced thermogenesis.
Food diaries will be analyzed using software to determine energy intake and macronutrient content.
An automated respirometer system plus a whole-body direct calorimeter, as the gold standard, will be used to measure energy expenditure and substrate utilization, aerobic fitness, resting metabolic rate, diet-induced thermogenesis, and markers of protein and fat catabolism to evaluate whether dietary temperature influences metabolism and surrogate end points of obesity in participants, such as weight and waist-hip ratio.
## Duration of Study
The optimum length of the study will be 12 weeks, if feasible, and if not, at least 2 weeks would be necessary.
## Assignment of Interventions
For treatment allocation, randomization (1:1:1:1) and blinding protocol will be used as described previously [71].
## Primary Outcome Measures to Be Assessed
Subset A denotes the change in BMI, total body fat, body weight, percentage body fat, lean body mass, percentage lean body mass, waist circumference, and waist-hip ratio from baseline to 12 weeks (time frame: 0, 6, 12, and n weeks). The measurements will be performed with light clothing before breakfast and preferably after voiding.
Subset B denotes changes in HSP-27, HSP-65, HSP-70, HSP-72, and HSP-90 (in plasma and total leukocytes) and anti–HSP-27, anti-HSP-65, anti-HSP-70, anti-HSP-72, and anti-HSP-90 antibodies from baseline to 12 weeks (or whatever time is feasible, time frame: 0, 6, 12, and n weeks).
Subset C denotes changes in motilin, fasting glucose, insulin, C-peptide, 2-hour glucose, and insulin plus homeostasis model assessment–estimated insulin resistance from baseline to 12 weeks (or whatever time is feasible, time frame: 0, 6, 12, and n weeks).
## Secondary Outcome Measures to Be Assessed
Changes in total cholesterol, triglycerides, low-density lipoprotein, high-density lipoprotein, reactive oxygen species, reactive nitrogen species, and NF-κB from baseline to 12 weeks (or whatever time is feasible, time frame: 0, 6, 12, and n weeks) are to be assessed.
## Other Outcome Measures to Be Assessed
Subset A denotes plasma levels of leptin and adiponectin from baseline to 12 weeks (or whatever time is feasible, time frame: 0, 6, 12, and n weeks).
Subset B denotes possible changes in appetite and food intake (using a 3-day dietary recall questionnaire). Immediately before and after each diet, participants will rate hunger, satiety, and prospective food consumption using 100-mm visual analog scales. Gastric motility will be measured using ultrasound imaging systems.
## A Short Proposal to Conduct a Crossover Design
As there are between-individual responses to heat stress [72], there are also interindividual differences in the serum concentration of HSPs [73-75], which may confound statistical analyses when calculating interactions between frequency of drinking or eating hot foods and time elapsed after eating or drinking. To overcome issues with interindividual differences in its broad sense, a crossover design with necessary modifications can be performed. In this case, it will be necessary that participants can eat ad libitum.
A 2-week washout period and thermoneutral diet between conditions will be included, in which participants will eat boluses of soups served at approximately 37 °C. In this case, the order in which participants participate in each treatment will be randomized. The participants will be assigned 4 weeks of each treatment with a 2-week washout period between each of the 4 crossover treatments.
Before starting the second round of the trial, all 3 treatment groups will return to a thermoneutral bolus diet for another 5 weeks (5 weeks of active treatment, 2 weeks of washout period, and 5 weeks of crossover round).
Adipose tissue biopsies and plasma samples will be obtained at 2 time points: before and after dietary treatment diet.
## Statistical Analyses
For statistical analysis, Bayesian adaptive approaches [76] will be applied whenever feasible because this approach is more likely to allocate participants to better-performing arms at each interim analysis.
In addition to standard analyses of RCTs, causal mediation analysis can be performed to determine how much of the weight change is mediated by the increase in HSP levels or appetite or both. Future trials should seek to identify alternative mechanisms with the aim of further refining the intervention.
This seminal clinical trial can provide preliminary data for causal mediation analysis to facilitate the exploration of the causal mechanisms underlying possible energy contributions through higher dietary temperatures. Proper methods will be used for this purpose [77,78].
## Sample Size Calculation
As the choice of type I errors (α, false positive), type II errors (β, false negative), and effect size may be quite arbitrary [79], we suggest calculating the sample size or power using the R value (eHSP-70 to iHSP-70 ratio) for a pilot study (alternatively, owing to feasibility, a preliminary study may be conducted on mice or rats).
Furthermore, as providing a priori sample size calculation is misleading and the presentation of CIs—although serving the same purpose—is superior [80], it would be better to use the CI of the R value to calculate the sample size based on the feasibility of a priori assumptions about the study results and available resources. In addition, because foods and drinks may be served and consumed at even wider temperature ranges, it would be instrumental to calculate sample sizes based on wider R ranges (approximately 0.2-6).
According to Krause et al [30], assuming the ratio R = (eHSP-70) / (iHSP-70) = 1 for the controls (resting and unstimulated) and assuming a moderate-hot meal temperature may produce a shift in R to up to 5, which is paralleled by an increase in inflammatory markers and stimulation of cell proliferation, R values >5 can be considered as an exacerbated proinflammatory response. However, for a conservative calculation, we suggest using $R = 2.$ According to Hunter-Lavin et al [81], heat-shocked cells simultaneously measured HSP-70 at 37 °C ($R = 1.00$), 39 °C ($R = 1.45$), 42 °C ($R = 0.65$), 43 °C ($R = 0.48$), and 45 °C ($R = 1.97$), this later temperature confirms a proinflammatory response.
Note that at 45 °C ($R = 1.97$), there is a turning point compared with that at 43 °C. This means that if a study intends to investigate wider ranges of meal temperature, it is necessary to calculate an array of expected R values and then choose the maximum calculated sample size. For an R value between 0.22 and 1.1, a sample size of 9 participants is needed to achieve $80\%$ power [1], whereas an R value between 1.8 and 6 would require 75 participants to achieve $90\%$ power [2].
Changes in the calculation of the R value are suggested to be valid during heat exposure, as R values are associated with the heat exposure of peripheral blood mononuclear cells to different temperatures within a physiological range [30]. Precautions that should be considered could include, but are not limited to, correction for noncompliance of the participants, adjustment for multiple comparisons, and innovative study design.
A total of 80 patients will be randomized equally (1:1:1:1) between the 3 active treatments and the thermoneutral group. Overall, 20 participants will be randomly allocated to the thermoneutral diet group (37 °C): 20 at 42 °C, 20 at 49 °C, and 20 at 54 °C. The primary analysis will be intention-to-treat for the whole study cohort.
It is possible that there are multiple roles of HSPs in body weight, weight regain, and composition, probably related to the exposure time during diet ingestion (and also dietary temperature acclimatization during short-term periods, midterm periods, and long-term periods and presence or absence as well as the degree of insulin resistance in people). Thus, conducting a clinical trial for 12 weeks would provide a sufficient time course to investigate this point.
## Further Considerations and Opportunities
Due to HSPs’ intertissue differences [30], it would be helpful to obtain samples of plasma, lymphocytes, central adipose tissue, and muscle (and liver tissue in case of animal study), if feasible, to attempt to find correlations between HSPs and other variables. This study will provide a great opportunity to administer an array of questionnaires to determine the quality of life, anxiety, depression, food frequency, physical activity, etc, and these data in combination with regular examinations will also help to decide whether and when this trial should be continued or stopped. As a preliminary study, it might be proposed to conduct an observational study to retrospectively investigate the mean differences in variables and odds ratios between people who report meals as cold and those who report consuming their meals while it is hot, in terms of serum baseline HSP levels. Subjective dietary temperatures should then be defined as objective temperatures with appropriate measures. This proposed trial can be replicated in healthy people, people with obesity, and people with T2DM (and in long-term patients vs newly diagnosed patients).
## Further Technical Issues That Need to Be Addressed in the Pilot Clinical Trial
One might argue whether the study discriminates between other possible influences of meal temperature on energy balance mentioned in the paper (eg, effect on gastric emptying and diet-induced thermogenesis). Evidently, these issues should also be controlled. Participants can eat ad libitum during the 12 weeks of intervention and are fed only soups for 3 meals a day for 12 weeks and are supposed to stay in the laboratory during the entire experiment. This can confound the assessment of hunger or satiety obtained using the visual analog scale using different meal sizes. If participants receive standardized and isocaloric meals, it can mask a possible effect on appetite as well. A very important issue regarding meal temperature is the experienced pleasantness of the meal, which can influence food intake. This should be assessed as well. We have proposed an intention-to-treat analysis for the pilot study. Such a long intervention probably might result in a significant dropout rate, so it is questionable if sufficient participants will conclude the study.
## Ethical Considerations
As this study involves human participants, the approval by Institutional Review Board (IRB) will need to be obtained before beginning any research activities. This will include an informed consent process, in which participants will be required to provide explicit informed consent for data collection, analysis, and reporting before the inclusion in the study. Such IRB approval will require privacy and confidentiality protection such as the deidentification of participant data, which would be appropriate for this trial. The compensation for participation in research will need to be considered by the specific IRB and research team.
## Results
This trial protocol has not been initiated and funding has not been sought at the time of this publication.
## Principal Findings
Apart from the obvious need to improve weight loss or weight loss maintenance, elucidating the roles that HSP may play in the energy balance is important for future considerations in dietary assessment. The lack of control for dietary temperatures may have produced confounding effects in the data analysis of clinical trials of obesity and medical situations with obesity-related pathology. Dietary assessment tools are widely used in most nutritional and medical studies; therefore, it is critical to have accurate and validated tools. Several meta-analyses and systematic reviews have shown that dietary intake assessment tools have fundamental errors [82-84]; some researchers have proposed that their application in energy balance or obesity and body weight studies must be ceased [85]. It was recently proposed that self-reported energy intake should not be used for the study of energy balance in research on obesity because energy underreporting varies as a function of BMI, and interindividual variability in the underreporting of self-reported energy intake automatically attenuates diet-disease relationships [86]. Furthermore, there is a dispute regarding the validation of dietary assessment tools and the inaccuracy of energy intake measures [87-90]. If dietary temperatures prove to affect energy balance, then future attempts to correct for underreporting and misreporting of diet outcomes cannot avoid taking dietary temperature into account, regardless of whether these tools are kept or abandoned and new tools are developed.
We have presented our hypothesis that dietary temperatures influence energy balance, and we have followed this with a clinical trial that would address this question. The lack of literature regarding possible direct and independent effects of higher dietary temperatures on indexes of obesity warrants a well-designed clinical trial to shed light on the ambiguous role of dietary temperature on daily energy contribution. If meal or drink temperature proves to contribute to energy homeostasis, depending on its contribution and scale, future clinical trials should attempt to adjust this effect when analyzing data. In addition, previous research and established relationships of disease states with dietary patterns, energy, and nutrient intake should be revisited.
This hypothetical proposal is important in several ways. Previous studies [69,70] have only examined the effect of water temperature on gastric emptying and thereby appetite and energy intake; however, they did not consider a potential and direct effect of higher meal or drink temperature through body thermodynamics and HSPs expression. Fujihira et al [69] were the first to investigate how different water temperatures impact gastric motility and energy intake. They showed that consuming 500 mL of water at 2 °C can suppress gastric contractions and ad libitum energy intake compared with consuming 500 mL of water at 37 °C and 60 °C. The subjective appetite perception of hunger in their study tended to be lower after consuming 500 mL of water at 2 °C than at 60 °C. They reported that reduced energy intake after consuming cold water (ie, at 2 °C) is accompanied by a change in gastric contractions. These findings suggest that water temperature may modulate gastric motility to influence energy intake. Indeed, the results of well-conducted experiments confirm that liquid and solid meals at 60 °C accelerated gastric emptying compared with those at 37 °C during the initial 30 minutes [66,70]. However, their findings can be simultaneously criticized, as they have not considered the potential biochemical effect of higher dietary temperatures on cell biology and body thermodynamics. For the first time, we argue that dietary temperature may directly influence energy balance through the induction of HSP-27, HSP-65, HSP-70, HSP-72, and HSP-90 expression.
## Conclusions
Our hypothetical proposal potentially adds new knowledge to the existing literature that food temperature may directly contribute to energy balance and thereby influence weight management. Our proposal has profound and widespread implications. For instance, if dietary temperatures prove to affect energy balance, future clinical trials involving metabolic studies have to take dietary temperature into account. In addition, the results of many published metabolic clinical trials may need to be revisited. The results of this proposal will have deep implications for dietary recommendations, such as the recommended daily energy intake for different age groups and diet planning in different health and disease states. Efficacy studies of nutritional interventions will also need to consider this point. Furthermore, some cultures are accustomed to consuming hot meals and drinks. Comparative studies, particularly those dealing with weight or metabolism, may need to account for biases created by different dietary temperatures. Our hypothesis may also explain the discrepancies in different studies (that did not control for dietary temperature), despite similar methodologies.
The proposed trial does carry limitations. There is no specific dynamic model for real-time monitoring or measuring of temperature changes of digested meals or drinks along the alimentary tract; therefore, we will have to estimate the inner food and drink temperature using the inverse heat conduction problem methodology, which may influence the precision of the results.
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|
---
title: 'Frailty transition and depression among community-dwelling older adults: the
Korean Longitudinal Study of Aging (2006–2020)'
authors:
- Nataliya Nerobkova
- Yu Shin Park
- Eun-Cheol Park
- Jaeyong Shin
journal: BMC Geriatrics
year: 2023
pmcid: PMC10024357
doi: 10.1186/s12877-022-03570-x
license: CC BY 4.0
---
# Frailty transition and depression among community-dwelling older adults: the Korean Longitudinal Study of Aging (2006–2020)
## Abstract
### Background
Frailty is recognized as a geriatric syndrome associated with depression. The consequences and mechanism of frailty transitions are still understudied. This study assessed the influence of frailty transitions on new-onset depressive symptomology using longitudinal, nationwide data of Korean community-dwelling older adults.
### Methods
Longitudinal population-based study conducted in every even-numbered year starting from 2006 to 2020 (eight waves) with a sample of older adults aged ≥ 60 years old. After the application of exclusion criteria, a total of 2,256 participants were included in the 2008 baseline year. Frailty transition was determined through the biennial assessment of change in frailty status using the frailty instrument (FI); depression was measured using the Center for Epidemiological Studies Depression 10 Scale. We employed the lagged general estimating equations to assess the temporal effect of frailty transition on obtaining depressive symptoms.
### Results
Compared to non-frail individuals, the risk of depression was higher in transitioned into frailty and constantly frail participants over a 2-year interval: men (odds ratio (OR) 1.26, $95\%$ confidence interval (CI) 1.21–1.32; OR 1.29, $95\%$ CI 1.21–1.38), women (OR 1.34, $95\%$ CI 1.28–1.40; OR 1.51, $95\%$ CI 1.41–1.62), respectively.
### Conclusions
Frailty transition is found to be associated with new-onset depressive symptoms. Frail individuals and those who transitioned into frailty were associated with a higher risk of depression. Particular attention should be paid to these frailty transitioned groups. Early intervention and implementation of prevention strategies at physical, nutritional, and social levels are warranted to ameliorate frailty and depression in late life.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-022-03570-x.
## Background
Frailty is an aging-related condition highly prevalent in the older population and emerging as a risk factor for adverse health outcomes, including falls, disability, hospitalization, and an increased risk of morbidity and mortality [1–4]. As frailty is a geriatric syndrome that severely affects the aging population, it has gained increasing attention among researchers.
In South Korea (hereafter, Korea), an aging population and a decline in birth rate are the greatest public health concerns [4–6]. The proportion of the general aged population is expected to increase substantially to $24.5\%$ by 2030 and $41.0\%$ by 2060 [7]. Thus, measures for curbing the incidence of frailty among older adults are warranted.
Frailty syndrome is a broad concept with various causative risk factors. Numerous instruments [8, 9] and scales have been developed to measure frailty syndrome [10, 11]. However, some factors, including weakening of handgrip strength and self-reported exhaustion, are common issues in the use of models [8]. At present, frailty is considered a multidimensional dynamic measure based on various age-related deficits [12, 13], as opposed to the earlier perception of frailty in a non-dimensional and only physical manner [14, 15]. Frailty is a dynamic condition, and its changes are characterized by a transition to a worsened or improved state over time. The frailty instrument (FI), a frailty measure that was developed and validated for the Korean population, is utilized for rapid assessment of frailty and determination of adverse health outcomes in older adults [4, 16, 17]. The FI is based on a broader approach to the measurement of frailty and includes physical (handgrip strength), psychological (exhaustion), and social (social isolation) factors. Evaluating the changes in frailty over time using the FI allows for consideration of the bidirectional aspect of transitions in frailty status.
The dynamic nature of frailty has been investigated in some longitudinal studies. However, most of these previous studies focused on the predictive risk factors of frailty transitions rather than on frailty transition outcomes as a changing continuous risk factor itself. Previous longitudinal studies conducted in Korea have established the impact of frailty transition on the cognitive functions of older adults [4, 18]. However, the impact of frailty transition on depressive symptoms among older Korean adults remains unclear.
Depression is a well-known risk factor for many health-related conditions [19–21]. Hence, studies have been conducted with the aim of preventing, slowing, and ameliorating depressive symptoms in vulnerable populations. The association between frailty and depression has been evaluated in several cross-sectional and longitudinal studies. However, little attention has been paid to the relationship between changes in frailty status over time and the development of depressive symptoms. Hypotheses of comparable biological mechanisms of frailty and depression have been proposed [22]. Although the results of cross-sectional studies indicate a positive association between depression and frailty [23, 24], findings from cohort studies are less consistent [25]. In addition, several studies conducted to examine the bidirectional relationship between depression and frailty showed controversial results [26–28].
To date, little is known about the effect of more comprehensive conceptualizations of frailty and its transitions on the development of depressive symptoms. Therefore, the aim of this study was to investigate the effect of frailty transitions on new-onset depressive symptoms among community-dwelling older adults in Korea using the FI and the Center for Epidemiological Studies Depression 10 Scale (CES-D-10).
## Data source and sample
This study was conducted using data collated over 12 years from the first to the eighth wave (2006 to 2020) of the Korean Longitudinal Study of Aging. Since its establishment in 2006, the Korea Labor Institute has been collecting regular panel data of the same population sample of older adults aged more than 45 years from all regions in Korea. The total number of participants surveyed in 2008 was 8,688 (approximately $84.7\%$ of the original 10,254 participants surveyed in 2006). The survey was conducted every even-numbered year starting from 2006, primarily using the same survey categories. The sample retention rate in 2020 was $63.3\%$. Information on the family background, demographic characteristics, family composition, health, employment, income, assets, and subjective quality of life of the respondents were collected for the survey [29]. Additional information about the survey is available on the panel survey organization website (https://survey.keis.or.kr/klosa/klosa01.jsp). The exclusion criteria for the survey included cognitive impairment and depression status during the first wave [2006], age below 60 years, missing information on the employed variables, and loss to follow-up. Application of these criteria led to the inclusion of 2,256 participants in 2008, 2,039 in 2010, 1,896 in 2012, 1,690 in 2014, 1,529 in 2016, 1,346 in 2018, and 1,192 in 2020. The selection process of the participants is shown in detail in Fig. 1.Fig. 1Flowchart of the study participants from 2006 to 2020 The KLoSA survey was approved by the National Statistical Office and Institutional Review Board of the Korea Centers for Disease Control and Prevention. All methods were conducted in accordance with the relevant guidelines and regulations. As the KLoSA database has been published to the public for scientific use, ethical approval was not required for the study. All participants were required to provide written informed consent to participate in the KLoSA survey and agreed to be used in further scientific research. The data were anonymized and de-recognizable with no personal information, with cautious protection on confidentiality.
## Variables
The variable of interest, “frailty transition,” was assessed as a time-varying covariate that reflects changes in frailty status as defined using the FI, which was developed and validated using the community-dwelling older adult population of Korea. The FI allows for rapid assessment of frailty and associated adverse outcomes, including disability, morbidity, institutionalization, and mortality, and has high predictive validity, discrimination, and calibration power [29]. The FI depicts the sociopsychological and physical components of frailty based on three criteria: exhaustion, social isolation, and weakness of handgrip strength [4, 17, 30]. The exhaustion criterion is estimated using self-reported measures of feeling that every task required effort during the previous week. Social isolation status is determined if respondents report not participating in any social group activity. Handgrip weakness is evaluated using sex-specific grip strength thresholds: < 24 kg for men and < 15 kg for women. The three variables are graded using a three-point scale, with ≥ 2 points classified as frail and ≤ 1 point as non-frail. In the survey, the lag function was used to detect changes in frailty status in the prior and the succeeding waves, following a two-year gap. Therefore, frailty transitions were categorized into four groups: [1] Non-frail → Non-frail, [2] Non-frail → Frail, [3] Frail → Frail, and [4] Frail → Non-frail.
The outcome variable, “depression,” was identified by measuring depressive symptoms using the CES-D-10. The 10-item version of the CES-D, established on the work of Andresen et al., was extrapolated from the original 20-item version of the CES-D by applying item-total correlations and eliminating redundant items [31]. The CES-D-10 is a validated screening tool used to identify major depressive symptoms in older adults [32–34]. The validity of the Korean version of CES-D-10 for screening of depressive symptoms is well based [35, 36]. Responses are graded on a four-point scale, coded 0–1, with a total score of 10 points. Higher scores indicate greater distress. A cut-off score of ≥ 4 points was set for the detection of depression in the survey participants, which is consistent with the proposed use of the CES-D-10 as a screening instrument [31, 37, 38].
Data on sociodemographic characteristics and health-related conditions were added as potential confounders in this study. Sociodemographic characteristics included sex (men, women), age (60–69, 70–79, ≥ 80 years), educational level (middle school or below, high school or above), marital status (married, not married), occupational status (working, not working) and income level per month in quartiles (low, middle-low, middle-high, and high). Additionally, we considered the participants’ regions of residence (urban or rural areas). Limitations in activities of daily living (ADL) were determined if the respondents had difficulty performing any daily, necessary tasks, including getting dressed, washing their face and hands, bathing, eating meals, leaving a room, and using the toilet. Limitations in Instrumental Activities of Daily Living (IADL) were defined as difficulties with performing social function-related tasks, including making/receiving phone calls, managing finances, companionship, mental support, transportation usage, household chores, preparation of meals, shopping, taking medications, and doing laundry. Cognitive function was assessed using the Korean version of the Mini-Mental State Examination (MMSE). The MMSE is a 30-point questionnaire, with 24 points being the cut-off for cognitive impairment. The chronic diseases considered in the present study included hypertension, diabetes mellitus, cancer, lung disease, heart disease, and cerebrovascular disease. Comorbidities were grouped into three categories depending on the number of diseases a participant had (0, 1, or ≥ 2 diseases). In addition, we considered smoking status (smoker, non-smoker), body mass index (normal, abnormal: underweight and overweight), and life satisfaction (bad, normal, and good).
## Statistical analysis
We evaluated relationships between the two-year frailty transition and CES-D-10 score using a 2-year lagged multivariable lagged generalized estimating equations (GEE) model that is an extension of the quasi-likelihood approach used to analyze longitudinal correlated data. The GEE model allows for repeated measurement analysis of longitudinal panel survey data and considers the correlation within the subject to generate odds ratios (ORs) and $95\%$ confidence intervals (CIs), and the corresponding p-value. All statistical analyses were performed separately for men and women to examine sex-specific differences in terms of the diverse impact of frailty transition on depressive symptoms. A total of eight waves were used for the analysis, and repeated measurements were carried out for each individual up to seven times. Two-year lagged changes in frailty transition were calculated using the frailty status in the preceding and follow-up waves (2006–2008, 2008–2010, 2010–2012, 2012–2014, 2014–2016, 2016–2018, and 2018–2020) following a two-year interval. Furthermore, a subgroup analysis was performed to reveal the relationship between frailty transition and depression status. We estimated the lagged GEE analyses for each FI with respect to the CES-D-10 score. Differences were considered statistically significant with a p-value of < 0.05. Statistical analyses were performed using the GENMOD procedure in SAS (version 9.4; SAS Institute Inc., Cary, NC, USA) with link identity and distribution normal.
## Results
The sex-stratified baseline characteristics of the study population are summarized in Table 1. A total of 2,256 people were included in the survey in the baseline year (1,256 men and 1,000 women). The percentage of women with a CES-D-10 score ≥ 4 was almost twice that of men (14.6 and $8.8\%$, respectively). Regarding frailty status, $39.3\%$ of the men and $59.1\%$ of the women transitioned into frailty, and $45\%$ of the men and $44\%$ of the women with a sustained frailty status showed depressive symptoms. There were significant differences in other covariates, such as age, occupational status, ADL, IADL, and MMSE status, between men and women with a CES-D-10 score ≥ 4. CES-D-10 score distributions for the main variables were additionally summarized as the median and interquartile range (Supplementary Table 1).Table 1General characteristics of the study population (baseline 2006→2008)VariablesCenter of Epidemiologic Studies Depression Scale, 10-item version (CES-D-10)MenWomenTotal< 4≥ 4Total< 4≥ 4N%N%N%N%N%N%Total $$n = 22561256100$.0114691.21108.81000100.085485.414614.6$*Frailty status* Non-frail → Non-frail111288.5105194.5615.584784.776590.3829.7 Non-frail → Frail846.75160.73339.3888.83640.95259.1 Frail → Frail201.61155.0945.0252.51456.01144.0 Frail → Non-frail403.23382.5717.5404.03997.512.5Age 60–6966152.662093.8416.257657.651389.16310.9 70–7949739.644789.95010.137337.330581.86818.2 ≥ 80987.87980.61919.4515.13670.61529.4Region Urban area54343.250492.8397.247147.140886.66313.4 Rural area71356.864290.07110.052952.944684.38315.7Educational level Middle school or below49039.043689.05411.067467.456684.010816.0 High school or above76661.071092.7567.332632.628888.33811.7Occupational status Working52942.149994.3305.717417.415790.2179.8 Non-working72757.964789.08011.082682.669784.412915.6Marital status Married116492.7106891.8968.264464.455886.68613.4 Not married927.37884.81415.235635.629683.16016.9Household income Quartile 1 (low)47237.640986.76313.344044.037084.17015.9 Quartile 237930.235593.7246.327627.625090.6269.4 Quartile 324019.122794.6135.415915.913081.82918.2 Quartile 4 (high)16513.115593.9106.112512.510483.22116.8Chronic disease 059347.254591.9488.143943.939389.54610.5 144735.640490.4439.637837.831182.36717.7 2 or more21617.219791.2198.818318.315082.03318.0ADL Normal123998.6113891.81018.298998.984985.814014.2 Abnormal171.4847.1952.9111.1545.5654.5IADL Normal110387.8102092.5837.595095.081886.113213.9 Abnormal15312.212682.42717.6505.03672.01428.0MMSE ≥ 24108886.6101793.5716.575175.167189.38010.7 < 2416813.412976.83923.224924.918373.56626.5Smoking status Non-smoker49139.145592.7367.397797.783885.813914.2 Smoker76560.969190.3749.7232.31669.6730.4BMI Normal118894.6108791.51018.593293.280686.512613.5 Abnormal685.45986.8913.2686.84870.62029.4Satisfaction of Life Bad16112.813282.02918.015015.010167.34932.7 Normal77761.970991.2688.860260.252086.48213.6 Good31825.330595.9134.124824.823394.0156.0 Table 2 depicts the findings of the lagged GEE model analyses of the association between changes in frailty status and the risk for a CES-D-10 score ≥ 4. We noted that in both men and women, those who showed a Non-frail → Frail transition (men: OR 1.26, $95\%$ CI 1.21–1.32; women: OR 1.34, $95\%$ CI 1.28–1.40) and Frail → Frail transition (men: OR 1.29, $95\%$ CI 1.21–1.38; women: OR 1.51, $95\%$ CI 1.41–1.62) had higher ORs than non-frail older adults. Owing to a large number of missing data and participants lost to follow-up, as well as the overlap of the exhaustion item with CES-D-10, several sensitivity analyses (Supplement Tables 2, 3, 4) were performed. The received findings were mainly consistent with the primary outcome. Table 2Generalized linear model using the GEE with CES-D-10 score in 2008–2020VariablesCES-D-10 score ≥ MenWomenORa$95\%$ CIORa$95\%$ CIFrailty status Non-frail → Non-frail1.001.00 Non-frail → Frail1.26(1.21—1.32)1.34(1.28—1.40) Frail → Frail1.29(1.21—1.38)1.51(1.41—1.62) Frail → Non-frail1.04(1.00—1.08)1.00(0.96—1.04)a Adjusted for other covariates Figure 2 shows the lagged GEE model analysis results of the effect of the two-year changes in FI status on the risk of depressive symptoms. We observed statistically significant associations between depressive symptoms and each change in FI status. However, the most significant association was between depressive symptoms and the exhaustion domain of the FI. Men and women who transitioned into an exhausted state (men: OR 1.63, $95\%$ CI 1.56–1.71; women: OR 1.71, $95\%$ CI 1.64–1.79) or maintained an exhausted state (men: OR 1.85, $95\%$ CI 1.71–1.99; women: OR 1.90, $95\%$ CI 1.79–2.07) had higher ORs than their non-exhausted counterparts. Fig. 2Subgroup analysis of Frailty Instrument (FI) components with depression. The exhaustion domain of the FI showed the most significant association with depression The findings of the independent subgroup analysis of the variables associated with the effect of changes in frailty status on a CES-D-10 score ≥ 4 are shown in Table 3. The results indicated that the Non-frail → Frail and the Frail → Frail groups had the highest ORs among participants who were experiencing cognitive impairment: MMSE score lower than 24 points was significantly associated with depressive symptoms: Non-frail → Frail (men: OR 1.25, $95\%$ CI 1.17–1.33; women OR 1.39, $95\%$ CI 1.31–1.48), Frail → Frail (men: OR 1.36, $95\%$ CI 1.23–1.50; women OR 1.62, $95\%$ CI 1.49–1.76).Table 3Subgroup analysis using the GEE of CES-D-10 score with frailty transition in 2006–2020VariablesCES-D-10 score ≥ 4MenWomenNon-frail → Non-frailNon-frail → FrailFrail → FrailFrail → Non-frailNon-frail → Non-frailNon-frail → FrailFrail → FrailFrail → Non-frailORORa$95\%$ CIORa$95\%$ CIORa$95\%$ CIORORa$95\%$ CIORa$95\%$ CIORa$95\%$ CIMMSE≥ 241.001.29(1.22—1.35)1.25(1.15—1.36)1.04(0.99—1.08)1.001.29(1.21—1.38)1.40(1.26—1.55)1.03(0.99—1.08)< 241.001.25(1.17—1.33)1.36(1.23—1.50)1.06(0.99—1.14)1.001.39(1.31—1.48)1.62(1.49—1.76)0.98(0.93—1.04)a Adjusted for other covariates
## Discussion
Depression is a common medical illness among older adults that is associated with numerous adverse health outcomes. The potential risk factors for the development of late-life depression likely comprise complex interactions among genetic factors, cognitive dysfunction, age-associated neurobiological fluctuations, and stressful events [39]. Thus, strategies developed through a detailed and precise examination of the above-mentioned risk factors and specifically designed to minimize the risks of depression and maintain well-being in later life are warranted. In the present study, we investigated the association between frailty transition and the onset of depressive symptoms among community-dwelling Korean adults over 60 years old. The results showed that frailty (transition into frailty or maintenance of frailty over a two-year period) was significantly associated with new-onset depressive symptomatology compared with continuous non-frailty. Furthermore, we suggest that transitional endpoints, particularly transitioning to a frailty state, might be the main features correlated with depression, given that baseline status may only influence the effects on follow-up status. Notably, the results also indicated that while improvement of frailty in men reduced depressive symptoms, participants still showed signs of depression compared to their non-frail counterparts.
The relationships between older age, frailty, and depression have been evaluated in previous studies. The results of the studies demonstrated a bidirectional association between frailty and depression. In addition, several prospective studies have been conducted to examine whether the presence or absence of frailty at baseline predicts new-onset incident depression. In a population-based cohort study of older adults aged ≥ 65 years who were followed up at 3, 6, and 9 years, $30.6\%$ of the participants without depression developed a depressed mood during follow-up, and the frail state was associated with a significant risk of new onset of depression in adjusted models [40]. In another study, follow-up analysis at 2 and 4 years revealed significant associations between frailty and the onset of depression in adjusted models [41]. These findings and those of the present study suggest that frailty status and transition are key causes of emotional distress (such as feelings of worthlessness or hopelessness) [42], which, in turn, may result in new-onset depressive symptomatology.
In the present study, subgroup analysis of independent variables indicated that respondents with cognitive impairment during follow-up showed an association between frailty status or transition to frailty and new onset of depressive symptoms. Previous studies have also demonstrated an association between frailty, cognition, and depression in older persons [43, 44].
Subgroup analysis of our variable of interest showed that negative transitions in individual components of the FI are associated with depressive symptomatology. Self-reported exhaustion was more significantly associated with depression in both men and women than other components of the FI. Some previous studies have revealed a strong correlation between vital exhaustion and depression [45, 46]. In addition, the impacts of the weakness of handgrip strength and social isolation on new-onset depressive symptoms have been investigated in previous research conducted in some countries [47, 48], including Korea [47, 49].
The etiology of the association between frailty and depression is not fully established. However, several possible explanatory mechanisms have been suggested. The findings of the above-mentioned studies support the concept of a uni- or bidirectional relationship between frailty and depression. However, interpretations of whether frailty and depression are causally related are limited owing to methodological weaknesses in the designs of the studies and the definitions and various measurement analyses of frailty status.
An alternative explanation for the considerable association between frailty and depression is that their indicators belong to overlapping domains of the same construct. Depressive symptoms are often included as some of several factors that constitute frailty measurement [50, 51]. Results of a previous confirmatory factor analysis of the indicators of depression and frailty suggested that these constructs capture distinct aspects of health, even though these aspects are highly related to each other [52]. The interdependence between frailty and depression may be explained by the impacts of their common causes, which exert similar effects on both of them. Therefore, frailty and depression may share a common susceptibility to the same factors, resulting in a significant association between them [53].
The current study has several limitations. First, all the data was self-reported and collected via survey, thus, we cannot exclude the risk of biased results. Second, the data of those who did not answer the essential covariate questions and those with cognitive impairment and depression at the baseline were excluded. We attempted to minimize the potential bias attributable to missing data by the employment of the imputation-based approach presented in the Supplementary materials, however, we cannot entirely eliminate the possible misestimation of the findings resulting in lower generalizability of the study findings. Third, biological risk factors that might significantly affect variables adjustment could be overlooked. Lastly, although the FI was developed and validated in the Korean population, the measure of frailty used in this study is not a universally used instrument. Furthermore, as this scale depends on self-reported estimation towards social and psychological aspects, personal or cultural differences may lead to information bias. Finally, the overlap of the exhaustion item with the CES-D-10 scale may also lead to a misestimation of found results. Further research using a broadly acceptable frailty measuring approach with higher validity and reliability measures are warranted.
Nonetheless, the strengths of our study include the relatively large sample size and longitudinal design, with results being representative of the Korean community-dwelling adult population over 60 years old. The panel data we employ allow us to temporally order our analysis to reduce the probability that associations between frailty and depression reflect its influence on the probability of becoming and remaining frail. Another strength is that the study provides an in-depth and broader view of frailty transition and related to its risk of depressive symptoms. Hence, exploring the dynamics of frailty status change over time on depression provides novel information compared to previous studies. The study provides longitudinal evidence to the growing body of literature that proposes that frailty and depression share common pathways and risk factors.
## Conclusions and implications
This study was conducted to assess the influence of frailty transitions on new-onset depressive symptoms using longitudinal, nationwide data of community-dwelling older adults in Korea. The findings of this study suggest that two-year frailty transitions are associated with new-onset depressive symptoms in older adults. Participants who transitioned into frailty or maintained a frailty status had a higher risk of depression than their non-frail counterparts. The results also demonstrated that exhaustion is a major component of the FI that leads to depression. Frail older adults who experience cognitive impairment showed stronger effects with depression. Early intervention and implementation of prevention strategies at physical, nutritional, and social levels are warranted to ameliorate frailty and depression in late life. Our study can contribute to the development of intervention strategies to better identify depression in later life of individuals who may be at greater risk due to their frailty conditions. Given that handgrip strength and social and psychological well-being can be measured at routine health check-ups, this study provides a substantial basis for policymakers to implement a frailty status screening through community-based healthcare programs for older people.
## Supplementary Information
Additional file 1: Supplementary Table 1. General characteristics of the study population (baseline 2008).Additional file 2: Supplementary Table 2. Generalized linear model using the GEE with CES-D-10 score in 2008-2020 with employing imputation-based approach for missingdata. Additional file 3: Supplementary Table 3. Generalized linear model using the GEE with CES-D-10 score in 2008-2020 without employing imputation-based approach formissing data. Additional file 4: Supplementary Table 4. Generalized linear model using the GEE with CES-D-10 score in 2008-2020.
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|
---
title: Race/ethnicity-specific associations between breastfeeding information source
and breastfeeding rates among U.S. women
authors:
- Stephanie M. Quintero
- Paula D. Strassle
- Amalia Londoño Tobón
- Stephanie Ponce
- Alia Alhomsi
- Ana I. Maldonado
- Jamie S. Ko
- Miciah J. Wilkerson
- Anna María Nápoles
journal: BMC Public Health
year: 2023
pmcid: PMC10024358
doi: 10.1186/s12889-023-15447-8
license: CC BY 4.0
---
# Race/ethnicity-specific associations between breastfeeding information source and breastfeeding rates among U.S. women
## Abstract
### Background
Despite evidence of the impact of breastfeeding information on breastfeeding rates, it is unknown if information sources and impact vary by race/ethnicity, thus this study assessed race/ethnicity-specific associations between breastfeeding information sources and breastfeeding.
### Methods
We used data from the 2016–2019 Pregnancy Risk Assessment Monitoring System. Race/ethnicity-stratified multinomial logistic regression was used to estimate associations between information source (e.g., family/friends) and breastfeeding rates (0 weeks/none, < 10 weeks, or ≥ 10 weeks; < 10 weeks and ≥ 10 weeks = any breastfeeding). All analyses were weighted to be nationally representative.
### Results
Among 5,945,018 women (weighted), $88\%$ reported initiating breastfeeding (≥ 10 weeks = $70\%$). Information from family/friends (< 10 weeks: aORs = 1.58–2.14; ≥ 10 weeks: aORs = 1.63–2.64) and breastfeeding support groups (< 10 weeks: aORs = 1.31–1.76; ≥ 10 weeks: aORs = 1.42–2.77) were consistently associated with breastfeeding and duration across most racial/ethnic groups; effects were consistently smaller among Alaska Native, Black, and Hispanic women (vs White women). Over half of American Indian and one-quarter of Black women reported not breastfeeding/stopping breastfeeding due to return to school/work concerns.
### Conclusions
Associations between breastfeeding information source and breastfeeding rates vary across race/ethnicity. Culturally tailored breastfeeding information and support from family/friends and support groups could help reduce breastfeeding disparities. Additional measures are needed to address disparities related to concerns about return to work/school.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15447-8.
## Background
Breastfeeding has many benefits for the infant and breastfeeding person. For example, longer breastfeeding duration can decrease risk of respiratory infections and mortality among infants and decrease risk of breast cancer and cardiovascular disease for the breastfeeding person [1]. Breastfeeding has also been linked to future behaviors such as increased resilience to psychosocial stressors [2] and decreased childhood maltreatment [3]. The American Academy of Pediatrics recommends that breastfeeding people exclusively breastfeed their infants for the first 6 months and continue to breastfeed with the introduction of complementary foods for a year or longer [1]. Despite the known benefits of breastfeeding, significant racial/ethnic disparities exist in the rates of initiation and duration of breastfeeding within the United States (U.S.) For instance, non-Hispanic Black ($75\%$ vs $83\%$ of all children born in 2018) and American Indian/Alaska Native people ($81\%$ vs. $84\%$ of all children born in 2017) are the least likely to initiate breastfeeding [4, 5]. Non-Hispanic Black ($49\%$), Hispanic/Latino ($53\%$), and multiracial adults ($53\%$) are also less likely to breastfeed for ≥ 6 months, compared to the U.S. national rate ($57\%$) [4, 5].
Multiple interrelated factors likely contribute to racial/ethnic disparities in breastfeeding, including historical, cultural, social, economic, political, and psychosocial factors [6]. Structural factors, such as mode of newborn delivery, socioeconomic status, and return to work, have also influenced breastfeeding rates in the U.S [7, 8]. While many barriers for increasing breastfeeding initiation and duration are structural, breastfeeding education through various information sources is a cost-effective intervention to improve breastfeeding rates overall and among diverse racial/ethnic populations [9–12].
Despite studies suggesting that breastfeeding information impacts breastfeeding rates, to our knowledge, there have been no studies that investigated if the impact of breastfeeding information sources differs across race/ethnicity in terms of improving breastfeeding initiation and duration. Thus, the purpose of this study is to 1) estimate the prevalence of breastfeeding information sources across race/ethnicity, and 2) determine whether the effect of breastfeeding information source on any breastfeeding and breastfeeding duration differs by race/ethnicity. A more nuanced understanding of the impact of specific sources of breastfeeding information among diverse populations could aid in streamlining and tailoring education interventions to decrease racial/ethnic disparities in breastfeeding.
## Study population
We used data from the Pregnancy Risk Assessment Monitoring System (PRAMS), a multi-site, population-based surveillance system that samples women 2–6 months after a live birth. PRAMS draws information from each participating state’s birth certificate file and asks questions about maternal health behaviors and experiences before, during, and after pregnancy. Details on the PRAMS study design and methodology have been described elsewhere [13]. For this study, we used the most recent PRAMS survey data, Phase 8, 2016–2019.
All women who reported currently living with their infant were eligible for inclusion. Women were excluded if they were missing race/ethnicity ($$n = 5$$,349) or reported as “other race” ($$n = 5$$,071); were < 18 years old or missing age ($$n = 1$$,762); completed the PRAMS survey < 10 weeks after giving birth ($$n = 112$$); or had missing data on breastfeeding outcomes ($$n = 5$$,494) or on breastfeeding information sources ($$n = 9$$,379). After exclusions, our final study cohort included 116,132 (5,945,018 weighted) individuals who gave birth between 2016 and 2019 from 37 states, Puerto Rico, and New York City. Henceforth, we will refer to PRAMS survey respondents as women, since all individuals included in our analysis self-identified as such. Demographics for women included in the study, stratified by race/ethnicity, are reported in Supplemental Table 1.
## Dependent variable: breastfeeding initiation and duration
Any breastfeeding and breastfeeding duration were captured using the question “How many weeks or months did you breastfeed or feed pumped milk to your new baby?” Any breastfeeding and duration were categorized into one 3-level variable categorized as 1) did not breastfeed or breastfed 0 weeks/none, 2) breastfed < 10 weeks, and 3) breastfed ≥ 10 weeks. Since all women completed the survey > 10 weeks after birth, those who reported “currently still breastfeeding” were categorized as breastfed ≥ 10 weeks. This definition has been used previously [14].
Each PRAMS participating site could include additional questions from a standardized library. These questions expand on topics covered in the core questionnaire. Two additional questions asked: “What were your reasons for stopping breastfeeding?” and “What were your reasons for not breastfeeding your new baby?” Data from these questions (22 sites included the first question and 20 included the second question) were included in analyses. A complete list of the sites and response options are found in the PRAMS documentation [15].
## Independent variable: sources of breastfeeding information
Breastfeeding information sources were captured using the question “Before or after your new baby was born, did you receive information about breastfeeding from any of the following sources (check all that apply)?” Responses included: my doctor; a nurse, midwife, or doula; a breastfeeding or lactation specialist; my baby’s doctor or health care provider; a breastfeeding support group; a breastfeeding hotline or toll-free number; family or friend; and other. Participants were instructed to select all that apply with response options of yes or no to each source. Because a breastfeeding or lactation specialist is often contacted when women are having difficulty breastfeeding (i.e., after breastfeeding initiation), this source was excluded in our modeling analyses but prevalence by race/ethnicity was reported.
## Other variables of interest
Race/ethnicity and other maternal demographics were obtained through birth certificate records and were available in PRAMS. Race identification options were American Indian, Alaska Native, Black, Asian, Native Hawaiian, mixed race, and White. Hispanic ethnicity was captured separately. We combined race/ethnicity into a single variable, in which anyone who identified as Hispanic was included as Hispanic.
Other birth certificate variables of interest involved age at delivery, education (i.e., elementary/some high school, high school degree, some college, and college degree or higher), and prenatal care adequacy (i.e., inadequate, intermediate, adequate, and adequate plus). Adequacy of prenatal care was characterized using the Kotelchuck Index [16] (i.e., Adequacy of Prenatal Care Utilization), which is a summary score based on the timing of initiation of prenatal care and total number of prenatal care visits.
## Statistical analyses
Multivariable multinomial logistic regression was used to estimate the association between breastfeeding information sources (baby’s doctor, personal doctor, family/friends, support group, hotline, nurse/midwife/doula) and breastfeeding initiation/duration (≥ 10 weeks, < 10 weeks, no breastfeeding [reference]), adjusting for age at delivery, education, adequacy of prenatal care (Kotelchuck index). All breastfeeding information sources (besides lactation specialist) were included in the model. Therefore, models estimated the association between receiving information from a specific source (e.g., baby’s doctor), compared to not receiving breastfeeding information from that source with the breastfeeding initiation/duration outcomes, adjusted for the variables listed above and the other information sources. To estimate the associations between breastfeeding information sources and breastfeeding initiation/duration within each racial/ethnic group, separate models were run within each race/ethnicity. Due to the small sample size of women who identified as Native Hawaiian ($$n = 44$$ unweighted), they were excluded from all modeling.
As a sensitivity analysis, we also assessed the impact of breastfeeding information sources by language (i.e., English- vs. Spanish-speaking) among Hispanic women. Similar to above, we ran two separate multivariable multinomial logistic regression models, one among English-speaking Hispanic women and one among Spanish-speaking Hispanic women, adjusting for age at delivery, education, and adequacy of prenatal care. We also ran a sensitivity analysis where lactation specialist was included as a breastfeeding information source in our models.
All analyses were conducted using SAS version 9.4 using complex survey methods and weighting to obtain national estimates.
## Results
Overall, $88\%$ of women reported any breastfeeding; Black ($77\%$) and American Indian ($82\%$) women were least likely to report any breastfeeding, compared to other groups ($89\%$-$100\%$), Table 1. Among those who reported any breastfeeding ($$n = 5$$,204,758), Black ($57\%$) and American Indian ($61\%$) women were also least likely to breastfeed ≥ 10 weeks, followed by Hispanic women ($64\%$), compared to other racial/ethnic groups ($70\%$-$81\%$). Native Hawaiian (> $99\%$), Asian ($93\%$), and Alaska Native ($92\%$) women were the most likely to report any breastfeeding, and Asian and Native Hawaiian women were the most likely to report breastfeeding for ≥ 10 weeks ($81\%$ for both groups. Over $90\%$ of both English- (≥ 10 weeks: $56.7\%$; < 10 weeks: $34.1\%$) and Spanish-speaking (≥ 10 weeks: $61.5\%$; < 10 weeks: $30.4\%$) Hispanic women reported any breastfeeding. Table 1Any breastfeeding, breastfeeding duration, reasons for not starting breastfeeding (among those reporting they did not breastfeed/breastfed 0 weeks/none only), and reasons for stopping breastfeeding (among those reporting any breastfeeding but < 10 weeks duration), stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016–2019OverallAmerican IndianAlaskaNativeAsianBlackHispanicNative HawaiianWhiteMultiracialAny breastfeeding Yes5,204,758(87.5)30,464(82.3)5,555(91.5)293,247(92.6)655,357(77.1)954,835(91.2)968(99.5)3,138,993(88.5)125,338(89.7) No740,260(12.5)6,546(17.7)514(8.5)23,461(7.4)195,124(22.9)91,723(8.8)5(0.5)408,433(11.5)14,455(10.3)*Breastfeeding durationa* < 10 weeks1,563,609(30.0)12,033(39.5)1,473(26.5)56,912(19.4)280,310(42.8)340,377(35.6)183(18.9)834,960(26.6)37,362(29.8) ≥ 10 weeks3,641,149(70.0)18,432(60.5)4,082(73.5)236,335(80.6)375,047(57.2)614,458(64.4)785(81.1)2,304,033(73.4)87,976(70.2)Reasons for not startingb Sick/on medicine44,984(12.9)228(28.8)N/Ad254(7.1)11,104(9.9)5,352(14.3)N/Ad27,097(14.2)948(16.7) Take care of other children82,408(23.5)65(8.2)N/Ad286(8.1)22,314(19.9)9,568(25.6)N/Ad48,867(25.6)1,308(23.1) Too many household duties48,399(13.8)86(10.8)N/Ad155(4.4)15,171(13.6)5,214(13.9)N/Ad26,623(14.0)1,151(20.3) Didn’t like it94,815(27.1)264(33.4)N/Ad639(18.0)36,089(32.3)9,251(24.7)N/Ad47,115(24.8)1,458(25.7) Tried but too hard56,589(16.2)78(9.9)N/Ad620(17.5)20,848(18.6)10,769(28.8)N/Ad23,135(12.1)1,138(20.1) Didn’t want to163,751(46.8)614(77.5)N/Ad971(27.3)56,698(50.7)10,616(28.4)N/Ad92,298(48.4)2,554(45.1) Went back to work66,154(18.9)211(26.6)N/Ad505(14.2)23,260(20.8)4,785(12.8)N/Ad36,270(19.0)1,123(19.8) Went back to school9,943(2.8)187(23.7)N/Ad232(6.5)4,324(3.9)289(0.8)N/Ad4,816(2.5)94(1.7) Other77,773(22.2)113(14.3)N/Ad691(19.6)17,196(15.4)10,125(27.1)N/Ad47,856(25.1)1,792(31.6)Reasons for stoppingcN/AdN/Ad Baby had difficulty latching232,286(38.7)1,559(37.7)N/Ad10,486(42.4)33,670(32.9)42,987(39.5)N/Ad138,607(40.2)4,956(34.8) Breastmilk not enough230,168(38.5)1,320(32.1)N/Ad11,138(45.3)30,183(29.5)45,788(42.3)N/Ad136,081(39.5)5,653(39.8) Baby not gaining enough weight102,866(17.2)792(19.2)N/Ad2,878(11.7)14,245(13.9)16,497(15.2)N/Ad66,266(19.2)2,183(15.3) Nipple pain/soreness152,869(25.5)1,404(34.1)N/Ad5,661(23.0)33,284(32.5)29,089(26.8)N/Ad79,475(23.0)3,939(27.7) Not producing enough milk344,969(57.6)2,491(60.5)N/Ad14,961(60.7)49,920(48.8)63,826(58.7)N/Ad204,420(59.2)9,278(65.2) Got sick/medical reasons62,011(10.4)494(12.0)N/Ad2,031(8.3)10,714(10.5)14,581(13.4)N/Ad32,837(9.5)1,336(9.5) Too many household duties103,157(17.2)900(21.9)N/Ad3,876(15.8)20,670(20.2)15,433(14.2)N/Ad59,395(17.2)2,866(20.3) Felt it was right time to stop65,227(10.9)460(11.2)N/Ad1,726(7.0)14,397(14.1)11,722(10.8)N/Ad35,129(10.2)1,776(12.6) Went back to work99,867(16.7)655(16.0)N/Ad4,793(19.5)24,404(23.8)15,548(14.3)N/Ad52,617(15.3)1,782(12.6) Went back to school15,600(2.6)165(4.0)N/Ad718(2.9)4,918(4.8)3,369(3.1)N/Ad5,598(1.6)832(5.9) Other115,428(19.3)827(20.1)N/Ad3,791(15.3)14,596(14.3)18,877(17.3)N/Ad74,873(21.7)2,463(17.3)a Among those who reported any breastfeedingb Among women who reported they did not breastfeed/breastfed 0 weeks/none; sites that asked this question included: AL, AR, IA, IL, KY, LA, ME, MI, MO, MT, NC, ND, NH, PR, RI, SD, TX, and VAc Among women who reported breastfeeding < 10 weeks; sites that asked this question included: AL, IA, IN, KY, ME, MI, MO, MT, NC, ND, NE, NH, YC, NY, PR, SD, VA, WA, and WYd There were no women who identified as Alaska Native or Native Hawaiian within the subset of states that asked additional questions about reasons for not starting or stopping breastfeeding “Didn’t want to” ($47\%$), “didn’t like it” ($27\%$), and having to take care of children ($24\%$) were the most common reasons women reported for never breastfeeding. American Indian ($78\%$) and Black ($51\%$) women were most likely to report “didn’t want to” as the reason for not breastfeeding, Table 1. Compared to other groups, American Indian women were more likely to report being sick or on medications ($29\%$) or having to go back to work or school ($50\%$, combined) as reasons for not breastfeeding. Over a quarter of Hispanic women ($29\%$) reported trying to breastfeed but found it too difficult.
Among women who stopped breastfeeding before 10 weeks post-partum, the most common reasons included not producing enough milk ($57\%$), baby experiencing difficulty latching ($38\%$), and breastmilk alone not satisfying their baby ($38\%$). Not producing enough milk was a common reason for stopping breastfeeding among all groups ($49\%$-$65\%$). We saw fewer racial/ethnic differences among reasons for stopping breastfeeding, Table 1. About 1 in 5 women who did not breastfeed or breastfed for < 10 weeks reported returning to work or school as a reason for not breastfeeding or stopping; this was more common among American Indian and Black women, Supplemental Fig. 1.
Personal doctor was the most prevalent source of breastfeeding information ($77\%$) followed by nurse/midwife/doula ($74\%$), baby’s doctor ($68\%$), and family/friends ($64\%$), Supplemental Table 2. Support groups ($23.3\%$) and hotline/toll-free numbers ($10\%$) were the least common. A majority of women ($73\%$) reported talking to a lactation/breastfeeding specialist. Almost all women reported receiving breastfeeding information from multiple sources (mean number of sources 3.98; SD 1.7). Overall, $24\%$ of women reported receiving information from a personal doctor, baby’s doctor, family/friends, and nurse/midwife/doula, $10\%$ reported receiving information from a personal doctor, baby’s doctor, and nurse/midwife/doula, and $8\%$ received information from every source except for a hotline, Supplemental Fig. 2.
After adjustment, receiving information from family/friends (aOR = 2.14, $95\%$ CI = 2.01–2.29) or a support group (aOR = 2.02, $95\%$ CI = 1.85–2.19) doubled the likelihood that a woman would breastfeed for ≥ 10 weeks, compared to not receiving information from the source. Information from family/friends (aOR = 1.93, $95\%$ CI = 1.80–2.07) or a support group (aOR = 1.46, $95\%$ CI = 1.33–1.59) increased the odds also of breastfeeding for < 10 weeks, compared to never breastfeeding, Table 2. Information from the baby’s doctor and from a nurse/midwife/doula were also associated with increased breastfeeding; receiving information from a personal doctor or a hotline did not increase breastfeeding initiation or duration, Table 2.Table 2Prevalence of sources of breastfeeding information, stratified across breastfeeding initiation and duration, and adjusted associations between breastfeeding information from sources and likelihood of breastfeeding < 10 weeks and ≥ 10 weeks (vs. not breastfeeding), weighted to be nationally representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016–2019. Due to small sample size ($$n = 44$$ unweighted), Native Hawaiian women were excluded from the analysesBreastfed ≥ 10 weeksBreastfed < 10 weeksDid not Breastfeed (reference)%aOR ($95\%$ CI)a%aOR ($95\%$ CI)a%Sources of information Baby’s doctor61.91.43 (1.33, 1.54)27.41.40 (1.29, 1.51)10.3 Personal doctor59.10.49 (0.45, 0.53)28.10.78 (0.71, 0.85)12.9 Family/friends64.92.14 (2.01, 2.29)26.61.93 (1.80, 2.07)8.5 Support group66.62.02 (1.85, 2.19)25.91.46 (1.33, 1.59)7.5 Hotline61.10.83 (0.74, 0.92)29.30.94 (0.84, 1.06)9.6 Nurse/midwife/doula63.41.71 (1.60, 1.84)26.31.45 (1.35, 1.57)10.3Abbreviations: OR odds ratio, CI confidence intervala Adjusted for age at birth, education, Kotelchuck index (prenatal care), race/ethnicity, and all other sources of breastfeeding information; reference group = did not breastfeed
## Race/ethnicity-stratified effects of breastfeeding information
The proportion of women who received breastfeeding information from each source, stratified by race/ethnicity, are presented in Fig. 1 and Supplemental Table 2. Alaska Native ($82\%$), Black ($76\%$), and American Indian ($75\%$) women were more likely to receive breastfeeding information from the baby’s doctor vs. other groups (65–$70\%$), Fig. 1A. Support groups were more commonly used by Native Hawaiian ($34\%$), Hispanic ($33\%$), and Black ($29\%$) women, compared to other racial/ethnic groups (19–$25\%$), Fig. 1B. Almost all Native Hawaiian women ($91\%$) reported receiving information from their nurse/midwife/doula, although rates were also high in the other groups as well (73–$86\%$), Fig. 1C. Fewer racial/ethnic differences were seen in receiving information from family/friends, Fig. 1D.Fig. 1The percentage of women who reported receiving breastfeeding information from A baby’s doctor, B support groups, C nurse/midwife/doula, and D family or friends before or after giving birth, stratified by race/ethnicity, weighted to be nationally representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016–2019 Information from the baby’s doctor increased breastfeeding for < 10 weeks among American Indian, Asian, and White women (< 10 weeks: aORs = 1.55–1.76; ≥ 10 weeks: aORs = 1.34–1.60); information from the baby’s doctor also increased the odds of Black and Hispanic women breastfeeding ≥ 10 weeks (aOR = 1.22 and 1.35, respectively), Fig. 2A and Table 3.Table 3Effect of receiving breastfeeding information from each information source on breastfeeding < 10 weeks and ≥ 10 weeks (compared to not breastfeeding), stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016–2019Baby’s DoctorPersonal DoctorFamily/FriendsSupport GroupHotlineNurse/Midwife/DoulaaOR ($95\%$ CI)aaOR ($95\%$ CI)aaOR ($95\%$ CI)aaOR ($95\%$ CI)aaOR ($95\%$ CI)aaOR ($95\%$ CI)aBreastfed < 10 weeksRace/ethnicity American Indian1.55 (1.11, 2.15)0.72 (0.51, 1.02)1.80 (1.38, 2.33)1.52 (1.08, 2.16)0.76 (0.48, 1.21)1.58 (1.13, 2.20) Alaska Native1.06 (0.48, 2.35)1.11 (0.45, 2.73)1.69 (0.93, 3.05)1.11 (0.55, 2.23)1.14 (0.45, 2.91)1.82 (0.78, 4.25) Asian1.65 (1.11, 2.45)0.74 (0.47, 1.16)1.63 (1.15, 2.31)0.92 (0.60, 1.40)0.82 (0.50, 1.32)1.39 (0.94, 2.07) Black1.04 (0.87, 1.24)1.05 (0.84, 1.31)1.58 (1.37, 1.83)1.45 (1.22, 1.73)0.97 (0.79, 1.20)1.26 (1.07, 1.49) Hispanic1.22 (0.99, 1.50)0.81 (0.63, 1.05)1.61 (1.34, 1.93)1.31 (1.07, 1.59)0.99 (0.77, 1.26)1.02 (0.83, 1.26) White1.60 (1.45, 1.77)0.70 (0.63, 0.79)2.22 (2.02, 2.44)1.69 (1.46, 1.96)1.04 (0.85, 1.26)1.64 (1.49, 1.81) Mixed race0.92 (0.56, 1.49)1.19 (0.68, 2.08)2.14 (1.42, 3.25)1.76 (0.99, 3.11)1.24 (0.61, 2.54)2.21 (1.42, 3.44)Breastfed ≥ 10 weeksRace/ethnicity American Indian1.34 (0.99, 1.81)0.59 (0.40, 0.87)1.94 (1.50, 2.50)2.12 (1.52, 2.97)0.74 (0.48, 1.14)1.68 (1.16, 2.43) Alaska Native1.31 (0.62, 2.78)0.90 (0.40, 2.03)1.67 (0.97, 2.87)1.17 (0.62, 2.21)0.64 (0.27, 1.53)0.86 (0.41, 1.81) Asian1.94 (1.37, 2.76)0.63 (0.42, 0.94)1.76 (1.29, 2.41)1.11 (0.76, 1.62)0.73 (0.48, 1.12)1.43 (1.02, 2.01) Black1.22 (1.03, 1.44)0.61 (0.50, 0.74)1.73 (1.50, 1.99)1.80 (1.52, 2.12)0.89 (0.72, 1.10)1.42 (1.21, 1.67) Hispanic1.35 (1.11, 1.65)0.56 (0.44, 0.72)1.63 (1.37, 1.94)1.42 (1.17, 1.71)1.05 (0.83, 1.32)1.19 (0.97, 1.46) White1.54 (1.40, 1.69)0.43 (0.39, 0.48)2.54 (2.33, 2.77)2.77 (2.42, 3.17)0.80 (0.66, 0.96)1.97 (1.80, 2.15) Mixed race0.77 (0.50, 1.19)0.59 (0.37, 0.95)2.64 (1.82, 3.83)2.32 (1.38, 3.90)0.94 (0.47, 1.86)2.71 (1.86, 3.96)Abbreviations: OR odds ratio, CI confidence intervala Adjusted for age at delivery, education, and prenatal care (Kotelchuck index); associations within each racial/ethnic group were modeled separately; reference group = did not breastfeedFig. 2The association between breastfeeding information from A baby’s doctor, B support groups, C nurse/midwife/doula, and D family or friends before or after giving birth and breastfeeding duration < 10 weeks and ≥ 10 weeks (vs. not breastfeeding), stratified by race/ethnicity, weighted to be nationally representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016–2019. Due to small sample size ($$n = 44$$ unweighted), Native Hawaiian women were excluded from the analyses Support groups only increased breastfeeding rates among American Indian, Black, Hispanic, White, and multiracial women (< 10 weeks: aORs = 1.31–1.76; ≥ 10 weeks: aORs = 1.42–2.77) and had no significant effect among Alaska Native or Asian women, Fig. 2B and Table 3. Information from nurse/midwife/doula increased breastfeeding rates among American Indian, Black, White, and multiracial women (< 10 weeks: aORs = 1.26–2.21; ≥ 10 weeks: aORs = 1.42–2.71), Fig. 2C. Information from family/friends was consistently associated with increased reporting of any breastfeeding and breastfeeding duration across all racial/ethnic groups (< 10 weeks: aORs = 1.58–2.14; ≥ 10 weeks: aORs = 1.63–2.64), Fig. 2D and Table 3. Overall, receiving breastfeeding information from support groups, nurse/midwife/doulas, the baby’s doctor, and family/friends substantially benefitted American Indian, Asian, White, and multiracial women, with weaker associations among Alaska Native, Black, and Hispanic women.
When Hispanic women were stratified by English- and Spanish-speaking, receiving information from family/friends had a greater association with breastfeeding among English-speaking Hispanic women (< 10 weeks: aOR = 1.81, $95\%$ CI 1.44–2.28; ≥ 10 weeks: aOR = 2.02, $95\%$ CI = 1.62–2.52) compared to their Spanish-speaking counterparts (< 10 weeks: aOR = 1.33, $95\%$ CI 1.00–1.77; ≥ 10 weeks: aOR = 1.25, $95\%$ CI = 0.96–1.63), Supplemental Table 3. Despite a greater percentage of Spanish-speaking Hispanic women obtaining information from a support group compared with their English-speaking counterparts ($39.5\%$ vs. $27.4\%$), support groups improved breastfeeding among English-speaking Hispanic women (< 10 weeks: aOR = 1.39, $95\%$ CI 1.05–1.83; ≥ 10 weeks: aOR = 1.63, $95\%$ CI = 1.25–2.12) with no statistically significant impact among their Spanish-speaking peers (< 10 weeks: aOR = 1.16, $95\%$ CI 0.86–1.57; ≥ 10 weeks: aOR = 1.11, $95\%$ CI = 0.83–1.47), Supplemental Table 3. Receiving breastfeeding information from the baby’s doctor was also only associated with increased breastfeeding among English-speaking Hispanic women (< 10 weeks: aOR = 1.42, $95\%$ CI = 1.10–1.84; ≥ 10 weeks aOR = 1.65, $95\%$ CI = 1.29, 2.11), Supplemental Table 3.
When lactation specialist was included in the models, results were relatively consistent, although baby’s doctor was no longer significantly associated with increased breastfeeding, Supplemental Table 4. Receipt of information from lactation specialists was strongly associated with increased breastfeeding (< 10 weeks: aOR = 5.18, $95\%$ CI = 4.83–5.56; ≥ 10 weeks aOR = 5.25, $95\%$ CI = 4.86–5.67). The association between lactation specialists and breastfeeding was statistically significant among all racial/ethnic groups but was stronger among White women (< 10 weeks: aOR = 7.39, $95\%$ CI = 6.65–8.22; ≥ 10 weeks aOR = 7.17, $95\%$ CI = 6.52–7.87), compared to other racial-ethnic groups (< 10 weeks: aORs = 2.44–4.83; ≥ 10 weeks aOR = 2.79–4.30), Supplemental Table 5.
## Discussion
In an analysis of nationally representative surveillance data, we found that receiving breastfeeding information during pregnancy or shortly after delivery was common, with a personal doctor, nurse/midwife/doula, and baby’s doctor being the most common sources. While family/friends, support groups, baby’s doctor, and nurse/midwife/doula were consistently associated with increased breastfeeding rates, the effects of these sources were smaller among Alaska Native, Black, and Hispanic women, compared to White women. Receiving information from a personal doctor or from breastfeeding hotlines did not increase breastfeeding initiation/duration among any groups. Thus, the impact of breastfeeding information varied by source and across race/ethnicity.
Similar to other studies which have found that support groups have multiple benefits including increasing breastfeeding, [17, 18] we found that information from a support group was strongly and consistently associated with breastfeeding initiation/duration among American Indian, Black, Hispanic (English-speaking only), White, and multiracial women; however, the association was smaller among English-speaking Hispanic and Black women, and absent among Alaska Native, Asian, or Spanish-speaking Hispanic women. Our study indicated that racial/ethnic disparities exist related to effect of breastfeeding information on breastfeeding initiation/duration, such that this information tends to be less effective for certain ethnic populations. These differences may be due to the dearth of culturally tailored support groups that meet the needs of marginalized populations, and/or deliver information in participant’s native languages. Most randomized trials of breastfeeding support groups have been conducted among non-Hispanic White women, and culturally tailored interventions for racial/ethnic minority women have been generally graded as lower-quality in a systematic review [9]. Access to breastfeeding support groups for diverse racial/ethnic populations may also be an issue. At least one study has found that access to Special Supplemental Nutrition Program for Women, Infants, and Children breastfeeding support services, a federal assistance program for healthcare and nutrition for low-income populations, was most limited in predominantly Black communities [19]. More efforts are needed to improve access to culturally tailored breastfeeding supportive services, especially in under-resourced communities.
Consistent with prior studies showing that people rely heavily on their social support networks for guidance and advice for breastfeeding, [20] in our study, family and friends were also an important source for increasing breastfeeding initiation/duration among all racial/ethnic groups except Spanish-speaking Hispanic women. Despite the importance of friends and family in breastfeeding, Black and American Indian women were less likely to report receiving breastfeeding information from their family/friends compared to other racial/ethnic groups. Prior studies suggest that lack of familial support is a major barrier to breastfeeding [20]. Among inner city African American women, research has shown that they were less likely to witness an African American woman breastfeeding within their community and were less likely to receive supportive breastfeeding advice from family and friends [21]. In our study, Black and American Indian women were also most likely to report not wanting to breastfeed or not liking breastfeeding. This may be due to intergenerational and historical trauma of White supremacy (e.g., oppression, gendered dehumanization of enslavement, wet nursing, genocide, cultural erasure, and forced removal from ancestral lands) among American Indian and African American women and breastfeeding persons [22, 23]. The decision to breastfeed among African American women remains deeply attached to the generational trauma of wet nurses during slavery where African American women were forced to breastfeed White children at the expense of their own, perpetuating the stereotype of African American infants being needy, sicker, and less well-behaved [23]. A promising approach among American Indian women is employing grandmothers as advocates to strengthen cultural ties to traditional breastfeeding practices, which has been shown to significantly increase breastfeeding rates [24].
Social media and online resources are also important sources to consider. The use of social media for breastfeeding support groups is associated with longer breastfeeding duration through the sharing of knowledge that can increase positive breastfeeding experiences, social connections, and a sense of belonging and breastfeeding self-efficacy [25]. Technology and social media can be leveraged to provide real-time help, such as peer support provided via texting, which was found to increase rates among Hispanic women [26]. However, social media can also spread misinformation and unmoderated support groups can contain inaccurate information. Although not captured directly in this study, misinformation could account in part for the proportion of women reporting “other” as a reason for not initiating or stopping breastfeeding.
Our findings have implications for public health, clinical, and policy efforts to decrease racial/ethnic disparities in breastfeeding. The Centers for Disease Control and Prevention has previously outlined the need to increase peer support programs and inclusion of relatives (e.g., spouses, grandmothers) into breastfeeding programming [27]. Programs that leverage family and friends as a source of breastfeeding information and culturally and linguistically appropriate peer-delivered community-based interventions have been shown to significantly reduce racial/ethnic disparities in breastfeeding [9, 12, 24, 28]. These findings point to the potential benefits of family and friend centered interventions, especially, among Spanish-speaking Hispanic women. The high rates of receipt of information from nurses, midwives and doulas and high rates of longer-term breastfeeding among Native Hawaiian women in particular could reflect such contextually embedded programming and needs to be better understood. For Asian women, further understanding of the heterogeneity across sub-populations might also inform cultural tailoring of breastfeeding interventions and increase their effectiveness. Further disaggregation of Asian women was not possible in our analysis due to small sample sizes, but further analysis is needed to understand the nuances of breast feeding behaviors within this group.
Although information from a personal doctor was the most prevalent source of breastfeeding information, it was not associated with increased breastfeeding initiation/duration overall or among any racial/ethnic group. By contrast, information from the baby’s doctor was slightly less common ($68.6\%$ vs. $77.2\%$), and it was associated with increased breastfeeding rates among almost all racial/ethnic groups. Notably, receiving information from the baby’s doctor was associated with longer breastfeeding among English-speaking Hispanic women but not among those who were Spanish speaking, possibly due to the need for and lack of language concordant physicians and trained interpreters to assist in the provision of breastfeeding information during visits. Strong encouragement from language concordant physicians could be effective among Spanish-speaking Hispanic women who tend to defer to physicians.
The limited impact of breastfeeding information from a personal doctor may be due to time constraints during appointments, leading to prioritization of other health topics, [29] which limits the quantity and quality of breastfeeding information imparted and received. The limited impact of personal physicians might also be due to barriers in accessing post-partum care and missing postpartum appointments due to work time constraints [30]. Furthermore, our findings that the main reasons for stopping breastfeeding were not producing enough milk, difficulty latching, and not enough breastmilk, indicate that early support is critical [31]. Increasing the availability of resources and remote surveillance, and bundling of services, e.g., referring women to toll-free hotlines, support groups, and lactation specialists, could result in greater impact. For example, toll-free hotlines were underutilized (only $10\%$ of women reporting accessing these) and could provide real-time support (especially if accessible in multiple languages), supplementing other sources of information.
Of note, only family/friends appeared to be consistently associated with breastfeeding among Alaska Native women, suggesting that resources provided through clinicians and support groups are not adequately meeting their needs. Since all Alaska Native women in our sample resided in Alaska, information sources and public health programming in Alaska may need to be made more easily accessible and culturally tailored for Alaska Native women.
Improving access to and quality of breastfeeding information and resources will not fully address breastfeeding disparities or maximize breastfeeding rates in the U.S. In our study, among women who did not breastfeed, one-quarter of Black women and half of American Indian women reported not initiating breastfeeding because they had to go back to work or school. Among those who breastfed < 10 weeks, $28\%$ of Black and $20\%$ of American Indian women reporting stopping because they had to go back to work or school. Overall, almost 1 in 5 women who did not breastfeed for ≥ 10 weeks reported returning to the workforce/school as a reason for stopping/not initiating breastfeeding.
Lack of access to space, time, and resources for breastfeeding or pumping at work or school need to be addressed. Consistent national workplace protections could help increase breastfeeding duration [20]. Intention to work full-time as well as shorter work leaves are both associated with lower rates of initiation and shorter breastfeeding duration [20, 32]. The U.S. federal Family and Medical Leave Act (FMLA), a labor law requiring employers to provide employees with job-protected unpaid leave, only offers leave for up to 12 weeks (much shorter than the recommended 6 months of breastfeeding), and $40\%$ of the workforce is ineligible for FMLA, leaving many post-partum people from low-income and racial/ethnic minority groups unable to take work leave [33]. Support programs and policies which increase access to paid leave, school and workplace accommodations (e.g., providing private and comfortable lactation sites) are crucial to meet the Healthy People 2030 goal of $42.4\%$ of infants exclusively breastfed for the first six months, [34] and may provide the best avenue for reducing breastfeeding disparities [33].
This study has limitations. First, given the nature of the PRAMS dataset and available questions, we were unable to measure or account for other important covariates such as 1) quantity, quality, and content of breastfeeding information, including if the woman gave birth at a hospital with breastfeeding initiatives, 2) access to paid leave and workplace protections for pumping/breastfeeding and other resources needed to promote breastfeeding after returning to work, or 3) participants’ views on breastfeeding (e.g., support, time commitment, exhaustion, experiences of discrimination). Second, since PRAMS surveys women within the first few post-partum months, we were unable to assess the proportion of women who met the recommendation to breastfeed for the first 6 months and its association with specific types of breastfeeding information sources,overall or among specific racial/ethnic groups. Although one of the strengths of this study was the ability to stratify individuals into the major racial/ethnic groups, they are still large and heterogeneous, e.g., Asian women. It is likely that varied cultural practices and values surrounding breastfeeding exist within these groups. For example, there are significant differences in breastfeeding initiation between Black immigrants and African Americans [21, 35]. Future studies should sample to allow for further disaggregation of these populations. Third, recall bias may be present as all PRAMS data on breastfeeding, including breastfeeding information sources are self-reported. However, we believe this bias would be minimal because the surveys were conducted within the post-partum period. Finally, due to the small sample size of Native Hawaiian women in PRAMS, we were unable to assess the impact of breastfeeding information sources on breastfeeding rates in this group. Future studies are needed to better understand breastfeeding practices in this population.
## Conclusions
Using multi-state, nationally representative surveillance data from 2016–2019, we found that breastfeeding disparities continue to persist among diverse racial/ethnic groups despite ongoing public health efforts. Receiving breastfeeding information from family, friends, or a support group were consistently associated with improved breastfeeding rates; however, these resources had smaller or no impact among Alaska Native, Black, and Hispanic women. Receiving information from the baby’s doctor also had limited benefit among racial/ethnic minorities, and receipt of breastfeeding information from a personal doctor had no impact on breastfeeding initiation/duration. Public health interventions should consider enhancing clinician information with other breastfeeding information and support services, including early surveillance and assistance. Improving access to and acceptability of breastfeeding among communities through linguistically and culturally appropriate efforts continues to be a public health challenge. Finally, while individual resources may improve breastfeeding rates among racial/ethnic minorities, paid leave and protections for pregnant and breastfeeding individuals in the workplace or schools are needed to address breastfeeding disparities in the U.S. and improve health equity among marginalized populations.
## Supplementary Information
Additional file 1: Supplemental Table 1. Demographics of women, stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Supplemental Figure 1. Women having to go back to work or school as reason for not breastfeeding or stopping breastfeeding (<10 weeks), stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Supplemental Table 2. Percentage of women who received breastfeeding information from each source, stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Supplemental Figure 2. Frequency of women receiving information from various combinations of breastfeeding information sources, sorted by most to least prevalent, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Black dots indicate that breastfeeding source was part of the combination (e.g., the first bar represents the number of women who received information from family/friends, baby’s doctor, nurse/midwife/doula, and their personal doctor). The bars next to the information sources under the x-axis represent the total number of women who received information from that source. Supplemental Table 3. Effect of receiving breastfeeding information from each information source on any breastfeeding and breastfeeding duration, stratified by English and Spanish speaking Hispanic women, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Supplemental Table 4. Prevalence of sources of breastfeeding information, stratified across any breastfeeding and breastfeeding duration, and adjusted associations between breastfeeding information from sources and likelihood of breastfeeding <10 weeks and ≥10 weeks (vs. not breastfeeding), weighted to be nationally representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019. Due to small sample size ($$n = 44$$ unweighted), Native Hawaiian women were excluded from the analyses. Supplemental Table 5. Effect of receiving breastfeeding information from a lactation specialist on breastfeeding <10 weeks and ≥10 weeks (compared to not breastfeeding), stratified by race/ethnicity, weighted to be site representative, Pregnancy Risk Assessment Monitoring System Phase 8, 2016-2019.
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|
---
title: 'Trophoblast retrieval from the cervical canal to predict abnormal pregnancy
early in gestation: a pilot study'
authors:
- Xiaoke Yang
- Liuyezi Du
- Yue Li
- Lin Liang
- Linlin Ma
- Shaowei Wang
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10024359
doi: 10.1186/s12884-023-05499-4
license: CC BY 4.0
---
# Trophoblast retrieval from the cervical canal to predict abnormal pregnancy early in gestation: a pilot study
## Abstract
### Background
The current detection of fetal chromosomal abnormalities by non-invasive prenatal testing (NIPT) mainly relies on the cell free DNA(cfDNA) in the maternal blood. However, a gestational age of less than 12 weeks or a high maternal BMI affects cfDNA fetal fraction and further the detection by NIPT negatively. In this study, we aim to retrieve the trophoblast cells from the maternal cervix to develop a new sampling method for NIPT enabling an earlier use of NIPT.
### Methods
We enrolled three patients who wanted to undergo induced abortion at Beijing Hospital between January 2022 and March 2022. Peripheral blood, cervix specimen, and the abortion tissue were collected and processed for each patient. Allele frequencies of the mutated gene loci of the maternal blood and the cervix sample were compared and the Sex Determining Region Y (SRY) gene was tested.
### Results
The allele frequencies of the mutated gene loci showed no significant difference between the maternal blood and the cervix sample. But we successfully detected signal of the SRY gene in the cervix sample of the only patient carrying a male fetus.
### Conclusions
The detection of the SRY gene in a cervix sample indicated a successful retrieval of trophoblast cells from the cervix canal. Further study needs to be conducted to verify our finding before its application to the clinical settings.
## Background
Chromosomal abnormalities such as trisomies and sex chromosomal abnormalities may negatively affect fetus and cause arrested embryo growth, organ abnormalities, or even death in utero [1, 2]. Traditional non-invasive methods for detecting abnormal chromosomes such as ultrasound and prenatal serum screening may cause a high proportion of false negatives and false positives. Whereas, invasive testing methods for accurate diagnosis such as chorionic villus sampling are expensive and may lead to miscarriage [3].
The discovery of fetal DNA in the cell-free plasma of pregnant women by Lo et al [4] and the development of next-generation sequencing (NGS) based methods enabled more accurate screening of fetal chromosomal abnormalities by non-invasive prenatal testing (NIPT). However, studies have found that an early gestation or a high maternal BMI may lead to an unsatisfactory cell free DNA(cfDNA) fetal fraction. Thus, NIPT is normally conducted after a gestation of 12 weeks and its performance for pregnant women with a high BMI is not very satisfactory [5].
Since the discovery of cfDNA, several collection methods from different parts of the genital tract have emerged, they include cotton swab insertion, endocervical mucus aspiration, and intrauterine or endocervical lavage [4, 6–8]. These techniques require a deep insertion of sampling material into the genital tract of pregnant women, which present some degree of invasiveness. In 2014, the Armant group presented the “Trophoblastic Retrieval and isolation of Cervix” (TRIC) method. This method collects endocervical samples between 5 and 20 weeks of gestation by inserting a brush approximately 2 cm into the endocervical canal, followed by rotations to trap mucus [9].
Other studies also have shown that trophoblast cells could be collected through the cervix as early as 5 ~ 7 weeks of pregnancy [10]. In our pilot study, we aim to retrieve the trophoblast cells from the cervix canal to explore a new sampling method for NIPT. The successful isolation of trophoblast cells from the cervix canal early during gestation will enable an earlier application of NIPT to detect chromosomal abnormalities.
## Ethical approval
The study was approved by the institutional review board of Beijing Hospital. Written informed consent from all the patients were obtained. Qualified professionals ensured that all the contents of the informed consent form were read and understood before signing.
## Accordance statement
All methods were performed in accordance with the relevant guidelines and regulations.
## Study participants
Between January 2022 and March 2022, we enrolled three patients who wanted to undergo induced abortion at Beijing Hospital. The women were aged 31, 35, 44 years old with a gestation of 11.9, 7.3 and 8.3 weeks, respectively. The BMI was 17.6, 21.6 and 23.4, respectively.
## Inclusion criteria
[1] Women between 5 and 12 weeks of gestation[2] Intrauterine pregnancy confirmed by ultrasound[3] Women who planned a termination of pregnancy[4] No trichomonas or mycobacterial infections and vaginal cleanliness degree I (determined by routine vaginal discharge tests)[5] Absence of acute or chronic inflammation of the cervix[6] No fever or vaginal bleeding during pregnancy[7] No sexual intercourse in the last five days[8] No history of using drugs to promote uterine contraction or prostaglandin-like drugs to promote cervical maturation during pregnancy[9] Voluntarily participated in this study
## Exclusion criteria
Women who have any contraindications to abortion in early pregnancy: [1] acute phase of any disease requiring hospitalization after treatment; [2] genital inflammation; [3] systemic condition unable to tolerate surgery; [4] body temperature ≥ 37.5℃ twice 4 h apart before surgery.
Note: The gestational age of the subjects was extrapolated using the last menstrual method and ultrasound method. If the embryo length is sucessfully measured by ultrasound, gestational week = embryo length (cm) + 6.5, if not, gestational week = maximum internal diameter of the gestational sac (cm) + 3. To define the early pregnancy embryonic arrest specimen, the ultrasound needs to indicate that the embryo had stopped developing (no heart tube pulsation, no germ visible, etc.).
## Sample collection
For each woman, after informed consent, 10 ml of peripheral blood, a specimen collected from the cervix, and the abortion tissue were obtained. Cells from the cervical canal were obtained noninvasively with a special cytobrush before performing manual negative pressure aspiration. Chorionic villus tissue was obtained aseptically during the abortion for routine chorionic villus cell culture. The collected exfoliated cell specimen was washed in sterile saline. A large amount of cervical mucus was mixed in the specimen, and the cervical mucus in the specimen was obtained by adding appropriate amount of collagenase digestion.
The cervix samples were all collected before termination of pregnancy(TOP). The blood of patient A was collected soon after TOP and the blood of patient B and C were collected before TOP.
## Specimen processing
The genomic DNA (gDNA) was extracted from the cervix samples using QIAamp DNA Blood Kit (Qiagen, Hilden, Germany). For the blood and abortion samples, we isolated the gDNA with MagPure Tissue DNA LQ Kit (Magen, Guangzhou, Guangdong, China). The gDNA was fragmented to around 200 bp by Qsonica Sonicator, after which the gDNA library was constructed by VAHTS Universal Pro DNA Library Prep Kit for MGI (Vazyme, Nanjing, Jiangsu, China). xGen CNV Backbone Hyb Panel (IDT, Coralville, Iowa, USA) was used to capture the target regions of gDNA libraries. The post-capture libraries were sequenced on MGISEQ-2000 platform (MGI, Shenzhen, Guangdong, China) with 100 bp paired-end sequencing strategy. All procedures were performed according to relevant guidelines and manufacturers’ instructions.
## Statistical analysis
The statistical analysis was conducted for each individual separately.
## Selection of gene loci for analysis
The main challenge of this method is to diffrentiate the signal of the fetus from the mother’s in the cevix. If a mutation happens in the mother and her fetus at the same time, conclusion cannot be drawn that the mutation detected in the cervix represent the mutation in the fetus. To solve this problem, we filtered each gene loci with a different mutation type in the mother and her fetus, which is a exclusive signal for the fetus, coming from the paternal origin.
As shown in Fig. 1, we conducted the selection for each patient individually. For patien A, we selected all the mutated homozygote gene loci in her blood sample ($$n = 2292$$). Then they were intersected with all the mutated heterozygote gene loci in abortion tissue A($$n = 3943$$). Then we get a group of 877 gene loci that are heterozygote in the fetus but homozygote in the mother. As for the cervix samples, we filtered all the mutated gene loci($$n = 5931$$). Finally we got an intersection of the 877 gene loci and 5931 gene loci for analysis(n-870). The final intersection represent the heterozygote mutaion happens only in fetus, not the mother. This rule out the influence of the mother’s signal in the cervix sample. The same selection was conducted for patient B and C. Patient A,B and C got 870, 866, 859 gene loci for analysis, respectively. Fig. 1Flowchart of gene loci selection
## Calculation of allele frequencies
The allele frequency in the blood sample was calculated by.
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{Allele frequency in the blood sample}=\frac{Allele\ depth\ of\ the\ reference\ genotype\ in\ the\ blood\ sample}{Allele\ depth\ of\ the\ reference\ genotype\ in\ the\ blood\ sample + Allele\ depth\ of\ the\ variant\ genotype\ in\ the\ blood\ sample}$$\end{document}Allele frequency in the blood sample=AlleledepthofthereferencegenotypeinthebloodsampleAlleledepthofthereferencegenotypeinthebloodsample+Alleledepthofthevariantgenotypeinthebloodsample The allele frequency in the cervix sample was calculated by.
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\text{Allele frequency in the cervix sample} = \frac{Allele\ depth\ of\ the\ reference\ genotype\ in\ the\ cervix\ sample}{Allele\ depth\ of\ the\ reference\ genotype\ in\ the\ cervix\ sample + Allele\ depth\ of\ the\ variant\ genotype\ in\ the\ cervix\ sample}$$\end{document}Allele frequency in the cervix sample=AlleledepthofthereferencegenotypeinthecervixsampleAlleledepthofthereferencegenotypeinthecervixsample+Alleledepthofthevariantgenotypeinthecervixsample
## SRY detection
There was a total of four Y chromosomal genomic intervals in our panel, which was confirmed to be exclusive for male fetuses. Then, we detected these four SRYs in the samples of patient A, the only patient who carried a male fetus.
## Results
In Fig. 2, we presented the allele frequencies of each gene loci in the cervix samples and maternal blood samples for three women. The differences of the allele frequencies between the cervix and blood samples were plotted in Fig. 3. The mean and median values were presented in Table 1. *One* gene loci corresponds to two alleles, so the estimated percentage of trophoblastic content from cervix samples was $0.243\%$, $0.192\%$, $0.186\%$, respectively. Based on the Mann-Withney-Wilcoxon test, p values for three women were all larger than 0.01 which does not indicate a significant difference. Fig. 2Allele frequencies in cervix and blood samples. The allele frequencies for the cerix and blood samples of patient A, B, C are shown. Abbreviations: A Cervix sample A, Ab Blood sample A, B Cervix sample B, Bb Blood sample B, C Cervix sample C, Cb, Blood sample CFig. 3Differences of the allele frequencies between the cervix and blood samples. The differences of allele frequencies of each gene loci in the cervix and blood sample were plotted for each individual patientTable 1Allele frequencies and trophpoblast content in cervixSampleNAvg. C %Avg. B %Med. C%Med. B%P value%TrophoblastA$8700.122\%$$0.095\%$$0.037\%$$00.500.243\%$B$8660.096\%$$0.095\%$$0.036\%$$00.630.192\%$C$8590.093\%$$0.088\%$$0.030\%$$00.560.186\%$Abbreviations: N Number of gene loci for analysis, Avg. C%, Percentage of average allele frequency in cervix sample, Avg. B%, Percentage of average allele frequency in blood sample, Med. C%, Percentage of median allele prequency in cervix sample, Med. B%, Percentage of median allele frequency in blood sample, P value P value of Mann-Withney-Wilcoxon for allele frequency in cervix and blood sample, % Trophoblast, estimated percentage of trophoblast from cervix samples We also detected the Sex Determining Region Y (SRY) in the samples of patient A, who carried a male fetus. As shown in Table 2, in the abortion tissue, the SRY signal was detected in all four regions. For the cervix sample, 4 reads were found to be matched for region chrY_p11.31_001_SRY_Y. No reads were matched for the other three SRYs. Table 2Signal for Sex Determining Region Y (SRY) for subject ASex Determining Region Y(SRY)Raw dataCervixBloodAbortion TissuechrY_p11.31_001_SRY_Y403119chrY_p11.2_001_rs402756200375chrY_q11.221_001_rs978604300354chrY_q11.222_001_rs390000896
## Discussion
Previous studies had explored the use of trophoblast cells in prenatal testing, and the procedure typically involves two parts. To seperate trophoblast cells, immunomagnetic separation and flow cytometry is often used, but a certain amount of target cells may be lost due to highly specificified antibodies and antigen destruction caused by specimen pretreatment [8]. Laser Capture Microdissection (LCM) can also be used but the the cutting precision may lead to the damage of the nucleus. Differential adhesion method uses the different adherent speed of trophoblast cells and maternal contaminated cells to remove maternal suspended cells [8]. However, in Yuan et al. ’s research, only $38\%$ of fetuses’ gender was correctly identified. For trophoblast cells identification, immunohistochemical staining is usually used, but its efficiency is highly dependent on the specificity of antibody and antigen destruction [11]. Instead of using seperation before identification, we extract DNA directly and then perform the sequencing procedure. To a great extent, this avoids the possible loss of target cells.
For the “allele frequnecy” method, we filtered the gene loci that was mutated in the cervix cells, and homozygous in blood but heterozygous in the abortion tissue. This step rules out the the major maternal contribution to the fetus for our further analysis. However, the homozygous genotype does not necessarily represent a $100\%$ homozygous signal. For example, geno loci X of a mother is homozygous with a $99.9\%$ homozygous allele frequency with $0.1\%$ heterozygous allele frequncy. Then, $99.9\%$ homozygous signal and $0.1\%$ heterozygous signal will be detected in all maternal cells, including blood and the cervix. Eventhough we filrtered the homozygous gene loci in the maternal blood and the heterozygous gene loci in cervix sample, the mother’s heterozygous signal in the blood and the cervix sample cannot be completely ruled out. To address this concern, we compared the allele frequncies of the cervix sample with those in the maternal blood. If the allele frequncies in the cervix sample are not significantly different from that in the blood sample, the signal detected in the cervix might come from the mother but not the fetus. If the signal we detected in the cervix cells is significantly different from that in the maternal blood, the contribution from the mother can be ruled out and the signal from the trophoblastic content is detected. The mean allele frequencies for the three subjects were $0.122\%$, $0.096\%$ and $0.093\%$ respectively. Since one gene loci has two alleles, the estimated trophoblast content was $0.243\%$, $0.192\%$, $0.186\%$, respectively. Although the signal was small, it was comparable to the previous study with a $\frac{1}{2000}$ content of trophoblast [12]. However, as can be seen from Figs. 1 and 2, the allele frequencies in the cervix cells were very similar to those in the blood cells. We compared the allele frequencies in the cervix and blood cells using paired-test wilcoxon test. The P values were all larger than 0.1, indicating an insignificant difference of allele differences between the gene loci in the cervix and blood cells. Therefore, using the “allele frequency” method, the signal of trophoblastic cells was not detected successfully.
However, we successfully detected the SRY signal in a cervix sample, indicating a preliminary successful retrieval of trophoblast from the cervix canal. The finding was consistent with a recent study in Belgium [13]. However, we only had one male fetus, the sample size was too small and we aim to include more patients for further study before its clinical application.
## Conclusion
We successfully detected the SRY signal in a cervix sample, indicating a successful start of trophoblast retrieval from the cervix canal. Further study needs to be conducted to verify our finding and hopefully, the successful retrieval of trophoblast from the cervix canal will be applied to the early detection of fetal chromosomal abnormalities by NIPT.
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|
---
title: 'Therapeutic effects of resveratrol and Omega-3 in mice atherosclerosis: focus
on histopathological changes'
authors:
- Shamsi Sadat Mosavi
- Soghra Rabizadeh
- Amirhossein Yadegar
- Sara Seifouri
- Fatemeh Mohammadi
- Reihane Qahremani
- Salome Sadat Salehi
- Armin Rajab
- Alireza Esteghamati
- Manouchehr Nakhjavani
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10024363
doi: 10.1186/s12906-023-03899-9
license: CC BY 4.0
---
# Therapeutic effects of resveratrol and Omega-3 in mice atherosclerosis: focus on histopathological changes
## Abstract
### Background
Resveratrol and omega-3 have been shown to prevent atherosclerosis. However, histopathological changes and their comparison have not been studied well. This study investigated the therapeutic effects of resveratrol and omega-3 in experimental atherosclerosis of mice.
### Methods
We divided sixty 6-week-old male C57BL/6 mice into six groups and followed for 10 weeks: [1] standard diet, [2] atherogenic diet, [3] atherogenic diet along with resveratrol from the start of the sixth week, [4] atherogenic diet along with omega-3 from the start of the sixth week, [5] standard diet along with resveratrol from the start of the sixth week, [6] standard diet along with omega-3 from the start of the sixth week.
### Results
The mice fed on an atherogenic diet had a larger fat area and a thicker aortic wall thickness than mice fed on a standard diet. The use of omega-3 and resveratrol in the mice with an atherogenic diet resulted in a significantly reduced fat area (p-value = 0.003), and resveratrol had a significantly higher effect. Omega-3 or resveratrol induced a significant reduction in aortic wall thickness in mice on an atherogenic diet, and there was no significant difference between them. Among the mice with a standard diet, this study did not observe any significant changes in the fat area or the aortic wall thickness with the consumption of omega-3 or resveratrol.
### Conclusions
Resveratrol and omega-3 had a regressive and therapeutic role in atherosclerosis, with a more significant effect in favor of resveratrol.
## Introduction
Atherosclerosis is the primary cause of the cardiovascular disease (CVD), including coronary artery disease (CAD), myocardial infarction (MI), stroke, congestive heart failure, and peripheral artery disease, and is a chronic inflammatory condition. Atherosclerosis is mainly located in the intima of medium to large arteries, mainly at the bifurcation site [1]. All the risk factors attributed to atherosclerosis play a part in exacerbating the underlying inflammatory process [2, 3]. One factor shown to be an early event in the atherosclerosis process is oxidation [4]. Radical oxygen species (ROS) are imperative cellular signaling molecules that lead to vascular lesions and endothelial dysfunction [5]. The turning point of vascular endothelial dysfunction is the imbalance between the production of vascular protective substances and vascular relaxants, which precedes many vascular pathological processes and the onset of cardiovascular diseases [6–8].
The formation of atherosclerotic lesions is characterized by excessive accumulation of cholesterol in the arterial intima [9]. Macrophages play an essential role in all stages of atherosclerosis. Disruption of lipid homeostasis in macrophages in atherosclerosis leads to cholesterol accumulation and foam cell formation [10]. Earlier studies have shown that plaque formation is not just an ongoing process associated with aging. It is a dynamic process that can be slowed down, stopped, or reversed [2, 11]. Current treatments of atherosclerotic and cardiovascular diseases mainly focus on statins, which regress plaque by reducing lipid content [12]. Although resveratrol may be effective in atherosclerosis, it may have additional potential benefits as a polyphenol with many pleiotropic actions. Resveratrol has been shown to have various beneficial biological effects such as anti-inflammatory, antioxidant, anti-glycosylation, anti-cancer, anti-aging, and neuroprotective [13].
Resveratrol is a polyphenol that was first found in the roots of *Veratrum gandiflorum* back in 1939 [14]. This polyphenolic phytoalexin is found in grapes, wine, berries, peanuts, and tea. Resveratrol prevents cardiovascular diseases by inhibiting radical oxygen species (ROS), platelet aggregation, and low-density lipoproteins oxidation [15]. Also, resveratrol elicits a regulatory effect on the metabolism of lipids [16]. In a study performed by Cheng et al. on obese mice with a high-fat diet, the anti-inflammatory and antioxidant effects of resveratrol inhibited the accumulation of lipid droplets in hepatocytes, resulting in the protection of the mice from hepatic steatosis [17]. In another study by Huo et al., the anti-inflammatory effects of resveratrol on diabetic mice with coronary diseases led to pancreatic tissue protection and a lower serum sugar level, resulting in cardiovascular protection [18]. Resveratrol is well tolerated in healthy people and by experimental models without significant side effects [13]. Resveratrol has low bioavailability and rapid metabolism, but despite this, it shows a relevant biological effect that may be due to its conversion/interconversion to sulfonate and glucuronide metabolites and/or its binding/dissociation to plasma proteins, the main routes of resveratrol delivery at target organ sites [13].
Omega-3 is a polyunsaturated fatty acid (PUFA) with its double bond three atoms away from its methyl terminus [19]. It is mainly known for its anti-inflammatory effects partially due to one of its substrates, eicosapentaenoic acid (EPA) [20]. Also, omega-3 has antithrombotic effects and reduces blood pressure, pulse rate, and triglyceride levels [21]. Two randomized clinical trials (JELIS and REDUCE-IT) that were carried out in 2007 and 2020 demonstrated the potential role of EPA in reducing atherosclerosis [22, 23]. Omega-3 fatty acids act as a substrate for forming a group of lipid mediators that relieve inflammation [24].
Pramaningtyas and Faruqy et al. examined the aorta of diabetic rats using Image J software and reported that exercise did not affect aortic wall thickness [25]. Bonanno et al., using ultra-high-resolution 3D imaging, showed significant differences in average vessel wall thickness of the cardiovascular system in atherosclerotic rats on an atherogenic diet [26].
Although resveratrol has been shown to have various beneficial effects, including effects on atherosclerosis, to the best of our knowledge, no studies have been conducted to show the impact of resveratrol on the histopathology of atherosclerosis. Also, the histopathological effects of omega-3 on atherosclerosis and its comparison with resveratrol are not well studied.
This study explored and compared the potential beneficial effects of resveratrol and omega-3 in the prevention and regression of atherosclerosis. Also, this study investigated histopathological factors, including the aortic wall thickness and the area occupied by fat droplets in the aortic wall.
## Materials and methods
Sixty 6-week-old male C57BL/6 mice were obtained from the Pasteur Institute of Iran (Tehran, Iran). The experiment complied with ARRIVE guidelines. This study followed the National Research Council’s Guide for the Care and Use of Laboratory Animals. This study was approved by the Research Ethics Committee of Tehran University of Medical Sciences (Approval number: 94–03–191-30,088). All mice were housed in plastic cages with a stainless-steel gird lid and wood shaving scattered on the floor. The vivarium was maintained at 23 °C on a 12-h light-dark cycle with lights off at 7 pm. The mice were acclimatized for 2 weeks and fed a regular commercial mouse diet (Behparvar co, Iran. This company produces food for laboratory animals, including mice). At the start of the trial, the mean mouse weight was 24 ± 2.6 g. Throughout the experiment (10 weeks), the mice were given free access to food and water.
To produce the atherogenic diet, $1.25\%$ cholesterol, $0.5\%$ cholic acid, and $15\%$ fat were added to the powdered standard diet pellets. The components were mixed for 45 min, formed into a dough with double-distilled water, rolled into pellets, and then allowed to dry for 1 day in a dehydrator at 29 °C and an additional 2 or 3 days in a 37 °C room [27, 28]. In addition, the pellets were provided to the animals every week, and the mice were fed fresh food from the refrigerator every day. The mice were also randomly divided into six groups. Ten mice were in each group.
Group 1 (control) had a standard diet for 10 weeks. Group 2 had an atherogenic diet for 10 weeks. Group 3, on the other hand, had an atherogenic diet for 10 weeks along with 20 mg/kg/day resveratrol [29] from the start of the sixth week. Group 4 also had an atherogenic diet for 10 weeks, yet along with 600 mg/kg/day omega-3 [30] from the beginning of the sixth week. However, group 5 had a standard diet for 10 weeks and 20 mg/kg/day resveratrol from the start of the sixth week, and group 6 had a standard diet for 10 weeks along with 600 mg/kg/day omega-3 from the beginning of the sixth week. In this study, mice were given the prescribed diet for 5 weeks to reach a balanced state, and at the end of the fifth week, we started the treatment with resveratrol or omega-3 in the target groups.
The resveratrol - a Trunature Company, USA product - was in the form of soft gels containing 250 mg of this substance. The omega-3 from Zahravi Company (Tabriz, Iran) was also in the form of soft gels containing 360 mg of this compound. Resveratrol and omega-3 were added to the drinking water of the animals. Each mouse consumed about 5 mL of water per day [31].
The mice were weighed weekly. At the end of the trial, the mice were fasted between 8 to 12 hours. Then they were killed by carbon dioxide inhalation [32]. Immediately after euthanasia, about 0.5 and 1.0 mL of blood was collected from each mouse using cardiac puncture. The blood was transferred to a test tube, and then its serum was separated by centrifugation at 1500 r/min for 15 minutes. ELISA analyzed the serum to measure the lipid profile, and LDL/HDL cholesterol ratio was calculated.
Furthermore, heart tissue was examined under a loupe to find the aortic arch. The tissues taken from animals were fixed in $10\%$ neutral buffered formalin, processed with the standard histological method, and the sections were stained with Hematoxylin and Eosin (H&E) [33, 34]. The current study used a simple, accessible, yet accurate method to achieve results and evaluate the aortic wall thickness. Besides that, this study investigated the intensity of fat accumulation by using and presenting a method, including measuring the areas occupied by fat. In other words, the current study tried to evaluate this aspect of atherosclerotic lesions using a method that easily yields logical results. Histopathological changes in the aorta of mice were studied by a microscope equipped with a camera (Tucsen, H series). This study measured the thickness of aortic walls (μm) and the areas occupied by fat (fatty streaks) (μm2) by Radical IS Capture Pro software.
The Radical IS Capture Pro software (Radical Scientific Equipment PVT. LTD) can measure the length or distance between any two specified points. Therefore, we were able to measure the thickness of the aorta. This study also determined the range of fat droplets using this software. The average area occupied by fat in all slides of each group is a sample of the fat area of that group.
Data analysis was performed using the SPSS software version 22 for windows (SPSS, Inc.). Results were expressed as the mean ± standard deviation. The six groups were compared using one-way ANOVA. Mean levels of serum lipid profile were calculated. We considered a p-value lower than 0.05 statistically significant.
## Results
The stages of progression and regression of the fat area and the aortic wall thickness of the mice are given in Table 1. The mice fed on an atherogenic diet had a larger fat area and a thicker aortic wall than those fed on a standard diet. The largest fat area was observed in mice fed on a drug-free atherogenic diet (1880.0 ± 16.6 μm2). Compared to the drug-free atherogenic diet group, the use of omega-3 and resveratrol in the mice fed on an atherogenic diet significantly reduced fat area (1481.7 ± 29.3 and 1007.2 ± 13.3 μm2) (p-value < 0.001). Also, we observed a significantly higher effect of resveratrol than omega-3 in limiting fat area progression in the atherogenic diet mice (p-value < 0.001). Similar to the fat area surface, the thickest aortic wall was found in mice who received a drug-free atherogenic diet (115.6 ± 9.1 μm). Omega-3 or resveratrol consumption significantly reduced aortic wall thickness (100.9 ± 16.1 and 89.6 ± 14.9 μm) among mice fed on an atherogenic diet (p-value < 0.05). However, there was no significant difference between the effects of omega-3 and resveratrol on aortic wall thickness in mice fed an atherogenic diet (p-value = 0.279).Table 1Stages of progression and regression of fat area surface and thickness of the aortic wall in this studyAtherogenic dietStandard dietP-value–Omega-3Resveratrol–Omega-3ResveratrolFat area surface (μm2)1880.0 ± 16.6 *1481.7 ± 29.3 *1007.2 ± 13.3 *213.2 ± 10.2 #207.7 ± 10.5 #202.8 ± 10.9 #0.003Thickness of the aortic wall (μm)115.6 ± 9.1 *100.9 ± 16.1 $89.6 ± 14.9 ¥38.3 ± 5.0 #37.9 ± 7.4 #37.2 ± 7.2 #0.011Data are presented as mean ± SD*: p-value ≤ 0.05, vs. all groups#: p-value ≤ 0.05, vs. atherogenic diet with or without drug$: p-value ≤ 0.05, vs. all groups except atherogenic diet with resveratrol¥: p-value ≤ 0.05, vs. all groups except atherogenic diet with omega-3 Among the mice fed on a standard diet, this study did not observe any significant changes in the fat area with the consumption of omega-3 (207.7 ± 10.5 μm2) or resveratrol (202.8 ± 10.9 μm2) compared to the group with a drug-free diet (213.2 ± 10.2 μm2) (all p-values > 0.05). Similar results were applied for the thickness of the aortic wall among mice fed on a standard diet (38.3 ± 5.0 μm), standard diet and omega-3 (37.9 ± 7.4 μm), and standard diet and resveratrol (37.2 ± 7.2 μm) (all p-values > 0.05). Figure 1 shows the histopathological changes. Fig. 1Histopathological changes of the aorta in the studied groups Lipid profile components in the studied groups, including TG, TC, LDL-C, and HDL-C levels, are shown in Fig. 2. The mean levels of TC and LDL-C showed a decreasing trend among groups that received treatment (P-trend = 0.532), while mean levels of HDL-C did not change significantly and were relatively stable after 10 weeks (P-trend = 0.315). Furthermore, the LDL/HDL cholesterol ratio was lower in mice receiving treatment, especially resveratrol, either fed on a standard or atherogenic diet (Fig. 3).Fig. 2Lipid profile in the studied groupsFig. 3LDL/HDL ratio in the studied groups
## Discussion
This study evaluated the potential effects of resveratrol and omega-3 on atherosclerosis. Features of atherosclerosis, such as incidence of fatty streaks, thickened aortic wall, and increased serum LDL/ HDL ratio, were observed in the mice receiving atherogenic diets in the fifth week but more severe in the tenth week. The area occupied by fat droplets was considered and measured as an indicator of the fatty streak. According to the author’s knowledge, this is the first time that the fat-occupied area in the aortic wall is calculated as a symbol of the fatty streak.
In the current study, resveratrol and omega-3 significantly inhibit the process of atherosclerosis in mice by reducing the area occupied by fat, the aortic wall thickness, and the LDL/HDL ratio. Even in mice receiving standard diets, the addition of treatments reduced fat area, the aortic wall thickness, and the LDL/HDL ratio. Also, resveratrol showed to be more effective than omega-3.
Various strategies have been proposed to prevent and reverse plaque formation, including diet modification, dietary supplements, and medications [20, 24, 35, 36]. Some essential oil compounds, such as eucalyptol, can prevent atherosclerotic lesions in the rat model by decreasing glycation, oxidative stress, and inflammatory mediators [37]. Since 1992, when the cardioprotective effects were attributed to red wine’s moderate and chronic consumption, several studies have been conducted to prove this effect and find its mechanism [38–40]. Resveratrol is a phenolic compound in red wine that has attracted attention for providing an alcohol-free antioxidant [41].
*The* general mechanisms of the anti-atherosclerotic effect of resveratrol and omega-3 are almost identical. The antioxidant effect mainly exerted by resveratrol is due to increased nitric oxide synthesis [15, 42], while it is exerted by omega-3 further due to reduced production of inflammatory mediators and cytokines [20].
Resveratrol and omega-3 reduce platelet aggregation and inhibit thrombus formation [20, 35, 43]. Reducing the number of macrophages within the plaque [21] and modulating the LDL uptake by them (diminished foam cells) under the influence of omega-3 [44], as well as inhibiting LDL peroxidation and vasorelaxant effect in the influence of resveratrol [15, 16, 43, 45], lead to limited fatty streak and plaque size. Decreased plaque size and the number of foam cells occurred in both treatment groups in this study.
In addition to these similar mechanisms, resveratrol and omega-3 have different and unique mechanisms that may have different effects. Based on the results of this study, resveratrol was able to be more effective than omega-3 in the prevention and the regression of atherosclerosis in mice, which may be due to its phytoestrogenic properties [43] or its ability to increase mitochondrial regeneration and activity [35, 43, 46]. With such a protective effect, resveratrol can be an excellent complementary to statins, which have been mainly used to prevent and treat atherosclerosis [47]. Studies have shown that statins may interfere with mitochondrial activities, and some adverse effects may be caused directly or indirectly by the mitochondrial pathway [48]. In addition, statins stabilize atherosclerotic plaque with thickened fibrous caps and macrocalcifications [12].
Kakoti et al. showed that combination therapy with resveratrol and omega-3 is more valuable, especially in chronic diseases such as Alzheimer’s and atherosclerosis. However, the findings were focused on serum concentrations of inflammatory mediators and not on histopathological parameters. The combined effect was to reduce inflammation by reducing nitric oxide (resveratrol effect) and prostaglandins (omega-3 impact) [35].
In this study, the LDL/HDL ratio (as a marker of atherosclerosis [49]) was significantly increased in mice fed on an atherogenic diet (p-value = 0.008). Both resveratrol and omega-3 reduced the LDL/HDL ratio in mice receiving an atherogenic diet. At the same time, neither of these substances significantly affected the LDL/HDL ratio in mice fed on a standard diet.
In the current study, mean levels of TC and LDL-C had a non-significant downward trend among groups receiving treatment. However, after 10 weeks, no significant changes were observed in mean HDL-C levels. The effect of resveratrol or omega-3 on serum lipid profile is highly controversial. Recent in vivo studies have failed to show a significant effect of resveratrol on serum cholesterol levels [43, 50]. However, in some cases, total cholesterol was reduced in resveratrol-treated hypercholesterolemic rats [43, 51]. Penumathsa et al. conducted a study on rats. They showed that the lipid level was decreased in all the treatment groups, more significantly in the statin-treated group when compared to the resveratrol-treated group [42]. The hypocholesterolemic effect of resveratrol may be due to its phenolic hydroxyls, which lead to the oxidation of unsaturated fatty acids and the reduction of circulating cholesterol [52]. Studies reported that omega-3 does not affect serum cholesterol levels [53], while other studies showed that omega-3 could increase HDL [54, 55] or decrease the ratio of total cholesterol to HDL [55, 56]. The increase in HDL-C induced by omega-3 may be explained by increased lipoprotein lipase (LPL) activity [54]. The controversial results may be due to the different conditions of the studies.
There are several limitations in this study:The current study did not investigate the treatment with the combination of resveratrol and omega-3 and its histopathological changes. This study did not look for changes at the cellular scale and did not use monoclonal antibodies and IHC. We only wanted to study histopathological changes and look for an easy way to record changes in aortic wall thickness and fat deposition. Oil Red O staining was not used in this study.
Further studies can investigate the potential effects of combination therapy in atherogenic or standard diet groups. Also, the treatment can be applied for a more extended period. A comparative study between resveratrol and statins may add to the knowledge. Further research is needed, especially clinical trials in humans, to confirm the protective action and demonstrate the clinical aspects of resveratrol.
## Conclusion
In the current study, resveratrol and omega-3 significantly had a regressive and therapeutic role in atherosclerosis, with a more significant effect in favor of resveratrol. More studies are needed to investigate histopathological changes and better understand their mechanism of action.
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|
---
title: 'Examining the relationship between health literacy and quality of life: evidence
from older people admitted to the hospital'
authors:
- Gholamhossein Mehralian
- Ali Reza Yusefi
- Esmat Rezabeigi Davarani
- Sudabeh Ahmadidarrehsima
- Parnian Nikmanesh
journal: BMC Geriatrics
year: 2023
pmcid: PMC10024369
doi: 10.1186/s12877-023-03838-w
license: CC BY 4.0
---
# Examining the relationship between health literacy and quality of life: evidence from older people admitted to the hospital
## Abstract
### Introduction
Literacy has become an increasingly serious problem, especially as it relates to health care. In this regard, health literacy (HL), as a cognitive skill, has proven to be an influential factor to improve of the quality of life (QOL). This study aimed to examine the level of HL and its relationship with the QOL of older people at the time of discharge from the hospital in the south of Iran.
### Methods
This descriptive-analytical cross-sectional study included 300 older people admitted and treated in 10 teaching-therapeutic hospitals affiliated with the Shiraz University of Medical Sciences in 2021. The standard Health Literacy for Iranian Adults (HELIA) questionnaire and the World Health Organization Quality of Life Questionnaire (WHOQOL-BREF) were used to collect the required data. Data were analyzed with SPSS software version 23 software using descriptive and inferential statistics, Pearson’s correlation coefficient, T-test, ANOVA, and multiple linear regression at $$p \leq 0.05.$$
### Results
The mean scores of Hl and QOL for older people were 48.22 ± 9.63 (out of 100) and 61.59 ± 12.43 (out of 120), respectively. Moreover, there was a significant direct correlation between the participants’ HL and their QOL ($r = 0.388$, $p \leq 0.001$). All dimensions of HL, including comprehension (β=0.461, $p \leq 0.001$), decision-making and behavior (β=0.434, $p \leq 0.001$), access (β=0.397, $p \leq 0.001$), reading skill (β=0.362, $$p \leq 0.002$$), and assessment (β=0.278, $$p \leq 0.004$$), were significant relationship with QOL. A statistically significant difference was revealed between the mean scores of HL regarding the participants’ gender ($$p \leq 0.04$$) and level of education ($$p \leq 0.001$$). Furthermore, the mean scores of QOL were significantly different with regard to older people’s gender ($$p \leq 0.02$$), marital status ($$p \leq 0.03$$), level of education ($$p \leq 0.002$$), and income ($$p \leq 0.01$$).
### Conclusion
The findings revealed the participants’ inadequate HL and average QOL. Considering the relationship of HL with QOL, it is recommended to develop comprehensive programs and effective interventions to develop HL skills and subsequently improve QOL among older people.
## Background
Aging is a sensitive phase of human life, and attention to the problems and needs of this age group is of paramount importance [1]. This phase of life is always considered one of the main economic, social, and health challenges in all countries [2]. According to estimates in 2012, older people account for $0.8\%$ of the world’s population. Meanwhile, in 2015, the proportion of older people population increased by $0.5\%$ and reached $8.5\%$ of the total population 2015. The frequency of older people is predicted to reach one billion or 0.12 of the total population by 2030. By 2050, older people are expected to cover $16.7\%$ of the total world population and reach above two billion persons [3]. On the other hand, older people in most societies are more likely to experience decreased physical, mental, and cognitive abilities and consequently hospitalization in health service centers such as hospitals. Accordingly, they are more likely to depend on formal and informal support to maintain health, performance, and self-sufficiency [4]. In this regard, attention to self-care and responsibility of older people towards different diseases is one of the support solutions requiring high levels of HL [5].
HL refers to individuals’ ability to interpret and understand basic information required for making decisions in the field of health [6]. This literacy encompasses skills, including reading, listening, analysis, and decision-making, and the ability to apply these skills in health situations, which are not necessarily correlated with years of education or general reading ability [7]. According to the World Health Organization, HL is defined not only as an individual character but also as a key determinant of health at the population level [8], which is a critical component in making appropriate and correct health decisions [9]. The appropriate level of HL makes individuals obey the orders of the health and treatment staff, promotes the effectiveness of medical consultations and health promotion and self-care programs, and enhances individuals’ desire to participate in screening programs [10]. On the other hand, inadequate and low HL in older people is associated with factors such as reduced cognitive ability, decreased physical health, increased risk of dementia, and risk factors of chronic diseases in this age group [11–13]. Moreover, some studies have documented that low HL in older people is associated with increased depression [14], adoption of some high-risk health behaviors [15], and generally unfavorable physical and mental health [16]. Accordingly, older people must reach an acceptable level of HL to maintain and improve their health level [17].
Moreover, experts believe that individuals with acceptable HL usually enjoy better QOL [18]. QOL is considered one of the key components of general health [19] and one of the indicators reflecting the health status of older people [20]. According to the WHO, QOL refers to individuals’ assessment and comprehension of their life situation under the influence of the cultural and value system and the setting where they live. Individuals’ goals, expectations, standards, and desires significantly affect their physical and psychological condition, level of independence, social relationships, and beliefs [21, 22]. Compared to other age groups, older people experience a unique QOL due to conditions such as older age and special experiences and skills [23]. On the other hand, changes in the disease models, i.e., a decrease in the rate of infectious diseases and an increase in life expectancy and chronic diseases, have led to increased attention to the concepts of health and QOL among older people in recent decades [24]. The importance of QOL is so much that experts have introduced the focus of health care in the current century to be developing QOL [25]. Examining the level of HL and QOL in older people can provide comprehensive information about their health conditions to promote their literacy level and consequently reach acceptable QOL in this age group. To this end, the present study aimed to examine the level of HL and its relationship with the QOL of older people at the time of discharge from the hospital settings.
## Hypothesis development
Physical impairment increases with age, and its negative effect on the ability to maintain independence increases the need for assistance, thereby decreasing QOL among older people [26]. In this regard, some studies have reported HL as one of the factors affecting QOL [27–32]. According to Wang et al. and Skevington et al., low HL is correlated with poor QOL, which can be caused by decreased accessibility and less use of medical care, poor self-management of the disease, reduced self-efficacy and ability to exercise control over life and surrounding environment, and increased stress aroused by daily life challenges [33, 34]. Yusefi et al. found a significant relationship between HL and QOL [11]. In their study, Panahi et al. reported a significant direct correlation between HL with the physical and mental dimensions of QOL and the overall QOL [35]. Kooshyar et al. also suggested that individuals with sufficient HL had a higher QOL and a significant relationship between HL and the physical and mental dimensions of QOL [28]. Zheng et al. [ 36], Couture et al. [ 29], and Wang et al. [ 33], also indicated that HL had an impact on QOL. Alnawajha claimed that QOL is the result of HL [37]. According to Hosieni et al., HL is the predictor of the QOL score [38]. Finally, Lee et al. state that HL in older people has a potential effect on promoting their health and QOL and decreases health care costs accordingly [39].
Moreover, some studies have revealed a significant relationship between HL and demographic variables such as gender [40–42] and level of education [40, 43–46]. Moreover, the relationship between QOL and variables such as gender [47–50], marital status [44, 50–52], level of education [52, 53], and income [48, 50] has also been documented in some other studies. According to the theoretical framework and various reviews of HL and QOL [11, 27–39], the following research hypotheses were formulated: The HL and its dimensions are positively related to older people’s QOL.The mean scores of HL and QOL for older people were significantly different regarding demographic variables.
## Design and setting
This descriptive-analytical study was cross-sectional which conducted in first-level teaching therapeutic hospitals ($$n = 10$$) affiliated with the Shiraz University of Medical Sciences in the south of Iran from July to November 2021.
## Participants
Participants in the study were older people who received medical advice in the discharge room of hospitals at the time of discharge. Most of these elderly people were in the internal medicine department of hospitals due to diseases such as gastrointestinal, cardiovascular, diabetes, and kidney problems.
According to the following Eq. [ 54] and given the correlation between HL and QOL ($r = 0.201$) based on a pilot study in Iran [55], at a confidence level of $95\%$ and β=0.1, the sample size was estimated to be at least 259 persons. To increase accuracy and avoid bias caused by sample size drop, 300 persons were included in the study.
1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$n = {\left[{\left({{Z_1} - \alpha {/_2} + {\rm{ }}{Z_{1 - \beta }}} \right){\rm{ }}/{\rm{ }}W} \right]^2} + {\rm{ }}3$$\end{document} In Eq. [ 1], W is calculated using the following equation: 2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$W = \raise.5ex\hbox{$\scriptstyle 1$}\kern-.1em/\kern-.15em\lower.25ex\hbox{$\scriptstyle 2$} {\rm{ }}ln\left({1 + r/1 - r} \right)$$\end{document} In Eq. 2, r is the estimated coefficient of correlation between responsiveness and service quality in a pilot study in Iran [55]. Questionnaires were submitted to the 300 participants in hospitals, proportional to the frequency of older people population in each of the studied hospitals. For this purpose, after visiting each hospital, the total frequency of older people (> 60 years) at the time of discharge in different wards was recorded. After calculating the total number of older people, the proportional sampling method was used to distribute 300 questionnaires. Older people were randomly selected in each hospital according to the number of older people in each ward. Inclusion criteria were informed consent, willingness to participate in the study, 60 years of age or older, the ability to speak and communicate. According to the WHO, 60 years of age or older is defined as the cutoff point of old age in developing countries [56]. Since cognitive disorders such as delirium and major and minor neurocognitive disorders affect individuals’ cognitive abilities (learning, memory, perception, and concentration) [57], older people with these disorders were excluded from the study. To this end, with the help of the supervisor and the doctor, the patient’s medical records were reviewed, and those with the aforementioned disorders were detected. Additionally, people who did not wish to continue cooperating were excluded.
## Instruments
The data collection instrument was a three-section standard questionnaire. The first section of the questionnaire encompassed demographic information (including age, gender, marital status, level of education, and income), and the second and third sections contained the standard HELIA [58] and WHOQOL-BREF [59] questionnaires. The 33-item HELIA measures individuals’ ability in five subscales of health literacy, including reading skills ($$n = 4$$), access ($$n = 6$$), comprehension ($$n = 7$$), assessment ($$n = 4$$), and decision-making and behavior ($$n = 12$$). The scale is scored on a 5-point Likert scale. The reading skill items are scored 5 (completely easy), 4 (easy), 3 (neither easy nor difficult), 2 (difficult), and 1 (completely difficult). For the other four subscales, the scoring scale contained 5 (always), 4 (often), 3 (sometimes), 2 (rarely), and 1 (never). To score the questionnaire, the raw scores in each subscale are obtained from the algebraic sum of the scores. Then, to convert this score into a range of zero to one hundred, the following formula is used: Adjusted score = (The raw score obtained-minimum possible score/maximum possible-minimum possible score). Finally, to obtain the total score, the scores of all dimensions (ranging from zero to one hundred) are added and divided by the number of dimensions ($$n = 5$$). Then health literacy was categorized into four levels inadequate (0–50 points), not enough (50.1–66), adequate (66.1–84), and excellent (84.1–100) [11]. Montazeri et al. ‘s confirmed the validity and reliability (Cronbach’s alpha coefficient=0.89) of this questionnaire [58].
The third section consisted of the standard WHOQOL-BREF questionnaire with 24 items in four domains: physical health ($$n = 7$$ items), psychological health ($$n = 6$$), social relations ($$n = 3$$), and living environment ($$n = 8$$). The items were scored on a 5-point Likert scale with 1 (very bad), 2 (bad), 3 (moderate), 4 (good), and 5 (very good). Older people’s QOL was also classified as follows: unfavorable (24–48), moderate (49–72), acceptable (73–96), and excellent (97–120). The validity and reliability of this questionnaire (with Cronbach’s alpha coefficient > 0.7) are confirmed in previous studies [59]. The validity and reliability of these two questionnaires are confirmed in previous studies [56, 59]. In the present study, the validity of the questionnaires was confirmed by 12 members of the academic staff and experts in the field of health and treatment management from the Iranian universities of medical sciences. Moreover, the calculated Content Validity Index (CVI) and Content Validity Ratio (CVR) were 0.88 and 0.90 for the HELIA questionnaire and 0.87 and 0.89 for the WHOQOL-BREF questionnaire, respectively. Regarding their reliability, a sample of 30 older people was pre-tested, and Cronbach’s alpha coefficient was 0.87 for the HELIA questionnaire and 0.79 for the WHOQOL-BREF questionnaire; hence, the reliability of the questionnaire was confirmed.
## Procedures and statistical analysis
To collect the required data, two researchers (PN and ERD) visited the hospitals on different weekdays in the morning, evening, and night shifts and distributed and collected questionnaires. To observe the ethical considerations, older people were voluntarily included in the study and filled out the questionnaires. After explaining the research objectives of the participants, they were ensured of the confidentiality of their information, and their verbal consent was obtained, and then the questionnaires were distributed in person among older people under study and collected on the same day. The questionnaire was completed by the participants. However, some older people asked the research team (PN and ERD) to help them complete the questionnaires. Then the collected data were imported to IBM SPSS software version 23. To investigate the correlation between older people’s HL and QOL and examine the correlation between these two variables and age, Pearson’s correlation coefficient was used. A T-test was used to detect the difference in the mean score of the two main research variables regarding gender. The ANOVA test was used to investigate the difference between the mean scores of HL and QOL in older people regarding their marital status, level of education, and income level. Finally, multiple linear regression was used to investigate the simultaneous relationship of different aspects of HL with QOL in older people.
## Results
The mean age of older people was 69.74 ± 5.23 years, with most of them ($57.33\%$) being in the age group of 60–70 years. Moreover, $53.67\%$ of the participants were females, and the others were males. Most of the respondents had elementary education ($46\%$) and were married ($85\%$) with an income of 10–20 million Rials ($54.67\%$). Table 1 shows the frequency distribution of the participants’ demographic information.
Table 1Frequency distribution of participants’ demographic information ($$n = 300$$)VariablesCategoryFrequency% Age (year) 60–7071–8080<1721072157.3335.677Total------300100 Gender MaleFemale13916146.3353.67Total------300100 Marital Status SingleMarriedDivorcedWidow72559292.338539.67Total------300100 Level of Education Unable to Read and WriteReading and WritingElementary SchoolDiplomaBSc and higher3448138611911.33164620.336.34Total------300100 Income Level (Rials) < 10,000,00010,000,000–20,000,00020,000,001–30,000,000> 30,000,00073164491424.3354.6716.334.67Total------300100 As presented in this table, the mean score of HL for older people was 48.22 ± 9.63 (out of 100), indicating inadequate HL. Among the HL dimensions, the maximum and minimum mean scores were obtained for access (50.19 ± 9.82 out of 100) and assessment (46.13 ± 9.32 out of 100). As presented in Table 2, the mean score of the participants’ QOL was 61.59 ± 12.43 out of 120, indicating their moderate QOL (Table 2).
Table 2Mean and standard deviation of participants’ HL and QOLMain VariableDimensionScore DomainMeanStandard Deviation HL AccessReading SkillsComprehensionAssessmentDecision-Making and Behavior0-10050.1949.3147.1846.1348.289.829.679.749.329.59 Total HL 0-100 48.22 * 9.63 QOL Physical HealthPsychological HealthSocial RelationsLiving Environment7–356–303–158–4019.4112.959.0120.225.534.873.255.61 Total QOL 61.59 ** 12.43 * Score out of 100** Score out of 120 The findings revealed a positive and significant correlation between HL and its dimensions with QOL among older people ($r = 0.388$, $p \leq 0.001$). Among the HL dimensions, comprehension had the highest correlation with QOL ($r = 0.411$, $p \leq 0.001$) (Table 3).
Table 3Correlation between older people’ HL and QOLHLTotal HL QOL Dimension AccessReading SkillsComprehensionAssessmentDecision-Making and BehaviorPhysical Healthr=0.442p<0.001r=0.455p=0.001r=0.492p<0.001r=0.429p=0.001r=0.475p<0.001r=0.457p<0.001Psychological Healthr=0.459p<0.001r=0.444p=0.001r=0.506p<0.001r=0.431p=0.002r=0.453p<0.001r=0.486p<0.001Social Relationsr=0.383p=0.001r=0.371p=0.002r=0.394p<0.001r=0.351p=0.003r=0.389p<0.001r=0.379p=0.001Living Environmentr=0.259p=0.002r=0.253p=0.002r=0.282p=0.001r=0.221p=0.004r=0.271p=0.001r=0.247p=0.003 Total QOL $r = 0.384$$p \leq 0.001$r=0.372p=0.001r=0.411p<0.001r=0.369p=0.002r=0.395p<0.001 $r = 0.388$ $p \leq 0.001$ To determine the relationship of different HL dimensions with the QOL of older people, the results of the multiple linear regression analyses showed that the significant variables in the model determined using the Enter method were comprehension, decision-making and behavior, access, reading skill, and assessment, respectively. Moreover, the coefficient of determination in the processed model (R-Adjusted) of this test was 0.62, indicating that $62\%$ of the variation in the QOL score can be explained by the variables in this model. According to the multiple linear regression analyses, the linear equation of the participants’ QOL was obtained as follows: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{array}{l}$Y = 1.813${\rm{ }} + {\rm{ }}0.461\,{x_1} + {\rm{ }}0.434\,{x_2}\\\,\,\, + {\rm{ }}0.397\,{x_3} + {\rm{ }}0.362\,{x_4} + {\rm{ }}0.278\,{x_5}\end{array}$$\end{document} Where, Y is the QOL score, and x represents different dimensions of HL in the studied population (Table 4).
Table 4Multiple linear regression test results for variables affecting older people’s QOLVariablesUnstandardized coefficientsStandardized coefficient βt-statisticsP-Value β Std. Error --- (Constant)1.8130.312-3.210.001 x 1 Comprehension0.4610.0990.3842.87<0.001 x 2 Decision-Making And Behavior0.4340.0840.3712.65<0.001 x 3 Access0.3970.0760.3392.57<0.001 x 4 Reading Skill0.3620.0650.2962.490.002 x 5 Assessment0.2780.0530.2282.080.004 The results showed significant differences among participants’ mean HL scores regarding gender ($$p \leq 0.04$$) and level of education ($$p \leq 0.001$$). In this regard, the mean HL scores of older people women (48.58 ± 9.74 out of 100) and those with bachelor’s degrees and higher education (49.48 ± 10.22 out of 100) were higher. Moreover, there were significant differences among the mean scores of older people’s QOL regarding their gender ($$p \leq 0.02$$), marital status ($$p \leq 0.03$$), level of education ($$p \leq 0.002$$), and income ($$p \leq 0.01$$). Accordingly, the mean QOL scores were higher for the females (62.55 ± 12.56 out of 120) than the males, for the married older people (64.97 ± 13.28 out of 120), for those with bachelor’s degrees and higher education (64.42 ± 13.16 out of 120), and for the participants with the income level above 30 million Rials per month (65.25 ± 13.48 out of 120) (Table 5).
Table 5Relationship between HL and QOL regarding participants’ demographic informationVariablesCategoryHLQOLMean ± SD(From 100)P-ValueMean ± SD(From 120)P-Value Age (year) 60–7049.53 ± 10.260.1063.94 ± 13.220.0871–8048.52 ± 9.6361.17 ± 12.4780<46.61 ± 8.6759.66 ± 12.17 Gender Male47.86 ± 9.32 0.04 60.63 ± 11.86 0.02 Female48.58 ± 9.7462.55 ± 12.56 Marital Status Single48.83 ± 9.490.0963.49 ± 12.61 0.03 Married49.07 ± 9.8364.97 ± 13.28Divorced46.56 ± 9.6159.21 ± 11.29Widow48.42 ± 9.5558.69 ± 11.76 Level of Education Unable to Read and Write47.31 ± 8.36 0.001 58.71 ± 11.53 0.002 Reading and Writing47.82 ± 8.6259.19 ± 11.47Elementary School48.22 ± 9.3562.05 ± 13.52Diploma48.27 ± 10.0863.58 ± 12.37BSc and higher49.48 ± 10.2264.42 ± 13.16 Income Level (Rials) < 1000000046.21 ± 9.730.1157.46 ± 11.93 0.01 10000000–2000000047.52 ± 9.6259.31 ± 11.5220000001–3000000049.39 ± 10.1164.34 ± 12.74> 3000000049.77 ± 9.8265.25 ± 13.48
## Discussion
The findings indicated the inadequate HL of older people. Consistent with this finding, Słońska et al. in Poland reported that most of older people aged 65 years and above had inadequate HL [60]. Moreover, in studies in different regions of Iran (e.g., Kooshyar et al. [ 28], Ansari et al. [ 40], Borji et al. [ 43], Mohseni et al. [ 44], Reisi et al. [ 61]), most of older people’s HL level is reported to be inadequate. In comparison, Nezafati et al. [ 45] and TamizKar et al. [ 62] reported that more than half of older people had adequate HL. Similarly, Meier et al. in Switzerland found that $68.6\%$ of older people had adequate HL [41]. According to Sørensen et al., HL adequacy in eight European countries ranged from 29 to $62\%$ [63]. This inconsistency seems to be associated with the participants’ level of education in different studies. On the other hand, HL is a complicated construct, and several factors such as economic and social factors, cultural status, individual characteristics, and others may have effects on this construct. The results of the present study indicated the participants’ moderate QOL. Older people’s QOL has been reported differently in different studies. Similarly, Azadi et al. [ 47] and Moghadam et al. [ 64] reported moderate QOL for older people. According to a systematic review and meta-analysis, the QOL of the Iranian older people was almost moderate [65]. While Izadi et al. showed that the overall QOL of older people was at an acceptable level [66]. Miranda et al. in Brazil [67] and Rantakokko et al. in the United States [68] found out that most of older people had acceptable QOL. In addition to differences in the individual, social, economic, and cultural characteristics of the studied groups, the differences in the preparedness of different communities to face the challenges caused by aging can also be a reason for the inconsistencies in older people’s QOL in different societies.
The findings of the study revealed a statistically significant and positive correlation between older people’s HL and their QOL. Furthermore, the HL dimensions, including comprehension, decision-making and behavior, access, reading skills, and assessment, were significant relationship with QOL in older people. In Wang et al. ’s study in China, high HL was significantly correlated with QOL in patients with high blood pressure [33]. Lee et al. showed that HL was one of the factors affecting health-related QOL in older people aged above 65 years in South Korea [39]. In the studies in Iran, there was a significant relationship between QOL and HL in older people [28, 69, 70]. Some studies have also documented that older people with a higher level of HL exhibit higher self-care behaviors and are healthier [46, 71–73]. Individuals with a higher level of HL may pay more attention to their health status and thus choose healthy behavioral habits, increasing their health-related QOL.
An investigation of the relationship between the main research variables and the participants’ demographic characteristics suggested that the participants’ mean HL scores were significantly different regarding their gender and level of education. Accordingly, older people women had higher HL than older people men. Moreover, with an increase in the level of education, older people’s HL increased as the HL of older people with a bachelor’s degree or higher education was higher than others. According to the results, the mean score of HL of the women was higher than that of men in Quaidi et al. ’s study on patients aged above 40 years with type 2 diabetes [42]. In Ansari et al. ’s study, the level of HL was higher in older people women [40]. Furthermore, Meier et al. showed that older people Swiss women’s HL was higher than that of men [41]. Perhaps, women’s search for more health-related content and more frequent visits to doctors and health centers to participate in screening programs and receive information from health workers are some reasons explaining their higher HL. Moreover, the role of women as the caretakers of other family members may encourage them to search for health-related information. Contrary to the results of the present study, older people men in some studies had higher HL [43, 74, 75]. The differences in individuals’ characteristics may be one of the reasons for inconsistencies in different studies.
In line with the results of this study, various studies have reported a significant relationship between HL and level of education; hence, the level of HL increases with an increase in individuals’ level of education [40, 41, 43–46, 74]. The elderlies with higher education have more ability to search for health-related content in virtual space and have more access to scientific information and texts. They can also establish effective communication with health care workers, and this has an effect on promoting their HL. In contrast to the aforementioned studies, Qaidi et al. provided different results in their study. In their study, the HL of all older people individuals with type 2 diabetes and a degree higher than a diploma was significantly lower than those with a diploma. The researchers concluded that a higher level of education does not guarantee higher HL [42]. Those with elementary education probably have more leisure time to search for health-related information from virtual social networks and receive information from mass communication media and have more opportunities to visit health centers for screening, care services, and information.
Finally, the mean score of older people’s QOL was significantly higher in older people women who were married and had a bachelor’s degree and higher education, with an income level above 30 million Rials. Accordingly, with an increase in older people’s level of education and income, their QOL increased.
In other studies in Iran (e.g., Azadi et al. [ 47], Rajabi et al. [ 48], Babak et al. [ 49], Raei et al. [ 50]), older people men’s mean QOL score was higher than older people women’s scores. Orfila et al. in Spain showed that older people women’s QOL scores were lower than that of men [76]. This contradicts the results of the present study. One of the reasons for older people men’s higher QOL scores in most studies may be the further financial independence of men than women. Moreover, in many cultures, after divorce or the death of a spouse, women have less chance of remarrying than men, and their isolation and loneliness may have a negative impact on their QOL. According to the results of the present study, the married older people had a higher QOL than older people who divorced from their spouses or whose spouses were not alive [44, 47, 48, 50–52]. One of the risks and problems of old age is loneliness and isolation; hence, attention to this issue and the provision of supportive and emotional conditions is one of the necessities of aging and affects the QOL of older people.
In line with the findings of this study, previous studies have claimed that the mean score of QOL increases with an increase in the level of literacy and education [47–49, 52, 53]. Given that higher education increases one’s competence in many fields, it can ultimately lead to higher QOL. The level of education not only is directly correlated with QOL by improving healthy behavior and lifestyle but also is indirectly by providing a better job, thereby making older people less vulnerable to economic and social problems and others. In line with the results of the present study, older people with higher incomes had a higher QOL [48, 50]. According to a review study in Iran, the results of studies on older people’s QOL revealed that this group is in a difficult situation due to the financial burden of the disease, and the researchers concluded that improving the economic situation can increase older people’s QOL [11]. Most elderlies may lose their source of income in old age and become economically dependent on others. On the other hand, medical expenses increase annually, and older people, especially those without additional insurance, are no longer able to pay medical costs. This implies that proper financial support is one of the factors affecting their QOL.
## Conclusion
The findings indicated inadequate and moderate levels of HL and QOL in older people, and there was a positive and significant statistical relationship between these two variables. In other words, improving HL leads to the improvement of QOL among older people. Inadequate HL in older people is considered a warning for officials, policy-makers, and health service providers, highlighting the need to pay further attention to HL in health promotion programs. Given that older people population in *Iran is* increasing, attention to the problems and needs of this group is a necessity. Accordingly, it is necessary to develop appropriate interventions to improve the HL level of older people to provide the grounds for improving their health status and promoting their QOL.
## Limitations
In this study, data collection was done by self-reporting, which may have an effect on the reporting of data by older people. Also, the use of a questionnaire and the inability to fully generalize the results to other societies and cultures are among the limitations of this study. Longitudinal studies in other regions with different cultures are highly recommended. As the study was conducted in Iran, it is recommended that similar studies be conducted in other countries so that a better understanding of the relationship between health literacy and quality of life can be obtained by comparing the results. However, despite the significant relationship between health literacy and quality of life in this study, there is a possibility that this relationship is influenced by a variety of factors, so future studies should explore the factors affecting the relationship between health literacy and quality of life.
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|
---
title: The cardiovascular and renal effects of glucagon-like peptide 1 receptor agonists
in patients with advanced diabetic kidney disease
authors:
- Yuan Lin
- Te-Hsiung Wang
- Ming-Lung Tsai
- Victor Chien-Chia Wu
- Chin-Ju Tseng
- Ming-Shyan Lin
- Yan-Rong Li
- Chih-Hsiang Chang
- Tien-Shin Chou
- Tzu-Hsien Tsai
- Ning-I Yang
- Ming-Jui Hung
- Tien-Hsing Chen
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10024371
doi: 10.1186/s12933-023-01793-9
license: CC BY 4.0
---
# The cardiovascular and renal effects of glucagon-like peptide 1 receptor agonists in patients with advanced diabetic kidney disease
## Abstract
### Background
To determine whether glucagon-like peptide 1 receptor agonists (GLP-1RAs) have cardiovascular and renal protective effects in patients with advanced diabetic kidney disease (DKD) with an estimated glomerular filtration rate (eGFR) < 30 mL/min per 1.73 m2.
### Methods
In this cohort study, patients with type 2 diabetes mellitus and eGFR < 30 mL/min per 1.73 m2 with a first prescription for GLP-1RAs or dipeptidyl peptidase 4 inhibitors (DPP-4is) from 2012 to 2021 ($$n = 125$$,392) were enrolled. A Cox proportional hazard model was used to assess the cardiorenal protective effects between the GLP-1RA and DDP-4i groups.
### Results
A total of 8922 participants [mean (SD) age 68.4 (11.5) years; 4516 ($50.6\%$) males; GLP-1RAs, $$n = 759$$; DPP-4is, $$n = 8163$$] were eligible for this study. During a mean follow-up of 2.1 years, 78 ($13\%$) and 204 ($13.8\%$) patients developed composite cardiovascular events in the GLP-1RA and DPP-4i groups, respectively [hazard ratio (HR) 0.88, $95\%$ confidence interval CI 0.68–1.13]. Composite kidney events were reported in 134 ($38.2\%$) and 393 ($44.2\%$) patients in the GLP-1RA and DPP-4i groups, respectively (subdistribution HR 0.72, $95\%$ CI 0.56–0.93).
### Conclusions
GLP-1RAs had a neutral effect on the composite cardiovascular outcomes but reduced composite kidney events in the patients with advanced DKD compared with DPP-4is.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01793-9.
## Background
Cardiovascular diseases are the leading causes of mortality in both patients with type 2 diabetes mellitus (type 2 diabetes) and chronic kidney disease (CKD) [1, 2]. Diabetic kidney disease (DKD) is also a major cause of end-stage kidney disease (ESKD) and dialysis [3]. The prevalence of diabetes mellitus (DM) is estimated to be $10.5\%$ globally [4], and the cardiovascular mortality rate in DKD patients is more than two folds higher compared to patients with type 2 diabetes with preserved kidney function [5]. An estimated glomerular filtration rate (eGFR) < 60 mL/min per 1.73 m2 (unit omitted below) has been associated with a higher risk of cardiovascular death [5]. Therefore, it is important to prevent cardiovascular and kidney events in DKD patients, especially in those with poor kidney function.
Although the first-line medication for type 2 diabetes is metformin, glucagon-like peptide-1 receptor agonists (GLP-1RAs) along with sodium–glucose cotransporter 2 inhibitors (SGLT2is) are recommended for patients with established atherosclerotic cardiovascular disease (ASCVD) or multiple ASCVD risk factors [6, 7]. Cardiorenal benefits including the prevention of major adverse cardiovascular events (MACEs) and a reduction in new-onset macroalbuminuria have been demonstrated in previous landmark studies [8–13]. In addition, adding GLP-1RAs to SGLT2is in diabetic patients with heart failure has been shown to significantly reduce composite cardiovascular events [14]. Moreover, GLP-1RAs have been proposed to be a potential candidate for the prevention of obesity-related cardiovascular diseases [15]. GLP-1RAs have also been confirmed to reduce all-cause and cardiovascular mortality in patients with type 2 diabetes and to improve left ventricular diastolic function in heart failure patients; hence it is conceivable that GLP-1RAs may be beneficial for patients with advanced DKD [16, 17]. Furthermore, SGLT2is but not GLP1-RAs have been associated with a lower risk of atrial fibrillation when compared with dipeptidyl peptidase 4 inhibitors (DPP-4is) [18]. SGLT2is are recommended for DKD patients with an eGFR > 25 and urine albumin-to-creatinine ratio (UACR) > 300 mg/g creatinine, while the role of GLP-1RAs in advanced DKD patients remains controversial [6].
Research on the effect of GLP-1RAs on cardiovascular outcomes in patients with advanced DKD (eGFR < 30) is limited. Previous GLP-1RA trials have mainly excluded advanced DKD patients, and completely excluded those with ESKD. Results from the LEADER study showed favorable cardiovascular outcomes in terms of MACEs in patients receiving liraglutide with an eGFR 30–60 compared to a placebo cohort [19]. Liraglutide, lixisenatide, dulaglutide and semaglutide have been shown to reduce the development of macroalbuminuria, indicating a renal protective effect [20]. However, solid evidence of cardiovascular protective effects with lixisenatide, dulaglutide and semaglutide in advanced DKD patients is lacking. Hence, the cardiovascular impact of GLP-1RAs in patients with advanced DKD and ESKD is worth investigating.
This study enrolled type 2 diabetes patients with a first prescription for GLP-1RAs and an eGFR < 30, and compared their effect to DPP-4is. The primary outcomes were composite cardiovascular outcomes including cardiovascular death, myocardial infarction (MI) and ischemic stroke, and composite renal outcome including a decline in eGFR > $50\%$, progression to ESKD with dialysis, and cardiovascular death. The aim of the study was to investigate the potential cardiovascular and renal protective effects of GLP-1RAs in DKD patients with moderate to severe kidney function Additional file 1.
## Data source
Data were acquired from Chang Gung Research Database (CGRD). The CGRD is the largest multi-institutional electronic medical record (EMR) database in Taiwan [21], including 2 medical centers and five general hospitals, and information on more than 11 million patients from 2001 to 2019.
## Patients and study design
The study cohort included patients with a first prescription for GLP-1RAs or DPP-4is from January 1, 2012 to December 31, 2021. The date of first prescription was defined as the index date. GLP-1RAs included liraglutide and dulaglutide, and DPP-4is included sitagliptin, vildagliptin, saxagliptin, and linagliptin. Patients with missing demographic data (age or sex), type 1 DM, < 40 years old, eGFR > 30, missing baseline eGFR data, and those with a follow-up period < 3 months were excluded. Patients with prescriptions for GLP-1RAs not of interest in this study, including exenatide and lixisenatide, were also excluded. The Modification of Diet in Renal Disease (MDRD) equation was used to calculate the eGFR. Patients were followed until the occurrence of an outcome (e.g., MACE), death, drug switch, or adding on another drug (e.g., GLP-1RAs to DPP-4is or GLP-1RAs added to DPP-4is) or December 31, 2021, whichever occurred first. Due to the retrospective nature of this study, no formal sample size calculation based on estimated effect size was performed.
## Covariates
The baseline characteristics included demographics, severity of DM, kidney function and stages, comorbidities, vital signs, laboratory data, and concomitant medications. Demographic data including age, sex, body mass index (BMI) and smoking were recorded. The duration of DM, baseline glycated hemoglobin (HbA1c) level, DM retinopathy and DM neuropathy were used as a proxy for the severity of DM. Kidney function and stages were categorized as an eGFR between 15 and 30, < 15, and dialysis. The baseline comorbidities included hypertension, hyperlipidemia and seven others. Charlson’s Comorbidity Index (CCI) score was also recorded. Vital signs included systolic and diastolic blood pressure and heart rate. The laboratory data included triglycerides, total cholesterol and three others. Concomitant medications were classified into glucose-lowering therapies (sulfonylurea, insulin and four others) and cardiovascular agents (antihypertension agents, lipid-lowering agents, and antiplatelet agents).
## Outcomes
Outcome measurements included clinical events and continuous outcomes. The primary cardiovascular outcome was a composition of cardiovascular death, MI, and ischemic stroke. Cardiovascular death was defined according to the standard definitions for cardiovascular and stroke endpoint events in clinical trials by the US Food and Drug Administration. The definitions of MI and ischemic stroke were acute episodes requiring hospitalization. The renal outcomes included a decline in eGFR > $50\%$, and progression to ESKD with dialysis. ESKD with dialysis was defined as the need for permanent dialysis regardless of hemodialysis or peritoneal dialysis. The composite renal outcome was defined as any one of a decline in eGFR > $50\%$, ESKD with dialysis, and cardiovascular death. The secondary outcomes were all-cause death, heart failure admission, admission due to any cause, composite major adverse limb events including newly diagnosed peripheral arterial disease, claudication, clinical limb ischemia, limb revascularization or amputation, hypoglycemia, diabetic ketoacidosis (DKA) or hyperosmolar hyperglycemic state (HHS), and infection death. The date, place and causes of death were extracted using data linked to the Taiwan Death Registry.
Continuous outcomes included systolic and diastolic blood pressure, body weight, HbA1c, eGFR, and heart rate. The continuous outcomes were extracted at baseline, and then 6, 12, 18 and 24 months of follow-up. Since the data of the patients were substantially impacted by dialysis, the continuous outcomes after dialysis at baseline or during follow-up were not analyzed.
## Statistical analysis
A propensity score matched cohort was created to compare outcomes. The propensity score was the predicted probability to be in the GLP-1RA group derived from a multivariable logistic regression model. All of the variables listed in Table 1 were included in the calculation of propensity score, except for the follow-up year which was replaced with the index date. The caliper was set as 0.2, the algorithm was greedy, and replacement was not allowed. Each patient in the GLP-1RA group was matched to 1 or more (at most 4) counterparts in the DPP-4i group. As some data on the continuous covariates were missing, single expectation–maximization imputation was performed before conducting propensity score matching. The balance of baseline characteristics between the two groups was assessed using standardized difference (STD), where an absolute STD value < 0.2 was considered to be a non-substantial difference between groups [22].Table 1Baseline characteristics of the patients before and after propensity score matchingVariableBefore matchingAfter matchingTotal ($$n = 8922$$)GLP1RA ($$n = 759$$)DPP4i ($$n = 8163$$)STDGLP1RA ($$n = 602$$)DPP4i ($$n = 1479$$)STDDemographics Age, years68.4 ± 11.565.3 ± 11.368.7 ± 11.5− 0.3065.9 ± 11.566.3 ± 11.0− 0.04 Male, n (%)4516 (50.6)389 (51.3)4127 (50.6)0.01305 (50.7)784 (53.01)− 0.05 Body mass index, kg/m226.0 ± 4.528.1 ± 4.925.7 ± 4.40.5127.7 ± 4.827.3 ± 4.50.10 Smoker, n (%)1696 (19.0)163 (21.5)1,533 (18.8)0.07122 (20.3)299 (20.22) < 0.01Severity of DM Duration of DM, year6.4 ± 5.910.7 ± 6.26.0 ± 5.70.7810.0 ± 6.28.8 ± 6.50.19 Baseline HbA1c, mmol/mol Baseline HbA1c, %62 ± 21 7.8 ± 1.975 ± 22 9.0 ± 2.060 ± 19 7.6 ± 1.80.7372 ± 19 8.7 ± 1.868 ± 24 8.4 ± 2.20.15 DM retinopathy, n (%)2310 (25.9)321 (42.3)1989 (24.4)0.39232 (38.5)491 (33.20)0.11 DM neuropathy, n (%)2644 (29.6)463 (61.0)2181 (26.7)0.74341 (56.6)733 (49.56)0.14Kidney function and stage eGFR, ml/min/1.73 m219.2 ± 14.820.8 ± 16.019.0 ± 14.60.1220.7 ± 15.220.6 ± 16.3 < 0.01 15–30, n (%)4123 (46.2)367 (48.4)3756 (46.0)0.05291 (48.3)726 (49.09)− 0.01 < 15, n (%)1532 (17.2)68 (9.0)1464 (17.9)− 0.2760 (10.0)163 (11.02)− 0.03 Dialysis, n (%)3267 (36.6)324 (42.7)2943 (36.1)0.14251 (41.7)590 (39.89)0.04Baseline comorbidity Hypertension, n (%)7862 (88.1)717 (94.5)7145 (87.5)0.24563 (93.5)1364 (92.22)0.05 Hyperlipidemia, n (%)4560 (51.1)586 (77.2)3974 (48.7)0.62442 (73.4)982 (66.40)0.15 Coronary heart disease, n (%)2751 (30.8)323 (42.6)2428 (29.7)0.27237 (39.4)536 (36.24)0.06 Heart failure hospitalization, n (%)1438 (16.1)160 (21.1)1278 (15.7)0.14120 (19.9)280 (18.93)0.03 Coronary intervention, n (%)1027 (11.5)158 (20.8)869 (10.6)0.28104 (17.3)241 (16.29)0.03 Ischemic stroke, n (%)1023 (11.5)100 (13.2)923 (11.3)0.0679 (13.1)166 (11.22)0.06 Myocardial infarction, n (%)806 (9.0)124 (16.3)682 (8.4)0.2484 (14.0)184 (12.44)0.04 Atrial fibrillation, n (%)708 (7.9)57 (7.5)651 (8.0)− 0.0244 (7.3)117 (7.91)− 0.02 Peripheral arterial disease, n (%)1009 (11.3)121 (15.9)888 (10.9)0.1590 (15.0)209 (14.13)0.02Charlson’s Comorbidity Index score5.5 ± 2.86.6 ± 2.75.4 ± 2.80.426.5 ± 2.76.2 ± 2.80.11Vital sign Systolic blood pressure, mmHg141.9 ± 25.5142.5 ± 24.4141.8 ± 25.60.03142.1 ± 23.6142.1 ± 23.9 < 0.01 Diastolic blood pressure, mmHg73.9 ± 13.674.7 ± 15.673.8 ± 13.40.0674.5 ± 15.774.4 ± 13.00.01 Heart rate, beat/min82.2 ± 14.882.8 ± 13.882.1 ± 14.90.0583.0 ± 13.782.9 ± 13.90.01Biochemistry data Triglyceride, mg/dL184.6 ± 133.2224.9 ± 162.6180.5 ± 129.20.30211.9 ± 144.4201.0 ± 143.60.08 Total cholesterol, mg/dL173.4 ± 50.1172.5 ± 51.1173.5 ± 50.0− 0.02171.8 ± 46.7172.4 ± 48.1− 0.01 High-Density Lipoprotein, mg/dL40.8 ± 13.439.8 ± 13.240.9 ± 13.5− 0.0840.4 ± 12.240.6 ± 12.7− 0.01 Low-density lipoprotein, mg/dL74.5 ± 14.172.7 ± 14.974.7 ± 14.0− 0.1473.1 ± 13.573.5 ± 14.2− 0.03 UACR, mg/g974 [130, 3006]1036 [149, 2791]967 [129, 3014]NA2138 [769, 3427]2221 [898, 3400]NAConcomitant glucose lowering therapies Sulfonylurea, n (%)5349 (60.0)479 (63.1)4870 (59.7)0.07377 (62.6)904 (61.12)0.03 Thiazolidinedione, n (%)757 (8.5)180 (23.7)577 (7.1)0.47121 (20.1)217 (14.67)0.14 Glinide, n (%)1930 (21.6)140 (18.4)1790 (21.9)− 0.09117 (19.4)280 (18.93)0.01 Alpha glucosidase, n (%)1193 (13.4)152 (20.0)1041 (12.8)0.20116 (19.3)254 (17.17)0.05 SGLT2i, n (%)178 (2.0)64 (8.4)114 (1.40)0.3344 (7.3)86 (5.81)0.06 Insulin, n (%)2792 (31.3)310 (40.8)2482 (30.4)0.22237 (39.4)545 (36.85)0.05Concomitant cardiovascular agents ACEi/ARB, n (%)5485 (61.5)485 (63.9)5000 (61.3)0.05384 (63.8)936 (63.29)0.01 Beta-blocker, n (%)3128 (35.1)321 (42.3)2807 (34.4)0.16252 (41.9)577 (39.01)0.06 DCCB, n (%)6050 (67.8)505 (66.5)5545 (67.9)− 0.03397 (65.9)998 (67.48)− 0.03 Thiazide, n (%)352 (3.9)32 (4.2)320 (3.9)0.0122 (3.7)55 (3.72) < 0.01 MRA, n (%)630 (7.1)59 (7.8)571 (7.0)0.0347 (7.8)110 (7.44)0.01 Nitrates, n (%)2249 (25.2)204 (26.9)2045 (25.1)0.04154 (25.6)393 (26.57)− 0.02 Vasodilator, n (%)950 (10.6)70 (9.2)880 (10.8)− 0.0562 (10.3)157 (10.62)− 0.01 Statins, n (%)4372 (49.0)566 (74.6)3806 (46.6)0.60428 (71.1)1000 (67.61)0.08 Fibrates, n (%)647 (7.3)96 (12.6)551 (6.7)0.2065 (10.8)139 (9.40)0.05 Aspirin, n (%)3007 (33.7)303 (39.9)2704 (33.1)0.14232 (38.5)566 (38.27)0.01 Clopidogrel/Ticagrelor/Prasugrel, n (%)1662 (18.6)179 (23.6)1483 (18.2)0.13136 (22.6)332 (22.45) < 0.01Follow-up, year3.2 ± 2.52.1 ± 1.83.3 ± 2.6− 0.532.2 ± 2.02.1 ± 2.10.06Data are presented as frequency (percentage), mean ± standard deviation or median [25th, 75th percentile]GLP1RA glucagon-like peptide-1 receptor agonist; DPP4i dipeptidyl peptidase 4 inhibitor; DM diabetes mellitus; HbA1c glycated hemoglobin; eGFR estimated glomerular filtration rate; UACR urine albumin-to-creatinine ratio; SGLT2i sodium-glucose cotransporter 2 inhibitor; ACEi angiotensin converting enzyme inhibitor; ARB angiotensin receptor blocker; DCCB dihydropyridine calcium channel blocker; MRA mineralocorticoid receptor antagonist The risk of a fatal outcome (e.g., cardiovascular death, all-cause death) between groups was compared using a Cox proportional hazard model. The incidence of nonfatal clinical events (e.g., MI, eGFR decline > $50\%$) between groups was compared using the Fine and Gray subdistribution hazard model which considered all-cause death during follow-up as a competing risk. Post hoc subgroup analysis of composite cardiovascular outcome and new-onset dialysis was further conducted. The selected subgroup variables were age (< 65 vs. ≥ 65 years), sex, duration of DM (< 10 vs. ≥ 10 years) and ten others. Changes in the continuous outcomes from baseline to follow-up measurements between groups were compared using a linear mixed model, with the random intercept and slope. The duration from baseline to dialysis during follow-up was compared between the two groups using the Mann–Whitney U-test. The cause of death between groups was compared using the chi-square test. Statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC, USA). All statistical tests were 2-sided, and a P value < 0.05 was considered significant.
## Patient inclusion
This study enrolled 125,392 patients with a first prescription for GLP-1RAs or DPP-4is between January 1, 2012 and December 31, 2021 (Table S1, S2). According to the exclusion criteria, a total of 759 GLP-1RA users and 8,163 DPP-4i users were eligible for analysis (Fig. 1). In the matched cohort, 212, 117, 59 and 214 patients in the GLP-1RA group were matched to 1, 2, 3 and 4 counterparts in the DPP-4i group, respectively, resulting in a total of 1479 patients in the DPP-4i group and 602 in the GLP-1RA group. Fig. 1Selection of Study Patients. GLP-1RA glucagon-like peptide 1 receptor agonist; DPP-4i dipeptidyl peptidase 4 inhibitor; DM diabetes mellitus; eGFR estimated glomerular filtration rate
## Demographic data
The mean age of the participants was 68.4 ± 11.5 years, and 4,516 ($50.6\%$) were male (Table 1). The mean duration of DM was 6.4 ± 5.9 years, and the baseline HbA1c was 62 ± 21 mmol/mol (7.8 ± $1.9\%$). Compared to the patients with DPP-4is, those with GLP-1RAs were younger, had a higher BMI, longer duration of DM, higher baseline HbA1c level, less CKD stage 5 (eGFR < 15), higher prevalence of DM retinopathy and neuropathy, hypertension, hyperlipidemia, coronary heart disease, coronary intervention and MI, greater CCI scores, higher triglyceride level, and were more likely to take thiazolidinedione, alpha glucosidase, SGLT2is, insulin, statins and fibrates (absolute SD values > 0.2). After matching, there were no significant differences in the baseline characteristics between groups (absolute SD values < 0.2).
## Clinical events
The mean follow-up in the matched cohort was 2.1 years (standard deviation = 2.1 years). The results showed that the risk of composite cardiovascular outcome was not significantly different between the GLP-1RA and DPP-4i groups ($13\%$ vs. $13.8\%$, hazard ratio [HR] 0.88, $95\%$ confidence interval CI 0.68–1.13) (Fig. 2A). The risks of each component of the composite cardiovascular outcome were also not significantly different between the two groups, including MI, ischemic stroke and cardiovascular death. With regards to the renal outcomes, the GLP-1RA group showed a greater protective effect than the DPP-4i group, including progression to ESKD with dialysis ($23.4\%$ vs. $27.45\%$, subdistribution HR [SHR] 0.72, $95\%$ CI 0.56–0.93) (Fig. 2B), decline in eGFR > $50\%$, and the composite renal outcomes. The median duration to new-onset dialysis was significantly longer in the GLP-1RA group (median: 1.9 years, interquartile range: 0.9–2.8 years) than in the DPP-4i group (median: 1.3 years, interquartile range: 0.6–2.4 years) (Fig. S1A).Fig. 2Cumulative Event Rate of Primary CV outcomes, Progression to Dialysis, All-cause Mortality, and MALEs. GLP-1RAs had a neutral effect on composite CV outcomes, but delayed progression to dialysis, and reduced all-cause mortality and MALEs compared with DDP-4is. CV cardiovascular; GLP-1RA glucagon-like peptide 1 receptor agonist; DPP-4i dipeptidyl peptidase 4 inhibitor; MALEs major adverse limb events For the secondary outcomes, the risks of all-cause death ($18.4\%$ vs. $25.1\%$, HR 0.71, $95\%$ CI 0.57–0.88) (Fig. 2C) and all-cause readmission were significantly lower in the GLP-1RA group (Table 2). In addition, the risk of composite major adverse limb events in the GLP-1RA group was borderline significantly lower than that in the DPP-4i group (Fig. 2D). The common causes of death in the advanced DKD patients included malignancy, infection, CV diseases, DM, and kidney disease. There were no significant differences between the GLP-1RA and DPP-4i groups. The other causes of death were significantly lower in the GLP-1RA group (Fig. S1B).Table 2Clinical events of the patients in the propensity score matched cohortOutcome (HR or SHR)GLP1RA ($$n = 602$$)DPP4i ($$n = 1479$$)HR/SHR ($95\%$ CI) of GLP1RAPn (%)Incidence ($95\%$ CI)an (%)Incidence ($95\%$ CI)aCardiovascular outcome Composite CV outcomeb (HR)78 (13.0)6.0 (4.7–7.4)204 (13.79)6.9 (6.0–7.9)0.88 (0.68–1.13)0.308 Cardiovascular death (HR)34 (5.6)2.5 (1.7–3.4)81 (5.5)2.6 (2.0–3.1)0.97 (0.66–1.44)0.894 Myocardial infarction (SHR)41 (6.8)3.1 (2.2–4.1)99 (6.7)3.3 (2.7–4.0)0.96 (0.67–1.38)0.843 Ischemic stroke (SHR)20 (3.3)1.5 (0.9–2.18)52 (3.5)1.7 (1.2–2.15)0.91 (0.54–1.52)0.708Renal outcome (SHR) eGFR decline > $50\%$113 (32.2)17.1 (14.0–20.3)319 (35.9)22.4 (20.0–24.9)0.74 (0.60–0.91)0.005 Progression to ESKD with dialysis82 (23.4)11.4 (8.9–13.9)244 (27.5)15.9 (13.9–17.9)0.72 (0.56–0.93)0.010 Composite renal outcomec134 (38.2)23.2 (19.3–27.1)393 (44.2)34.3 (30.9–37.7)0.75 (0.61–0.93)0.009Secondary outcome All-cause death (HR)111 (18.4)8.3 (6.8–9.9)371 (25.1)12.0 (10.8–13.2)0.71 (0.57–0.88)0.002 Heart failure admission (SHR)77 (12.8)6.2 (4.8–7.5)222 (15.1)8.0 (6.9–9.0)0.80 (0.61–1.05)0.102 Admission due to any cause (SHR)343 (57.0)42.5 (38.0–47.0)906 (61.3)54.7 (51.2–58.3)0.81 (0.71–0.91)0.001 Composite MALE outcomed (SHR)42 (7.0)3.3 (2.3–4.3)132 (8.9)4.5 (3.7–5.2)0.74 (0.52–1.05)0.094 Hypoglycemia (SHR)45 (7.5)3.5 (2.5–4.5)117 (7.9)4.0 (3.3–4.7)0.89 (0.64–1.23)0.479 DKA/HHS (SHR)83 (13.8)6.8 (5.3–8.2)170 (11.5)6.0 (5.1–6.9)1.15 (0.88–1.51)0.315Data are presented as frequency (percentage)GLP1RA glucagon-like peptide-1 receptor agonist; DPP4i dipeptidyl peptidase 4 inhibitor; HR hazard ratio; SHR subdistribution hazard ratio; CI confidence interval; eGFR estimated glomerular filtration rate; ESKD end-stage kidney disease; MALE major adverse limb event; DKA diabetic ketoacidosis; HHS hyperglycemic hyperosmolar stateaNumber of events per 100 person-yearsbComposite of cardiovascular death, myocardial infarction or ischemic strokecComposite of eGFR decline > $50\%$, progression to ESKD with dialysis or cardiovascular deathdComposite of newly-diagnosed peripheral arterial disease, claudication, clinical limb ischemia, limb revascularization or amputation
## Discussion
In this cohort study of patients with advanced DKD, we evaluated the associations between cardiovascular and kidney outcomes in patients with GLP-1RAs versus DPP-4is. GLP-1RAs and DPP-4is have been compared in patients with fair kidney function in previous studies, which have reported a decrease in HbA1c [23–25] and reduction in body weight [25]. Compared with the DPP-4i group, the GLP-1RA group exhibited modest benefits in terms of the composite cardiovascular outcome including cardiovascular death, MI, and ischemic stroke. In addition, the GLP-1RAs had a more favorable renal protective effect than DPP-4is in terms of a decline in eGFR > $50\%$ and progression to ESKD with dialysis. Moreover, the GLP-1RA group had a lower rate of all-cause death and admission due to any cause. Taken together, our findings showed that the use of GLP-1RAs in type 2 diabetes patients with advanced DKD resulted in a neutral cardiovascular effect, better kidney function preservation, and lower mortality.
## Cardiovascular outcomes
GLP-1RAs have been associated with a significant reduction in composite cardiovascular outcomes in type 2 diabetes patients with relatively fair kidney function (eGFR > 30) [9, 10, 26], whereas neutral composite cardiovascular outcomes have been reported in patients with poor kidney function (eGFR < 30) [9, 10, 27]. However, these previous studies were mainly based on subgroup analysis or included only a limited sample size. Our study focused on DKD patients with an eGFR < 30 to evaluate the exact effect of GLP-1RAs on cardiovascular outcomes. We found that GLP-1RAs did not significantly improve the composite cardiovascular outcome. The pathophysiological mechanism between DKD and cardiovascular diseases is complex and multifactorial. Increased rates of cardiovascular events or death have been associated with deteriorating kidney function [28]. The SUSTAIN-6 study reported that the reduction in composite cardiovascular events was mainly attributed to nonfatal stroke [10]. In addition, the patients with advanced DKD had more resistant or difficult-to-control hypertension, which is also a major risk factor for ischemic stroke. In addition, GLP-1RAs act through several brain receptors, including the arcuate nucleus, paraventricular nucleus and subfornical organ, leading to reduced appetite, oxidative stress and inflammation [29]. These histopathological changes can contribute to mitochondrial dysfunction, subsequently leading to oxidative stress and inflammation [29], which may increase the risk of stroke in CKD patients. Other factors associated with stroke in CKD patients include alterations in cardiac output, platelet function, regional cerebral perfusion, accelerated systemic atherosclerosis, altered blood brain barrier, and disordered neurovascular coupling [30]. These CKD-related factors may have precipitated stroke and diminished the protective effect of GLP-1RAs in our study cohort, which may explain the insignificant effect on cardiovascular outcomes.
## Renal outcomes
In contrast, a significant renal protective effect was found in the GLP-1RA group compared to the DPP-4is group with regards to a decline in eGFR > $50\%$ and ESKD progression to dialysis. The time to dialysis initiation was 6 months later in the GLP-1RA group than in the DPP-4is group. There are multiple hypotheses for the kidney protective effect of GLP-1RAs, however the mechanism remains unclear. Possible indirect factors include appropriate body weight maintenance and glycemic control, while direct factors target the kidneys. GLP-1RAs have several extra-pancreatic functions, including reducing oxidative stress-induced autophagy and endothelial dysfunction [31]. GLP-1RAs have also been shown to reduce albuminuria and glomerular sclerosis by suppressing oxidative stress and local inflammation [32]. In addition, natriuresis and potential renal protection have been proposed via sodium–hydrogen exchanger 3 (NHE3) in healthy and obese male participants [33]. A previous GLP-1RA trial in patients with relatively fair kidney function demonstrated notable renal protective effects. The LEADER study (liraglutide, eGFR > 30) revealed benefits on composite renal outcome, mostly due to a reduction in new-onset persistent macroalbuminuria [12], which is a known predictive factor of kidney-related outcomes [34]. The ELIXA study (lixisenatide, eGFR > 30) showed a reduction in UACR and lower risk of new-onset macroalbuminuria [13], and the REWIND study (dulaglutide, eGFR > 15) reported improvements in new macroalbuminuria, a sustained decline in eGFR of $30\%$ or more, or chronic renal replacement therapy [8]. The SUSTAIN-6 study (semaglutide, eGFR > 30) reported the amelioration of persistent macroalbuminuria, doubling of serum creatinine and creatinine clearance < 45 mL/min, or continuous renal replacement therapy [10]. Nevertheless, these studies basically excluded patients with advanced CKD, especially those with an eGFR < 30. Moreover, GLP-1RA acts on the kidneys to increase renal plasma flow and glomerular filtration rate via GLP-1 receptors, and the effect of GLP-1RAs may fluctuate with different pathological status of the kidneys [35]. Thus, the actual renal protective effect of GLP-1RAs in patients with advanced DKD remains inconclusive. Our study provides evidence of a protective effect on kidney function and delay in the timing of dialysis with GLP-1RA treatment, even in patients with CKD stage 4 or 5 and type 2 diabetes.
## Secondary outcomes
We also found a significant reduction in all-cause death and admission due to any cause in the GLP-1RA cohort, which is compatible with a previous study on patients with ESKD [36]. Previous studies have generally emphasized admission due to heart failure, however the LEADER [9], ELIXA [37], REWIND [8], SUSTAIN-6 [10], PIONEER-6 (oral semaglutide) [38], EXSCEL (exenatide) [39], and Harmony (albiglutide) [40] studies all reported no significant difference in heart failure admission. The same trend was also revealed in our investigation. In addition, the LEADER, EXSCEL, and PIONEER-6 studies indicated that patients with GLP-1RAs had a lower rate of all-cause death, which is compatible with our findings [9, 38, 39]. Our GLP1-RA group did not show superiority in the composite cardiovascular outcome or cardiovascular death compared to the DDP4i group. Therefore, the decrease in all-cause death cannot be explained by heart failure admission or cardiovascular events. It is possible that the reason for the lower all-cause death rate may be related to renal death or infection death. A Scandinavian register-based cohort study demonstrated a significantly lower admission rate for kidney events in patients receiving GLP-1RAs [41]. We also demonstrated the renal protective effect of GLP-1RAs. Furthermore, GLP-1RAs have been shown to modulate sepsis. Lipopolysaccharide-induced endotoxemia, endotoxic shock, vascular dysfunction, and inflammatory markers were ameliorated by liraglutide in rat model [42]. The anti-inflammatory function of GLP1-RAs was suggested to be through the inhibition of tumor necrosis factor alpha (TNFα) and decreases in vascular cell adhesion protein 1 (VCAM-1), intercellular adhesion molecules 1 (ICAM-1) and E-selectin expression in an animal sepsis model [43]. In addition, septic acute kidney injury has been shown to induce the expression of GLP-1 receptors in renal tubules to reduce kidney injury [44]. GLP-1 receptors are expressed in several organs including the pancreas, kidneys and heart [45]. GLP-1RAs modulate not only glycemic control but also inflammation. These sophisticated interactions of GLP-1RAs including the decrease in renal and infection events may explain the decrease in all-cause death and admission due to any cause.
## Limitations
Although this study is based on real-world data on outcomes of patients with advanced DKD receiving GLP-1RAs, there are several limitations. First, we cannot infer causal associations between GLP-1RAs and cardiovascular or kidney outcomes due to the retrospective observational design of this study. Nevertheless, we enrolled patients who received GLP-1RAs and DPP-4is and evaluated the same parameters and outcomes in both groups. Therefore, the causal relationship should be relatively valid in this study. Second, background heterogeneity existed in the GLP-1RA and DPP-4i cohorts. The GLP-1RA users usually had a longer DM duration, more complications, and a refractory tendency to antiglycemic agents. These differences may have interfered with the outcomes; however, we mitigated sampling bias using propensity score matching to balance covariates including DM duration, DM complications, drug categories, and laboratory data. Therefore, we believe that the study outcomes should not be influenced by heterogeneity. Third, it is difficult to avoid coding errors in database research. We diminished possible miscoding by pairing diagnostic code and drug registration data. For instance, hypertension was defined as patients receiving antihypertensive agents and a diagnosis of hypertension, and similar definitions were also applied to other diseases. We also defined kidney function using direct eGFR data rather than CKD stage diagnosis code, which may have been coded inappropriately. In addition, the outcome measurements including ischemic stroke and MI required admission records. Therefore, disease miscoding in this study should be limited. Fourth, the GLP-1RAs in this study only included the human GLP-1-like analogues liraglutide and dulaglutide. Semaglutide was not included because few patients used this drug as it was relatively new in Taiwan during the enrollment period. We excluded exendin-4-like analogues such as exenatide and lixisenatide because they are different drug subcategories. Although the outcomes were limited to liraglutide and dulaglutide, the results should be robust and homogenous. Finally, we cannot ensure medication compliance in each patient, which is a common limitation in real-word observational studies. However, the National Health Insurance Administration in Taiwan created the Diabetic Shared Care Program (DSCP) to ensure that diabetic patients receive standard care in Taiwan. The DSCP team includes physicians, nurses, nutritionists and pharmacists who receive standard care courses to provide integrated care. This approach should increase the medication adherence of diabetic patients in Taiwan.
## Conclusions
GLP-1RAs had no influence on the composite cardiovascular outcomes but reduced composite kidney events including a decline in eGFR > $50\%$ and progression to ESKD with dialysis, all-cause mortality, and admission in patients with advanced DKD (eGFR < 30) compared with DPP-4is.
## Supplementary Information
Additional file 1: Figure S1. Time to Dialysis Distribution and Causes of Death. Table S1. Number of patients with advanced chronic kidney disease with prescription of GLP1RA and DPP4i. Table S2. Number of patients receiving dialysis with prescription of GLP1RA and DPP4i.
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|
---
title: Effect of PCL nanofiber mats coated with chitosan microcapsules containing
cinnamon essential oil for wound healing
authors:
- Mahmoud Osanloo
- Fariba Noori
- Alireza Tavassoli
- Mohammad Reza Ataollahi
- Ali Davoodi
- Morteza Seifalah-Zade
- Ali Taghinezhad
- Narges Fereydouni
- Arash Goodarzi
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10024394
doi: 10.1186/s12906-023-03905-0
license: CC BY 4.0
---
# Effect of PCL nanofiber mats coated with chitosan microcapsules containing cinnamon essential oil for wound healing
## Abstract
### Introduction
Cinnamon is one of the most common spices that has been studied for its anti-inflammatory, antioxidant, and antibacterial properties in wound healing. The purpose of this study was to evaluate the effectiveness of polycaprolactone nanofiber mats coated with chitosan microcapsules loaded with cinnamon essential oil in wound healing.
### Material and methods
For this purpose, chitosan microcapsules containing cinnamon essential oil (µCS-CiZ) were prepared by ion gelation and PCL nanofibers by electrospinning. The size of the µCS-CiZ and the morphology of nanofibers were evaluated by DLS and FESEM methods. In order to evaluate wound healing, 48 rats in 4 groups of Control, µCS-CiZ, PCL, and PCL + µCS-CiZ and were examined on days 7, 14, and 21 in terms of macroscopy (wound closure rate) and histology (edema, inflammation, vascularity, fibrotic tissue, and re-epithelialization).
### Results
The particle size of the µCS-CiZ and the diameter of the nanofibers were estimated at about 6.33 ± 1.27 μm and 228 ± 33 nm, respectively. On day 21, both µCS-CiZ and PCL groups showed a significant decrease in wound size compared to the control group ($P \leq 0.001$). The PCL + µCS-CiZ group also showed a significant decrease compared to the µCS-CiZ ($P \leq 0.05$) and PCL groups ($P \leq 0.05$). Histological results showed further reduction of edema, inflammation, and vascularity in granulation tissue and appearance of moderate to marked fibrotic tissue in PCL + µCS-CiZ group compared with the other groups.
### Conclusion
The results of the study showed that the combined use of PCL + µCS-CiZ indicates a synergistic effect on improving wound healing.
## Introduction
Cinnamon is the bark of trees of genus Cinnamomum and is one the most common spices used worldwide especially in Asian ones. There are two main Cinnamomum species; *Cinnamomum zeylanicum* (CiZ) and *Cinnamomum cassia* (CC); the latter is known as *Cinnamomum aromaticum* or Chinese cinnamon. Nearly all parts of Cinnamon tree like leaves, bark, flowers, roots, and fruits are beneficial in medicinal and culinary utilizations. The essential oils obtained from different part of Cinnamon tree vary noticeably in chemical compositions which show different medicinal and pharmacological properties [1]. The bark, leaf, and root mostly possess cinnamaldehyde, eugenol, and camphor, respectively which have the same hydrocarbon backbones with various derivatives [2]. Therefore, this chemical diversity is likely the reason for medicinal and pharmacological benefits which highlights its worthiness to the different industries [3].
Cinnamomum zeylanicum (CiZ), also known as Ceylon cinnamon or true cinnamon, originates from Sri-Lanka and India [3]. Almost $83\%$ of the constituents of cinnamon are three essential oils extracted from the bark of the CiZ tree, cinnamaldehyde, eugenol and linalool [4], of which cinnamaldehyde accounts for about 50–$63\%$ of the total bark oil [5, 6].
One of the important differences between chemical composition of CiZ and CC is their coumarin content [7]. The level of coumarins in CC seems to be considerably high, which contains approximately 2.1–4.4 gr per kg of CC powder [8]. This means that one teaspoon of CC powder contains about 5.8–12.1 mg of coumarin, which is extremely higher than the Tolerable Daily Intake standards for coumarin reported by European Food Safety Authority (EFSA) [8, 9]. Coumarin is one of the most potential carcinogenic, anticoagulant, and hepato-toxic materials whose underlying mechanisms are yet to be well defined [9]. Consequently, the European Food Safety Authority (EFSA) has warned of health risks from regular consumption of CC powder due to its coumarin content [10].
There are several studies indicating numerous advantageous health effects of CiZ, including anti-tumor, anti-inflammatory, cardio-protective, anti-microbial, and anti-fungal properties [11]. Another important feature of CiZ is wound healing, which has been studied in the form of extracts and essential oils that may be formulated in different methods. Farahpour co-workers [2012] examined wound healing effect of aqueous and ethanolic extract of Ceylon cinnamon in an animal excision wound model. They showed significant wound enclosure rate and epithelialization in cinnamon groups compared to control and placebo groups [12, 13]. Han co-workers [2017] applied CiZ essential oil of bark (CBEO) on primary human neonatal fibroblast cells, and investigated the effect of CBEO on inflammatory and remodeling biomarkers. They showed that CBEO has intense anti-proliferative, anti-inflammatory, anti-remodeling effect and potency to modulate and alter signaling pathways [14].
Therefore, new technologies have developed opportunities to improve the stability, release, quality increase, and effectiveness of natural products. Electrospinning is a method of producing nanofibers from multiple polymer solutions under high-voltage electrical field [15]. This technique is used to produce large-scale complicated structures through different strategies such as multiple-jet [16] and nozzle-less electrospinning [17] with biomedical, drug delivery, and advanced composite properties [18–22]. Nanofibers have particular characteristics, for example high surface to volume ratio, high porosity, inter-connected porous networks, and flexible function which are widely used in medical applications [23–25]. Polycaprolactone (PCL) is one of synthetic polymers, extensively applied for biomedical usage, which is about slow biological degradation and biocompatible properties. The combination of PCL characteristics and individual nanofiber structure properties could lead to a promising scaffold for diverse applications [26–28].
Gosh co-workers [2013] synthesized stable and fine CiZ oil micro-emulsions (CMF4) droplets of very small size of about 6 nm, using non-ionic surfactant Tween-20 and water. CMF4 represented no erythema and anti-bacterial activity and improved wound healing process in rats [29]. Salehi co-workers [2019] fabricated cinnamon loaded into polycaprolactone/gelatin (cin/PCL/Gel) nanofibers in order to improve wound healing. They demonstrated that PCL/Gel $5\%$ cinnamon showed the best wound closure of about $98\%$ compared the other groups after 14 days [30]. Kossyvaki co-workers [2020] also fabricated PVP/keratin electrospun fibers containing cinnamon essential oil UVB burn model. This study was conducted to apply chitosan microparticles containing *Cinnamomum zeylanicum* essential oils to conserve EO stability and effectiveness besides PCL nanofibers to develop an extracellular matrix (ECM)-like scaffold to evaluate wound healing [31].
## Materials
The materials were purchased as follows: polycaprolactone (PCL) (Sigma–Aldrich, Germany), chitosan (Easter Holding Group, China, MW: 100 KDa, deacetylaton degree: $93\%$), *Cinnamomum zeylanicum* (CiZ) essential oil (Zardband Pharmaceutcals Co, Iran), tripolyphosphate (TPP), tween 80, tween 20, Hexafluoro-2-propanol (HFIP), hematoxylin and eosin stain (Merck, Germany), glacial acetic acid and ethanol (> $99.7\%$, Dr. Mojallali, Iran), and Masson’s trichrome staining kit (Asiapajohesh, Iran).
## PCL fabrication
Electrospun PCL NFs were prepared as previously described [32], with slight modifications. The PCL solution was prepared by dissolving the granules in HFIP ($15\%$ w/v) at room temperature overnight. The prepared polymer was loaded into a 10 mL syringe with stainless steel needle (22 gauge) connected to 17 kV DC voltage in an electrospinning apparatus (Fnm co. Ltd., Tehran, Iran). The polymer solution was injected with a feed rate of 0.5 mL/h at the distance of 140 mm from the needle tip to collector rotating at a speed of 100 rpm. For easy separation of formed NFs on the collector, an aluminum foil was wrapped on it.
## Surface morphology analysis
The surface morphology of polycaprolactone nanofibers was analyzed using field emission scanning electron microscopy (FESEM) (MIRA3, TESCAN Co, Czech). The images were taken after the gold plating process at an acceleration voltage of 20 kV (Q150R ES, Quorum Technologies, UK). Then, the average diameter of nanofibers was measured using ImageJ (National Institute of Health, USA) software with a sample size of 100 nanofibers.
## Synthesis of µCS-CiZ
Chitosan microcapsules (µCS) containing CiZ essential oils (µCS-CiZ) were prepared as previously published [33]. Briefly, chitosan solution was prepared by dissolving chitosan (1.5 w/v) in a dilute solution of acetic acid ($1\%$ v/v). The chitosan solution, tween 80 ($2\%$ w/v), tween 20 ($8\%$ w/v) was mixed to form a homogeneous solution and then the essential oil ($1\%$ w/v) was added for 5 min and 1500 rpm. Finally, aqueous solution of TPP ($0.3\%$ w/v) was drop-wisely added and stirred for another 40 min and left for few days to increase the viscosity and form a gel.
## Size measurement of µCS-CiZ
Particle size and particle size distribution of µCS-CiZ were evaluated using dynamic light scattering (DLS, scatteroscope-I, K-ONE, Korea). d50 (d: median diameter of particles at 50 cumulative percent) as reported by the DLS instrument was considered as particle size and particle size distribution was calculated using the following equation.
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$particle\;size\;distribution\;=\;\sqrt{d75}\;\div d25$$\end{document}particlesizedistribution=d75÷d25
## FT-IR
FT-IR (Nicolet iS10 FT-IR spectrometer, USA) was used to confirm CiZ loading in the chitosan microcapsules. The FT-IR spectra of CiZ, chitosan, tween 20, tween 80, and microcapsules without CiZ were recorded in wavenumber range of 400–4000 cm−1, using KBr pellets.
## In vivo study
In this study, 48 male Wistar rats, which were about 2 months old and weighed 200–250 g, were obtained from animal house of Fasa medical school and used as wound healing model. The rats were kept in polystyrene cages according to the rules of keeping and observing animal rights, with a light cycle of 12 h, standard temperature conditions (25 ± 2 °C), and humidity and free access to food and water. All procedures and experiments involving animals were approved by Bioethics Committee of Fasa University of Medical Sciences (Ethics code: IR.FUMS.REC.1400.070) performed in accordance with the guideline for the care and use of laboratory animals in Iran. All methods are reported in accordance with ARRIVE guidelines.
Prior to the experiments, the cages and chambers were exposed to ultraviolet radiation for 24 h and disinfected using ditol to create a pathogen-free environment. The rats were anesthetized by intraperitoneal injection of ketamine-xylzine (70:30). Then, the animal's back hair was shaved and thoroughly cleaned on the wound area. The expected wound area was disinfected with $10\%$ Betadine solution. Then, using a stencil ruler, a 2 × 2 cm wound was created by a full-thickness surgical razor. Finally, the treatments were applied directly to the newly created wound at the same time of surgery.
## Study groups
Rats were randomly divided into four groups. In each of these groups, there were 4 rats for the study on days 7, 14 and 21. The study groups were studied as follows:- Control group (wound washing with normal saline and bandage with common sterile gas)- The rats treated with chitosan microcapsules containing cinnamon essential oil (µCS-CiZ group)- The rats treated with nanofibers (PCL group)- The rats treated with microcapsules + nanofibers (PCL + µCS-CiZ group) The same amount of 2.5 ml of microcapsules and 2.5 × 2.5 cm of nanofibers were used for the experimental groups. This value was selected for the nanofibers based on the size of the wound and for the microcapsules to the extent that it can cover the entire wound. Then, they were kept in separate cages in a special room until the end of the study.
## Macroscopic assessment of the wounds
Wound healing rate was assessed by measuring the length and width of the wound with a caliper on days 7, 14, and 21. Wound closure was performed in different groups with 4 repetitions and the results were reported as mean ± standard deviation.
## Histology study
After sacrificing the animals by CO2 euthanasia on day 21, about 2 mm of the area around the wound was removed with scissors and forceps in full-thickness. Then, the wound tissue was fixed in $10\%$ formalin solution, dehydrated in ascending grade of alcohols, embedded in paraffin, sectioned and mounted on the slides for Hematoxylin–Eosin and Masson’s Trichrome staining and further histological examinations. Qualitative scoring was performed according to Table 1.Table 1Histopathological scoring of the wound area according to the repairing processNoScoreGranulation tissueFibrotic tissueMasson's trichrome stainingEdemaVascularityInflammation10Not seenNot seenNot seen20–1SlightSlightSlight31MildMildMild41–2Mild to moderateMild to moderateMild to moderate52ModerateModerateModerate62–3Moderate to markedModerate to markedModerate to marked73MarkedMarkedMarked83–4Moderately markedModerately markedModerately marked94Very markedVery markedVery marked
## Statistical analysis
Statistical analyzes were performed using GraghPad Prism 6.0 software. The normality of the data was measured by Kolmogorov–Smirnov test. One-way ANOVA and Tukey post hoc test was performed to compare the mean ± standard deviation (SD) of more than two groups (wound size and re-epithelialization). The edema, inflammation, vascularity, fibrosis tissue, and Masson's trichrome staining severity were analyzed by Kruskal–Wallis test and Mann–Whitney U test was used to determine significance among the groups. Significance level in all analyzes was considered less than 0.05 ($p \leq 0.05$).
## Characterization of nanofibers and µCS-CiZ
Figure 1 showed that the particle size of µCS-CiZ was 6.33 µm with distribution of 1.27 µm. Fig. 1Morphology and DLS of chitosan microcapsules containing cinnamon essential oil
## Nanofiber morphology
Figure 2 shows that the PCL nanofibers were formed in the form of an interconnected and non-woven network with a uniform morphology without bead and with numerous pores. By measuring the diameter of 100 nanofibers with ImageJ software, the average diameter of nanofibers was estimated to be about 228 ± 33 nm. Fig. 2SEM illustration of PCL nanofibers
## Fourier transform infrared spectroscopy (FT-IR)
Figure 3 shows FT-IR spectra of µCS, µCS-TPP, CiZ essential oil, µCS-TPP-CiZ essential oil, and PCL nanofiber. Chitosan powder shows to have specific peaks at different wavenumbers (cm-1) such as 3400–3500 (O–H and N–H (amine I) stretching) and 1550–1650 (N–H (amid II) bending) [33, 35].µCS were formed after addition of an aqueous solution containing TPP into aqueous solution of chitosan, tween 20, and tween 80. Because of electrostatic interactions between phosphoric and ammonium ions of TPP and chitosan, new peak (P = O) appears around 1000–1100 cm−1. During the encapsulation process, it leads to slight shift of amide II bending peaks toward shorter wavelengths [33].Fig. 3FT-IR of µCS, µCS-CiZ and PCL nanofiber Essential oils are usually composed of different ingredients. Characteristic peaks of cinnamon essential oil are mostly seen in the range of 600–1800 cm−1. The main peaks of cinnamon essential oil (1727 cm−1) correspond to high levels of cinnamaldehyde and aldehyde of saturated fatty acid, and 1679 cm−1 and 1626 cm−1 are related to stretching vibration of carbonyl aldehyde C = O. To confirm the presence of cinnamon essential oil in the microcapsules, the presence of the most constituent substance is examined. Therefore, the appearance of a peak at 1727 cm−1 is related to cinnamaldehyde and confirms the presence of cinnamon essential oil in the formulation of chitosan microcapsules [34].
The polycaprolactone spectrum showed peaks related to -C-H at about 2943, 2894, 2865 cm−1 and a very sharp signal band corresponds to the carbonyl group at 1721 cm−1. It was also observed that 1470 and 1396 cm−1 bands were related to CH2 groups and a signals group around 1292 cm-1 that could be related to asymmetric C–O–C.
## Wound closure
Figure 4 shows macroscopic images of wounds on days 7, 14 and 21 in different groups. Statistical analysis of these images in Fig. 5 and Table 2 shows that the rate of wound closure in different groups on day 7 are not significantly different. On day 14, the µCS-CiZ group showed a significant decrease compared to the control group ($P \leq 0.01$), the nanofibers group compared to the control group ($P \leq 0.001$) and the PCL + µCS-CiZ group compared to the control group ($P \leq 0.001$). However, on day 21, the PCL + µCS-CiZ group alone showed a significant decrease compared to the control group ($P \leq 0.001$), the PCL + µCS-CiZ group also showed a significant decrease compared to the µCS-CiZ group ($P \leq 0.05$) and the nanofibers group ($P \leq 0.05$).Fig. 4Macroscopic images of wounds of different groups on days 7, 14 and 21. Control control group, µCS-CiZ chitosan microcapsules containing Cinnamon essential oil group, PCL PCL nanofibers group, PCL + µCS-CiZ PCL nanofibers coated with chitosan microcapsules containing cinnamon essential oil groupFig. 5Wound size comparison of control, µCS-CiZ, PCL, and PCL + µCS-CiZ on days 7, 14, and 21, *: $P \leq 0.05$, **: $P \leq 0.01$, and ***: $P \leq 0.001$ shows the difference of significance level between the groups. Control control group, µCS-CiZ chitosan microcapsules containing Cinnamon essential oil group, PCL PCL nanofibers group, PCL + µCS-CiZ PCL nanofibers coated with chitosan microcapsules containing cinnamon essential oil groupTable 2Wound size of four groups on days 7, 14, 21ControlµCS-CiZPCLPCL + µCS-CiZDay 7267.98 ± 66.41252.09 ± 32.70206.68 ± 34.23203.19 ± 28.96Day 14144.68 ± 21.1565.05 ± 18.8953.27 ± 33.4324.51 ± 10.74Day 2160.76 ± 8.2242.26 ± 18.5641.74 ± 14.9012.28 ± 6.49Control control group, µCS-CiZ chitosan microcapsules containing Cinnamon essential oil group, PCL PCL nanofibers group, PCL + µCS-CiZ PCL nanofibers coated with chitosan microcapsules containing cinnamon essential oil group
## Histological studies
In the control group, ulceration in the epidermal layer, loose granulation tissue, micro-abscesses, and moderate to marked infiltration of inflammatory cells in wound area indicated early stages of granulation tissue formation and incomplete healing (Fig. 6A-B). Masson's trichrome staining also showed that granulation tissue does not convert to fibrotic scar tissue in any of the control groups. The light blue color in the wound site is mainly due to protein and fibrin leakage from the vessels of granulation tissue (Fig. 7A-B).Fig. 6Hematoxylin–Eosin staining of Control group (A (40X)-B (100X)), µCS-CiZ group (C (40X)-D (100X)), PCL group (E (40X)-F (100X)), PCL + µCS-CiZ group (G (40X)-H(100X)) on day 21. Star sign: granulation tissue, Cross sign: fibrotic scar tissue, Arrow sign with letter I: inflammation, Arrow sign with letter A: micro-abscess, Arrow sign with letter E: edema, and Head arrow: re-epithelializationFig. 7Masson's trichrome of Control group (A (40X)-B (100X)), µCS-CiZ group (C (40X)-D (100X)), PCL group (E (40X)-F(200X)), PCL + µCS-CiZ group (G (40X)-H(100X)) on day 21. Star sign: granulation tissue, Cross sign: fibrotic scar tissue, Arrow sign with letter I: inflammation, Arrow sign with letter A: micro-abscess, Arrow sign with letter E: edema, and Head arrow: re-epithelialization In the µCS-CiZ group, re-epithelialization of the thickness of the epidermal layer is about 17–$50\%$ of the adjacent layer. There is primary granulation tissue formation as well as infiltration of inflammatory cells and edema (Fig. 6C-D). Masson's trichrome staining also showed that mild to moderate fibrotic scar tissue fills the subcutaneous wound area (Fig. 7C-D).
In the PCL group, epidermal ulcers, micro-abscesses, and vascular granulation tissue are present with moderate to marked infiltration of inflammatory cells. In this group, epidermal regeneration is not seen in contrast to the µCS-CiZ group, so that the microcapsule seems to play an important role in re-epithelialization (Fig. 6E-F). Masson's trichrome staining also showed moderate color for fibrotic scars (Fig. 7E-F).
In the PCL + µCS-CiZ group, reduction of vascular inflammation and edema in the granulation tissue (Fig. 6G-H) and in Masson's trichrome staining, moderate to high fibrotic ulcer (Fig. 7G-H) implied that the combined effect of µCS-CiZ and PCL displaces more fibrotic scar tissue by the granulation tissue. However, in this group, re-epithelialization does not occur completely and the wound surface is observed in most cases of this group.
## Statistical analysis of histological staining
In this study, three parameters of granulation tissue including edema, inflammation, and vascularity were examined separately. Figure 8 showed that edema severity had a significant decrease in the PCL and PCL + µCS-CiZ groups compared to the control group ($P \leq 0.05$). Vascularity severity also decreased significantly in the PCL + µCS-CiZ group ($P \leq 0.05$). Statistical studies also showed that the PCL + µCS-CiZ group had a significant increase in fibrosis tissue accumulation and replacement with granulation tissue compared to the control group ($P \leq 0.01$) and µCS-CiZ group ($P \leq 0.05$). This result was confirmed by the results of Masson's trichrome staining, as it showed that the PCL + µCS-CiZ group had a significant increase in the collagen filament secretion intensity compared to the control group ($P \leq 0.05$) and µCS-CiZ group ($P \leq 0.01$). *In* general, the results showed that PCL + µCS-CiZ could improve the passage of granulation tissue stage and the onset of fibrous tissue secretion significantly faster than the control group. Fig. 8Statistical analysis of histological factors: Edema severity (A), Inflammation severity (B), Vascularity severity (C), Fibrosis severity (D), Masson’s trichrome (E), Re-epithelialization percentage (F). *: $P \leq 0.05$, **: $P \leq 0.01$, and ***: $P \leq 0.001$ shows the difference of significance level between the groups. Control control group, µCS-CiZ chitosan microcapsules containing Cinnamon essential oil group, PCL PCL nanofibers group, PCL + µCS-CiZ PCL nanofibers coated with chitosan microcapsules containing cinnamon essential oil group Statistical results showed that re-epithelialization had a significant increase in µCS-CiZ, PCL, and PCL + µCS-CiZ groups compared to the control group ($P \leq 0.001$). In addition, this parameter showed a significant increase in the µCS-CiZ group compared to the PCL group ($P \leq 0.05$) (Fig. 8).
## Discussion
This study was performed to evaluate the effects of PCL nanofibers scaffold coated with chitosan microcapsules containing cinnamon essential oil on the healing of full-thickness wounds in an animal model of rats. Inflammation, fibroblast proliferation, collagen deposition, wound contraction, and re-epithelialization are the most important stages of wound healing and have close relationship with each other. Therefore, intervention in any of these stages using beneficial drugs can ultimately promote or inhibit one or all of the recovery stages [35]. Herbal medicines are increasingly used worldwide due to their effectiveness and safety. Cinnamon is a valuable medicinal plant that has multiple healing properties. Recent studies reported the antioxidant activity of cinnamon essential oil [36]. Cinnamon essential oil can inhibit hepatic 3-hydroxy-3-methyl glutaryl CoA reductase (HMG-CoA) activity in mice and lipid peroxidation by increasing the activity of liver antioxidant enzyme [37]. Moreover, having Eugenol content, cinnamon oil has anti-inflammatory property like COX antagonist, which accelerates wound healing [38, 39].
It has been shown that cinnamon extract, whether aqueous or alcoholic, in addition to its specific antioxidant properties, may also have antimicrobial effects, which could be the basis for the wound healing activity of cinnamon [12, 40, 41]. Cinnamaldehyde, a bioactive compound in cinnamon, has significant antibacterial activity against gram-positive and gram-negative bacteria in vitro [42, 43]. In addition, cinnamaldehyde also inhibits the growth of fungi such as yeast, filamentous mold and dermatophytes, and eggs and adult head lice [44]. Therefore, all these reported properties of cinnamon have anti-inflammatory, antioxidant, and antimicrobial properties that promote wound healing, mostly due to the substances contained in its essential oil, especially Eugenol and Cinnamaldehyde.
In this study, chitosan microcapsules containing cinnamon essential oil were used for wound healing. Histological results show that both collagen production and re-epithelialization improved in the µCS-CiZ group alone compared to the control group. Wound dressings are classified as inactive and active according to their role in wound healing. Dressings that only cover wounds are classified as passive dressings while dressings that, in addition to their primary use, heal wounds are considered active dressings [45]. Recently, special attention has been paid to the manufacture of drug-carrying dressings to target different stages of wound healing [46, 47]. For this purpose, several drug carriers have been formulated, among which, polymer particles have attracted a lot of attention due to their new properties and functions compared to conventional drug delivery systems [48]. Degradable and biocompatible chitosan particles are a group of these carriers that can act as drug carriers for wound healing drugs [49]. These particles were used in this study to carry cinnamon essential oil due to its antibacterial, antioxidant and wound healing properties. On the other hand, low solubility and rapid degradability of cinnamon essential oil disrupts its bioavailability in the wound area, which can be preserved by trapping polymer particles [50, 51]. Various studies have also shown that the use of chitosan at the wound site can accelerate wound contraction. The wound area of chitosan-treated animals was significantly lower than untreated groups. Chitosan can be depolymerized and release acetyl glucosamine (N-acetyl-β-D-glucosamine) which promotes fibroblast proliferation and increases collagen deposition at the wound site [52, 53]. Studies have shown that chitosan is able to reduce the number of bacteria, inflammatory factors, and oxidative stress, thereby helping to heal wounds [54, 55].
In this study, polycaprolactone nanofibers were also used as a scaffold to improve wound healing. Histological results show that in the nanofibers group alone, the rate of collagen production and conversion of granulation tissue to fibrotic tissue has improved compared to the control group, but not better than the other groups. New technologies have created opportunities to improve the sustainability, release and increase the quality and effectiveness of natural products. Electrospinning is a method for producing nanofibers from a polymer solution under a strong electric field [15]. This method, through various strategies such as multi-nozzle electrospinning [16] and nozzle-less electrospinning [17], is used to produce complex large-scale structures with biomedical properties, drug delivery systems and advanced composite nanofibers with fillers for biocompatible scaffolds [18–22].
Nanofibers have unique properties widely used in medical applications, such as high surface to volume ratio, high porosity, interconnected porous network, and flexible performance [23–25]. Polycaprolactone (PCL) is a synthetic polymer that is widely used in medical applications due to its slow biodegradation and biocompatibility properties. The combination of PCL (biocompatibility and slow biodegradation) properties with the unique structural properties of nanofibers has led to the creation of a promising substrate for a variety of applications, including medical applications [26–28]. PCL nanofibers can be used as a skin replacement or as a wound dressing. Flexibility to combine with other bioactive substances (such as growth factors, nanoparticles, antimicrobials, anti-inflammatory agents, and wound healing drugs) is another advantage in nanofibers [56–59]. Nanofiber membranes with their barrier-like function are considered as a physical barrier to the penetration of microbes and thus prevent infections. In addition, the pores in nanofiber scaffolds (typically 1 to 10 microns) are small enough to prevent bacteria from infiltrating. In addition, due to their extracellular matrix-like structure, they facilitate and promote cell migration from the outer edges of the wound to the center. Finally, PCL nanofiber membranes provide an effective method for faster wound healing [60].
This study also shows that concomitant use of PCL nanofibers with chitosan microcapsules containing cinnamon essential oil has a synergistic effect on wound healing. Combined use of both scaffolds has caused faster transformation of the granulation to fibrotic tissue and the wound closure faster than the control group, as well as the use of each one separately. These results show that the combined use of microcapsules containing cinnamon essential oil with nanofibers creates greater compatibility.
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|
---
title: In vitro antimicrobial efficacy of Cassia alata (Linn.) leaves, stem, and root
extracts against cellulitis causative agent Staphylococcus aureus
authors:
- Seng Chiew Toh
- Samuel Lihan
- Scholastica Ramih Bunya
- Sui Sien Leong
journal: BMC Complementary Medicine and Therapies
year: 2023
pmcid: PMC10024395
doi: 10.1186/s12906-023-03914-z
license: CC BY 4.0
---
# In vitro antimicrobial efficacy of Cassia alata (Linn.) leaves, stem, and root extracts against cellulitis causative agent Staphylococcus aureus
## Abstract
### Background
Cellulitis is a common skin disease encountered in medical emergencies in hospitals. It can be treated using a combination of antibiotics therapy; however, the causative agent *Staphylococcus aureus* has been reported to develop resistance towards the currently used antibiotics. Therefore, the search for more alternative herbal origin antimicrobial agents is critical. Aim: *In this* study, maceration and Soxhlet extraction of the whole plant of *Cassia alata* Linn. ( leaves, roots, and stem) were performed using four solvents with different polarities, namely n-hexane, ethyl acetate, ethanol and distilled water. The crude extracts were screened using agar well diffusion, colorimetric broth microdilution, grid culture and bacterial growth curve analysis against Staphylococcus aureus. The phytochemicals in the crude extracts were identified using Gas Chromatography-Mass Spectrometry (GC–MS).
### Results
Agar-well diffusion analysis revealed that extraction using ethyl acetate showed the largest inhibition zone with an average diameter of 15.30 mm (root Soxhlet extract) followed by 14.70 mm (leaf Soxhlet extract) and 13.70 mm (root maceration extract). The lowest minimum inhibitory and minimum bactericidal concentration in root Soxhlet extract using ethyl acetate was 0.313 and 0.625 µg µL−1, respectively. Our study proved that crude extract of the plant suppressed the growth of S. aureus as evidenced from a significant regression extension ($p \leq 0.06$, $$p \leq 0.00003$$) of lag phase for 6 h after the treatment with increased concentration. Based on the GC–MS analysis, 88 phytochemicals consist of fatty acids, esters, alkanes, phenols, fatty alcohols, sesquiterpenoids and macrocycle that possibly contributed to the antimicrobial properties were identified, 32 of which were previously characterized for their antimicrobial, antioxidant, and anti-inflammatory activities.
### Conclusion
Ethyl acetate crude extract was better than the other investigated solvents. The root and stem of C. alata showed significant antimicrobial efficacy against S. aureus in this study. The remaining 56 out of 88 phytochemicals of the plant should be intensively studied for more medicinal uses.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12906-023-03914-z.
## Background
Cellulitis is commonly defined as an acute, spreading pyogenic inflammatory condition involving the dermis and subcutaneous tissues caused by bacterial infection [1–3]. It is, however; not contagious despite the rapid spreading and pyogenic nature of cellulitis [4]. Cellulitis is a general medical emergency in hospitals because it has caused more than 600,000 hospitalizations, about $3.7\%$ of total emergency admissions in the US since 2010; the severity varies from mild to life-threatening [5, 6]. The Infectious Disease Society of America (IDSA) suggests that *Staphylococcus aureus* is the main culprit of cellulitis based on combined data from the studies of specimen cultures on punch biopsies, needle aspirates, and serological studies [7, 8].
S. aureus is a coagulase-positive, non-motile, and non-spore-forming facultative gram-positive anaerobe that colonize half of the adult population. Approximately 20 to $30\%$ of them are permanent while the other $30\%$ are just transient carriers [9]. It increases the risks of cellulitis by two to ten times [10]. When a staphylococcal infection is suspected or the aetiology that leads to cellulitis infection is unknown, patients are usually treated first with intravenous flucloxacillin and amoxicillin [11, 12]. In most cases, penicillin remains the first-choice drug for S. aureus infections [9]. However, it has been reported to develop resistance towards penicillin [13, 14], linezolid or daptomycin [15], glycopeptides, vancomycin, and teicoplanin [16] and significant resistance against the current generation antibiotics is anticipated in the near future.
The resistant of bacteria towards antimicrobial agents, increase in treatment costs and the adverse effects of synthetic drugs have necessitated the development of alternative, safe, efficient, and cost-effective natural medicines from plants [17, 18] and microbes [19–21]. Natural drugs are relatively cheaper; they have fewer side effects, better patient tolerance, and are acceptable due to a long history of use [22, 23]. The essential oils derived from medicinal plants are potential sources of antimicrobial agents against multiple-drug resistant (MDR) bacteria. These oils consist of phytochemicals such as terpenoids that can easily diffuse across cell membranes to induce biological reactions [24]. It reduces the opportunity for bacteria to develop resistance as the bacteria can be targeted via several mechanisms [25]. Besides, the essential oils also confer synergetic effects when used in combination with less effective antibiotics [26]. Hence, researchers are increasingly drawing their attention to medicinal plants for new leads to develop better solutions against MDR bacteria [27].
Cassia alata (Linn.) is known as the candle shrub by the locals in Sarawak, Borneo. It is a valuable plant, particularly among traditional practitioners in Malaysia. The locals use the plant as a prescriptive medicine to treat ringworm infection [22]. C. alata has been characterized by various bioactive compounds, including alkaloids, phenolics, flavonoids, tannin, steroids, and triterpenoids [28]. For example, kaempferol, anthraquinone, hexadecanoic acid methyl ester, hexadecanoic acid, and kaempferol-3-O-β-D-glucopyranoside were identified in leaves, while ziganein, apigenin, and 1,3,8-trihydroxy-2-methyl-anthraquinone were found in stem [28]. These phytochemicals possess exciting biological and pharmacological properties such as antimicrobial, antifungal [29, 30], antioxidants [31], antiseptic [32], anti-inflammatory [22], analgesic [33], and anti-hyperglycaemic [17, 34]. Previous studies have also shown the efficacy of the extracts and phytochemicals from C. alata against some clinical isolates of MDR bacteria [35, 36], which attracted researchers’ attention to explore the full potential of the plant as antibacterial and anti-oxidative agents. Hence, the main objective of this study was to determine the chemical compositions and antimicrobial properties of crude extracts from C. alata (Linn.) for potential application in the pharmaceutical sector against the cellulitis agent, S. aureus.
## Plant material
Whole plants of C. alata Linn. were collected in June 2018 from a site located near Kampung Sungai Bako Jaya (N 1°40′25.5" E 110°27′12.8"), Malaysia. Botanical identification of the collected plant materials was done by Associate Professor Dr Mohd Said Saad of Plant Genetics Unit, Institute of Bioscience, Universiti Putra Malaysia. The voucher specimen was deposited at the Phyto-medicinal Herbarium of Institute Bioscience under the accession number SK$\frac{179}{02.}$
## Sample collection
C. alata plants (leaves, roots, and stems) were collected in sterile polyethene bags to avoid external contamination. The samples were labelled and transported directly to the laboratory in a chilled icebox.
## Sample processing
The leaves, roots, and stems were processed as described by Odeyemi et al. [ 37] with modifications. First, the samples were sorted according to appearance and condition, while those in a spoilt state were discarded. It was followed by surface disinfection of selected samples by soaking in $2\%$ sodium hypochlorite (Merck, Germany, 6–$14\%$ active chlorine) for 10 min, followed by $70\%$ ethanol (Merck, Germany, EMSURE® ACS) for a minute and then rinsed at least five times with sterile distilled water. The samples were then oven-dried at 40 °C for 72 h until a constant weight was obtained. After that, the dried samples were finely ground into small particle sizes (< 0.2 mm) and then transferred into sterile containers for storage in a dry condition.
## Maceration and Soxhlet extraction
Maceration was performed according to Yeo et al. [ 38] and Azwanida [39] with modifications. Four extraction solvents with different polarities were selected for the maceration process, namely n-hexane (Hex) (Merck, Germany, EMSURE® ACS), ethyl acetate (EA) (Merck, Germany, EMSURE® ACS), undenatured absolute ethanol (EtOH) (Systerm, Malaysia, ChemAR $99.8\%$), and sterile distilled water (dH2O). Approximately 20.0 g of ground samples were weighed and transferred to the screw-capped amber conical flasks. These samples were then soaked in the respective solvent in the ratio of 1 (sample): 10 (solvent) for 48 h and constantly mixed at room temperature on the platform shaker. After that, the sample-solvent mixtures were filtered and collected in sterile amber chemical bottles. A new batch of respective solvents was added to filtered samples for another round of extraction. These filtration and extraction processes were repeated four times to allow maximum recovery of bioactive compounds from the plant materials.
Soxhlet extraction was performed according to Redfern et al. [ 40] with modifications. Hex, EA, EtOH and dH2O were applied in the extraction process. Approximately 10.0 g of ground samples were weighed and transferred into cellulose extraction thimbles inside the Soxhlet extraction chamber. 200 mL of extraction solvent was added to the extractor flask and heated using an isomantle heater. These Soxhlet extractions were repeated for 20 cycles at 50 °C. After the extraction process, extraction solvents were collected in amber chemical bottles before rotary evaporation.
## Rotary evaporation
Crude extracts were dried in a rotary evaporator (Hei-VAP, Heidolph, Germany) to remove the excess of extraction solvents. Supplementary 1 lays the conditions of rotary evaporation for the respective extraction solvent. These crude extracts were rotary evaporated until a minute volume was left inside the flask. The leftover was then transferred into a pre-weighed sterile beaker and the flask was rinsed using a small volume of extraction solvent to allow maximum extract recovery. The beakers were dried at room temperature in the fume hood. These crude extracts were kept in a freezer at -20 °C for storage and further study.
## Solvent reconstitution
The extract colloids were reconstituted by using $100\%$ (v/v) dimethyl sulfoxide (DMSO) (Merck, Germany, EMSURE® ACS). Several concentrations of plant extracts were calculated and prepared.
## Standardization of bacteria culture
A clinical strain of S. aureus was obtained from the Faculty of Medical and Health Science, Universiti Malaysia Sarawak. Unless stated otherwise, the antimicrobial assay was carried out using Mueller–Hinton broth (MHB) (Oxoid, UK). The bacterial culture was incubated at 37 °C for 18 h to obtain bacteria culture in the log phase and then standardized to 0.5 MacFarland at 600 nm.
## Agar well diffusion assay
The agar well diffusion assay was performed according to Magaldi et al. [ 41] and Valgas et al. [ 42] with slight modifications. Muller-Hinton agar (MHA) (Oxoid, UK) were equally divided into four different sections and labelled with types, extraction methods and concentrations of plant extract tested. Standardized S. aureus from overnight MHB culture was lawn cultured using a sterile cotton swab and then allowed to dry for 15 min. After that, 7 mm bores were punched through the seeded MHA and 50 μL of extracts with adjusted concentrations (1, 1.5, 2, 10 gL−1) were carefully transferred into the bores using a pipette. 50 μL of $100\%$ (v/v) DMSO was used as the negative control. The agar plates were allowed to acclimate at room temperature for 15 min before being incubated at 37 °C for 18 h. The growth inhibition zones developed around the bores were measured in diameter using a pair of callipers. The assay was performed in triplicate, and the antimicrobial activities of the crude extracts were expressed as the mean of inhibition diameters in millimetres (mm).
## MIC using colorimetric broth microdilution assay
Broth microdilution assay was performed using sterile 96-wells round-bottom microtiter plates (TPP, Switzerland) according to Salvat et al. [ 43] and CLSI [44] standard with modifications. 100 μL of sterile MHB was dispensed using a multi-channel pipette into wells from rows B to H for columns 1 to 4, columns 6 to 9; rows A to H for columns 10 and 12. 200 μL of four prepared crude extracts with 10.0 gL−1 concentration were dispensed into row A, columns 1 to 4 for assays and columns 6 to 9 as the extract control. After that, 100 μL of crude extracts from row A were transferred and serially diluted to 2-fold from rows A to H for columns 1 to 4 and columns 6 to 9. It was followed by dispensing 200 μL of $100\%$ (v/v) DMSO into the well in row A column 12 and serially diluted to 2-fold from rows A to H. For microbial inoculum, 0.5 MacFarland standardized S. aureus was further diluted 1:150 to obtain the bacteria concentration at 1 × 106 CFU mL−1. Finally, 100 μL of the standardized bacteria inoculum was dispensed into the wells of rows A to H for columns 1 to 4 for assay and 12 for bacteria growth control. The same volume of sterile MHB was dispensed into wells from columns 6 to 9 for the extract control. The microtiter plates were read at 600 nm using an M965 microplate reader (Metertech Inc., Taiwan) after a minute of high-speed shaking before being incubated at 37 °C for 18 h.
After incubation, the plates were read again with the same set of conditions. Then, 20 µL of 0.45 µm syringe filtered 3-(4,5-dimethylthiazol-2-yl)-2,5- diphenyltetrazolium bromide (MTT) (AMRESCO, Ohio, USA, ultra-pure grade) solution (5.0 gL−1 in PBS) was subsequently added to all the 96-wells. The microtiter plates were wrapped with aluminium foil and incubated again at 37 °C for an hour. Purple formazan crystal was observed at the bottom of the wells shortly after the incubation. Three-quarters of the uncoloured MTT solution was carefully removed using a pipette without disturbing the formazan crystals. Next, the wells were washed using sterile PBS solution and the microtiter plates were placed on an orbital shaker at 120 rpm for three hours to settle down the formazan crystal. These processes were repeated twice to allow better of unbound MTT from the wells. After that, approximately 100 μL of formazan solubilizing agent ($0.5\%$ SDS, 36 mM HCL acidified isopropanol) was dispensed into all the 96-wells and mixed properly to dissolve all the formazan crystals. The microtiter plates were read at 540 nm after high-speed shaking for three minutes. The reduction of bacteria was calculated in percentage according to the formula shown below:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Percentage}\;\mathrm{of}\;\mathrm{bacteria}\;\mathrm{reduction}\;\left(\%\right)\;=\;\left[\frac{\left(Treated\;-\;blank\right)\;-\;\left(Control\;-\;blank\right)}{\left(Control\;-\;blank\right)}\right]\;\times\;100\%$$\end{document}Percentageofbacteriareduction%=Treated-blank-Control-blankControl-blank×$100\%$
## Minimum Bactericidal Concentration (MBC) using grid culture
MBC was performed under a standardized set of conditions as described in document M26-A [44]. The MBC was determined by subculturing from wells after broth microdilution assay to a non-selective agar, and negative microbial growth was yielded. Briefly, 3 µL of extract-bacteria mixture from the assay wells of broth microdilution assay after 18 h of incubation was pipetted onto the MHA plate that was gridded and labelled. Six extract concentrations were tested in triplicate: 5, 2.5, 1.25, 0.625, 0.313 and 0.156 µg µL−1. The inoculated MHA plates were acclimatised to room temperature for about 15 min before incubating at 37 °C for 18 h. The growth of S. aureus in the spaces was observed and the results were tabulated.
## Bacterial growth curve analysis
Bacterial growth curve analysis was performed according to Husain et al. [ 45] with modifications to determine the effects of crude extract on the bacterial growth curve of S. aureus. The analysis was conducted in the 96-wells round-bottom microtiter plates (TPP, Switzerland), and the absorbance was read using a microtiter plate reader. The identified MIC of the extracts against S. aureus from previous colorimetric broth microdilution assay was applied in this test. Sterile MHB containing the extracts was prepared at a final concentration of 0.5, 1 and 2 × MIC on a microtiter plate for testing and extract background control while sterile MHB without extract served as the blank and bacteria growth control. The S. aureus inoculum was prepared according to the previous broth microdilution assay. After that, one volume of standardized bacteria was added to the extract and bacteria growth control while a similar volume of sterile MHB was dispensed into extract background control and blank. The plates were incubated at 37 °C for varied time intervals (0, 2, 4, 6, 8, 12 and 18 h) and then read at 600 nm wavelength after a minute of high-speed shaking. Analysis was conducted in triplicate, and the bacterial growth curves expressed in the mean of turbidity where the absorbance was plotted against time intervals.
Further analysis of bacterial growth curve was performed for REA Soxhlet, SEA maceration, SEA Soxhlet, and REA maceration extracts that showed a large inhibition zone with low MIC and MBC endpoints and ratios. Figure 3 shows the growth curves of S. aureus treated with several MICs of REA Soxhlet, SEA maceration, SEA Soxhlet and REA maceration extract at 600 nm wavelength across 18 h of incubation. From the growth curves, the untreated S. aureus (orange lines, Fig. 3) managed to reach the log phase after 2 to 4 h of incubation at 37 °C. It started to grow exponentially afterwards and recorded an optical density (OD) of 0.424 after 18 h of incubation. However, the treated S. aureus generally recorded a proportional extension of lag phase for about 2 to 6 h and reduction in bacterial growth rate after the extract treatment with increasing extract concentration. At 0.25 × MIC concentration (red lines, Fig. 3), the SEA maceration, REA maceration and REA Soxhlet extracts-treated S. aureus showed a minor increment after 4 h of incubation, while those treated with SEA Soxhlet had a major increment in OD within a similar incubation. Meanwhile, S. aureus treated with the 1 × MIC extract concentration (blue lines, Fig. 3) of REA Soxhlet, SEA maceration and SEA Soxhlet extract only showed slight or no increment after 8 h of incubation, while those treated with REA Soxhlet extract had an OD decline after 4 h. The regression study between extract concentration and the optical densities of treated S. aureus was significant ($p \leq 0.06$, $$p \leq 0.00003$$). The increase in OD indicates that the crude extracts were unable to further induce the bacteriostatic or bactericidal effects due to depleting antimicrobial phytochemicals in the extracts, confirming the concentration dependency. Fig. 3Growth curves of S. aureus treated with several MICs of SEA maceration, SEA Soxhlet, REA maceration and REA Soxhlet extract at 600 nm wavelength across 18 h of incubation where (A, B, C, D) represents SEA maceration, SEA Soxhlet, REA maceration, REA Soxhlet, respectively. Solvents used: EA, ethyl acetate. S and R represents stem and root
## Gas Chromatography-Mass Spectrometry (GC–MS)
GC–MS was performed as described by Samling et al. [ 46] with modifications. The analysis was conducted on Shimadzu GC–MS model QP 2010 PLUS (Shimadzu, Japan) equipped with a single quadrupoles mass analyser and a non-polar GC BPX-5 cross-linked column ($5\%$ Phenyl Polysilphenylene-siloxane) of 30 m length, 0.25 mm internal diameter and 0.25 µm film thickness. The temperature of the GC oven was initially programmed at 50 °C for 1 min then ramped to 240 °C at the rate of 8.5 °C min-1 and held for 10 min. Injector and detector temperatures were programmed at 260 °C. The interface temperature was set at 260 °C and the inert ion source was programmed at 200 °C while 70 eV of electron impact ionization energy was used with a scan rate and mass range of 909 s/spectra and 40–500 m/z. Helium gas ($99.999\%$ purity) was used as a carrier gas with a flow rate of 1.0 mL min-1. The injection volume was 1 µL with a splitting ratio of 20:1. The interpretation of mass-spectrum was conducted using a mass spectral library search in the National Institute Standard and Technology (NIST) database incorporated with the GC–MS data system for the potential identification of compounds. Name, molecular mass, and structure of the components in the crude extracts were analysed and recorded.
REA Soxhlet, SEA maceration, SEA Soxhlet, and REA maceration extract were subjected to GC–MS analysis to identify the chemical constituents of the extracts. A total number of 88 individual phytochemical compounds were identified and 32 of them were readily known from previous studies to have antimicrobial, antioxidant, or anti-inflammatory properties. The individual compounds were identified based on mass spectra fragmentation patterns using the NIST14 library database (Table 3).Table 3Phytochemicals identified in C. alata (stem, leaves, roots) extracts that potentially use to treat cellulitisPhytochemicalsExtractsR.T/minArea (%)Molecular FormulaM. WNatureBiological functionsReferencesn-Hexadecanoic acidSEA macerationSEA SoxhletREA Soxhlet15.09415.12015.19512.2828.7934.34C16H32O2256Saturated long-chain fatty acidAntibacterial, anti-inflammatory, antioxidant, antiandrogenic, 5α-reductase inhibitorShows pesticidal, nematocidal, haemolytic, hypocholesterolemic effects[47–50]9, 12-Octadecadienoic acid (Z, Z)-SEA macerationSEA SoxhletREA Soxhlet16.23316.26116.34111.6538.0010.07C18H32O2280Unsaturated fatty acid(plant glycosides)Nematicide, antiacne, insectifuge, antiarthritic, hepatoprotective and cancer-preventive, antiandrogenic, antieczemic, antihistaminic, anti-inflammatory, anti-coronary, 5α-reductase inhibitor, and shows hypocholesterolemic effects[51–53]StigmasterolSEA maceration18.7605.72C29H48O412PhytosterolShow strong antibacterial activity against multiple-drug resistant mycobacteria. Strong antioxidant, anti-inflammatory, antioxidative properties that can inhibit tumour promotion[54–57]γ-SitosterolSEA maceration20.8495.03C29H50O414Plant steroidProphylactic activity, antioxidant, antibacterial, anti-diarrhoeal, and anti-inflammatory[56, 58–60]β-Sitosterol acetateSEA maceration26.4163.95C31H52O2456Steroid esterAntioxidant and free radical scavengers[55]Octadecanoic acidSEA macerationSEA SoxhletREA Soxhlet16.37216.38816.4663.016.683.16C18H36O2284C18 straight-chain saturated fatty acidAntibacterial, antifungal, and antitumor activities[52, 61]Eicosanoic acidSEA maceration12.13013.6811.070.62C20H40O2312C20 straight-chain saturated fatty acidAnticancer and anti-inflammatory activities[55, 62]OctadecaneSEA maceration12.3671.14C18H38254C18 straight-chain alkaneAntimicrobial, antioxidant, anticancer, and hypoglycaemic activities[61, 63]Dibutyl phthalateSEA maceration15.1650.99C16H22O4278Phthalate ester and a diesterAntimicrobial, antimalarial, and antifungal effects[56, 61]Phenol, 3, 5-bis (1, 1-dimethylethyl)-SEA maceration11.7570.76C14H22O206Alkylated phenolAnti-inflammatory effects[64]1-EicosanolSEA macerationSEA Soxhlet17.12617.1270.730.78C20H42O298Straight-chain fatty alcoholAntimalarial, antifungal and antioxidant[65, 66]n-Nonadecanol-1SEA maceration13.8500.64C19H40O284Long-chain fatty alcoholAntimicrobial and cytotoxic effects[61]TetradecaneSEA maceration10.6680.63C14H29Cl232C14 straight-chain acyclic alkaneAntifungal, antiviral, antibacterial, nematocidal, antitumor and wound healing activities[61, 67, 68]Pentadecanoic acidSEA macerationSEA Soxhlet14.40014.4400.550.92C15H30O2242C15 straight-chain saturated fatty acidAntibacterial activities[61, 68, 69]1,2-Benzene dicarboxylic acid, bis(2-methylpropyl) esterSEA maceration14.4940.53C16H22O4278Phthalate ester and a diesterAntimicrobial, antifungal, and antifouling activities[49, 61, 70]Bicyclo[3.1.1]heptan-3-ol,6,6-dimethyl-2-methylene-,[1S-(1α, 3α, 5α)]SEA maceration17.3880.45C10H16O152Pinane monoterpenoid, Secondary alcohol,Carbobicyclic compoundAntimicrobial activities[60]NeophytadieneSEA macerationREA macerationREA Soxhlet14.15314.16914.1700.441.983.57C20H38278SesquiterpenoidA good analgesic and induce anti-inflammatory, antimicrobial, and antioxidant effect[57, 71, 72]TetratetracontaneSEA maceration15.8980.43C44H90618C44 long-chain unbranched alkaneAnti-inflammatory, antibacterial, antioxidant, antiulcerogenic and hypoglycaemic effects[48, 61, 68]Cycloheptasiloxane, tetradecamethyl-SEA maceration10.8830.43C14H42O7Si7518Organosiloxane and a macrocycleAntioxidant, antimicrobial and hypocholesterolemic effects[73]TetracontaneSEA Soxhlet19.0478.86C40H82562Straight-chain aliphatic alkaneAnalgesic and anti-inflammatory activity[61, 69]Palmitoleic acidSEA Soxhlet14.9760.99C16H30O2254Long chain minor monounsaturated fatty acidInhibit bacterial enoyl-acyl carrier protein reductase (FabI) and increase the permeability of bacterial membrane because of surfactant action[74]Tetradecanoic acidSEA SoxhletREA Soxhlet13.68113.7140.850.58C14H28O2228Long-chain saturated fatty acidNematicide and cancer-preventive agent, antimicrobial, antioxidant, antifungal, hypercholesterolemic effects[51, 61, 70, 75]Heptadecanoic acidSEA Soxhlet15.7460.59C17H34O2270C17 long, straight-chain saturated fatty acidAntimicrobial, antioxidant, and antifungalUsed as surfactant[51, 61, 76]9,10-Anthracenedione, 1, 8-dihydroxy-3-methyl-SEA Soxhlet18.0370.58C15H10O4254TrihydroxyanthraquinoneAntiviral and anti-inflammatory effects[53]Hexadecanoic acid, methyl esterREA macerationREA Soxhlet14.82614.82522.185.44C17H34O2270Fatty acid methyl esterAntifungal, antioxidant, 5α-reductase inhibitor, nematicide, pesticide, antiandrogenic, flavour hypocholesterolemic, haemolytic effects and potent antimicrobial activities[48, 61, 68–70, 77]9-Octadecenoic acid (Z)-, methyl esterREA macerationREA Soxhlet16.02115.9918.565.12C19H36O2296Fatty acid methyl ester derived from oleic acidAntioxidant, antimicrobial and nematocidal, anticarcinogenic, antihypertensive[67, 77, 78]Cyclohexasiloxane, dodecamethyl-REA maceration9.4152.12C12H36O6Si6444Organosiloxane and a macrocycleAntifungal, antimicrobial, and used as a preservative and health-related products[73, 79]PhytolREA Soxhlet16.0596.66C20H40O296Acyclic diterpene alcoholAntimicrobial, antioxidant, anti-inflammatory and anticancerIt can be used as a precursor for the manufacture of synthetic forms of vitamin E and vitamin K1[50, 51, 57, 77, 80]SqualeneREA Soxhlet21.1581.43C30H50410Natural isoprenoidPrecursor of various hormones in animals and sterols in plants. Cancer and chemopreventive agent, antibacterial, antioxidant, antitumor, immunostimulant, a lipoxygenase-inhibitor, and free radical scavenger[48, 49, 51, 57, 61, 68]3, 7, 11, 15-Tetramethyl-2-hexadecen-1-olREA Soxhlet14.4811.29C20H40O296Acyclic diterpene alcoholInsecticidal, anti-inflammatory, anti-tuberculosis, antimicrobial, and antioxidant[57, 61, 62]n-Heptadecanol-1REA Soxhlet17.1520.86C17H36O256Long-chain fatty alcoholAnti-arthritis and treatment of skin diseases[61]2-Pentadecanone, 6, 10, 14-trimethyl-REA Soxhlet14.2530.57C18H36O268SesquiterpenoidAntibacterial activity against both gram-positive and gram-negative bacteria[52, 61, 67, 70] The SEA maceration had 50 major phytochemicals identified from the GC–MS, however, only 19 were previously characterized. Among the 19 phytochemicals, SEA maceration extract shared six phytochemicals in common and the remaining were found only specialized to the extract. N-hexadecanoic acid ($12.28\%$), 9, 12-octadecadienoic acid (Z, Z)- ($11.65\%$), octadecanoic acid ($3.01\%$), 1-eicosanol ($0.73\%$), pentadecanoic acid ($0.55\%$) and neophytadiene ($0.44\%$) were common, while stigmasterol ($5.72\%$), γ-sitosterol ($5.03\%$), β-sitosterol acetate ($3.95\%$), eicosanoic acid ($1.07\%$), octadecane ($1.14\%$), dibutyl phthalate ($0.99\%$), phenol, 3, 5-bis (1, 1-dimethylethyl)- ($0.76\%$), n-nonadecanol-1 ($0.64\%$), tetradecane ($0.63\%$), 1,2-benzene dicarboxylic acid, bis(2-methylpropyl) ester ($0.53\%$), bicyclo[3.1.1]heptan-3-ol,6,6-dimethyl-2-methylene-,[1S-(1α, 3α, 5α)] ($0.45\%$), tetratetracontane ($0.43\%$) and cycloheptasiloxane, tetradecamethyl- ($0.43\%$) were found only in SEA maceration extract.
SEA Soxhlet had 17 major phytochemicals identified, however, only ten were previously characterized. Six were in common, namely 9, 12-octadecadienoic acid (Z, Z)- ($38\%$), n-hexadecanoic acid ($28.79\%$), octadecanoic acid ($6.68\%$), pentadecanoic acid ($0.92\%$), tetradecanoic acid ($0.85\%$) and 1-eicosanol ($0.78\%$). Meanwhile, tetracontane ($8.86\%$), palmitoleic acid ($0.99\%$), heptadecanoic acid ($0.59\%$), 9,10-anthracenedione, 1, 8-dihydroxy-3-methyl- ($0.58\%$) were the four phytochemicals that were found only in SEA Soxhlet extract.
Of the 17 phytochemicals identified in REA maceration from the GC–MS, only four were previously characterized, namely hexadecanoic acid, methyl ester ($22.18\%$), 9-octadecenoic acid (Z)-, methyl ester ($8.56\%$) and neophytadiene ($1.98\%$). Cyclohexasiloxane, dodecamethyl- ($2.12\%$) was the only phytochemical found explicitly to REA maceration.
There were 30 phytochemicals identified in REA Soxhlet, 12 of which were previously characterized and seven phytochemicals were found in common [n-hexadecanoic acid ($34.34\%$), 9, 12-octadecadienoic acid (Z, Z)- ($10.07\%$), hexadecanoic acid, methyl ester ($5.44\%$), 9-octadecenoic acid (Z)-, methyl ester ($5.12\%$), neophytadiene ($3.57\%$), octadecanoic acid ($3.16\%$), tetradecanoic acid ($0.58\%$)]. Meanwhile, phytol ($6.66\%$), squalene ($1.43\%$), 3, 7, 11, 15-tetramethyl-2-hexadecen-1-ol ($1.29\%$), n-heptadecanol-1 ($0.86\%$) and 2-pentadecanone, 6, 10, 14-trimethyl- ($0.57\%$) were the five explicit phytochemicals found in the REA Soxhlet extract.
## Agar-well diffusion assay
Table 1 shows the average diameter of inhibition zones produced by C. alata crude extracts from both maceration and Soxhlet extraction tested in several concentrations with different plant’s parts and extraction solvents. The results showed that ethyl acetate extracts from both maceration and Soxhlet extraction generally exhibited stronger antimicrobial activities ($p \leq 0.06$, $$p \leq 0.0001$$) compared to the other extracts. The largest inhibition zone was observed in REA Soxhlet extract with an average diameter of 15.30 mm at 10 µg µL−1 extract concentration, followed by LEA Soxhlet (14.70 mm) and REA maceration (13.30 mm). These findings implied that ethyl acetate could be a better solvent than the others in isolating the phytochemicals responsible for antimicrobial activities towards S. aureus from C. alata. Generally, crude extracts of C. alata demonstrated a concentration-dependant antimicrobial activity because most of the extracts exhibited larger size of inhibition zones at higher concentrations and the sizes decreased with lower concentration. When the extract concentration was gradually reduced to 1 µg µL−1, there were only three extracts that remained susceptible towards S. aureus ($p \leq 0.06$, $$p \leq 0.01$$). Those extracts were SEA maceration extract (9.00 mm), REA maceration extract (9.70 mm) and REA Soxhlet extract (9.00 mm). Except for LdH2O Soxhlet extract at 10 µg µL−1 concentrations, most of the water extracts did not show clear inhibition zones. Figure 1 shows the agar well diffusion assay among leaves, roots, and stem parts of C. alata against S. aureus from both maceration and Soxhlet extraction at 10 µg µL−1 concentration. Table 1Average diameter of inhibition zones (mm) produced by C. alata crude extracts tested against Staphylococcus aureusExtractionPartsSolventAverage diameter of inhibition zone (mm)10 µg µL−12 µg µL−11.5 µg µL−11 µg µL−1MacerationStem partsn-Hexane11.7 ± 0.580.00.00.0Ethyl acetate13.0 ± 0.009.3 ± 0.609.7 ± 0.609.0 ± 1.00Ethanol10.7 ± 0.608.7 ± 0.608.3 ± 0.600.0dH2O0.00.00.00.0Leavesn-Hexane10.3 ± 0.600.00.00.0Ethyl acetate13.3 ± 0.609.0 ± 0.000.00.0Ethanol13.3 ± 0.6010.3 ± 0.600.00.0dH2O0.00.00.00.0Rootn-Hexane0.00.00.00.0Ethyl acetate13.7 ± 0.609.3 ± 0.609.3 ± 0.609.7 ± 0.60Ethanol9.3 ± 0.608.7 ± 0.609.3 ± 0.600.0dH2O0.00.00.00.0Soxhlet extractionStem partsn-Hexane10.3 ± 0.6010.0 ± 0.000.00.0Ethyl acetate13.3 ± 0.6012.0 ± 0.0010.0 ± 0.000.0Ethanol8.3 ± 0.600.00.00.0dH2O0.00.00.00.0Leavesn-Hexane0.00.00.00.0Ethyl acetate14.7 ± 0.600.00.00.0Ethanol11.3 ± 0.500.00.00.0dH2O10.3 ± 0.600.00.00.0Rootn-Hexane11.7 ± 1.200.00.00.0Ethyl acetate15.3 ± 0.6012.7 ± 0.6010.7 ± 0.609.0 ± 0.00Ethanol11.7 ± 0.609.3 ± 0.609.3 ± 0.600.0dH2O0.00.00.00.0Positive control- Rifampin antibiotic disc (5 µg)26.0 ± 1.00Positive control- Gentamicin antibiotic disc (10 µg)22.0 ± 0.00Positive control- Streptomycin antibiotic disc (10 µg)14.5 ± 0.50Negative control-$100\%$ (v/v) DMSO solution (50 µL)0.0Fig. 1Agar well diffusion assay of the roots, stem, and leaves extract of C. alata from both maceration and Soxhlet extraction tested at 10 µg µL−1 concentration, where (A, B, C, D, E, F) represents Stem Soxhlet extract, Leaves Soxhlet extract, Root Soxhlet extract, Stem maceration extract, Leaves maceration extract, Root maceration extract, respectively. G represents Negative control-$100\%$ DMSO and positive controls-Rifampin, Gentamicin and Streptomycin antibiotic disks. Solvents used: Hex, n-hexane; EA, ethyl acetate; EtOH, undenatured ethanol & DH2O, sterile distilled water
## Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC)
Table 2 shows the MIC, MBC endpoints and MBC/MIC ratio of crude extracts among leaves, roots and stem of C. alata from both maceration and Soxhlet extraction and the reduction percentage of bacteria identified at MIC endpoints. Figure 2 shows the overnight growth of S. aureus culture with the MBC values of the crude extracts against S. aureus. The maceration and Soxhlet extraction of stem, leaves and roots of C. alata using n-hexane, ethyl acetate and ethanol solvent produced most bactericidal agents (MBC/MIC ≤ 4), except for LEA maceration (MBC/MIC = 8) and LHex Soxhlet (MBC/MIC > 4) that are bacteriostatic agents. From the colorimetric broth microdilution assay, ethyl acetate extracts of C. alata with MIC endpoints ranging from 0.313 to 0.625 µg µL−1 showed strong antagonistic activities ($p \leq 0.06$, $$p \leq 0.00008$$) compared to the other extracts. Among the extracts tested, REA Soxhlet extract had the lowest MIC value, with 0.313 µg µL−1, while the other extracts ranged between 0.625 µg µL−1 to 1.25 µg µL−1. Almost all water extracts tested did not show any inhibition activity except for LdH2O Soxhlet extract at 1.25 µg µL−1 extract concentration. The REA soxhlet extract had exhibited the lowest MBC value with 0.625 µg µL−1 while the other extracts ranged between 1.25 to more than 5.00 µg µL−1. Almost all water-based crude extracts had higher MBC values, exceeding 5.00 µg µL−1.Table 2Minimum Inhibitory Concentration and Minimum Bactericidal Concentration of C. alata against Staphylococcus aureusExtractionPartsSolventBacteria Reduction PercentageMIC [µg/µL]MBC [µg/µL]MBC/MICMacerationStem partsn-Hexane-92.954 ± 11.2850.6252.5004Ethyl acetate-99.082 ± 0.9330.6251.2502Ethanol-101.520 ± 4.8251.2502.5002dH2O--5.000-Leavesn-Hexane-99.327 ± 1.0091.2505.0004Ethyl acetate-91.743 ± 5.2650.6255.0008Ethanol-100.337 ± 2.5541.2502.5002dH2O--5.000-Rootn-Hexane-99.760 ± 1.3971.2502.5002Ethyl acetate-98.740 ± 1.0040.6252.5004Ethanol-93.020 ± 11.9511.2502.5002dH2O-- > 5.000-Soxhlet extractionStem partsn-Hexane-90.910 ± 8.3760.6252.5004Ethyl acetate-97.219 ± 1.8720.6251.2502Ethanol-99.424 ± 1.7711.2501.2501dH2O--5.000-Leavesn-Hexane-99.618 ± 6.8841.250 > 5.000 > 4Ethyl acetate-98.810 ± 2.2590.6252.5004Ethanol-96.136 ± 6.2141.2502.5002dH2O-99.867 ± 0.9921.2501.2501Rootn-Hexane-99.031 ± 0.6190.6252.5004Ethyl acetate-95.937 ± 2.9750.3130.6252Ethanol-97.745 ± 1.8481.2502.5002dH2O-- > 5.000-Fig. 2MBCs of crude extracts among stem parts, leaves and roots of C. alata from both maceration and Soxhlet extraction against S. aureus. Spaces without visible bacterial growth indicate bactericidal effect, where (A, B, C, D, E, F) represents Stem Soxhlet extract, Leaves Soxhlet extract, Root Soxhlet extract, Stem maceration extract, Leaves maceration extract, Root maceration extract, respectively
## Discussion
Previous research has shown that the ethanolic and methanolic extracts of C. alata had stronger antimicrobial activities against S. aureus in agar-well or agar-disc diffusion assay compared to chloroform and ethyl acetate extracts [81, 82]. These differences in antimicrobial activities from different solvents and plant parts were also observed elsewhere [82, 83]. Hence, the present study expects significant antimicrobial activities from ethanol extracts. However, it was the extracts from ethyl acetate exhibited stronger antimicrobial activities. It implies that ethyl acetate may be a better solvent than others in extracting the phytochemicals responsible for antimicrobial activities against S. aureus from C. alata. Ethyl acetate is capable of extracting and dissolving active principal phytochemicals with semi-polar properties from plants, such as alkaloids, sterols, terpenoids, flavonoids, aglycons and glycosides [84]. The phytochemicals may be more soluble and potent when extracted using ethyl acetate compared to the other solvents [85]. Therefore, stronger antimicrobial activities demonstrated by ethyl acetate extract compared with other extraction solvents may be attributed to the presence of impurities or ashes that are more soluble in other solvents due to dilution effect of the active phytochemicals in the extracts [85].
The crude extracts of C. alata also demonstrated a concentration-dependant antimicrobial activity. These findings were in line with previously reported findings [85–87]. When the extract concentration was gradually reduced to 1 µg µL−1, only three extracts remained susceptible to S. aureus ($p \leq 0.06$, $$p \leq 0.01$$). Previous research has determined that the phytochemical distribution within C. alata varied between plant parts [82, 83, 85]. For example, the roots may accumulate more flavonoid quercetin, naringenin and kaempferol [42], while anthraquinones, flavonoids, quinines, and sterols in leaves [88]. This could explain why ethyl acetate root extract is more potent and exhibited stronger antimicrobial activities against S. aureus at all concentration levels compared to the leaves and stem extracts of same solvent [85].
On the contrary, most of the water extracts did not show clear inhibition zones at 10 µg µL−1 concentrations except for the LdH2O Soxhlet extract. These findings suggest that water extracts of C. alata did not possess any antimicrobial properties or inhibition effects against S. aureus [22]. Interestingly, previous research by Somchit et al. [ 30] and Timothy et al. [ 87] revealed the presence of certain phytochemicals in LdH2O Soxhlet extract that could inhibit the growth of S. aureus. However, findings from the present study contradict the indication that water extracts of C. alata shared the same potency as the other extracts tested in this study [89, 90]. The differences observed in antimicrobial activities of extracts from the same plant part tested are common in phytochemical research. This is because the concentration of plant constituents may vary from one geographical location to another, depending on the age of the plant, differences in topographical factors, soil nutrients, extraction methods as well as the method used for antimicrobial study [91].
From broth microdilution assay, the ethyl acetate extracts of C. alata demonstrated strong antagonistic activities. Similar findings were also reported by Wikaningtyas and Sukandar [92, 93], indicating that the ethyl acetate extracts could exhibit stronger antimicrobial activities against S. aureus compared to the other extracts of C. alata. The bacteria reduction rates of S. aureus at the MIC endpoints were higher than $90\%$, thereby, validating the MIC endpoint values of those extracts. For crude extracts with a bacteria reduction rate above $100\%$, the actual MIC endpoint values could be slightly higher than the values stated but that intermediate concentration was not determined. These findings substantiated the preliminary results from agar-well diffusion assay that most water extracts either only showed inhibition effects at very high concentrations or no inhibition effects at all towards S. aureus. However, these results contradicted previous findings where water extracts of C. alata were shown to be capable of inhibiting the growth of S. aureus and were just as effective as the other extracts [87, 91]. These differences in the antimicrobial activities could be attributed to variations in phytochemical concentrations that were active and potent against S. aureus, as well as water-soluble impurities from the extraction process [85, 91].
The extracts are only considered as bactericidal agents if the ratio MBC/MIC is less than or equal to 4, while those greater than 4 as bacteriostatic agent [89, 94]. The MBC/MIC ratio of crude extracts ranged between 1 and 4, except for LEA maceration extract with the ratio of 8. The MBC/MIC ratios of water-based extracts cannot be calculated due to the ambiguous values of MIC and MBC (except for LdH2O Soxhlet), indicating that water-based extracts possessed no or weak antimicrobial effects against S. aureus. Therefore, it can be inferred that most of the C. alata crude extracts could be utilised as the bactericidal agent while LEA maceration and LHex soxhlet as the bacteriostatic agent.
Bacterial growth curve analysis is an influential tool that can provide comprehensive information about the pharmacodynamics of an antibacterial agent that cannot be obtained simply through the endpoints assay such as the MIC and MBC [90, 95]. The primary focus of the analysis is the elongation of the lag phase, which is the time required for the bacterial culture to enter the exponential phase after the extract treatment, because the length of the lag phase directly depends on the bacteria’s growing condition [96]. The lag phase is also a critical window to protect the bacteria from antimicrobial stress and promote bacteria regrowth after the removal of antimicrobial agents [97]. Theophel et al. [ 98] emphasize the importance of the lag phase as the key stage for bacteria to develop strategies in resisting the killing by antimicrobial agents [98]. The researchers further highlighted the duration of lag phases as a more meaningful indicator of dose-dependent antibiotic inhibition. Hence, an in-depth understanding of how antimicrobial agents affect the lag phase of bacterium is essential.
In this study, S. aureus treated with C. alata extracts showed differences in the growth rate at the exponential phase (Fig. 3). For example, S. aureus treated with 0.25 × MIC concentration of SEA maceration extract showed an approximately $54.4\%$ decrease in growth rate at 8 to 12 h of incubation compared with the bacteria control. At the same time, those treated with 0.5 × MIC concentration of SEA maceration extract showed approximately $93.2\%$ of reduction for the same incubating period. Similar observations were also noted in other extracts with the increase of extract concentration. Meanwhile, S. aureus treated with REA Soxhlet extract at 0.25 × MIC concentration recorded higher optical density values after 18 h of incubation compared with the bacteria control. These observations could be due to the low inoculum concentration of REA Soxhlet extract (0.078 µg µL−1) and the promoting effect of bacterial regrowth upon the removal of antimicrobial agents after the extended lag phases [48, 99]. Based on the bacterial growth curve analysis, the period required for the extracts to show full potential efficacy against S. aureus was determined as 6 h of incubation. Hence, we suggest that S. aureus be treated with another concentration level at the 6th hour to maintain low OD readings.
There were 88 individual phytochemical compounds identified from the GC–MS analysis and 32 were readily known from previous research to have antimicrobial, antioxidant, or anti-inflammatory properties. According to the chromatogram, the largest peak area ($38.00\%$) was recorded by 9, 12-octadecadienoic acid (Z, Z)- (linoleic acid) in SEA Soxhlet extract. It is a plant glycoside with anti-inflammatory, antieczemic and inhibitory action against some bacteria species [51, 74]. However, despite the bacterial inhibitory action, most research effort has primarily focused on its anti-inflammatory properties [51, 52, 100]. In this study, the anti-inflammatory properties of 9, 12-octadecadienoic acid (Z, Z)- is a favourable property because it could reduce the inflammation symptoms caused by cellulitis.
Apart from that, n-hexadecanoic acid, octadecanoic acid, 9,12-octadecadienoic acid (Z, Z)- and neophytadiene were identified as the ubiquitous phytochemicals found in the extracts of C. alata except for REA maceration and SEA Soxhlet. Both extracts were found deficient in either one phytochemical from the list. Relatively higher concentrations of n-hexadecanoic acid were detected in REA Soxhlet with a peak area of $34.4\%$, followed by SEA Soxhlet ($28.7\%$) and SEA maceration ($12.28\%$). n-Hexadecanoic acid is also known as palmitic acid, which is the most common saturated fatty acid found in animals, plants and microorganisms with antibacterial, anti-inflammatory, and antioxidants properties [47, 101]. Meanwhile, the highest octadecanoic acid concentration was recorded in SEA Soxhlet with a peak area of $6.68\%$, followed by REA Soxhlet ($3.16\%$) and SEA maceration ($3.01\%$). Octadecanoic acid is one of the commonest saturated fatty acids and is commonly known by the name stearic acid. It exists as a glycerol ester and is found abundantly in most animals (up to $30\%$) and plant fats (typically < $5\%$) [102]. It was reported to exhibit antibacterial, antifungal and antitumour activities [61].
The REA maceration extract was devoid of n-hexadecanoic acid and octadecanoic acid, but it did contain $22.18\%$ of hexadecanoic acid, methyl ester, which was found explicitly in the root extracts of C. alata. The REA Soxhlet also contained about $5.44\%$ of hexadecanoic acid, methyl ester. The methyl ester of fatty acid has been reported to exhibit antioxidant and potent antimicrobial activities against Gram-positive and Gram-negative bacteria [48, 103]. A study showed that hexadecanoic acid, methyl ester exhibited antibacterial potency against the clinical strain of S. aureus, *Pseudomonas aeruginosa* and *Klebsiella pneumoniae* [99]. The antimicrobial activity of fatty acids is regulated by their structures, morphologies, carbon-chain length functions, and the presence, number, positioning, and orientation of double bonds [104]. Many organisms rely on fatty acid methyl ester as a defence mechanism against bacterial infection, with the mode of action being the bacteria’s cell membrane. In addition, the fatty acid methyl ester also interferes with cellular energy production, inhibits enzyme activity, and causes direct bacterial cell lysis [105]. The safety and activity of the fatty acid methyl ester make it a promising antimicrobial agent.
Although the phytochemicals have been previously identified and characterized in the extracts, 56 phytochemicals whose biological properties and functions remain unknown. Some of these phytochemicals occurred in relatively higher concentrations in the extracts. For example, 3-propylglutaric acid ($3.04\%$) in SEA Soxhlet extract; 4,22-cholestadien-3-one ($7.73\%$) and γ-sitostenone ($13.21\%$) in SEA maceration extract; henicosanal ($13.60\%$), methyl stearate ($5.96\%$) and hexadecanal ($4.83\%$) in REA maceration extract; and methyl 10-trans,12-cis-octadecadienoate ($6.76\%$) and heptadecanolide ($6.38\%$) in REA Soxhlet extract. The phytochemicals that contributed to the antimicrobial, anti-inflammatory and antioxidant properties of C. alata in this study should be further investigated before formulating an effective natural drug against the cellulitis causing agent, S. aureus.
## Conclusion
The ethyl acetate extracts of C. alata exhibited strong antimicrobial activities against the clinical strain of S. aureus. The extracts from Soxhlet extraction also exhibited stronger antimicrobial activities compared to maceration extraction. REA Soxhlet extract showed significant inhibition effects towards S. aureus, followed by SEA Soxhlet, SEA maceration and REA maceration extract of C. alata. Extracts derived from n-hexane, ethyl acetate and ethanol can be utilised as the bactericidal agents (MBC/MIC ≤ 4), except for LHex Soxhlet and LEA maceration extracts. Different elongation patterns of bacterial lag phases and reduction in growth rate were observed during the growth curve analysis. There was a significant regression extension ($p \leq 0.06$, $$p \leq 0.00003$$) of the lag phase for 2 to 6 h after the extract treatment with the increase of extract concentration. Based on the GC–MS analysis, a total of 88 phytochemicals that constitute phenolics, steroids, fatty acids, alcoholics, esters, and alkane hydrocarbons were detected, 32 of which were previously characterized for their antimicrobial, antioxidant, and anti-inflammatory activities.
## Supplementary Information
Additional file 1.Additional file 2.
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|
---
title: SETD8 inhibits ferroptosis in pancreatic cancer by inhibiting the expression
of RRAD
authors:
- Zekun Lu
- Qiangsheng Hu
- Yi Qin
- Hao Yang
- Bingkai Xiao
- Weibo Chen
- Shunrong Ji
- Guangchen Zu
- Zhiliang Wang
- Guixiong Fan
- Xiaowu Xu
- Xuemin Chen
journal: Cancer Cell International
year: 2023
pmcid: PMC10024404
doi: 10.1186/s12935-023-02899-6
license: CC BY 4.0
---
# SETD8 inhibits ferroptosis in pancreatic cancer by inhibiting the expression of RRAD
## Abstract
### Background
As an oncogene, SETD8 can promote tumour growth and tumour cell proliferation. This study aims to reveal the relationship between SETD8 and ferroptosis in pancreatic cancer and its role in pancreatic cancer to provide a possible new direction for the comprehensive treatment of pancreatic cancer.
### Methods
The downstream targets were screened by RNA sequencing analysis. Western blot, Real-time Quantitative PCR (qPCR) and immunohistochemistry showed the relationship between genes. Cell proliferation analysis and cell metabolite analysis revealed the function of genes. Chromatin immunoprecipitation (CHIP) assays were used to study the molecular mechanism.
### Results
The potential downstream target of SETD8, RRAD, was screened by RNA sequencing analysis. A negative correlation between SETD8 and RRAD was found by protein imprinting, Real-time Quantitative PCR (qPCR) and immunohistochemistry. Through cell proliferation analysis and cell metabolite analysis, it was found that RRAD can not only inhibit the proliferation of cancer cells but also improve the level of lipid peroxidation of cancer cells. At the same time, chromatin immunoprecipitation analysis (CHIP) was used to explore the molecular mechanism by which SETD8 regulates RRAD expression. SETD8 inhibited RRAD expression.
### Conclusions
SETD8 interacts with the promoter region of RRAD, which epigenetically silences the expression of RRAD to reduce the level of lipid peroxidation in pancreatic cancer cells, thereby inhibiting ferroptosis in pancreatic cancer cells and resulting in poor prognosis of pancreatic cancer.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12935-023-02899-6.
## Background
Pancreatic cancer is a highly malignant tumour. It is the seventh leading cause of death among human cancers [1, 2]. With the development of medical technology, targeted drugs have been applied to breast cancer, lung cancer and other tumours and have achieved good clinical results [3, 4]. However, no effective molecular target for targeted therapy has been found for pancreatic cancer. Pancreatic cancer is resistant to most chemotherapeutic agents, so surgical treatment remains the only way to cure pancreatic cancer. However, due to its late discovery, most patients have lost best opportunity for operation by the time they see a doctor [5]. Therefore, it is particularly important to explore the molecular biological mechanism of the occurrence and development of pancreatic cancer. Understanding the molecular biological mechanisms of pancreatic cancer can provide new therapeutic targets and directions for the comprehensive treatment of pancreatic cancer. Although the overall 5-year survival rate of pancreatic cancer is less than $10\%$, in clinical practice and other authors' literature, a small proportion of pancreatic cancer patients have a relatively good prognosis [6].
Lysine methyltransferase SETD8 (also known as PR-SET7, SET8 or KMT5A) is a member of the SET domain family. Its important and most common function is to regulate the cell cycle and tumour growth [7, 8]; SETD8 is unique among the KMTs discovered thus far. It is the only methyltransferase that can monomethylate histone H4 lysine 20 (H4K20). At the same time, the most important and common type of histone modification is methylation, and histone methylation is a common form of epigenetic disorder in tumorigenesis and development [9]. In addition, the methylation marker histone H4 lysine 20 (H4K20) is also considered to be an inhibitory signal of gene transcription, which plays a key role in DNA replication, DNA damage repair and silencing heterochromatin [10]. Therefore, enzymes that target the methylation of lysine residues in modified substrates are a potential direction of antitumour drug research. In our study, we found that SETD8 negatively regulates glucose metabolism and redox reactions in pancreatic cancer cells. *Through* gene expression profiling, we noticed that SETD8 reduces the expression of RRAD, a closely related gene of glycometabolism (RAS associated with diabetes) [11]. Therefore, we speculate that SETD8 may be involved in the regulation of lipid peroxidation in pancreatic cancer cells.
Ferroptosis is a new form of iron dependent programmed cell death that is different from apoptosis, necrosis and autophagy. It is characterized by lipid peroxidation of unsaturated fatty acids highly expressed on the cell membrane [12]. There is evidence that ferroptosis is associated with biological redox reactions and health. At the same time, inducing ferroptosis in cancer cells has great potential in cancer treatment. This is especially true in refractory malignant tumours that are resistant to traditional treatments such as radiotherapy and chemotherapy [13, 14]. Ferroptosis is closely related to RAS mutation of oncogenes. First, Brent R. Stockwell found that some small molecules, such as RAS-selective lethal 3 (RSL3) and erastin, can induce iron-dependent regulatory cell death different from other forms of cell death (such as apoptosis and necrosis) [15, 16]. Interestingly, nearly $95\%$ of all pancreatic cancers have mutations in the KRAS gene [17]. However, research on pancreatic cancer and ferroptosis is insufficient. At the same time, we can analyse the level of lipid peroxidation in pancreatic cancer cells by SETD8. Therefore, we speculate that SETD8 may be involved in the regulation of ferroptosis in pancreatic cancer.
In this study, we studied the effect of methylation of histone H4 lysine 20 by lysine methyltransferase SETD8 on ferroptosis in pancreatic cancer. We found that SETD8 inhibited the occurrence of ferroptosis in pancreatic cancer. We proved that RRAD (RAS associated with diabetes) is a key target gene for SETD8 and that RRAD can promote lipid peroxidation in pancreatic cancer cells. Mechanistically, SETD8 inhibits the transcriptional activity of RRAD by binding to the promoter region of RRAD, thus downregulating the expression of RRAD and resulting in a decrease in the incidence of ferroptosis in pancreatic cancer cells. Therefore, high levels of SETD8 and low levels of RRAD are closely related to poor prognosis in pancreatic cancer patients. At the same time, we found that the SETD8-RRAD-ferroptosis axis may be a potential target for the treatment of pancreatic cancer and provide a new strategy for the comprehensive treatment of pancreatic cancer.
## Cell culture
The human pancreatic cancer cell lines MIAPACA-2 and SW1990 were obtained from the American Type Culture Collection Center (ATCC, VA, USA) and cultured according to the ATCC-provided program. All cells used in the experiment were within 10 generations after thawing. All cell culture media contained 100 U/ml penicillin and 100 mg/ml streptomycin.
## Chemicals
The ferroptosis inducer RAS-selective lethal 3 (RSL3) and ferroptosis inhibitor ferrostatin-1 (Fer-1) were purchased from Selleckchem.
## Plasmids
The coding sequences of human RRAD and SETD8 were cloned into the whole virus vector p pCDH-CMV-MCS-EF1-puro (SBI, USA) to produce the expression plasmids of RRAD and SETD8. To inhibit the expression of the target gene, the pLKO.1 TRC cloning vector (Addgene plasmid 10878, Watertown, MA, USA) was used. The 21 bp targets for SETD8 were CCGAGGAACAGAAGATCAAAG and CGCAACAGAATCGCAAACTTA; the 21 bp targets for RRAD were CGTAGCTCGTAACAGCCGCAA and CACACCTATGATCGCTCCATT. The control interference shRNA (Addgene plasmid 1864) was used as a knockout control vector. The corresponding overexpression structures of SETD8 and RRAD were obtained by using the pCDH-CMV-MCS-EF1-Puro vector (System Biosciences, Palo Alto, CA, USA), and empty body (EV) was used as a control. Lentiviruses are composed of the target gene expression vectors psPAX2 and pMD2. 293 T cells were added at a ratio of 4:3:1. Lentivirus particles were used to infect MIAPACA-2 and SW1990 cells, followed by puromycin screening to obtain stable cell lines.
## Western blot
Cells were first collected and washed twice with PBS, RIPA buffer mixed with protease and phosphatase inhibitors (Beyotime Biotechnology, Shanghai, China) was added, and the cells were lysed on ice for 30 min. Then, the protein concentration of the lysate was determined by a BCA protein analysis kit (Beyotime Biotechnology, Shanghai, China). Approximately 20 μg protein samples were separated on $10\%$ SDS–polyacrylamide gels and then transferred to PVDF membranes (Millipore, Billerica, USA), and incubated with specific antibodies against SETD8 (Proteintech, 1:1000) and RRAD (Abcam, 1:1000). Next, the membrane was detected with a secondary antibody bound to HRP (protein, 1:5000). Finally, immunoblotting was incubated with an enhanced chemiluminescence detection kit (Millipore) and displayed by an imaging system (Clinx). The original figures of the western blots are all in Additional file 1.
## RNA extraction and real-time quantitative PCR
In summary, First, the cells were suspended in a 15 ml centrifuge tube and washed twice with PBS. Then, 2 × 10^7 cells were taken into a 1.5 ml EP tube and total RNA was extracted with Trizol reagent (Invitrogen, USA). cDNA was obtained by reverse transcription using the Takara primescript RT kit. Quantitative real-time PCR was used to determine the expression level of the target gene by an ABI 7900ht real-time PCR system (American Applied Biological Systems Company). β-Actin was used as the control, and the relative mRNA level was expressed by multiple changes compared with the control. The primers used were as follows: human SETD8: 5′-AAGATGTCCAAGCCCCGC-3′ (forward), 5′-TGTTCCTCGGACTTCATGGC-3′ (reverse); Human RRAD: 5′-ACATTTGGGAGCAGGACGG-3′ (forward), 5′-CTCTTGTTGCCCACGAGGAT-3′ (reverse); people β-Actin: 5′-CTACGTCGCCCTGGACTTCGAGC-3′ (forward), 5′-GATGGAGCCGCCGATCCACACGG-3′ (reverse). Subsequently, the delta-delta Ct method was used for data statistics. All tests were carried out in triplicate.
## Chromosome immunoprecipitation assay
Chromosome immunoprecipitation analysis was performed to evaluate the occupancy of SETD8 on the RRAD promoter according to the instructions provided by the Magna CHIP A/G Chromatin Immunoprecipitation Kit (Darmstadt Millipore, Germany). A pair of primers was used to amplify the chromatin region of RRAD. The primer sequences were as follows: forward primer (5′–3′): AGTTGCTGCTTTTGGCTGATTGGGTT, reverse primer (5′–3′): AGTTGCTGCTTTTGGCTGATTGGGTT. Simply put, the cells were crosslinked with $1\%$ formaldehyde for 10 min and then lysed and sonicated to an average size of 500 bp. The cross-linked protein/DNA complex was immunoprecipitated by anti-SETD8 antibody (Santa Cruz Biotechnology, USA) and isotype control IgG (Cell Signaling Technology, 3900), incubated at 4 °C, bound to protein magnetic beads, eluted from the complex and purified for DNA. CHIP-ReCHIP was carried out basically the same as primary CHIP. The target DNA sequence was finally analysed on agarose gel for CHIP experiments.
## C11-BODIPY staining
Half a million cells were seeded into each well in a six-well plate (Corning) and then pretreated with a ferroptosis inducer for 24 h. Before flow cytometry, the cells were separated, resuspended and washed, and then stained with 2 μmol/l C11 BODIPY for 30 min. Then, the fluorescence intensity was detected by flow cytometry (Beckman).
## MDA and GSH/GSSG determination
The cells were placed in a 6-well cell culture plate (Corning). The treatment conditions were consistent with c11-bodipy analysis. After obtaining the cell homogenate, the protein concentration was measured using a BCA protein analysis kit (Beyotime), and then MDA was detected using the lipid peroxidation MDA Analysis Kit (Beyotime). After obtaining the MDA content, the ratio of MDA to protein concentration was calculated. The ratio of GSH/GSSG was measured according to the Beyotime GSH/GSSG assay kit and standardized using the protein concentration of the cell lysate.
## CCK-8 and colony formation test
We used the Cell Counting Kit-8 (Beyotime) for cell proliferation and toxicity experiments. The cells were inoculated into 96-well plates (1 per well) × 103 cells), and then 10 μl the Cell Counting Kit-8 (CCK-8) solution was added to each well at 0, 24, 48, 72, 96 and 120 h and incubated at 37 °C in $5\%$ CO2 for 1.5 h, and then, the absorbance of each sample was measured at 450 nm wavelength using a microplate reader. In the cytotoxicity experiment, the cells were inoculated into 96-well plates (5000 cells per well) and then treated with a ferroptosis inducer and inhibitor. After 48 h, 10 μl CCK-8 solution was added to each well, incubated in $5\%$ CO2 at 37 °C for 1 h, and finally, the absorbance of each sample was measured at 450 nm using a microplate reader. We tested cell viability according to the instructions of the Cell Counting Kit-8 reagent (Beyotime). For colony formation, pancreatic cancer cells (150 cells per pore) were inoculated in 6-well plates for 7–10 days. The colonies were fixed with $4\%$ paraformaldehyde for 15 min, stained with $1\%$ crystal violet for 30 min, and then counted.
## Tissue samples and immunohistochemical (IHC) staining
The clinical tissue samples used in this study were clinical PDAC tissue samples from 80 patients confirmed by surgery and pathology and approved by the ethics committee of the Affiliated Tumour Hospital of Fudan University (FUSCC). The immunohistochemical staining of paraffin-embedded tissues adopts a two-step scheme. First, The antigen was extracted by dewaxing hydration and antigen retrieval, and then the slide was incubated with the following primary antibodies: anti-SETD8 (Proteintech) and anti-RRAD (Abcam). HRP binds affinity-purified sheep anti rabbit IgG (Proteintech) as a secondary antibody. Three different views were randomly selected under the microscope, and each slide was scored. According to the total area and intensity of staining, the protein expression level score was [1], < $5\%$ of the total cells; [2], 5–$25\%$; [3], 25–$50\%$; [4], 50–75 and > $75\%$: [5]. The final score was the average of the three views and was classified as follows: low (1 ≤ score < 3) and high (3 ≤ score ≥ 5).
## Statistical analysis
The experiment was repeated at least three times. All data were analysed by GraphPad Prism 8. Two-tailed unpaired Student’s t tests were used to compare the differences between the two groups. The χ2 test was used to analyse the relationship between the expression of SETD8 or RRAD and the corresponding clinicopathological features. Survival curves were drawn using the Kaplan‒Meier method and compared by the log-rank test. Differences were considered significant at *$P \leq 0.05$; **$P \leq 0.01.$ NS means there was no significant difference.
## SETD8 inhibits ferroptosis in pancreatic cancer cells
The most common and important function of SETD8 is to regulate the cell cycle and tumour growth. We silenced the expression of SETD8 in the SW1990 cell line using two SETD8-specific shRNA expression lentiviruses (shSETD8#1 and shSETD8#2). At the same time, the expression of SETD8 was enhanced in the Mia PaCa-2 cell line. The efficiency of SETD8 knockout and overexpression was confirmed by qPCR and Western blotting (Fig. 1a, b). We found that the level of lipid oxidation increased in the SW1990 cell line with SETD8 silencing, but decreased with the Mia PaCa-2 cell line’s overexpression of SETD8 (Fig. 1c). Therefore, we speculated that the expression level of glutathione as the main antioxidant [18] decreased in the cell lines with SETD8 knockout and increased in the Mia PaCa-2 cell lines with increased SETD8 expression. To confirm this hypothesis, we tested the GSH/GSSG ratio in the SW1990 cell line with silenced SETD8 expression and the Mia PaCa-2 cell line overexpressing SETD8 (Fig. 1d). As expected, SETD8 increased the expression of GSH. At the same time, GSH is closely related to GPX4, a key substance regulating ferroptosis [19]. Therefore, we used the BODIPY $\frac{581}{591}$C11 probe to detect the level of lipid peroxidation in the SW1990 cell line with silenced SETD8 expression and the Mia PaCa-2 cell line overexpressing SETD8 (Fig. 1f). The results showed that SETD8 inhibited the lipid peroxidation of cells. Lipid peroxidation is the most important marker of ferroptosis [20], and SETD8 inhibits lipid peroxidation. Therefore, SETD8 may inhibit ferroptosis. To verify this, we added the ferroptosis inhibitor ferrostatin-1 to the SW1990 cell line, which silenced SETD8 expression. The ferroptosis inhibitor ferrostatin-1 (Fer) reversed the lipid peroxidation induced by SETD8 silencing (Fig. 1e). A ferroptosis inducer (RSL3) was added to the Mia PaCa-2 cell line overexpressing SETD8. The results showed that a ferroptosis inducer (RSL3) could inhibit the increased cell viability of the cells overexpressing SETD8 (Fig. 1e). This indicates that SETD8 inhibits the occurrence of ferroptosis in pancreatic cancer cells. Fig. 1SETD8 declines ferroptosis in pancreatic cancer cells. A, B The shRNAs against SETD8 plasmid were transfected into SW1990 cell line. Plasmid overexpressing SETD8 was transfected into Mia PaCa-2 cell line. Western blot analysis and qPCR analysis were performed to exam SETD8 protein and mRNA levels, respectively. C The MDA was detected in SETD8-silenced SW1990 cell line and SETD8-overexpressed Mia PaCa-2 cell line. The results showed that SETD8 could inhibit the expression of MDA. D The GSH/GSSG ratio was detected in SETD8-silenced SW1990 cell line and SETD8-overexpressed Mia PaCa-2 cell line. It was found that silencing SETD8 could decrease the GSH/GSSG ratio and overexpression of SETD8 could increase the GSH/GSSG ratio. E Cell viability was detected in the SETD8-silenced SW1990 cell line in the presence or absence of 2 μmol/l Fer and the SETD8-overexpressed Mia PaCa-2 cell line in the presence or absence of 2 μmol/l RSL3. The results showed that the cell viability decreased by SETD8 knockdown could be reversed by the ferroptosis inhibitor Fer and the cell viability increased by SETD8 overexpression could be reversed by the ferroptosis inducer RSL3. F Flow cytometry analyzed the fluorescence of BODIPY$\frac{581}{591}$C11 (lower) and the relative content was calculated (upper). The results showed that SETD8 could reduce the level of intracellular lipid peroxidation. Two tailed unpaired Student t-test was used in the above experiments. * $P \leq 0.05$; **$P \leq 0.01$
## RNA expression profiling identified RRAD as a downstream target of SETD8 regulating ferroptosis
To study the molecular mechanism by which SETD8 regulates ferroptosis in pancreatic cancer cells, we examined the effect of SETD8 knockout on gene expression profiles. Specifically, two different shRNAs targeting SETD8 (shSETD8 # 1 and shSETD8 # 2) were used to silence the expression of SETD8 in SW1990 cells, and SETD8 was overexpressed in Mia PaCa-2 cells. After that, we compared the mRNA expression level differences between the above specific treatment cells by RNA sequencing. The results showed that a series of genes were upregulated and downregulated in SW1990 cells silenced by SETD8 and Mia PaCa-2 cells overexpressed by SETD8. ( Fig. 2a). The total number of genes in the final list is relatively limited, in part because two different shSETD8s were transfected into the cell line to minimize the off-target effects and false-positive results of shRNAs. These include the RAS-related GTPase subfamily member RRAD. RRAD is also known as the RAS family diabetes-related gene, can inhibit the proliferation and migration of tumour cells, and has been identified as a tumour suppressor gene in many tumours [21–24]. It has been reported that epigenetic genes usually play a key role in cancer cells by inhibiting the expression of tumour suppressor genes [25]. Therefore, we confirmed the gene RRAD through qPCR and found that its expression was significantly upregulated with SETD8 knockout, which was consistent with the sequencing data (Fig. 2b). At the same time, the expression of RRAD was also downregulated with SETD8 overexpression (Fig. 2b). In addition, we further examined the effect of SETD8 on RRAD at the protein level by Western blotting (Fig. 2c). Coincidentally, the result was consistent with that of qPCR. The expression of RRAD was significantly upregulated in SETD8-silenced cells and downregulated in SETD8-overexpressing cells. To further verify the relationship between SETD8 and RRAD expression, IHC was performed in human PDAC tissue (Fig. 2d). The results showed that there was a negative correlation between the expression of SETD8 and RRAD (r = − 0.344; $$P \leq 0.0018$$; $$n = 80$$).Fig. 2The downstream target gene of SETD8 is RRAD and negatively regulates RRAD expression. A In the gene heatmap of knockdown of SETD8 in SW1990 cells, RRAD was significantly overexpressed. In the heatmap of genes overexpressing SETD8 in Mia PaCa-2 cells, RRAD was significantly decreased. These results indicated that RRAD was a downstream target gene of SETD8 and was negatively regulated by SETD8. B qPCR analysis was performed to exam RRAD mRNA level. These results confirmed that SETD8 knockdown led to upregulation of the RRAD expression level, while SETD8 overexpression led to downregulation of the RRAD expression level. This further proves that SETD8 negatively regulates RRAD expression. C Western blot was performed to exam RRAD protein level. The results showed that SETD8 knockdown resulted in upregulation of the RRAD protein level, while SETD8 overexpression led to downregulation of the RRAD protein level. This further indicates that SETD8 negatively regulates RRAD expression. D Representative images of IHC staining of SETD8 and RRAD in PDAC tumours and their correlation. $$P \leq 0.0018.$$ It was further shown that SETD8 was negatively correlated with RRAD. * $P \leq 0.05$; **$P \leq 0.01$
## RRAD inhibits the proliferation of PDAC cell lines and is associated with a better prognosis in PDAC patients
To further study the function of RRAD in the development of pancreatic cancer, we overexpressed RRAD in the MIA PaCa-2 cell line and confirmed the efficiency of RRAD over expression by qPCR and Western blotting (Fig. 3a, b). By analysing the results of the following functional analysis, and we found that compared with the control group, the ability of RRAD-overexpressing cell lines to proliferate and form colonies decreased significantly (Fig. 3c, d), indicating that RRAD inhibited the proliferation of pancreatic cancer cells. At the same time, the expression of RRAD was closely related to the prolongation of overall survival (Fig. 3e, f, g). Overall, the above results indicate that the high expression of RRAD indicates that pancreatic cancer patients have a better prognosis and plays a role in inhibiting the occurrence and development of pancreatic cancer. Fig. 3RRAD inhibits the proliferation of pancreatic cancer cells and is associated with the prognosis of patients with pancreatic cancer. A, B Western blot analysis and qPCR analysis were performed to exam RRAD protein and mRNA levels, respectively. The overexpression of RRAD was confirmed in Mia PaCa-2 cell line. C CCK-8 assay showed that overexpression of RRAD decreased the proliferation of pancreatic cancer cells. D Colony formation assay showed that overexpression of RRAD reduced the proliferation of pancreatic cancer cells. E Representative images of IHC staining of RRAD in PDAC tumour tissues and paired adjacent normal tissues. The results showed that RRAD was underexpressed in pancreatic cancer. F lHC analysis further confirmed that RRAD protein levels were lower in cancer tissues. wilcoxon rank test $$P \leq 0.0015.$$ G Kaplan–Meier survival rate analysis for PDAC patients showed high RRAD expression was associated with longer over survival. * $P \leq 0.05$; **$P \leq 0.01$
## RRAD promotes ferroptosis in pancreatic cancer
RRAD can play a role in regulating the occurrence and development of pancreatic cancer. To further study its role in pancreatic cancer. We silenced the expression of RRAD in the SW1990 cell line by using two kinds of RRAD-specific shRNA expression lentiviruses (shRRAD#1 and shRRAD#2). At the same time, the expression of RRAD was enhanced in the Mia PaCa-2 cell line. The efficiency of RRAD knockout and overexpression was verified by qPCR and Western blotting (Fig. 4a, b). We found that the level of lipid oxidation decreased in the SW1990 cell line with silenced RRAD expression, while the level of lipid oxidation increased in the Mia PaCa-2 cell line overexpressing RRAD (Fig. 4c). Therefore, we speculate that the expression level of glutathione as the main antioxidant is increased in the cell line with knockout of RRAD expression and decreased in the Mia PaCa-2 cell line with increased RRAD expression. To test this hypothesis, we tested the GSH/GSSG ratio in the SW1990 cell line with silenced RRAD expression and the Mia PaCa-2 cell line overexpressing RRAD (Fig. 4d). As expected, RRAD decreased the expression of GSH. At the same time, GSH is closely related to GPx4 [19], a key substance regulating ferroptosis. Therefore, we used the BODIPY $\frac{581}{591}$C11 probe to detect the level of lipid peroxidation in the SW1990 cell line, which silenced RRAD expression, and the Mia PaCa-2 cell line, which overexpressed RRAD (Fig. 4f). The results showed that RRAD promoted lipid peroxidation in pancreatic cancer cells. Lipid peroxidation is the most important marker of ferroptosis [20], and RRAD promotes lipid peroxidation. This suggests that RRAD may promote ferroptosis. To verify this, we added a ferroptosis inducer (RSL3) to the SW1990 cell line to silence RRAD expression. The ferroptosis inducer (RSL3) inhibited the increased cell viability of RRAD-silenced cells. The ferroptosis inhibitor ferrostatin-1 was added to the Mia PaCa-2 cell line to overexpress RRAD. The results showed that the ferroptosis inhibitor ferrostatin-1 partially reversed the lipid peroxidation caused by RRAD overexpression (Fig. 4e). This indicates that RRAD promotes the occurrence of ferroptosis in pancreatic cancer cells. Fig. 4RRAD enhances ferroptosis in pancreatic cancer cells. A, B The shRNAs against RRAD plasmid were transfected into SW1990 cell line. Plasmid overexpressing RRAD was transfected into Mia PaCa-2 cell line. Western blot analysis and qPCR analysis were performed to exam SETD8 protein and mRNA levels, respectively. C The MDA was detected in RRAD-silenced SW1990 cell line and RRAD-overexpressed Mia PaCa-2 cell line. The results showed that RRAD could promote the expression of MDA. D The GSH/GSSG ratio was detected in RRAD-silenced SW1990 cell line and RRAD-overexpressed Mia PaCa-2 cell line. It was found that silencing RRAD could increase the GSH/GSSG ratio and overexpression of RRAD could decrease the GSH/GSSG ratio. E Cell viability was detected in the RRAD-silenced SW1990 cell line in the presence or absence of 2 μmol/l RSL3 and the RRAD-overexpressed Mia PaCa-2 cell line in the presence or absence of 2 μmol/l Fer. The results showed that the cell viability increased by RRAD knockdown could be reversed by the ferroptosis inducer RSL3 and the cell viability decreased by RRAD overexpression could be reversed by the ferroptosis inhibitor Fer. F Flow cytometry analyzed the fluorescence of BODIPY$\frac{581}{591}$C11 (lower) and the relative content was calculated (upper). The results showed that RRAD could increase the level of intracellular lipid peroxidation. Two tailed unpaired Student t-test was used in the above experiments. * $P \leq 0.05$; **$P \leq 0.01$
## SETD8 suppresses ferroptosis in pancreatic cancer by downregulating RRAD
To further investigate how SETD8 affects the occurrence of ferroptosis in pancreatic cancer, we speculated that SETD8 may inhibit the occurrence of ferroptosis by inhibiting the expression of RRAD. Therefore, we constructed a stable pancreatic cancer cell line that silenced SETD8 and RRAD and silenced SETD8 and RRAD simultaneously in SW1990 cell lines. We also constructed stable pancreatic cancer cell lines that stably expressed SETD8 and RRAD and overexpressed SETD8 and RRAD in MIA PaCa-2 cell lines. Then, we performed qPCR and Western blotting to verify the efficiency of knockout and overexpression of the above cell lines (Fig. 5a, b). Then, we used the BODIPY $\frac{581}{591}$c11 probe to detect the level of lipid peroxidation in the above treated cell lines at the same time (Fig. 5d). The results showed that the increase in lipid peroxidation levels in cancer cells caused by knockout of SETD8 could be reversed by silencing the expression of the RRAD gene. The increase in lipid peroxidation caused by overexpression of RRAD can be reversed by overexpression of the SETD8 gene. Fig. 5SETD8 influences the occurrence of ferroptosis in pancreatic cancer through RRAD. A, B qPCR and Western blot assay confirmed the efficiency of shRNAs targeting SETD8, RRAD or both in the SW1990 cell line and the overexpression efficiency of SETD8, RRAD or both in the Mia PaCa-2 cell line. C Cell viability was detected in SETD8-, RRAD- or both-silenced SW1990 cell lines and in SETD8-, RRAD- or both-overexpressed Mia PaCa-2 cell line. The results showed that overexpression of SETD8 enhanced cell viability, which was reversed by overexpression of RRAD. The increase in cancer cell activity caused by silencing RRAD expression was reversed by silencing SETD8 expression. D Flow cytometry analyzed the fluorescence of BODIPY$\frac{581}{591}$C11 (lower) and the relative content was calculated (upper). The results showed that SETD8 knockout induced elevated lipid peroxidation in cancer cells, which could be reversed by silencing RRAD expression. The elevated lipid peroxidation induced by RRAD overexpression was reversed by overexpression of SETD8. * $P \leq 0.05$; **$P \leq 0.01$ To more directly observe the effect of the SETD8 gene on the viability of pancreatic cancer cells, we detected the viability of these different pancreatic cancer cells. The results showed that overexpression of the SETD8 gene increased the activity of cancer cells, which could be reversed by overexpression of the RRAD gene. The increase in cancer cell viability caused by silencing the expression of the RRAD gene can be reversed by silencing the expression of the SETD8 gene (Fig. 5c). Therefore, the above results suggest that SETD8 can inhibit ferroptosis in pancreatic cancer by downregulating the expression of RRAD.
## SETD8 interacts with the promoter region of RRAD to inhibit its expression
To better understand the mechanism by which SETD8 affects RRAD expression, we carried out affinity purification and overexpression of SETD8 labelled with FLAG (FLAG-SETD8) in Mia PaCa-2 cells. Cell extracts were prepared and affinity purified using an anti-flag affinity gel. Double luciferase reporter analysis was performed in HEK293T cells, and SETD8 was overexpressed in the luciferase reporter driven by the RRAD promoter (Fig. 6a). The results showed that RRAD reporter activity decreased gradually with increasing SETD8 overexpression. To further confirm the specific binding of SETD8 to the RRAD promoter, SW1990 and MIA PaCa-2 cells were collected for chromatin immunoprecipitation (CHIP) analysis to verify the occupation of SETD8 on the RRAD promoter. The results confirmed that SETD8 occupied the RRAD promoter region (Fig. 6b, c). *In* general, SETD8 inhibits RRAD transcription by interacting with the RRAD promoter, thus inhibiting ferroptosis in pancreatic cancer cells (Fig. 6d).Fig. 6SETD8 binds to the promoter of RRAD to inhibit its expression. A Dual-luciferase assay showed that the fluorescence intensity decreased gradually with the increase of SETD8 expression. SETD8 suppressed RRAD promoter activity in HEK293T cells. B, C CHIP‒qPCR analyses confirmed that SETD8 specifically bound to the RRAD promoter in SW1990 cells and Mia PaCa-2 cells. D Schematic diagram of the mechanism of SETD8 inhibiting cell ferroptosis. SETD8 inhibits the transcription of RRAD by binding to the promoter of RRAD, which leads to a decrease in the level of lipid peroxidation, thus reducing the occurrence of ferroptosis in PDAC. Two tailed unpaired Student t-test was used in the above experiments. * $P \leq 0.05$; **$P \leq 0.01$
## Discussion
Pancreatic cancer is a highly malignant and aggressive tumour. The total survival rate of pancreatic cancer patients in the last 5 years is less than $10\%$ [26, 27]. The RAS superfamily plays an important role in cell physiological activities. Its mutation or abnormal activation will promote the occurrence and progression of cancer [28, 29]. KRAS mutations can be observed in more than $95\%$ of pancreatic cancers [17]. However, no obvious progress has been made in the treatment strategy of pancreatic cancer. Currently, the chemotherapy regimen is gemcitabine, anaxx, Forfield and albumin-bound paclitaxel. However, pancreatic cancer has strong chemoresistance, the prognosis of chemotherapy is still very poor [30]. Therefore, it is urgent to find compounds that can selectively kill RAS mutant cancer cells. Brent R. Stockwell found specific compounds that can kill RAS mutant cancer cells among tens of thousands of small compounds. In addition, they found that these compounds killed cancer cells in a manner different from apoptosis and necrosis [15, 16]; this method of death is named ferroptosis. Increasing evidence shows that ferroptosis has broad application prospects in the clinic. Ferroptosis holds great promise in cancer therapy, especially in treating tumors that have developed resistance to traditional therapies [31]. There has been evidence that drug-resistant cancer cells easily undergo ferroptosis. Therefore, ferroptosis can be used as a targeted therapy for cancer [14, 32]. At the same time, using nanomaterials as drug carriers to induce ferroptosis in cancer cells also provides another option [33]. In addition, studies have shown that the induction of ferroptosis in pancreatic cancer cells can enhance their sensitivity to chemotherapy, such as gemcitabine and cisplatin [34, 35]. Therefore, further study of the role of ferroptosis in pancreatic cancer will help to provide a new direction for the treatment of pancreatic cancer.
In this study, we found that lysine methyltransferase SETD8, a member of the SET domain family, plays an important and common role in regulating the cell cycle and tumour growth. It can increase the GSH/GSSG ratio in pancreatic cancer and reduce the level of lipid peroxidation. A high GSH/GSSG ratio and low lipid peroxidation can inhibit ferroptosis [36, 37]. We further demonstrated that SETD8 can inhibit the occurrence of ferroptosis in pancreatic cancer. To elucidate the underlying mechanisms, we screened gene expression profiles. In SETD8 downregulated genes, we found that RRAD, a member of the RAS-related GTPase subfamily and also known as the RAS family diabetes-related gene, can inhibit tumour cell proliferation and migration and has been identified as a tumour suppressor gene in many tumours [21–24]. We further found that RRAD could inhibit the proliferation of pancreatic cancer. In addition, RRAD can also reduce the GSH/GSSG ratio in pancreatic cancer and increase the level of lipid peroxidation. This indicates that RRAD can promote the occurrence of ferroptosis in pancreatic cancer. Our further experiments show that SETD8 inhibits RRAD transcription and that the SETD8 knockout-induced increase in lipid peroxidation levels in pancreatic cancer cells can be reversed by silencing RRAD gene expression. The increase in lipid peroxidation caused by overexpression of RRAD can be reversed by overexpression of the SETD8 gene. Accordingly, ferroptosis inhibitors can save the cell viability reduced by low expression of SETD8. At the same time, based on the results of IHC analysis, the higher the level of SETD8 or the lower the RRAD level, the worse the prognosis of pancreatic cancer patients. In order to further clarify how SETD8 inhibits RRAD transcription. Through CHIP experiments, we demonstrated that SETD8 regulates RRAD expression by specific binding to the RRAD promoter region.
SETD8 has been shown to affect the progression of diabetic nephropathy by regulating bach1 transcription [38]. It has also been found that SETD8 can promote tumour cell growth and metastasis through the receptor tyrosine kinase ROR1 [39]. Meanwhile, SETD8 promoted the development of endometrial cancer by inhibiting the function of tumour suppressor genes through H4K20 methylation and p53 expression [40]. In this study, we demonstrated that SETD8 can also promote tumor cell growth by inhibiting ferroptosis. Studies have found that RRAD can inhibit tumour cell proliferation, migration and Warburg effect by downregulating ACTG1 expression [41, 42]. We demonstrate for the first time an association of RRAD with ferroptosis. RRAD can inhibit tumour cell growth through ferroptosis. The expression of RRAD was closely related to the prognosis of pancreatic cancer patients. Pancreatic cancer patients with high RRAD expression have a better prognosis than those with low RRAD expression. Furthermore, we found that SETD8 inhibited the ferroptosis in pancreatic cancer by binding to the promoter region of RRAD, revealing the role of SETD8-RRAD-ferroptosis axis in the regulation of pancreatic cancer.
However, the limitation of this study is that it does not explain how SETD8 combines with the promoter region of RRAD to regulate the transcription of RRAD. We speculate that SETD8 may inhibit the transcription of RRAD by binding a transcription factor to the promoter of RRAD. GPx4 is a key gene in the regulation of ferroptosis and we did not further investigate the relationship between RRAD and GPx4. At the same time, the role of SETD8-RRAD-ferrodeath axis in pancreatic cancer was not further verified in animal experiments.
Taken together, SETD8 promotes the growth of pancreatic cancer cells by inhibiting ferroptosis. RRAD inhibits ferroptosis in pancreatic cancer cells. Meanwhile, low expression of RRAD is closely related to poor prognosis of pancreatic cancer patients. STED8 inhibits the transcription of RRAD by binding to the promoter region of RRAD and thus reduces the expression of RRAD. Finally, it inhibits the ferroptosis of pancreatic cancer cells and promotes the proliferation of pancreatic cancer. These results reveal that the SETD8-RRAD-ferroptosis axis may provide potential therapeutic targets and predictors for the treatment of pancreatic cancer.
## Conclusions
Our study reveals the role of SETD8-RRAD in the occurrence of ferroptosis in pancreatic cancer. The combination of SETD8 and the RRAD promoter subregion results in the inhibition of RRAD transcription, thereby affecting the occurrence of erroptosis in pancreatic cancer. These results may provide new strategies for the induction of ferroptosis in pancreatic cancer and provide a new direction for the comprehensive treatment of pancreatic cancer.
## Supplementary Information
Additional file 1. The original figures of the western blot.
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|
---
title: Endocannabinergic modulation of central serotonergic activity in healthy human
volunteers
authors:
- Barbara Emons
- Larissa Arning
- Vera-Estelle Makulla
- Maria-Theresia Suchy
- Dimitrios Tsikas
- Thomas Lücke
- Jörg T. Epplen
- Georg Juckel
- Patrik Roser
journal: Annals of General Psychiatry
year: 2023
pmcid: PMC10024405
doi: 10.1186/s12991-023-00437-2
license: CC BY 4.0
---
# Endocannabinergic modulation of central serotonergic activity in healthy human volunteers
## Abstract
### Background
The serotonergic and the endocannabinoid system are involved in the etiology of depression. Depressive patients exhibit low serotonergic activity and decreased level of the endocannabinoids anandamide (AEA) and 2-arachidonylglycerol (2AG). Since the cannabinoid (CB) 1 receptor is activated by endogenous ligands such as AEA and 2AG, whose concentration are controlled by the fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase, respectively, we investigated the effects on serotonergic utilization. In this study, we investigated the impact of the rs1049353 single-nucleotide polymorphism (SNP) of the cannabinoid receptor 1 (CNR1) gene, which codes the endocannabinoid CB1 receptor, and the rs324420 SNP of the FAAH gene on the serotonergic and endocannabinoid system in 59 healthy volunteers.
### Methods
Serotonergic activity was measured by loudness dependence of auditory-evoked potentials (LDAEP). Plasma concentrations of AEA, 2AG and its inactive isomer 1AG were determined by mass spectrometry. Genotyping of two SNPs (rs1049353, rs344420) was conducted by polymerase chain reaction (PCR) and differential enzymatic analysis with the PCR restriction fragment length polymorphism method.
### Results
Genotype distributions by serotonergic activity or endocannabinoid concentration showed no differences. However, after detailed consideration of the CNR1-A-allele-carriers, a reduced AEA (A-allele-carrier $M = 0.66$, SD = 0.24; GG genotype $M = 0.72$, SD = 0.24) and 2AG (A-allele-carriers $M = 0.70$, SD = 0.33; GG genotype $M = 1.03$, SD = 0.83) plasma concentration and an association between the serotonergic activity and the concentrations of AEA and 2AG has been observed.
### Conclusions
Our results suggest that carriers of the CNR1-A allele may be more susceptible to developing depression.
## Introduction
Major depression is among the most common and in terms of its severity the most underestimated disease. The fact, that the disease impacts the well-being and the quality of life, thus causing a high psychological strain [30]. Major depression is characterized by mental dejectedness as lead symptom and shows cyclic courses. Further symptoms can be detected in the emotional, motivating, cognitive and neuro-vegetative area and in the psychomotor activity [2]. The manifestation of the disease varies while the symptomatology shows a common characteristic [42]. Depressive diseases are heterogeneous illnesses, which can be traced back on the interaction of multiple genes with environment factors such as stress [10, 35, 31].
Disorders of the serontonergic system are being discussed as pathogenetic factors for the emergence and characteristic of psychiatric diseases such as depression [48]. In the pharmacological treatment of depression, the serotonergic system has proven to be an effective point of action for therapy [9]. The monoamine hypothesis of depression is the most popular theory, it states that a reduced monoaminerge transmission within the central nervous system is the reason for depression [52]. The successful application of serotonin and noradrenalin-based antidepressants like selective serotonin reuptake inhibitors (SSRIs) confirms this hypothesis [51]. The defective regulations of the serotonin balance during a depression phase are found in different parts of the serotonergic system [38, 53].
The endocannabinoid system plays an important role in the modulation of the serotonergic system [29]. There are two cannabinoid (CB) receptors (CB1 and CB2) in the endocannabinoid system, whose natural ligands and specific enzymes are responsible for their biosynthesis and metabolic inactivation. It has been shown that the endocannabinoid system is involved in the pathophysiology of depression [25, 54, 57]. The CB1-receptor is a G-protein-coupled receptor, which is expressed in the central nervous system [19] and plays a role in the serotonergic neurotransmission [4, 20]. Five endocannabinoids exists, whereof Anandamid (AEA) and 2-Arachidonoylglycerol (2AG) are the most potent substances. Endocannabinoids can be considered as neuromodulators at the CB1 receptor [14].
*Several* genetic studies show that there is an interaction between the CB1 receptor gene (CNR1), the serotonin receptor gene (SERT) and the anxiety disorder [37]. Several experimental examinations in humans reveal a significant association between the functional (AAT)n-polymorphism within the CNR1 gene and depression by patients with Parkinson’s disease [6]. It is, however, assumed that a changed CNR1 expression plays an important role for the pathogenesis of depression [57]. A lower CB1 receptor density has been found in the cortex of patients with major depression [36]. The CNR 1 rs1049353 A allele exhibits an increased risk for developing a depression particularly in haplotypic combination [34]. The effect of variation within the CNR1 gene for depression have been shown [43]. Furthermore, the CNR1 gene is negatively regulated by glucocorticoids. Hypercortisolemia is present in depressed patients, so this supports the hypothesis that there is decreased activity of the endocannabinoid system in these patients [39]. The CNR1 polymorphism studied, rs 1049353, could influence CNR1 receptor functions by affecting translation or mRNA stability [11].
The CB-1 receptor is activated by endogenous ligands such as AEA and 2AG. The hydrolytic enzymes Fatty Acid Amide Hydrolase (FAAH) and monoacylglycerol lipase (MAGL) are responsible for the metabolism of AEA and 2AG [12, 16]. In animal studies, CB1 receptor antagonist, endogenous cannabinoid reuptake inhibitor or inhibitor of FAAH enzymes generate an antidepressive effect [1, 20, 25]. The direct activation of central CB1 shows in animal models of depression (e.g. forced swim test, tail suspension test) a significant antidepressive effect [5, 20, 25, 56]. By analogy, depressive patients show a reduced 2AG-concentration in serum, whereby the concentration is negatively correlated with the duration of depression [28]. Single nucleotide polymorphism (SNP) in the rs324420FAAH genes lead to be relevant and have an impact on brain biochemistry, neurocircuitry, behavior and symptoms [15]. Moreover, this polymorphism reduces FAAH activity, resulting in increased anandamide serum concentrations [55].
Loudness dependence of auditory evoked potentials (LDAEP) is an established measure for the central serotonin-system activity [24, 33, 60]. A low LDAEP is implied by a high serotonergic neurotransmission [32]. A significant correlation in depressive patients is shown between the increased stimulus intensity of LDEAP measured with low serotonergic function and successful response on SSRIs [23, 32, 47]. Alterations in serotonin neurotransmission may increase the risk of psychiatric disorders, so “Loudness dependence of auditory evoked potentials” (LDAEP)—a non-invasive method may be applied to measure its activity. Preclinical and animal studies have shown that LDAEP is an indicator for serotonergic neurotransmission/activity in the brain [24, 33, 49, 60].
The aim of the present study was to investigate possible associations between the endogenous cannabinoid system and the central serotonergic activity in healthy volunteers. Central serotonergic transmission is determined indirectly by the loudness dependence of auditory evoked potentials. For this, we determine the distribution and effect of SNPs within the CNR1 gene and the FAAH gene and endocannabinoids concentration (AEA, 2AG) and the serotonergic activity.
Here, we hypothesize that CNR 1 rs1049353 A carriers and in the presence of SNPs in rs324420FAAH genes a reduced AEA and 2AG concentration in plasma is present. In addition, we suspect reduced serotonergic activity in the presence of these.
## Study population
The sample contained 59 healthy probands (31 females and 28 males, average age 28.47 ± 7.62 years). The test persons were right handed, normal hearing and non-smokers. Neither at the time of the survey nor in their health history they suffered from psychiatric diseases or acute, severe and unstable somatic illnesses. In their past, there was no substance abuse and no regular use of medicines. No psychiatric diseases of first degree relatives existed and there was also no low intelligence or suicidality. The investigation of psychometry was done by the following tests: Multiple Choice Word Test (MWT-B), Hamilton Depression Rating Scale (HAMD-21), Beck Depression Inventory (BDI), State-Trait Anxiety Inventory (STAI) und Brief Psychiatric Rating Scale (BPRS). Thereby, no psychiatric disorders were emerged. The mean score and the ranges of the psychometry are listed in the following: MWT-B 114.9 (90–129), HAMD-21 0.6 (0–4), BDI 1.54 (0–10), STAI 29.8 (20–58) and BPRS 18.1 (18–21).
## LDAEP measurement
To determine the central serotonin-system activity, the Loudness dependence of auditory evoked potentials (LDAEP) method was executed in a noise reduced and electrically shielded room adjacent to the measurement equipment. This was done via the subject´s eyes open using the Brain Vision BrainAmp ® MR method (Brain Products GmbH, Munich, Germany). The test persons sat in a slightly lying position on a chair with a headrest.
For the EEG acquisition, 32 non-polarized silver–silver chloride electrodes were attached to a suitable EEG cap (Easy Cap®) according to the $\frac{10}{20}$ system (impedance ≤ 10 kΩ). The acoustic stimulation contained five stimulations with an inter-stimulus interval randomized between 1800 and 2200 ms. Tone bursts of 1000 Hz (Hertz) with a duration of 40 ms (milliseconds) (rise-/fade-time 10 ms) were presented to the test persons in 5 intensities (60, 70. 80, 90, 100 dB SPL) via headphones. The stimuli were created with E-Prime Software (Presentation 11.3® Neurobehavioral Systems Inc. Albany, CA, USA). The eye movement is captured by an electrode, which is placed 1 cm below the outside left corner of the eye. The impedance was kept below of 5 kΩ (kilo ohms). The EEG was recorded with a sampling rate of 256 Hz and an analogue bandpass filter (0, 16–70 Hz). At least 40 artifact-free trials per intensity were included in the further analysis (Brain Products GmbH, Munich, Germany). A semi-automatic measurement at the Cz (central zero) electrode was carried out at peaks of N1 (60–125 ms) and P2 (110–210 ms). The N1/P2 (negativ/positiv) amplitude was defined as the difference in the peak amplitude between N1 and N2. The calculation of LDAEP was executed by means of the least square linear regression slope, while the stimulus intensity was considered as the independent variable and the N1/P2 amplitude was considered as the dependent variable.
## Genotyping SNPs
DNA of the participants was extracted from EDTA (ethylendiaminotetraacetat)-anticoagulated peripheral blood by using a QIAamp DNA mini Kit (Qiagen GmbH, Hilden, Germany) under the protocol of Qiagen. SNPs were chosen for genotyping by selecting SNPs from the literature (CNR1 rs1049353, FAAH rs324420). Genotyping/allel determination was conducted by polymerase chain reaction (PCR) and differential enzymatic analysis with the PCR restriction fragment length polymorphism method. Oligonucleotides were designed using Primer Express 2.0 Software (Applied Biosystems).
## Endocannabinoid measurements
Endocannabinoid measurements were performed at the Institute of Clinical Pharmacology, Hannover Medical School, Germany. Measurements were carried out on an ultra-performance liquid chromatograph model ACQUITY coupled with a tandem mass spectrometer (UPLC-MS/MS) model XEVO TQ MS (Waters, Milford, MA, USA) as reported elsewhere [61, 62]. We determined simultaneously the plasma concentration of AEA, 2AG and 1AG. Venous blood was collected early in the morning from subjects, who has fasted (not had anything to eat or drink) about 8 h. Venous blood samples were taken and drawn into EDTA-containing tubes (BD Vacutainer, Franklin Lakes, NY, USA) and centrifuged at 3500 rpm (rounds per minute). The time interval between blood sampling and centrifugation was kept below 10 min. Plasma aliquots were stored at − 80 °C until assay. The stored samples were thawed on ice and 300-µL aliquots thereof were spiked with a mixture of deuterated (d) analogs in ethanol, i.e., d4-AEA and d5-2AG, to reach a final concentration of 2.5 nM each. d4-AEA and d5-2AG, served as internal standards for AEA and 2AG, respectively. Then, plasma samples were incubated for 15 min on ice. Extraction was conducted by adding 1 mL toluene to each sample and by shaking twice in a Precellys®24 Dual homogenizer at 5000 rpm for 20 s with interruption of 5 s, so that the warming of the samples was prevented. The phase separation was carried out by centrifugation (4655×g, 4 °C, 5 min). The upper organic phase was transferred into a 1.5-mL glass vial. After solvent evaporation under a nitrogen stream, the residue was dried at room temperature (25 °C) under nitrogen. The residues were reconstituted in 40 µL of water–methanol (1:3, v/v) and mixed by vortexing for 10 s, and 10-µL aliquots were injected. Quantitative analyses were performed in positive electrospray ionization and selected–reaction monitoring modes as described previously [62].
## Statistical analysis
Pearson correlation analysis was used to determine the correlation of the whole sample and the eCS (endocannabinoids) concentration and the LDAEP value. For genotypes and alleles specific association with eCS concentration one-way ANOVA and t-Tests was conducted. SPSS Ver.21.0 for Windows (SPSS Inc.) was used for all statistical analysis. Data are reported as mean ± standard deviation (SD). p values less than 0.05 were considered statistically significant.
## Results
Healthy volunteers did not differ in age and an equal distribution existed for gender. The mean of the years of education was 16.56 ± 2.50. The mean score of the alcohol use was 2.6 drinks per week in a range of 0–15. Genotype and alleles distribution are displayed in Table 1. No psychiatric disorders were recorded. The central serotonergic activity, measured by LDAEP had a mean value of 0.252 ± 0.15. The consideration of the alleles distribution in matters of LDEAP showed, that the healthy volunteers with homozygous G (guanine) alleles of the rs 1049353CNR1 gene in mean was a trend towards lower LDEAP score than the A (adenosine) allele carriers (0.233 ± 0.121 vs. 0.277 ± 0.169, $$p \leq 0.173$$). In case of the alleles allocation for the rs 324420FAAH gene, the homozygous C (cytosine) allele carriers displayed in mean a nearly identical LDAEP score than the A-allele-carriers (0.255 ± 0.161 vs. 0.243 ± 0.114) (Table 1).Table 1Genotype and allele distribution of the rs 1049353CNR1 and rs 324420FAAH variations in healthy volunteers and mean and standard deviation of LDAEP score and concentration of the endocannabinoids (AEA, 2AG and totalAG) for the genotype and allele distributionGenotypeGroup of genotypeGGGAAAGGGA + AACNR1 (rs1049353) N (%)27 (40.5)30 (54.8)2 (4.7)27 (40.5)32 (59.5) LDAEP(Mean, SD)0.223 ± 0.120.272 ± 0.170.347 ± 0.930.223 ± 0.120.277 ± 0.17 AEA (nmol/L)(Mean, SD)0.71 ± 0.230.65 ± 0.200.51 ± 0.080.71 ± 0.230.64 ± 0.24 2AG (nmol/L)(Mean, SD)1.02 ± 0.820.66 ± 0.240.78 ± 0.161.02 ± 0.820.66 ± 0.24 TotalAG (nmol/L)(Mean, SD)2.88 ± 3.021.50 ± 0.461.94 ± 0.592.88 ± 3.021.53 ± 0.47CCCAAACCCA + AAFAAH (rs324420) N (%)44 (75.5)13 (21.2)2 (3.3)44 (75.5)15 [25] LDAEP(Mean, SD)0.255 ± 0.160.243 ± 0.120.247 ± 0.090.255 ± 0.160.243 ± 0.11 AEA (nmol/L)(Mean, SD)0.64 ± 0.210.74 ± 0.170.90 ± 0.550.64 ± 0.210.76 ± 0.22 2AG (nmol/L)(Mean, SD)0.76 ± 0.461.08 ± 0.900.42 ± 0.260.76 ± 0.460.99 ± 0.87 TotalAG (nmol/L)(Mean, SD)2.01 ± 1.622.67 ± 3.491.40 ± 0.622.01 ± 1.622.50 ± 3.27 *The plasma* concentration was 0.67 ± 0.22 nmol/L for AEA, 0.82 ± 0.59 nmol/L for 2AG, and 2.13 ± 2.14 nmol/L for total AG, i.e., 2AG + 1AG (Table 1). Looking closer into the data of alleles distribution of the endocannabinoid concentration revealed that homozygous C allele carriers for the rs 324420FAAH gene displayed lower concentrations of the analysed endocannabinoids than the A-allele-carriers (AEA: 0.64 ± 0.21 nmol/L vs. 0.76 ± 0.22 nmol/L, $$p \leq 0.076$$; 2AG: 0.76 ± 0.46 nmol/L vs. 2AG: 0.99 ± 0.87 nmol/L, $$p \leq 0.199$$; total AG 2.01 ± 1.62 nmol/L vs. 2.50 ± 3.27 nmol/L, $$p \leq 0.452$$) (Table 1). There were no statistical differences detected when comparing genotype and allele frequencies related to the mean score of plasma endocannabinoid concentrations.
Group comparison of the LDAEP scores to evaluate the effects of the genotypes and alleles distribution of the focused genes the rs 1049353CNR1 und rs 324420FAAH showed no statistical significances.
Group comparison by t test of homozygous GG carriers and A carrier of the rs 1049353CNR1 gene revealed a significant association of the CNR1 and the 2-AG concentration ($t = 2.352$, df = 56, $$p \leq 0.022$$; Fig. 1), as well as of CNR1 and total AG concentration ($t = 2.500$, df = 56, $$p \leq 0.015$$, Fig. 2) evident. Fig. 1Plasma level comparison of the endocannabinoid 2AG between the A-allele carriers and the GG carriers of the rs 1049353CNR1 variation (*$p \leq 0.005$, significant one-way ANOVA)Fig. 2Plasma level comparison of the endocannabinoid total AG between the A-allele-carriers and the GG carriers of the rs 1049353CNR1 variation. (* $p \leq 0.005$, significant one-way ANOVA) *Correlation analysis* of the whole population were calculated by Pearson, they detected no significant correlation between the LDAEP value and the concentration of the reviewed endocannabinoids (AEA, 2AG and total AG).
The t test of LDAEP scores to evaluate the effects of the genotypes and alleles distribution of the focused genes rs 1049353CNR1 und rs 324420FAAH show no significances as well.
There was a tendency toward a negative for the subgroup of A-allele-carrier of rs 1049353CNR1 gene in reference to the LDAEP value and the endocannabinoid AEA (r = − 0.185, $$p \leq 0.156$$). But the effect is not significant. In addition, for this subgroup, there was a trend toward negative correlation in terms of LDEAP values and the concentration of the endocannabinoid 2AG (r = − 0.234, $$p \leq 0.099$$).
## Discussion
The interaction between components of the endocannabinoid system and central serotonergic transmission in healthy volunteers was the basis of our studies, which examined, peripheral serum concentrations of the endocannabinoids 2-AG and anandamide and a single-nucleotide polymorphism of each of the CNR1 gene (rs1049353) and the FAAH gene (rs324420) and, LDAEP as an indirect measure of central serotonergic transmission.
*The* genetic aspects of polymorphism of central receptors or enzymes of the endocannabinoid system were of particular importance for the understanding of the processes. Delineating the underlying mechanisms improves our knowledge of depression and may contribute to better treat this disease. The present analysis investigating rs 1049353CNR1 and rs 324420FAAH gene SNP variants in healthy volunteers. In about half of the healthy subjects, a homozygous G allele was present in the rs 1049353CNR1 SNP studied. Whereas Monteleone et al. [ 44] displayed a three-quarter share in homozygous GG genotype of CNR1 gene in healthy controls. The difference of the distribution could be caused by the sample size. On the other hand, FAAH genotype distribution was nearly the same in both studies.
Evidence for the role of endocannabinoid signalling in the regulation of the serotonin system is suggested by behavioural studies showing a high level of functional overlap between the serotonin and the endocannabinoid system [40, 41, 46, 59].
Looking at the genotypes and alleles distribution of the SNPS rs 324420FAAH genes and rs 1049353CNR1 in healthy volunteers of our study, no statistical differences were detected with respect to serotonergic activity. Nevertheless, Dlugos et al. [ 17] showed a genetic variation of rs 324420FAAH is associated with specific mood responses, whereas clinical studies displayed that genetic variability of the CNR1 gene predicts an effect to etiology of major depression and clinical response [43]. Different results between our and the groups are likely to be due to differences in the study populations, for instance, patients versus healthy volunteers in our study.
With regard to the genotypes distribution of rs 1049353CNR1 and rs 324420FAAH genes in our study and the focus on the plasma concentration of AEA, 2AG and total AG, there were also no differences displayed in healthy volunteers. This also applies to the allele distribution of the FAAH gene. Whereas Chiang et al. [ 11] have shown that polymorphism in rs 324420FAAH determine in approximately half of the enzymatic activity of FAAH in humans, so that an increased activation of AEA and 2AG is triggered. As a consequence, the concentration of the investigated endocannabinoids had to be reduced. These results were also replicated in further investigations [15, 55]. In our study, we did not find higher AEA plasma concentrations suggesting that FAAH activity is not diminished in the healthy volunteers of our study. Following this line of investigation in animal models, a reduction of FAAH activity results in the increase level of endogenous cannabinoids AEA and 2AG, so an inhibition of the FAAH enzyme has antidepressant effect [20]. The CNR 1-A-allele exhibits an increased risk for developing a depression particularly in haplotypic combination [34]. A closer analysis of the allele distribution of the CNR1 gene, particularly the A-allele-carriers and the homozygous G-allele-carriers distribution, appears to show an association of the haplotype and the concentrations of the endocannabinoids AEA and 2AG. The measurement of the endocannabinoid concentrations in A-allele-carriers showed a lower concentration than in the homozygous G-allele-carriers. This is evident in various studies. Umathe et al. [ 56] showed an interaction between endocannabinoids (AEA, 2AG) and the serotonergic system in the regulation of depressive and compulsive behaviour. Reduced AEA and 2AG concentrations are determined in patients with major depression [26–28]. This means that a modification of the central endocannabinoid system caused a variation in major depression. Data of animal studies had already shown that regulation of either CB1 receptor antagonist, endogenous cannabinoid reuptake inhibitor or inhibitor of FAAH enzymes have generated an antidepressive effect [1, 20], [25]. The direct activation of central CB1 receptors with exogenous applied synthetic CB1-receptor agonists (e.g. HU210, WIN55, 212-2), by indirect activation of central CB1-receptor with selective FAAH inhibitors (e.g. URB597) or inhibitors of the membrane anandamid-transporter-receptor (e.g. AM404) showed in animal models of depression (e.g. forced swim test, tail suspension test) a significant antidepressive effect [20, 56, 25]. By administration of selective CB1-receptor antagonists (e.g. AM251, SR141716) nearby a CB1-receptor-mediated mechanism, a blocking of the effect is shown. By analogy, depressive patients show a reduced 2AG concentration in serum, whereby the concentration is negatively correlated with the duration of depression [28].
Further analysis of the FAAH-A-allele-carrier in terms of the dependency of the serotonergic activity and the plasma concentration of the investigated endocannabinoids indicated no interactions. Indeed, the same consideration of the subgroup of CNR1-A-allel-carriers showed specific coherencies. In this sense, it has been indicated that lower concentrations of the endocannabinoid AEA and 2AG came along with lower LDAEP score. These results point towards an influence of the CNR1 genetic variation on the endocannabinoid concentrations and the serotonergic activity. Considering that CNR1-A-allele carriers have an increased risk to predict a major depression these results are accompanied with the findings of other studies that endocannabinoid concentrations were decreased in major depression [26–28].
Our findings were reinforced by the results in clinical studies. These studies showed an interaction between the endocannabinoid system and depression. The THC-analogue Dronabinol® showed antidepressive effects [8], while the selective CB1-receptor-antagonist SR141716 tended to result in depressive symptoms [13, 45]. The functional interaction of the serotonergic and endocannabinoid system in brain plays a potential role of CB1 receptor signal in psychiatric disease like depression [18]. The interplay of cannabidiol and endocannabinoids [7] promote the activation of 5-HT1A receptors [50], so a possible connection of the endocannabinoid system and the serotonergic system in case of depression is supposable. There is evidence that increased endocannabinoid concentration and the stimulation of CB1 receptors reduce stress responses and anxiolytic effects [3, 21]. The fact that glucocorticoids modulate the excitability of 5-HT neurons results in an increased concentration of endocannabinoids. This gave an indication for a potential cellular mechanism, which is involved in the regulation of stress related behaviour [58]. Meanwhile, it is also known that the CB1 receptors are located on serotonergic neurons of the raphe nuclei. This implies that the endocannabinoid System modulates serotonergic functions [22]. These clinical results and our assumption indicate a relation between the serotonergic system and the endocannabinoid system in depression.
There are some limitations of the study. The small size of the genetic sample limits the power to detect small differences. Furthermore, the investigation comprised only one SNP of CNR1 and FAAH genes, there are many more applicable SNP within these genes. Due to the limited sample size, it was not possible to assess the differences that might exist between heterozygosity GA and homozygosity AA. Further limitation is the involvement of healthy volunteers. Extension of our conclusions to the major depression requires further studies on patients.
## Conclusion
In conclusion, our study shows no differences in serotonergic activity and plasma endocannabinoid concentration with respect to the genotype distribution of CNR1 and FAAH genes in healthy subjects. Finally, these results show that healthy subjects who are carriers of the CNR1-A allele have lower endocannabinoid concentrations than patients with major depression. Our results suggest that carriers of the CNR1-A allele may be more susceptible to the development of depression. In addition, an interaction between endocannabinoid concentration and the serotonergic system was demonstrated.
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|
---
title: 'Effects of maternal pre-pregnancy body mass index and gestational weight gain
on antenatal mental disorders in China: a prospective study'
authors:
- Xuan Zhou
- Lin Rao
- Dongjian Yang
- Tong Wang
- Hong Li
- Zhiwei Liu
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10024407
doi: 10.1186/s12884-023-05502-y
license: CC BY 4.0
---
# Effects of maternal pre-pregnancy body mass index and gestational weight gain on antenatal mental disorders in China: a prospective study
## Abstract
### Background
Maternal obesity is the most common medical condition among women of reproductive age worldwide. The pre-pregnancy body mass index and gestational weight gain have been suggested to be associated with maternal mental disorders. This study aimed to investigate the effects of the pre-pregnancy body mass index and gestational weight gain on antenatal depression, stress, and anxiety.
### Methods
In total, 4,890 pregnant women were enrolled in the present study, which is based on an ongoing prospective cohort study. We used self-reported pre-pregnancy weights and the last weights measured prior to delivery (using professional instruments) to calculate the pre-pregnancy body mass index and gestational weight gain. The questionnaires used included the Center for Epidemiologic Studies Depression Scale (CES-D), Self-Rating Anxiety Scale (SAS), and 10-item version of the Perceived Stress Scale (PSS-10). We used Pearson product-moment correlation and multivariable logistic regression models to examine the impact of the pre-pregnancy body mass index and gestational weight gain on different maternal mental disorders.
### Results
After adjusting for conception, annual household income, occupation, education, smoking status, and drinking status, excessive gestational weight gain during pregnancy was associated with a greater chance of anxiety symptoms in the entire sample (adjusted model: odds ratio = 1.479, $95\%$ confidence interval = 1.128, 1.938) and especially in women with a normal body mass index (adjusted model: odds ratio = 1.668, $95\%$ confidence interval = 1.209, 2.302). However, the relationship between the maternal pre-pregnancy body mass index and mental health was not significant.
### Conclusion
Pregnant women with a normal pre-pregnancy body mass index had a greater chance of experiencing anxiety symptoms before delivery if gestational weight gain was excessive; however, its effects on depression or stress symptoms were not observed. The maternal pre-pregnancy body mass index may not be independently associated with maternal mental disorders.
## Background
Pregnancy is a period of significant neurobiological and psychological changes brought about by physiological hormones, and is usually accompanied by an increase in various negative emotions [1]. Approximately one in five women experience antenatal mental disorders, such as depression and anxiety [2]. Perinatal depression (period prevalence: $18\%$ for prenatal depression and $14\%$ for postpartum depression) is one of the most common psychological problems in pregnant women [3]; its prevalence may be even higher in Asian populations ($24.3\%$) [4]. Perinatal anxiety and stress affect approximately $17\%$ and up to $84\%$ of all women, respectively [5]. A Chinese study revealed that more than $50\%$ of pregnant women have symptoms of anxiety and stress, especially in the late pregnancy and postpartum periods [6].Poor dietary intake and social support, an increased risk of preeclampsia, and pregnancy and labour complications are some harmful consequences of anxiety, depression, and stress during the antenatal period [7]. Furthermore, maternal mental disorders are associated with several adverse foetal and neonatal outcomes, such as low birth weights, preterm births, high rates of diarrhoea, poor breastfeeding practices, infectious illnesses, and poor cognitive development [8].
Maternal obesity is a growing public health concern; in fact, it is the most common medical condition in women of reproductive age worldwide [9].Women who are overweight or obese are more likely to experience excessive gestational weight gain (GWG) and postpartum weight retention [10]. The prevalence of obesity during pregnancy differs according to diverse guidelines [11]. Studies in population-based cohorts have shown that significant ethnic differences in the genetic background, living environment, and lifestyle lead to population-level differences in the body mass index (BMI) and GWG [12]. For instance, according to traditional Chinese customs, pregnant women are frequently required to overeat certain foods and reduce exercise during pregnancy [13]; however, overnutrition and a lack of exercise in mothers may lead to excessive GWG, postpartum weight retention, and macrosomia [14].
Evidence suggests that pre-pregnancy obesity and excessive GWG may increase the risk of adverse mental health outcomes in pregnant women [15–17]. Moreover, these factors may also negatively impact a woman’s self-image and self-esteem [18]. Ertel et al. found that pre-pregnancy obesity was associated with elevated depressive symptoms during the postpartum period [19]. A systematic review and meta-analysis also suggested that pre-pregnancy obesity was associated with an increased risk of maternal depressive symptoms and anxiety during pregnancy and in the postpartum period [17]. Kominiarek et al. conducted a survey to evaluate the association between prenatal stress and GWG; their findings suggested that the lowest stress scores were associated with adequate GWG [20]. However, all of these studies have considered a single symptom to measure the mental health of pregnant mothers and have ignored the multiple negative emotions experienced by these women. Stress, anxiety, and depressive symptoms are intercorrelated; their active assessment and management is required in the perinatal period [6].
Understanding the risk factors of perinatal emotion disorders, especially those that can be modified, can improve our ability to identify women at risk and provide additional ways for possible prevention and intervention. To the best of our knowledge, there are few studies (especially those considering racial factors) on the effects of pre-pregnancy BMI and GWG on antenatal mental disorders. In the present study, we aimed to assess the prevalence of pre-pregnancy BMI and GWG in and the sociodemographic characteristics of pregnant Chinese women. We hypothesized that the pre-pregnancy BMI and GWG would predict depression, stress, and/or anxiety symptoms during pregnancy.
## Design and participants
This prospective cohort study was performed as part of the ongoing China National Birth Cohort Study from March 2017 to May 2020. The study design was approved by the Ethics Committee of the International Peace Maternity and Child Health Hospital (affiliated with the Shanghai Jiao Tong University School of Medicine; approval no.: GKLW2016-21).Study participation was voluntary; the patients declared their willingness to participate only after examining the objectives and procedures of the study. All enrolled patients provided written informed consent.
The inclusion criteria were as follows: (i) women planning to seek prenatal care and deliver at the study hospital, (ii) women whose maternity files were established in the hospital, (iii) women with singleton pregnancies, (iv) women who could complete online questionnaires in Chinese, and (v) women willing to sign the consent form. The exclusion criteria were as follows: (i) women aged < 20 years; (ii) women with mental disorders, including depression or anxiety before pregnancy; (iii) women with mental disorders in the first and second trimesters of pregnancy; and (iv) women with serious underlying conditions. After enrolment, structured questionnaires were administered to pregnant women hospitalised for childbirth or before delivery. Thereafter, women with preterm births (i.e., delivery before 37 completed weeks of gestation); those who transferred to another hospital; those with insufficient pre-pregnancy BMI, weight, and height data; and those with other missing data were excluded. The final study sample comprised 4,890 women.
## BMI and GWG
BMI is a statistical index that is computed using a person’s weight and height; it provides an estimate of the body fat in men and women of any age. Pre-pregnancy BMI was calculated by dividing the participants’ pre-pregnancy weight (in kg) by their height (in m2). Based on the adult BMI classification standards for the Chinese population [21], we categorized our participants into the following four groups using their BMI: underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 24 kg/m2), overweight (24 kg/m2 ≤ BMI < 28 kg/m2), and obese (BMI ≥ 28 kg/m2).
GWG data were obtained by computing the difference between the last weight measured using professional instruments before delivery in the hospital and the self-reported pre-pregnancy weight. However, there are no official guidelines on GWG for the Chinese population. In the current study, maternal GWG was defined as excessive, adequate, and insufficient. We adopted the Institute of Medicine’s (IOM) recommendation for GWG and defined adequate GWG as being 12.5–18.0 kg, 11.5–16.0 kg, 7.0–11.50 kg, and 5.0–9.0 kg in underweight women, women with normal weight, overweight women, and obese women, respectively [22].
## Measurement of depressive symptoms
The Center for Epidemiologic Studies Depression Scale (CES-D) was used to measure the level of depressive symptoms in the enrolled women. The CES-D is a 20-item tool, with each item rated on a 4-point scoring system; the total score ranges from 0 to 60. Higher scores indicated a higher probability of an individual experiencing depression. A cut-off score of 16 was used to determine non-depression/depression in pregnant women and postpartum mothers [23]. The Chinese version of the scale has been reported to have good reliability and validity [24]. The Cronbach’s α was 0.924 in the current study.
## Measurement of anxiety symptoms
Anxiety symptoms were assessed using the Self-Rating Anxiety Scale (SAS), which was developed in 1971. It is primarily used to evaluate the severity of an individual’s anxiety. It is a 20-item scale, with each item rated using a 4-level score [25]. Higher scores indicate more severe anxiety symptoms. SAS has been reported to have good reliability and validity in China, and a standardised score of 50 is the upper limit for normative populations [26]. The Cronbach’s α was 0.894 in the current study.
## Measurement of perceived stress
Perceived stress was assessed using the 10-item Perceived Stress Scale (PSS-10). Each item was scored on a 5-point scale, with the total score ranging from 0 to 40 [27]. Higher scores indicate higher levels of perceived stress, and scores of 14 or above are indicative of moderate-to-high levels of perceived stress [28]. The Chinese version of this scale has demonstrated good reliability and validity [29]. The Cronbach’s α was 0.833 in the current study.
## Assessment of covariates
Several variables were assessed, including age, gestational age at delivery, method of conception, education, occupation, annual household income, parity, place of residence, ethnicity, drinking status, and smoking status. Sociodemographic data were assessed using an interviewer-administered questionnaire before delivery. If the gestational week differed from the delivery gestational week, the researcher manually modified it according to medical records.
## Statistical analysis
Descriptive statistics were used to summarise the participants’ characteristics. Continuous variables are expressed as means ± standard deviations, whereas categorical variables are expressed as percentages. An analysis of variance was performed to analyse the differences among the groups. The Chi-square and Fisher exact tests were performed to examine the association between two categorical variables. A Pearson’s correlation coefficient analysis was undertaken as an exploratory analysis to explore the relationship among the pre-pregnancy BMI, GWG, CES-D score, and SAS score. A binary logistic regression was performed to calculate the odds ratios (ORs) and $95\%$ confidence intervals (CIs) for the relationships among the pre-pregnancy BMI, GWG, and risk of exceeding scale thresholds (CES-D, SAS, and PSS-10 scores) in the entire sample. All statistical analyses were two-sided and performed using R version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria); $p \leq 0.05$ indicated statistical significance.
## Results
Among 4,890 participants, 999 ($20.4\%$), 2,059 ($42.1\%$), and 1,832 ($37.7\%$) had insufficient, adequate, and excessive GWG, respectively. Furthermore, $53.5\%$ and $43.4\%$ of the participants with an underweight and a normal BMI before pregnancy, respectively, had adequate GWG; conversely, $64.0\%$ and $73.2\%$ of the participants with an overweight and obese BMI before pregnancy had excessive GWG, respectively. Table 1 presents the participants’ demographic data according to the pre-pregnancy BMI categories. No significant differences were observed among the four groups in terms of the place of residence, occupation, ethnicity, smoking status, and drinking status ($p \leq 0.05$ for all). Women with pre-pregnancy obesity tended to be younger and had a higher proportion of excessive GWG (all $p \leq 0.05$). Significant intergroup differences were observed in terms of conception, annual household income, education, and parity: most women who conceived naturally and were underweight were more likely to have relatively a higher income and education level, and most were nulliparous before this pregnancy.
Table 1Characteristics of participants by pre-pregnancy BMIVariablesTotal($$n = 4890$$)Pre-BMI underweight($$n = 665$$)Pre-BMI normal($$n = 3474$$)Pre-BMI overweight($$n = 639$$)Pre-BMI obesity($$n = 112$$) F/χ 2 p Age, Mean ± SD30.59 ± 3.5731.94 ± 3.8832.65 ± 4.1032.71 ± 4.2330.59 ± 3.5734.848< 0.001Gestational weight gain, Mean ± SD39.21 ± 0.9939.26 ± 1.0239.19 ± 0.9838.89 ± 0.8839.21 ± 0.995.2820.001Gestational weight gain, n (%)382.345< 0.001 Insufficient999 (20.4)194 (29.2)741 (21.3)58 (9.1)6 (5.4) Adequate2059 (42.1)356 (53.5)1507 (43.4)172 (26.9)24 (21.4) Excessive1832 (37.5)115 (17.3)1226 (35.3)409 [64]82 (73.2)Method of conception, n (%)30.376< 0.001 Assisted reproductive technology859 (17.6)72 (10.8)627 [18]141 (22.1)19 [17] Conceived Naturally4031 (82.4)593 (89.2)2847 [82]498 (77.9)93 [83]Place of residence, n (%)-0.405 Rural218 (4.5)38 (5.7)148 (4.3)27 (4.2)5 (4.5) Urban4672 (95.5)627 (94.3)3326 (95.7)612 (95.8)107 (95.5)Annual household income (10,000 RMB (1410 USD)/year), n (%)44.903< 0.001 ~101542 (31.5)205 (30.8)1028 (29.6)259 (40.5)50 (44.6) 20 ~ 301491 (30.5)191 (28.7)1082 (31.1)190 (29.7)28 [25] 30~1857 [38]269 (40.5)1364 (39.3)190 (29.7)34 (30.4)Occupation, n (%)1.9630.580 Employed4376 (89.5)591 (88.9)3122 (89.9)564 (88.3)99 (88.4) Unemployed514 (10.5)74 (11.1)352 (10.1)75 (11.7)13 (11.6)Education, n (%)35.197< 0.001 ~College/vocational1187 (24.3)138 (20.8)821 (23.6)189 (29.6)39 (34.8) Undergraduate2566 (52.5)382 (57.4)1793 (51.6)334 (52.3)57 (50.9) Postgraduate~1137 (23.3)145 (21.8)860 (24.8)116 (18.2)16 (14.3)Parity, n (%)19.191< 0.001 Nulliparous3976 (81.3)581 (87.4)2799 (80.6)507 (79.3)89 (79.5) Multiparous914 (18.7)84 (12.6)675 (19.4)132 (20.7)23 (20.5)Ethnic, n (%)-0.858 Han4797 (98.1)655 (98.5)3404 [98]628 (98.3)110 (98.2) non-Han93 (1.9)10 (1.5)70 [2]11 (1.7)2 (1.8)a Smoking status, n (%)-0.110 No4772 (97.7)642 (96.5)3400 [98]622 (97.5)108 (97.3) Yes111 (2.3)23 (3.5)69 [2]16 (2.5)3 (2.7)b Drinking status, n (%)3.9270.270 No2999 (61.4)390 (58.6)2141 (61.7)404 (63.3)64 (57.7) Yes1884 (38.6)275 (41.4)1328 (38.3)234 (36.7)47 (42.3)Abbreviations: SD standard deviation, BMI body mass index, GWG gestational weight gaina Smoking during pregnancy or during the year before pregnancy. b Alcohol consumption during pregnancy or during the year before pregnancy Table 2 shows a comparison of the scale scores (CES-D, SAS, and PSS-10 scores) among different GWG subgroups according to the total sample and the pre-pregnancy BMI categories. In the total sample and among normal-weight women, the SAS scores were higher in those with excessive GWG than in those with insufficient or adequate GWG ($p \leq 0.001$). No such differences were observed among the GWG subgroups for the other pre-pregnancy BMI categories. Furthermore, $7.8\%$ and $7.2\%$ of the patients with normal pre-pregnancy BMI and in the total sample, respectively, had SAS scores ≥ 50. Among women with a normal BMI, the PSS-10 score was higher in those with excessive GWG than in those with insufficient and adequate GWG. No such differences were observed among the GWG subgroups for the other pre-pregnancy BMI categories. Furthermore, the likelihood of the PSS-10 score being ≥ 14 was higher in both the total sample and in women with a normal BMI than in the women from the other BMI categories. Conversely, the CES-D score did not differ significantly among the GWG subgroups in all the pre-pregnancy BMI categories (all $p \leq 0.05$).
Table 2Scale scores of pregnant women in subgroups of GWG by total sample and pre-pregnancy BMIVariables n GWGinsufficientGWGadequateGWGexcessive p Total 4890CES-D score8.90 ± 7.509.20 ± 7.639.40 ± 8.070.251 < 16807 (80.8)1647 (80.0)1444 (78.8)0.427 ≥ 16192 (19.2)412 (20.0)388 (21.2)SAS score35.78 ± 7.0635.97 ± 6.7436.85 ± 7.44< 0.001 < 50943 (94.4)1960 (95.2)1700 (92.8)0.006 ≥ 5056 (5.6)99 (4.8)132 (7.2)PSS-10 score9.44 ± 5.549.73 ± 5.479.95 ± 5.790.070 < 14780 (78.1)1562 (75.9)1346 (73.5)0.020 ≥ 14219 (21.9)497 (24.1)486 (26.5) Pre-BMI normal 3474CES-D score8.73 ± 7.539.15 ± 7.659.60 ± 8.090.052 < 16605 (81.6)1212 (80.4)953 (77.7)0.076 ≥ 16136 (18.4)295 (19.6)273 (22.3)SAS score35.62 ± 7.1035.79 ± 6.6836.94 ± 7.66< 0.001 < 50700 (94.5)1438 (95.4)1130 (92.2)0.001 ≥ 5041 (5.5)69 (4.6)96 (7.8)PSS-10 score9.31 ± 5.579.66 ± 5.4310.06 ± 5.790.015 < 14586 (79.1)1151 (76.4)888 (72.4)0.002 ≥ 14155 (20.9)356 (23.6)338 (27.6) Pre-BMI underweight 665CES-D score9.68 ± 7.389.72 ± 7.459.06 ± 7.720.716 < 16148 (76.3)276 (77.5)92 (80.0)0.751 ≥ 1646 (23.7)80 (22.5)23 (20.0)SAS score36.52 ± 6.8836.84 ± 6.9536.77 ± 7.250.871 < 50184 (94.8)335 (94.1)108 (93.9)0.921 ≥ 5010 (5.2)21 (5.9)7 (6.1)PSS-10 score9.61 ± 5.5810.26 ± 5.429.73 ± 6.190.370 < 14143 (73.7)260 (73.0)84 (73.0)0.984 ≥ 1451 (26.3)96 (27.0)31 (27.0) Pre-BMI overweight 639CES-D score8.72 ± 7.528.89 ± 7.769.08 ± 8.140.928 < 1649 (84.5)138 (80.2)331 (80.9)0.770 ≥ 169 (15.5)34 (19.8)78 (19.1)SAS score35.10 ± 7.1036.10 ± 6.6636.79 ± 7.220.181 < 5053 (91.4)164 (95.3)382 (93.4)0.472 ≥ 505 (8.6)8 (4.7)27 (6.6)PSS-10 score10.79 ± 4.999.62 ± 5.599.71 ± 5.660.281 < 1445 (77.6)130 (75.6)311 (76.0)0.953 ≥ 1413 (22.4)42 (24.4)98 (24.0) Pre-BMI obesity 112CES-D score6.50 ± 5.797.29 ± 7.828.59 ± 7.780.621 < 165 (83.3)21 (87.5)68 (82.9)0.900 ≥ 161 (16.7)3 (12.5)14 (17.1)SAS score37.33 ± 6.8333.50 ± 6.4735.83 ± 5.260.274 < 506 (100.0)23 (95.8)80 (97.6)0.611 ≥ 500 (0.0)1 (4.2)2 (2.4)PSS-10 score6.67 ± 4.187.04 ± 6.849.80 ± 5.860.112 < 146 (100.0)21 (87.5)63 (76.8)0.308 ≥ 140 (0.0)3 (12.5)19 (23.2)Abbreviations: CES-D Center for Epidemiologic Studies Depression Scale, SAS Self-Rating Anxiety Scale, PSS-10 10-item version of Perceived Stress Scale, BMI body mass index, GWG gestational weight gain Low correlations were observed among the pre-pregnancy BMI, GWG, and the three scale scores. No significant association was found among the PSS-10 score, SAS score, and pre-pregnancy BMI (all $p \leq 0.05$). However, a strong correlation was found between the CES-D and SAS scores (rho = 0.77, $p \leq 0.05$), CES-D and PSS-10 scores (rho = 0.74, $p \leq 0.05$), and SAS and PSS-10 scores (rho = 0.63, $p \leq 0.05$).This indicates that the three scale scores were positively correlated with each other (See Fig. 1 for details).
Fig. 1Correlation heat map of pre-pregnancy BMI, GWG, and the three scale scores A logistic regression analysis was performed to explore the relationship between GWG and maternal mental disorders (Table 3). Excessive GWG was found to be associated with exceeding the SAS score threshold in the entire sample (OR = 1.535; $95\%$ CI = 1.173, 2.008). After adjusting for potential confounders, excessive GWG was also found to be associated with a greater chance of anxiety symptoms in the entire sample (OR = 1.479; $95\%$ CI = 1.128, 1.938).
Another logistic regression analysis was performed to explore the relationship between GWG and depression, anxiety, and stress according to the pre-pregnancy BMI. Excessive GWG significantly increased the possibilities of anxiety in both the unadjusted and adjusted models (unadjusted model: OR = 1.767, $95\%$ CI = 1.284, 2.431; adjusted model: OR = 1.668, $95\%$ CI = 1.209, 2.302). Conversely, excessive GWG was significantly associated with the chance of exceeding the PSS-10 score thresholds in the unadjusted model (OR = 1.227; $95\%$ CI = 1.033, 1.459) but not in the adjusted model (OR = 1.151; $95\%$ CI = 0.966, 1.372).
Table 3Associations among maternal pre-pregnancy BMI, GWG and the exceeding scale thresholds in total sampleVariablesCES-DSASPSS-10GWGinsufficientGWG adequateGWGexcessiveGWGInsufficientGWG adequateGWGexcessiveGWGinsufficientGWG adequateGWGexcessive Total Model 1a0.951 (0.785,1.151)Reference1.075 (0.92,1.257)1.179 (0.842,1.651)Reference 1.535 (1.173,2.008) 0.88 (0.734,1.055)Reference1.133 (0.98,1.309)Model 2b0.961 (0.793,1.164)Reference1.052 (0.899,1.231)1.2 (0.855,1.683)Reference 1.479 (1.128,1.938) 0.905 (0.754,1.087)Reference1.073 (0.927,1.243) Pre-BMI normal Model 10.928 (0.741,1.163)Reference1.179 (0.98,1.419)1.222 (0.821,1.817)Reference 1.767 (1.284,2.431) 0.849 (0.686,1.051)Reference 1.227 (1.033,1.459) Model 20.939 (0.748,1.177)Reference1.14 (0.946,1.374)1.248 (0.838,1.86)Reference 1.668 (1.209,2.302) 0.875 (0.705,1.086)Reference1.151 (0.966,1.372) Pre-BMI underweight Model 11.072 (0.709,1.622)Reference0.862 (0.513,1.451)0.867 (0.4,1.88)Reference1.034 (0.428,2.499)0.966 (0.65,1.436)Reference0.9995 (0.6224,1.6052)Model 21.083 (0.709,1.655)Reference0.836 (0.493,1.418)0.85 (0.386,1.871)Reference1.055 (0.433,2.571)0.969 (0.644,1.459)Reference0.954 (0.588,1.548) Pre-BMI overweight Model 10.663 (0.287,1.529)Reference0.956 (0.61,1.499)1.971 (0.618,6.288)Reference1.449 (0.645,3.257)0.915 (0.45,1.86)Reference0.975 (0.644,1.478)Model 20.687 (0.293,1.612)Reference0.946 (0.6,1.49)2.145 (0.658,6.993)Reference1.459 (0.646,3.294)0.919 (0.445,1.899)Reference0.942 (0.619,1.435) Pre-BMI obesity Model 11.75 (0.143,21.384)Reference1.441 (0.378,5.501)0 (0,Inf)Reference0.575 (0.05,6.629)0 (0,Inf)Reference2.111 (0.567,7.855)Model 22.829 (0.162,49.469)Reference1.368 (0.324,5.784)0 (0,Inf)Reference0.229 (0.014,3.746)0 (0,Inf)Reference1.504 (0.379,5.958)Abbreviations: CES-D, Center for Epidemiologic Studies Depression Scale; SAS, Self-Rating Anxiety Scale;PSS-10, 10-item version of Perceived Stress Scale; BMI, body mass index; GWG, gestational weight gaina: Model 1 unadjusted logistic regression modelb: Model 2 was adjusted for age at conception, annual household income, occupation, education, smoking status, and drinking status
## Discussion
The main purpose of our study was to explore the impact of pre-pregnancy BMI and GWG on the psychological state of women. The most important finding of this study is that excessive GWG is strongly associated with greater chances of exceeding the SAS score thresholds in pregnant Chinese women with a normal pre-pregnancy BMI, while in no other category were the results significant. No effects of GWG on depression or stress symptoms were observed. In other words, pregnant women with normal weight before pregnancy are more likely to have anxiety symptoms if they gain too much weight during pregnancy. We did not find a strong association between the pre-pregnancy BMI and the perinatal psychological status. These findings suggest that avoiding excessive GWG can reduce the incidence of anxiety in pregnant women with a normal pre-pregnancy weight.
In the present study, excessive GWG did not affect the women’s stress and depression during pregnancy but aggravated their anxiety. Currently, the conclusions of various relevant studies are not consistent, which indicates that the relationship between GWG and prenatal anxiety, depression, and stress is complex and variable. Ertel et al. did not observe an association between GWG and prenatal depressive symptoms [19]. A study of 505 pregnant women showed that excessive gestational weight gain independently predicted greater postpartum depressive symptoms [16]. In a study focusing on the relationship between stress and GWG, the researchers believed that there might be evidence of an association between stress and maternal body weight and weight gain [30]. Eichler et al. found that GWG was significantly positively linked to stress only during the second trimester [31].The inconsistency in the reported findings can be explained by the following: [1] most of the evidence is based on self-reported body weight and height, which is associated with a higher potential for misclassification (when compared with prospective measurement of maternal BMI) [32] and [2] as a limitation of most studies, the analysed women underestimated their pre-pregnancy weight and overestimated their GWG [33].
Nevertheless, in our study, the negative effect of excessive GWG on perinatal anxiety could not be ignored. We found that women with normal pre-pregnancy weight in our study were at a greater chance of experiencing anxiety symptoms due to excessive GWG. The number of studies on anxiety and weight gain is rather small; however, the findings of some studies are consistent with our findings. Systematic literature suggests that obese pregnant women are at a higher risk of developing comorbid anxiety disorders [34]. Zanardo et al. indicate that women who experienced excessive GWG have a higher risk of developing anxiety [35]. In our study, the finding relates only to women who had normal pre-pregnancy nutritional status. A possible explanation is that women with normal weight before pregnancy receive less relevant education than women with substandard pre-pregnancy weight do; adequate health consultation can positively impact the lifestyle and dietary structure of pregnant women [36]. As healthcare providers tend to focus more on women with obesity [37], women with a normal pre-pregnancy weight may not be able to control their weight correctly in the absence of proper guidance. It is conceivable that women entering pregnancy at an underweight or normal-weight BMI may find the associated changes in body shape more difficult to accept [38]. Studies showed body dissatisfaction might have negative outcomes, including anxiety or feeling stressed, poor self-esteem, isolation, and social anxiety [39, 40]. Another explanation of how GWG affects maternal mental health is inflammatory markers. The study indicated that excessive GWG was associated with higher concentrations of inflammatory factors [41], and inflammation has been implicated in anxiety [42]. Thus, while paying attention to obese or overweight pregnant women, we should also provide adequate advice and health guidance to women with normal pre-pregnancy weights.
At the same time, it should be noted that the incidence of perinatal anxiety was relatively low in our study as compared to in previous studies; these previous studies have revealed that 15–$30\%$ of pregnant women have clinically significant levels of anxiety [43, 44]. With an SAS score of 50 as the cut-off, only $5.9\%$ of the women were diagnosed with anxiety. Furthermore, $7.8\%$ of women with a normal pre-pregnancy weight and excessive GWG were diagnosed with anxiety symptoms. The large difference in the incidence may also explain the inconsistencies in the reported effects of GWG on prenatal anxiety among different studies.
Our hypothesis that pre-pregnancy BMI was associated with depression, stress, and/or anxiety symptoms during pregnancy was not supported by our data; this is consistent with the findings of other studies. McPhie et al. reported that pre-pregnancy BMI could not predict depressive or anxiety symptoms [45]. However, there are conflicting findings regarding pre-pregnancy and antenatal mental disorders. A systematic review and meta-analysis of the impact of pre-pregnancy BMI on maternal depressive and anxiety symptoms revealed positive associations between pre-pregnancy weight and depression and anxiety [17]. Another study evaluating the prevalence and risk of antenatal mental disorders among obese and overweight women claimed that women who were obese when they became pregnant were more likely to experience elevated antenatal mental health problems [46].
To date, existing findings on the relationship between BMI and mental disorders during pregnancy are inconsistent, and the causal direction of the relationship between obesity and mental health problems in pregnant women is unclear. This inconsistency may be related to the differences in the methodologies adopted (including evaluation instruments and definitions) [47] and to the differences in the adjustments for confounding factors across the studies. In our study, the method of conception, annual household income, occupation, parity, and education were important confounding factors; $68.5\%$ of the participants’ families had a higher annual income, $75.8\%$ of the participants had a bachelor’s degree at the very least, and only $10.5\%$ of the participants were unemployed. In a similar study from Australia, only $61.3\%$ of the women had a Bachelor’s or Master’s degree [47], while Cheng et al. reported that $25\%$ of their participants were unemployed [6]. A community-based cross-sectional study in Aligarh also found that the prevalence of maternal mental disorders was significantly higher among mothers who were part of a higher age group, who belonged to a low socioeconomic class, who had no educational background or had a lower level of education, who were housewives, and who had higher parity [48]. Moreover, social and cultural factors may also affect the body image, weight satisfaction, and weight-gain attitudes of the women [19].
In the present study, only $41.93\%$ of the women met the IOM recommendations and gained an adequate gestational weight; however, as the pre-pregnancy BMI increased, this proportion decreased gradually and the proportion of excessive GWG increased gradually. It is particularly concerning that current evidence shows that up to $64\%$ of overweight women and $73.2\%$ of obese women have GWG that exceeds the GWG currently recommended by IOM; this is consistent with our findings [49, 50]. Healthcare providers should calculate the pre-pregnancy BMI of pregnant women at their first prenatal visit, so that they can provide optimal diet and exercise counselling for achieving the IOM-recommended GWG.
We also noticed positive correlations among the perceptions of depression, anxiety, and stress. Previous studies have proposed that perceived stress, anxiety, and depression are strongly correlated with pregnancy [51, 52]. Several comorbidities may exist among maternal mental disorders. Dindo et al. found high rates of co-occurrence of anxiety and stress disorders in women with depression [53]. Because of this increasing emphasis on screening for comorbidities during pregnancy, it is necessary to further study depression, stress, and anxiety as comorbidities as well.
To the best of our knowledge, this study included the largest sample to date to explore the relationship among the pre-pregnancy BMI, GWG, and maternal mental health in the Chinese population. However, our study has some limitations. First,although the weight during pregnancy was measured using professional measuring machines, the pre-pregnancy weight was self-reported; this may have led to some potential misclassification. Second, some major complications, such as gestational diabetes mellitus, hypertension disorders, and thyroid dysfunction, were not evaluated in our study, even though these are important confounders in the evaluation of maternal mental disorders. Finally, assessments of mental health were derived from self-reported questionnaires, which may be susceptible to measurement bias.
## Conclusion
This large prospective cohort study demonstrated that women with normal weight before pregnancy are more likely to have anxiety symptoms if they gain too much weight during pregnancy. However, our findings did not indicate a strong association between the pre-pregnancy BMI and the perinatal psychological status. Instead, our findings suggest that excessive GWG is associated with antenatal anxiety,in women with a normal pre-pregnancy weight. Thus, such women should receive sufficient attention and health guidance, because poor weight control during pregnancy might aggravate their anxiety symptoms. More than $50\%$ of the women in our study did not meet the IOM standards for adequate GWG. In the future, larger longitudinal studies are needed to explore the biological mechanisms linking GWG with maternal mental health. Screening and interventions should be performed to identify women at a high risk of excess GWG relatively early in pregnancy to prevent the occurrence of mental disorders.
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|
---
title: 'Spatial association between green space and COPD mortality: a township-level
ecological study in Chongqing, China'
authors:
- Aiping Gou
- Guanzheng Tan
- Xianbin Ding
- Jiangbo Wang
- Yan Jiao
- Chunyan Gou
- Qiang Tan
journal: BMC Pulmonary Medicine
year: 2023
pmcid: PMC10024412
doi: 10.1186/s12890-023-02359-x
license: CC BY 4.0
---
# Spatial association between green space and COPD mortality: a township-level ecological study in Chongqing, China
## Abstract
### Background
There are regional differences in the effect of green space on mortality of Chronic obstructive pulmonary disease (COPD). We conduct an ecological study, using the administrative divisions of Chongqing townships in China as the basic unit, to investigate the association between COPD mortality and green space based on data of 313,013 COPD deaths in Chongqing from 2012 to 2020. Green space is defined by Fractional vegetation cover (FVC), which is further calculated based on the normalised vegetation index (NDVI) from satellite remote sensing imagery maps.
### Methods
After processing the data, the non-linear relationship between green space and COPD mortality is revealed by generalised additive models; the spatial differences between green space and COPD mortality is described by geographically weighted regression models; and finally, the interpretive power and interaction of each factor on the spatial distribution of COPD mortality is examined by a geographic probe.
### Results
The results show that the FVC local regression coefficients ranged from − 0.0397 to 0.0478, $63.0\%$ of the regions in Chongqing have a positive correlation between green space and COPD mortality while $37.0\%$ of the regions mainly in the northeast and west have a negative correlation. The interpretive power of the FVC factor on the spatial distribution of COPD mortality is 0.08.
### Conclusions
Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. This study is the first to reveal the relationship between COPD mortality and green space in Chongqing at the township scale, providing a basis for public health policy formulation in Chongqing.
## Introduction
COPD is a common chronic disease with airflow obstruction and incomplete reversibility [1], and acute exacerbations of COPD increase the socio-economic burden [2]. It is a major cause of increased morbidity, mortality and health care costs for chronic diseases worldwide [3]. The prevalence of COPD in most regions of *China is* higher than that estimated by the World Health Organization Model [4], and the number of COPD-related deaths in China in 2013 was 910,809, accounting for $31.1\%$ of all COPD deaths in the world [5]. In order to make effective public health policy on COPD, it is important to explore the risk factors for COPD deaths.
Green space is an integral part of the habitat, and studies have shown that people in urban areas with high green space coverage are at lower risk of chronic diseases, including cardiovascular disease [6], asthma [7] and diabetes [8]. Greater exposure to the natural environment can promote overall human health and well-being [9].
However, there are inconsistencies in the effects of green space on respiratory health. A study in the Netherlands found that increased green space would reduce the prevalence of COPD [10], and another study in the UK found similar findings [11]. However, in contrast to the two studies, a national cross-sectional study in China found that green space in community may be a risk factor for increased COPD prevalence, especially true in the northern and north-eastern China [12]. In addition, a Korean cross-sectional study and a Hong Kong cohort study found no significant association between green space and mortality from respiratory diseases [13, 14]. The inconsistency of these studies may imply that there is inter-regional variability in the effect of green space on COPD.
There is no concrete evidence for this inconsistency, but it is generally accepted that possible explanations include some subjective bias in the quantification of green space in some studies, or related to inter-regional differences in vegetation types and methods of COPD ascertainment [12]. On one hand, green space can reduce air pollution and thus reduce adverse respiratory effects [15], on the other hand, some plants may release volatile organic compounds (VOCs) [16]. A study in the USA found a positive correlation between asthma in children and levels of green space around their homes, attributed to VOCs [17].
It is an effective tool for future planning, health management and evaluation to clarify the factors contributing to the spatial pattern of disease. There are few studies on the relationship between the green space and the prevalence of COPD, and the influence of the green space on the COPD mortality may be different in different regions. In order to improve health management and control COPD mortality in Chongqing, we conducted an ecological study in Chongqing based on township administrative divisions as the basic unit, with the following objectives: [1] To investigate the spatial distribution of COPD mortality in Chongqing; [2] To reveal the influence of green space on COPD mortality in Chongqing and its regional differences.
## Study area
This research is based on the administrative territorial entity of Chinese villages and towns, which is the fourth level of administrative division in China. The study area, Chongqing, is a municipality directly under the Central Government of China in southwest China, with a hilly and mountainous terrain and a population mainly concentrated in the nine districts of the main city of Chongqing. The total resident population is 32,124,000 [2022], covering an area of 82,400 square kilometers, including 38 districts and counties, with 1,031 township-level divisions [18].
## Data
Health Data COPD mortality data was collected from Chongqing Center for Disease Prevention and Control from all districts and counties, with a total of 313,013 COPD deaths from 2012 to 2020. The COPD mortality rate for each township was then obtained by field calculation in QGIS.
Green Space *In this* study, FVC was used to quantify the green space of each area. FVC quantifies the denseness of vegetation and reflects its growth status, which is an important fundamental data for describing ecosystems and has been widely used in various fields [19–21]. In this study, the Sentinel-2A satellite was selected by the Google Earth Engine platform to obtain remote sensing images with less than $20\%$ cloud cover in Chongqing in 2020, and the NDVI data were obtained by de-clouding, stitching and calculating. The vegetation coverage was then retrieved from the NDVI data using a pixel-wise dichotomous model [22], which is expressed as:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{vc}=\frac{NDVI-{NDVI}_{soil}}{{NDVI}_{veg}-{NDVI}_{soil}}$$\end{document}fvc=NDVI-NDVIsoilNDVIveg-NDVIsoil The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${NDVI}_{soil}$$\end{document}NDVIsoil is the minimum value of the pure soil image, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${NDVI}_{veg}$$\end{document}NDVIveg is the maximum value of the pure vegetation image. The values of the cumulative frequency of the NDVI image were selected as 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}$${NDVI}_{soil}$$\end{document}NDVIsoil and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${NDVI}_{veg}$$\end{document}NDVIveg respectively based on the $5\%$ and $95\%$ confidence intervals in the study. The NDVI frequency histograms were calculated after the removal of the water areas in order to avoid the influence of the large water areas. The final FVC raster data was obtained at 10 m resolution. Based on the raster data, the mean FVC values for each township in Chongqing were calculated in QGIS (Fig. 1).Fig. 1Mean values of FVC by quintile for each township Other data: *Population data* for each township are based on the sixth census [2010], from the Chongqing Municipal Bureau of Statistics [2022], where the proportion of elderly population is calculated from the number of people over 65 years in each region and the proportion of gender is calculated by females. Air pollution is widely considered to be significantly associated with COPD mortality [23–25]. PM2.5 and PM10 data were obtained from a 1 km resolution daily raster data set produced by the State Key Laboratory of Remote Sensing Science, Beijing Normal University [26, 27], and the annual average raster data of PM2.5 and PM10 were calculated separately in this paper by the raster package of R.
The spatial distribution of medical resources also has an important impact on the spatial distribution of COPD mortality. In this paper, Point of Interest (POI) spatial distribution data of medical resources within Chongqing city of Gaode Map was obtained through python, and a total of 1777 POI points were obtained. In order to conduct further statistical analysis, the kernel density of these POI points was estimated by QGIS, where the influence radius was set to 10 km, and the corresponding weights were assigned according to the different levels of medical institutions of the POI points, and finally the raster of the density distribution of medical institutions within Chongqing was calculated.
## Statistical analysis
The article summarizes the characteristics of the data in descriptive statistics. At the same time, in order to explore the spatial relationship of COPD mortality, a spatial autocorrelation analysis of COPD mortality was performed, and its spatial correlation was expressed by the Moran index [28, 29]. Then, to explore the binary relationship between the variables, a spearman correlation analysis [30] was performed on each variable to obtain the binary correlation coefficient between the variables. Based on the results of the spearman correlation coefficients, variables with significant collinearity and non-significant correlation with COPD mortality were removed and finally three variables of FVC, proportion of elderly population, and density of health facilities, were selected for regression analysis.
Based on the results of the spearman correlation analysis, the study first attempted to describe the relationship between COPD mortality and FVC using GAMs Model, which is able to examine the relationship between the dependent variable and multiple independent variables as well as to fit the model through a non-linear smoothing term [31]. *The* general expression for GAMs is:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g(Y)=\alpha +\sum_{$j = 1$}^{n} {f}_{i}\left({x}_{j}\right)+\varepsilon$$\end{document}g(Y)=α+∑$j = 1$nfixj+εwhere Y is the dependent variable, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g(Y)$$\end{document}g(Y) is the link function, \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 is the intercept term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{j}$$\end{document}xj is the independent variable, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${f}_{i}\left({x}_{j}\right)$$\end{document}fixj is the smoothing function, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varepsilon$$\end{document}ε is the random error. Where the smoothing parameters will be calculated by the restricted maximum likelihood method of smoothing (REML) to ensure stable and reliable results [32]. The process of constructing the GAMs in this paper consists of three models with increasing adjustment levels, as follows:3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Model} 1: g\left(Deaths\right)=\alpha +s\left(FVC\right)+\varepsilon$$\end{document}Model1:gDeaths=α+sFVC+ε4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Model }2: g\left(Deaths\right)=\alpha +s\left(FVC\right)+s(Hospital)+\varepsilon$$\end{document}Model2:gDeaths=α+sFVC+s(Hospital)+ε5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathrm{Model }3: g\left(Deaths\right)=\alpha +s\left(FVC\right)+s(Hospital)+s(Older)+\varepsilon$$\end{document}Model3:gDeaths=α+sFVC+s(Hospital)+s(Older)+εwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Deaths$$\end{document}*Deaths is* the COPD mortality rate in each township, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$FVC$$\end{document}FVC is the vegetation cover, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Hospital$$\end{document}*Hospital is* the spatial distribution density of medical institutions, and \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 natural spline smoothing function.
Based on the results of the spatial autocorrelation analysis, GWR was performed on the independent and dependent variables in order to expose the spatial association between green space and COPD mortality. GWR is essentially an improved global regression model, where GWR fits a local regression equation at each spatial location, resulting in local regression coefficients that reflect the relationship between the independent and dependent variables for each township unit, as well as the spatial heterogeneity of each region [33, 34]. *The* general formula for the GWR model is:6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${y}_{i}={\beta }_{0}\left({u}_{i},{v}_{i}\right)+\sum_{$k = 1$}^{t} {\beta }_{k}\left({u}_{i},{v}_{i}\right){x}_{ki}+{\varepsilon }_{i}$$\end{document}yi=β0ui,vi+∑$k = 1$tβkui,vixki+εiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${u}_{i}$$\end{document}ui is the latitude of the i-th location, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${v}_{i}$$\end{document}vi is the longitude of the i-th location, \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}β0 is the regression constant for the i-th location, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{k}$$\end{document}βk is the k-th regression parameter to be estimated for the i-th location, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${x}_{ki}$$\end{document}xki is the observed value of the k-th variable for the i-th location, t is the number of independent variables, 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 random error for i-th location. The spatial weight function of GWR affects the parameters such as local regression coefficients, and the Gauss function is used to determine the weights in this study. The value of bandwidth affects the analysis results of the model, and the method selected in this study is corrected Akaike Information Criterion (AICc) [35], which finally result in an optimal bandwidth of 18.
Finally, to further investigate the interaction between the spatial correlation intensity and variables of green space and COPD mortality, the Geo-detector was used for factor detection and interaction detection [36]. It is important to note that as the data used were continuous, the discrete transformation of spatial data into class variables was carried out before the detection of factors and interactions.
The construction of the GAMs model for this study was performed using the “mgcv” package in R (4.2.1), with the selection of spline curves relying on the “splines” package (R Core Team 2022); the GWR was constructed using the “GWmodel” package [37, 38]; the “GD” package was used for the Geo-detector [39]; and the spatial autocorrelation analysis was performed using GeoDa (1.10); the calculation and processing of raster data relied on the “raster” package [40], the “sf” package [41] and QGIS (3.26).
## Descriptive statistics and spearman correlation
The descriptive statistics for all variables are shown in Table 1. The multi-year mortality rates for each township administrative unit of COPD in Chongqing from 2012 to 2020 range from 0 to 0.1, with a mean of 0.0191 and a standard deviation standard deviation (SD) of 0.009. The Spearman correlation coefficient is able to describe the correlation between the variables. It takes values in the range − 1 to 1. A positive value means that the two variables are positively correlated, while a negative value means the opposite [30]. Figure 2 shows the Spearman correlation between all variables, with a correlation of 0.1 between COPD mortality and FVC ($p \leq 0.01$), and a significant correlation between FVC and PM2.5, PM10 (|correlation coefficient|> 0.85, $p \leq 0.01$), these variables were removed to avoid Multicollinearity effects. Figure 3 shows the COPD mortality rates by township administrative unit in Chongqing, with values ranging from 0 to 0.1007, with high values mainly in the central, northern and western regions of Chongqing around the municipality. The values of FVC range from 0.0223, to 0.928, with high values mainly in the southeast and northwest regions. Table 1Descriptive statisticsMin. Max. MedianMeanSDDeaths00.10070.0180.01910.009FVC0.02230.92800.65520.60910.1946Hospital0955.7013.622254.600153.8525Age0.00460.84990.13350.13790.0681PM2.520.3635.3829.1428.373.772PM1033.5455.9145.6944.565.8480Female0.40750.56930.49050.48980.01439Fig. 2Bivariate correlation matrix graph. Deaths is the mortality rate between 2012 and 2020 for each communal administrative unit; FVC is the fractional vegetation cover; *Hospital is* the nuclear density value of medical institutions in each region; *Age is* the percentage of population over 65 years old in each region; *Female is* the percentage of female population in each region. Red cells indicate a positive correlation, Blue cells a negative correlationFig. 3COPD mortality by quintile *Deaths is* the mortality rate between 2012 and 2020 for each communal administrative unit; FVC is the fractional vegetation cover; *Hospital is* the nuclear density value of medical institutions in each region; *Age is* the percentage of population over 65 years old in each region; *Female is* the percentage of female population in each region.
## Spatial autocorrelation of COPD mortality
To test whether COPD mortality is spatially autocorrelated, the study used Moran's I spatial autocorrelation analysis. The Moran's I index for COPD mortality was 0.257 ($p \leq 0.01$), implying a potential dependence on the spatial distribution of COPD mortality in Chongqing. The LISA Cluster Map (Fig. 4) also shows the spatial distribution of the clusters and the corresponding confidence of each region; the high value clusters are located in the central and western part of the main urban area; while the low value clusters are distributed in the north-eastern, south-eastern and western areas of the main city. Fig. 4A is the Significant level; B is Spatial autocorrelation
## Generalised additive models results
According to the variance inflation factor (VIF) test, the VIF of each variable is less than 5, and there is no multicollinearity between the variables [42]. Table 2 shows the results of the GAMs regression, where degrees of freedom greater than 1 means a non-linearly relationship between the explanatory variable and the COPD mortality; the F statistic reflects the degree of influence of the explanatory variables, the larger the F value the greater the influence; the P value is a parameter to judge the hypothesis test; and the R2 reflects the effect of the model fit. Model 1, Model 2 and Model 3 are three models with increasing adjustment levels respectively. Model 1 is a univariate regression model describing the relationship between FVC and COPD mortality; In Model 2, R2 was 0.183 after adding the medical resource covariates, and the rate of deviation explanation was $21.5\%$; In Model 3, the proportion of elderly population was also added and the coefficients of all variables are statistically significant, and the final model deviation interpretation rate is $32.4\%$.Table 2Parameters of GAMs resultsMode1 (R2 = 0.074)Model 2 (R2 = 0.183)Model 3 (R2 = 0.287)VariableedfRef.dfFPedfRef.dfFPedfRef.dfFPFVC6.8037.9189.96206.2607.4397.10705.36.5912.2760.02Hospital5.6646.79219.95702.8613.5583.5360.01Age3.5864.27042.9180Edf Estimated degree of freedom, Ref.df Reference degree of freedom
## Geographically weighted regression models results
The relationship between COPD mortality and FVC was spatially unstable as shown in Fig. 5. From the results of regional parameter (Table 3), the FVC local regression coefficients range from − 0.0397 to 0.0478, $63.0\%$ of the regions are positive and $37.0\%$ are negative. The negative correlation areas are mainly located in the northeast and west of Chongqing. The local regression coefficients for Hospital range from − 0.0001 to 0.0004, a smaller range than that of the other two variables, with $19.4\%$ of the area being positive and $81.6\%$ being negative, the positive impact area is distributed in the southeast region. The Age local regression coefficients range from − 0.0161 to 0.2428, $91.7\%$ of the areas are positive and $8.3\%$ are negative. The high-value regions are mainly in the central and northeastern parts of Chongqing. Fig. 5A is the R2 of the geographically weighted regression model; B is the coefficient of the variable FVC; C is the coefficient of the variable Age; D is the coefficient of the variable HospitalTable 3Parameters of GWR resultsVariableMinMedianMaxP value + (%) − (%)Intercept − 0.03750.00960.0362 < 0.00165.334.7FVC − 0.03970.00480.0478 < 0.00163.037.0Hospital − 0.0001 − 0.000010.0004 < 0.00119.481.6Age − 0.01610.06640.2428 < 0.00191.78.3+ Percentage of areas where the sign of the coefficient is a positive sign− Percentage of areas where the sign of the coefficient is a negative sign
## Geo-detector results
According to the Q-value (P value < 0.01) of Geo-detector factor detection, the three variables selected to explain the change in COPD mortality in Chongqing are Age, Hospital and FVC in descending order of explanatory power, with 0.257, 0.142 and 0.08 respectively. The difference in interpretive power when two factors work together on a single factor can be obtained by interactive probing. Among them, the interpretive power of FVC interaction with Age was 0.2885; the interpretive power of the interaction between FVC and Hospital was 0.2039; and the interpretive power of the interaction between Age and Hospital was 0.2846. All the results showed a two-factor enhancement, indicating that the interaction of factors had different degree of enhancement in explaining the mortality of COPD compared with single factor.
## Interpretation of results
This study found that the spatial distribution of COPD mortality in Chongqing has certain aggregation characteristics. The non-linear relationship between FVC and COPD mortality in Chongqing was described by GAMS model, and the model was made more stable by adding covariates. It was found that COPD mortality changed in segments as the FVC value increased. Based on the results of the GAMs, we hypothesized that there were regional differences in the association between COPD mortality and FVC, and the results of the GWR verified this hypothesis (Fig. 5). COPD mortality was positively correlated with FVC in $63\%$ of the township areas in Chongqing and negatively correlated in $37.0\%$ of the areas. It means that COPD mortality increases with increasing FVC in $63\%$ of the township areas in Chongqing, higher vegetation cover brings higher risk of COPD mortality, COPD mortality decreases with increasing FVC in $37\%$ of the township areas, and the negatively correlated areas are mainly located in the northeast of Chongqing. And according to the results of R2 distribution, the GWR model effect was better in this region. Overall, there was a spatially non-stationary relationship between COPD mortality and FVC in Chongqing.
Considering that regional differences in air pollution may affect the judgment of the relationship between FVC and COPD mortality, the study revealed the relationship between FVC and PM2.5, PM10 through Spearman correlation analysis, with significant negative correlation coefficients of − 0.88 and − 0.87, respectively. Implying that in Chongqing, the areas with high FVC have lower PM2.5 and PM10 concentrations. Therefore this study was able to rule out the possibility that high PM2.5 and PM10 concentrations would mask the beneficial effect of FVC on COPD mortality, making the findings more convincing. Also according to the results of the GAMs, GWR and Geo-detector models, the age factor makes the largest contribution to the distribution of COPD mortality, and the correlation coefficients for FVC are all relatively small. Such results are also in line with the reality.
Regional differences in the relationship between green space and COPD mortality is acceptable. A Belgian study including mortality data from five urban areas between 2001 and 2011 found a negative association between residential green space and respiratory disease mortality [43]; a Korean cross-sectional study and a Chinese cohort study found no significant association between green space and respiratory disease mortality [13, 14]; a national study of China by Fan [12] concluded that green space was a risk factor for increased COPD prevalence, and according to similar studies, findings on the relationship between green space and respiratory disease vary widely across regions.
In most townships in Chongqing where COPD mortality is positively associated with FVC, one possible explanation for the predominance is that areas with high FVC tend to be rural and mountainous, with limited access to medical resources, resulting in high mortality and high FVC occurring in the same areas. From Fig. 3, it can be seen that the mortality rate of COPD in the west of *Chongqing is* lower and the medical resource there is the best, while the townships surrounding the main urban area have correspondingly higher FVC values, but also lower availability of medical resources and higher COPD mortality rates. Another possible explanation is that some high-value FVC regions have lower urbanization with industrial structure dominated by primary and secondary industries, with high poverty and unemployment rates, low health insurance coverage, and a high proportion of the population currently or previously engaged in manual work. Studies have shown that people with low incomes are more susceptible to air pollution [13, 44], and people with low levels of education may also lack knowledge of air pollution disease prevention [45], resulting in higher COPD mortality. The regions where COPD mortality is negatively correlated with FVC in Chongqing are mainly located in the northeast. The possible reasons are that these regions have excellent natural environment and some districts and towns are all-area tourism demonstration areas, dominated by tertiary industries. The green space forms a complete natural barrier to optimize air quality and block air pollution particles, thus providing a good environment and reducing the risk of respiratory diseases [15].
There were significant regional differences in the spatial correlation between COPD mortality and green space in Chongqing. The reason for this may be related to the degree of influence of the FVC factor on the distribution of COPD mortality. According to the results of the geographic probe, the interpretive power of FVC on the spatial subdivision of COPD mortality is only 0.08, which is inferior to age and hospital availability; and according to the factor interaction detection, the interpretive power of multi-factor interaction is greater than that of FVC single factor, implying that the spatial subdivision of COPD mortality should be explained by multiple factors.
## Limitations
Although the results of this study are statically significant, there are some limitations. The results of this study may be confounded by some difficult-to-measure personal factors due to the use of the township as the basic study unit, where many data were missing and only a few covariates could be taken into account. Firstly, smoking is considered to be a major cause of COPD, but the results of this study cannot eliminate the possible impact of smoking habits in the population due to the lack of data. Secondly, increased mortality of COPD may also be related to the presence of underlying disease in the cases themselves, and the presence of underlying disease in the study subjects themselves may have an impact on the findings. Meanwhile, due to data limitations, there were differences between the dependent variable COPD mortality (2012–2020) and the independent variable FVC [2020], so the study could not exclude uncertainties stemming from temporal inconsistencies [46].
## Conclusion
The control of COPD mortality is of great importance to alleviate social stress and improve people's health and well-being. Due to the correlation between green space and respiratory diseases, we attempted to uncover the spatial relationship between green space and COPD mortality in Chongqing. It is expected to achieve the control of COPD mortality through planning policy. However, the results did not confirm this expectation. Green space may be a potential risk factor for increased COPD mortality in some regions of Chongqing. Therefore, this spatial differentiation needs to be taken into account in future green space planning and public health policy development in Chongqing.
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|
---
title: 'The association between receipt of home care rehabilitation services and acute
care hospital utilization in clients with multimorbidity following an acute care
unit discharge: a retrospective cohort study'
authors:
- Amanda Mofina
- Jordan Miller
- Joan Tranmer
- Wenbin Li
- Catherine Donnelly
journal: BMC Health Services Research
year: 2023
pmcid: PMC10024414
doi: 10.1186/s12913-023-09116-0
license: CC BY 4.0
---
# The association between receipt of home care rehabilitation services and acute care hospital utilization in clients with multimorbidity following an acute care unit discharge: a retrospective cohort study
## Abstract
### Background
Individuals experiencing multimorbidity have more complex healthcare needs, use more healthcare services, and access multiple service providers across the healthcare continuum. They also experience higher rates of functional decline. Rehabilitation therapists are well positioned to address these functional needs; however, little is known about the influence of rehabilitation therapy on patient outcomes, and subsequent unplanned healthcare utilization for people with multimorbidity. The aims of this study were to: 1) describe and compare the characteristics of people with multimorbidity receiving: home care rehabilitation therapy alone, other home care services without rehabilitation therapy, and the combination of home care rehabilitation therapy and other home care services, and 2) determine the association between home care rehabilitation therapy and subsequent healthcare utilization among those recently discharged from an acute care unit.
### Methods
This retrospective cohort study used linked health administrative data housed within ICES, Ontario, Canada. The cohort included long-stay home care clients experiencing multimorbidity who were discharged from acute care settings between 2007–2015 ($$n = 43$$,145). Descriptive statistics, ANOVA’s, t-tests, and chi-square analyses were used to describe and compare cohort characteristics. Multivariable logistic regression was used to understand the association between receipt of rehabilitation therapy and healthcare utilization.
### Results
Of those with multimorbidity receiving long-stay home care services, $45.5\%$ had five or more chronic conditions and $46.3\%$ required some assistance with ADLs. Compared to people receiving other home care services, those receiving home care rehabilitation therapy only were less likely to be readmitted to the hospital (OR = 0.78; $95\%$ CI: 0.73–0.83) and use emergency department services (OR = 0.73; $95\%$ CI: 0.69–0.78) within the first 3-months following hospital discharge.
### Conclusions
Receipt of rehabilitation therapy was associated with less unplanned healthcare service use when transitioning from hospital to home among persons with multimorbidity. These findings suggest rehabilitation therapy may help to reduce the healthcare burden for individuals and health systems. Future research should evaluate the potential cost savings and health outcomes associated with providing rehabilitation therapy services for people with multimorbidity.
## Introduction
The prevalence of multimorbidity, the co-occurrence of two or more chronic conditions, is estimated at upwards of $33.1\%$ globally [1]. Individuals experiencing multimorbidity are more likely to be admitted to hospital compared to those without multimorbidity [2]. Those with four or more physical chronic health conditions are nearly six times more likely to experience an unplanned hospital admission [2]. It is evident that individuals who experience multimorbidity interact with, and transition through, the health care system more frequently because they have higher health care needs that span across multiple health domains [2–7].
Interprofessional healthcare teams support individuals with complex health needs to navigate health care systems, particularly the transitions between systems [8]. Rehabilitation therapists (occupational therapists and physical therapists) are members of interprofessional healthcare teams that focus on improving patient function by considering multiple aspects of health such as physical, psychosocial, cognitive, addressing the person’s abilities as well as their environment, and social determinants of health [9–13]. As such, they are well positioned to address the complex functional needs of persons with multimorbidity.
There is a dearth of evidence examining the impact of rehabilitation therapy for individuals with multimorbidity, and the subsequent impact on healthcare utilization. A recent rapid review explored the relationship between home care rehabilitation, functional outcomes, and subsequent health utilization for those experiencing multimorbidity and found just four studies [12]. A retrospective cohort study ($$n = 99$$,764 home care clients) included within the rapid review reported that rehabilitation therapists can contribute to a reduction in hospital readmissions and institutionalization (long-term care admission) for people with musculoskeletal health conditions [11]. However, there was a gap in the literature with respect to understanding the association between receipt of home care rehabilitation therapy and subsequent health utilization following a discharge from an acute inpatient hospital unit among those with multimorbidity.
This study aims to address this gap in the literature through the following objectives: 1) To describe and compare the characteristics of people with multimorbidity who are referred and receiving home care rehabilitation therapy to those receiving home care for other services after recent discharge from an acute care unit in Ontario, and 2) to identify the association between home care rehabilitation therapy and subsequent health utilization (hospital readmission and emergency department use) by people with multimorbidity recently discharged home from an acute care unit in Ontario. Addressing these research gaps will build upon existing literature by determining the role of rehabilitation therapists in reducing unplanned healthcare use after transitions out of the hospital for people with multimorbidity.
## Study design and setting
This retrospective cohort study used linked health administrative data in Ontario, Canada between the years 2007–2015. This time range was selected because it corresponds with an eight-year period of structural stability in the home care delivery model in the province. This timeframe corresponds with the co-existence of Local Health Integration Networks (LHINs) and Community Care Access Centres (CCACs). The LHINs and CCACs were responsible for home care service funding, eligibility, and access in Ontario.
## Data sources
Health administrative data for Ontario residents are housed at ICES, a not-for-profit organization that aims to improve health care using existing data to further the evidence. ICES is a prescribed entity operating under data security policies and procedures approved by the Ontario Information and Privacy Commissioner. Multiple datasets housed within ICES were used and these datasets were linked using unique encoded identifiers and analyzed at ICES. The Registered Persons Database (RPDB) includes data related to population demographic characteristics. The Discharge Abstract Database (DAD) includes data on hospital discharges and the National Ambulatory Care Reporting System (NACRS) includes data regarding emergency department utilization. The Resident Assessment Instrument-Home Care database (RAI-HC database) was used to provide details about home care services received and key measures of functional status.
Additional databases were used to identify individuals with multimorbidity, which will be further outlined below.
## Datasets used in defining the multimorbidity population
An established ICES macro was used to identify individuals with multimorbidity for this analysis. Multimorbidity was defined as experiencing two or more co-occurring chronic conditions and was considered in the context of seventeen chronic conditions. The ICES cohort included the following chronic conditions based on prevalence and system-level burden: acute myocardial infarction (AMI), osteoarthritis and other arthritis (excluding rheumatoid arthritis), rheumatoid arthritis, asthma, all cancers, cardiac arrythmia, congestive heart failure, chronic obstructive pulmonary disease, coronary syndrome (excluding AMI), dementia, diabetes, hypertension, mood disorders (anxiety, depression and other nonpsychotic disorders), other mental illnesses, osteoporosis, renal failure, and stroke (excluding transient ischemic attacks) [7, 14–21]. The ICES derived chronic condition cohorts have been validated for eight of the 17 chronic conditions considered in the multimorbidity definition (bolded in the above list) [22–27]. The other nine conditions were defined using similar methods to the validated ICES chronic condition cohorts [22].
## Client population
Individuals were included in the cohort if they were: 1) diagnosed with multimorbidity as defined above, 2) were discharged home from the acute care unit, 3) long-stay home care clients, which refers to those who are expected to receive home care services for a minimum of 60 days [11], and had one RAI-HC assessment within 15 days from their hospital discharge, which is the index event (excluding home care discharge assessments), and 4) above the age of 18 and less than 105 years of age. The lookback window for capturing the chronic conditions used in the definition of multimorbidity was five years prior to the index date (the individuals’ first home care assessment following hospital discharge). The RAI-HC assessment tool is a validated standardized, mandated assessment completed with all long-stay home care clients in Ontario [28]. This assessment tool captures demographic information as well as aspects of cognitive health, psychoemotional health, physical functioning and mobility, and other domains of health. This tool also has embedded health subscales that capture some of these larger functional constructs, which include: Activities of Daily Living (ADL) Hierarchy Scale, Instrumental Activities of Daily Living (IADL), Pain Scale, Cognitive Performance Scale (CPS), Depression Rating Scale (DRS) and Changes in Health, End-stage Disease, Signs and Symptoms Scale (CHESS) used to further describe functional and health statuses [28–32]. The proximity of the RAI-HC assessment with the hospital discharge (within 15 days) was an important consideration in the transition from hospital care to home care because of the relationship being explored in the current study: the relationship between receipt of home care rehabilitation services and subsequent unplanned healthcare service utilization. Furthermore, this 15-day time-period aimed to exclude those with rapid readmissions who would not have been home long enough to have home care services initiated and/or implemented.
Exclusion criteria for the cohort included individuals: 1) with an invalid unique ICES identifier, 2) with an invalid code for age and/or sex, 3) who died at the hospital, or their date of death preceded the receipt of home care services, 4) who were non-Ontario residents, 5) resided in an institutionalized care environment and/or were discharged to an institutionalized environment (i.e., long-term care or hospital residence). The two cohorts, the home care cohort derived from the RAI-HC database and the acute care cohort derived from the DAD were then linked to create the study cohort of home care clients with multimorbidity who were discharged from an acute care unit. Figure 1 illustrates the cohort creation process. Of note, the individuals removed from the acute care discharges could populate more than one exclusion group; that is, these exclusion groups were not mutually exclusive at this stage. For example, one individual could be included in the missing age or sex, and age less than 18 groups and counted twice. The ‘DAD cohort’ however, is the calculated difference with individuals counted only once. Fig. 1Study population flowchart
## Variables
Home care referrals in Ontario can be made by a healthcare provider, a caregiver, and/or a self-referral. These referrals can occur at any point along the continuum of care and eligibility is determined by a case manager [33, 34]. For this study, the individuals were categorized into one of the following mutually exclusive groups using the treatment variable based on the home care that they received after their recent discharge from an acute care hospital (within 15 days). The groups were: 1) rehabilitation therapy only (occupational therapy and/or physical therapy); 2) rehabilitation therapy and other home care services; 3) other home care services excluding those receiving occupational therapy and/or physical therapy. The other home care services could include services such as, but not limited to, home care nursing, personal support work, and social work.
The outcomes of interest were unplanned hospital admission and emergency department visits after long-stay home care rehabilitation services. The three treatment groups were compared with respect to the outcome of interest. Hospital admissions and emergency department visits were dichotomized (yes/no) and captured at 3 months and 12 months from the time of an individuals’ first RAI-HC assessment post-initial hospital discharge. That is, if an individual was readmitted at any point within three months, they would be coded as having experienced an admission; similarly, if an individual was readmitted at any point within 12-month observation window, they were coded as experiencing an admission. When conducting the supplemental analysis, the sum of the readmissions at the three-month time frame and the sum of the readmissions for the 12-month timeframe were considered. The same coding structure was applied for emergency department utilization.
The RAI-HC subscales, along with other demographic characteristics derived from the assessment were used as potential covariates in the analysis (e.g., age, cognition, and functional performance in areas of activities of daily living and instrumental activities of daily living).
## Analysis
Descriptive statistics were used to summarize the characteristics of people with multimorbidity who were discharged from an acute care setting who received home care and those that did not. Descriptive statistics were also used to describe the characteristics of people with multimorbidity who received home care rehabilitation, those who received other home care services, and those that received rehabilitation and other home care services. T-tests and ANOVAs were used to compare the means across continuous variables and chi-square tests were used to compare categorical variables among baseline characteristics of people who received different types of home care. The primary analysis involved multivariable logistic regression to determine the relationship between home care rehabilitation therapy and subsequent hospital utilization. The initial step in building the model(s) included the development of an a priori list of potential covariates informed by the literature, to consider when examining the relationship between receipt of home care rehabilitation and subsequent health utilization (e.g., age, sex, number of chronic conditions, cognition, areas of functional performance, and indicators of marginalization) [10, 14, 19, 35–37]. These covariates aligned with the clinical and social characteristics discussed in the theoretical model proposed by Rogers et al. [ 10] that was foundational in guiding this research. Secondly, univariate logistic regression models were used to inform covariate selection. Covariates that were retained included those that were significant in the descriptive analysis and had statistically significant univariate relationship with hospital re-admissions and emergency department visits. These remaining covariates were entered into backwards elimination stepwise regression procedure. A decision was made to consider the covariates across two models to ensure methodological consistency. The models included: 1) covariates retained in the backwards elimination model, and 2) non-modifiable covariates only (age, sex, and number of chronic conditions).
Additionally, to assess the robustness of the findings, supplementary analysis involved examining the outcome, healthcare utilization, as a count variable over the 3-month and 12-month timeframe. Negative binomial regression was used to consider this relationship because it includes a dispersion term that corrects for a high number of ‘0’ values [38, 39]. In this study, ‘0’ values refer to no hospital admission or no emergency department interaction. This approach ensures a more accurate variance estimate [38, 39]. These models considered only non-modifiable covariates because there was less than $10\%$ difference between the above two multivariable logistic regression models.
This study was approved by the Queen’s University Health Sciences and Affiliated Teaching Hospitals Research Ethics Board in Kingston, Ontario, Canada (approval #6,025,299-REH-739–18). All statistical analyses were conducted using SAS Enterprise guide, version 7.1 (SAS Institute, Cary, NC).
## Results
There were 4,803,224 acute care inpatients discharged between January 2007 and December 2015. Of those, 1,875,207 had multimorbidity and were discharged without home care services. In the same timeframe, 710,549 individuals received long-stay home care services and of those 43,145 individuals had a diagnosis of multimorbidity, received long-stay home care services and were captured within a discharge timeframe of 15 days. Individuals who returned to the hospital within the 15-day timeframe and short-stay home care clients were not captured in this study. Of those receiving home care included in the study cohort, $54\%$ received home care rehabilitation therapy (occupational therapy, or physiotherapy) services.
Those who did not receive home care had an average age at discharge of 62.5 (sd = 17.0) years, a higher proportion were female ($54.3\%$), and the majority had two or three chronic conditions ($57.6\%$). Most clients receiving home care were over the age of 75 years ($67.6\%$), and female ($62\%$).
Of the clients receiving rehabilitation therapy services only, a higher proportion experienced mild, moderate, or severe cognitive impairment ($58.2\%$ vs $47.3\%$) and required supervision or assistance with activities of daily living ($49.7\%$ vs. $39.9\%$) compared to those not receiving rehabilitation therapy ($p \leq 0.001$) (Table 1). A higher proportion of those receiving rehabilitation therapy services had experienced falls compared to those receiving other home care services only ($51.6\%$ of those receiving rehabilitation therapy only, $46.6\%$ of those receiving rehabilitation therapy and other home care services, and $35.7\%$ of those receiving other home care services only; $p \leq 0.001$) (Table 1).Table 1Distribution of baseline characteristics and embedded health subscales across home care health professionsVariableOther Home care Services only $$n = 19$$,832 n(%)Home care rehabilitation therapy only $$n = 7$$,081 n(%)Home care rehabilitation therapy and other services $$n = 16$$,232 n(%)Total $$n = 43$$,145 n(%)P valueAgeMean (SD)76.6 (12.2)78.5 (11.8)77.7 (11.5)77.3 (11.9) <.001 < 551,198 ($6.0\%$)322 ($4.5\%$)753 ($4.6\%$)2,273 ($5.3\%$) <.001 55–8413,126 ($66.2\%$)4,308 ($60.8\%$)10,696 ($65.9\%$)28,130 ($65.2\%$) 85 + 5,508 ($27.8\%$)2,451 ($34.6\%$)4,783 ($29.5\%$)12,742 ($29.5\%$)Sex Female11,951 ($60.3\%$)4,527 ($63.9\%$)9,913 ($61.1\%$)26,391 ($61.2\%$) <.001Falls Frequency (within the last 90 days) 012,753 ($64.3\%$)3,427 ($48.4\%$)8,662 ($53.4\%$)24,842 ($57.6\%$) <.001 14,306 ($21.7\%$)2,062 ($29.1\%$)4,530 ($27.9\%$)10,898 ($25.3\%$) > 22,773 ($14.0\%$)1,592 ($22.5\%$)3,040 ($18.7\%$)7,405 ($17.2\%$)Diagnosis count 22,633 ($13.3\%$)923 ($13.0\%$)2,285 ($14.1\%$)5,841 ($13.5\%$) <.001 33,916 ($19.7\%$)1,331 ($18.8\%$)3,306 ($20.4\%$)8,553 ($19.8\%$) 44,158 ($21.0\%$)1,499 ($21.2\%$)3,479 ($21.4\%$)9,136 ($21.2\%$) 5 + 9,125 ($46.0\%$)3,328 ($47.0\%$)7,162 ($44.1\%$)19,615 ($45.5\%$)Ontario Marginalization Index Low level of marginalization (Score = 1,2)5,849 ($29.5\%$)2,150 ($30.4\%$)4,952 ($30.5\%$)12,951 ($30.0\%$) <.001 Medium level of marginalization (Score = 3)8,454 ($42.6\%$)2,939 ($41.5\%$)6,768 ($41.7\%$)18,161 ($42.1\%$) High level of marginalization (Score = 4,5)5,345 ($27.0\%$)1,945 ($27.5\%$)4,434 ($27.3\%$)11,724 ($27.2\%$)ADL Hierarchy Scale Independent11,922 ($60.1\%$)3,562 ($50.3\%$)7,681 ($47.3\%$)23,165 ($53.7\%$) <.001 Supervision/limited Assistance5,349 ($27.0\%$)2,043 ($28.9\%$)5,289 ($32.6\%$)12,681 ($29.4\%$) Moderate/Extensive Assistance2,561 ($12.9\%$)1,476 ($20.8\%$)3,262 ($20.1\%$)7,299 ($16.9\%$)IADL Involvement Scale Independent/Set-up assist650 ($3.3\%$)142 ($2.0\%$)191 ($1.2\%$)983 ($2.3\%$) <.001 Moderate/Extensive Assistance19,182 ($96.7\%$)6,939 ($98.0\%$)16,041 ($98.8\%$)42,162 ($97.7\%$)CHESS Scale No instability2,418 ($12.2\%$)759 ($10.7\%$)1,207 ($7.4\%$)4,384 ($10.2\%$) <.001 Minimal instability12,452 ($62.8\%$)4,690 ($66.2\%$)10,609 ($65.4\%$)27,751 ($64.3\%$) Moderate/severe instability4,962 ($25.0\%$)1,632 ($23.0\%$)4,416 ($27.2\%$)11,010 ($25.5\%$)Cognitive Performance Scale No cognitive impairment10,450 ($52.7\%$)2,965 ($41.9\%$)7,279 ($44.8\%$)20,694 ($48.0\%$) <.001 Mild cognitive impairment6,796 ($34.3\%$)2,894 ($40.9\%$)6,545 ($40.3\%$)16,235 ($37.6\%$) Moderate/Severe cognitive impairment2,586 ($13.0\%$)1,222 ($17.3\%$)2,408 ($14.8\%$)6,216 ($14.4\%$)Depression Rating Scale Score of 0 (no depressive symptoms)11,532 ($58.1\%$)4,021 ($56.8\%$)9,075 ($55.9\%$)24,628 ($57.1\%$)0.003 Score of 1 or 2 (minimal symptoms present in last 3 days)4,858 ($24.5\%$)1,766 ($24.9\%$)4,193 ($25.8\%$)10,817 ($25.1\%$) Score of 3,4,5 (moderate number of symptoms in last 3 days)2,421 ($12.2\%$)919 ($13.0\%$)2,105 ($13.0\%$)5,445 ($12.6\%$) Score 6 + (severe/all mood symptoms present in last 3 days)1,021 ($5.1\%$)375 ($5.3\%$)859 ($5.3\%$)2,255 ($5.2\%$)Pain Scale No pain6,898 ($34.8\%$)2,121 ($30.0\%$)5,130 ($31.6\%$)14,149 ($32.8\%$) <.001 Less than daily pain1,968 ($9.9\%$)726 ($10.3\%$)1,619 ($10.0\%$)4,313 ($10.0\%$) Daily pain10,966 ($55.3\%$)4,234 ($59.8\%$)9,483 ($58.4\%$)24,683 ($57.2\%$) *Over a* 3-month period, 10,100 individuals ($23.4\%$) with multimorbidity who received home care services after transitioning home from acute care were readmitted to acute care (the hospital). Over a 12-month period, 18,218 ($42.2\%$) were readmitted. At the 3-month follow-up time-frame, a higher proportion of individuals readmitted to the hospital were: 85 years of age and older ($30.2\%$), had experienced two or more falls ($18.6\%$), and had four or more chronic conditions ($71.4\%$). A higher proportion of those readmitted required assistance with activities of daily living ($52.2\%$ vs $44.5\%$), experienced moderate/severe health instability (as captured through the CHESS scale) ($35.5\%$ vs $22.5\%$) and experienced some level of cognitive impairment ($55.3\%$ vs $51.1\%$) ($p \leq 0.001$) (Table 2). Similar proportions were observed at the 12-month follow-up across these demographics. Table 2Distribution of baseline characteristics and embedded health subscales stratified by hospital re-admission statusVariable3 monthsP value12 MonthsP valueNot readmitted $$n = 33$$,045 n(%)Readmitted $$n = 10$$,100 n(%)Total $$n = 43$$,145 n(%)Not readmitted $$n = 24$$,927 n(%)Readmitted $$n = 18$$,218 n(%)Total $$n = 43$$,145 n(%)Home Care service group Other home care services only14,816 ($44.8\%$)5,016 ($49.7\%$)19,832 ($46.0\%$) <.00110,989 ($44.1\%$)8,843 ($48.5\%$)19,832 ($46.0\%$) <.001 Rehabilitation therapy only5,607 ($17.0\%$)1,474 ($14.6\%$)7,081 ($16.4\%$)4,293 ($17.2\%$)2,788 ($15.3\%$)7,081 ($16.4\%$) Home care rehabilitation therapy and other services12,622 ($38.2\%$)3,610 ($35.7\%$)16,232 ($37.6\%$)9,645 ($38.7\%$)6,587 ($36.2\%$)16,232 ($37.6\%$)Age < 551,804 ($5.5\%$)469 ($4.6\%$)2,273 ($5.3\%$)0.0021,451 ($5.8\%$)822 ($4.5\%$)2,273 ($5.3\%$) <.001 55–8421,554 ($65.2\%$)6,576 ($65.1\%$)28,130 ($65.2\%$)16,466 ($66.1\%$)11,664 ($64.0\%$)28,130 ($65.2\%$) 85 + 9,687 ($29.3\%$)3,055 ($30.2\%$)12,742 ($29.5\%$)7,010 ($28.1\%$)5,732 ($31.5\%$)12,742 ($29.5\%$)Sex Female20,817 ($63.0\%$)5,574 ($55.2\%$)26,391 ($61.2\%$) <.00116,064 ($64.4\%$)10,327 ($56.7\%$)26,391 ($61.2\%$) <.001Falls Frequency (within the last 90 days) 018,884 ($57.1\%$)5,958 ($59.0\%$)24,842 ($57.6\%$) <.00114,296 ($57.4\%$)10,546 ($57.9\%$)24,842 ($57.6\%$) <.001 18,634 ($26.1\%$)2,264 ($22.4\%$)10,898 ($25.3\%$)6,559 ($26.3\%$)4,339 ($23.8\%$)10,898 ($25.3\%$) 2 + 5,527 ($16.7\%$)1,878 ($18.6\%$)7,405 ($17.2\%$)4,072 ($16.3\%$)3,333 ($18.3\%$)7,405 ($17.2\%$)Diagnosis count 24,689 ($14.2\%$)1,152 ($11.4\%$)5,841 ($13.5\%$) <.0013,820 ($15.3\%$)2,021 ($11.1\%$)5,841 ($13.5\%$) <.001 36,813 ($20.6\%$)1,740 ($17.2\%$)8,553 ($19.8\%$)5,433 ($21.8\%$)3,120 ($17.1\%$)8,553 ($19.8\%$) 47,118 ($21.5\%$)2,018 ($20.0\%$)9,136 ($21.2\%$)5,479 ($22.0\%$)3,657 ($20.1\%$)9,136 ($21.2\%$) 5 + 14,425 ($43.7\%$)5,190 ($51.4\%$)19,615 ($45.5\%$)10,195 ($40.9\%$)9,420 ($51.7\%$)19,615 ($45.5\%$)Ontario Marginalization Index Low level of marginalization (Score = 1,2)9,947 ($30.1\%$)3,004 ($29.7\%$)12,951 ($30.0\%$)0.0117,527 ($30.2\%$)5,424 ($29.8\%$)12,951 ($30.0\%$)0.033 Medium level of marginalization (Score = 3)13,843 ($41.9\%$)4,318 ($42.8\%$)18,161 ($42.1\%$)10,469 ($42.0\%$)7,692 ($42.2\%$)18,161 ($42.1\%$) High level of marginalization (Score = 4,5)9,039 ($27.4\%$)2,685 ($26.6\%$)11,724 ($27.2\%$)6,777 ($27.2\%$)4,947 ($27.2\%$)11,724 ($27.2\%$)Marital Status Never married1,986 ($6.0\%$)532 ($5.3\%$)2,518 ($5.8\%$) <.0011,534 ($6.2\%$)984 ($5.4\%$)2,518 ($5.8\%$) <.001 Married14,537 ($44.0\%$)4,746 ($47.0\%$)19,283 ($44.7\%$)10,926 ($43.8\%$)8,357 ($45.9\%$)19,283 ($44.7\%$) Divorced, separated, widowed16,070 ($48.6\%$)4,696 ($46.5\%$)20,766 ($48.1\%$)12,111 ($48.6\%$)8,655 ($47.5\%$)20,766 ($48.1\%$) Other452 ($1.4\%$)126 ($1.2\%$)578 ($1.3\%$)356 ($1.4\%$)222 ($1.2\%$)578 ($1.3\%$)Bladder continence Continent21,364 ($64.7\%$)6,201 ($61.4\%$)27,565 ($63.9\%$) <.00116,363 ($65.6\%$)11,202 ($61.5\%$)27,565 ($63.9\%$) <.001 Usually continent6,518 ($19.7\%$)2,002 ($19.8\%$)8,520 ($19.7\%$)4,797 ($19.2\%$)3,723 ($20.4\%$)8,520 ($19.7\%$) Usually incontinent5,119 ($15.5\%$)1,872 ($18.5\%$)6,991 ($16.2\%$)3,737 ($15.0\%$)3,254 ($17.9\%$)6,991 ($16.2\%$)Bowel Continence Continent28,277 ($85.6\%$)8,052 ($79.7\%$)36,329 ($84.2\%$) <.00121,410 ($85.9\%$)14,919 ($81.9\%$)36,329 ($84.2\%$) <.001 Usually continent2,447 ($7.4\%$)994 ($9.8\%$)3,441 ($8.0\%$)1,783 ($7.2\%$)1,658 ($9.1\%$)3,441 ($8.0\%$) Usually incontinent2,288 ($6.9\%$)1,038 ($10.3\%$)3,326 ($7.7\%$)1,707 ($6.8\%$)1,619 ($8.9\%$)3,326 ($7.7\%$)ADL Hierarchy Scale Independent18,334 ($55.5\%$)4,831 ($47.8\%$)23,165 ($53.7\%$) <.00113,928 ($55.9\%$)9,237 ($50.7\%$)23,165 ($53.7\%$) <.001 Supervision/limited Assistance9,530 ($28.8\%$)3,151 ($31.2\%$)12,681 ($29.4\%$)7,106 ($28.5\%$)5,575 ($30.6\%$)12,681 ($29.4\%$) Moderate/Extensive Assistance5,181 ($15.7\%$)2,118 ($21.0\%$)7,299 ($16.9\%$)3,893 ($15.6\%$)3,406 ($18.7\%$)7,299 ($16.9\%$)IADL Involvement Scale Independent/Set-up assist772 ($2.3\%$)211 ($2.1\%$)983 ($2.3\%$)0.145603 ($2.4\%$)380 ($2.1\%$)983 ($2.3\%$)0.022 Moderate/Extensive Assistance32,273 ($97.7\%$)9,889 ($97.9\%$)42,162 ($97.7\%$)24,324 ($97.6\%$)17,838 ($97.9\%$)42,162 ($97.7\%$)CHESS Scale No instability3,638 ($11.0\%$)746 ($7.4\%$)4,384 ($10.2\%$) <.0012,761 ($11.1\%$)1,623 ($8.9\%$)4,384 ($10.2\%$) <.001 Minimal instability21,986 ($66.5\%$)5,765 ($57.1\%$)27,751 ($64.3\%$)16,764 ($67.3\%$)10,987 ($60.3\%$)27,751 ($64.3\%$) Moderate/severe instability7,421 ($22.5\%$)3,589 ($35.5\%$)11,010 ($25.5\%$)5,402 ($21.7\%$)5,608 ($30.8\%$)11,010 ($25.5\%$)Cognitive Performance Scale No cognitive impairment16,180 ($49.0\%$)4,514 ($44.7\%$)20,694 ($48.0\%$) <.00112,542 ($50.3\%$)8,152 ($44.7\%$)20,694 ($48.0\%$) <.001 Mild cognitive impairment12,310 ($37.3\%$)3,925 ($38.9\%$)16,235 ($37.6\%$)9,044 ($36.3\%$)7,191 ($39.5\%$)16,235 ($37.6\%$) Moderate/Severe cognitive impairment4,555 ($13.8\%$)1,661 ($16.4\%$)6,216 ($14.4\%$)3,341 ($13.4\%$)2,875 ($15.8\%$)6,216 ($14.4\%$)Depression Rating Scale Score of 0 (no depressive symptoms)19,272 ($58.3\%$)5,356 ($53.0\%$)24,628 ($57.1\%$) <.00114,574 ($58.5\%$)10,054 ($55.2\%$)24,628 ($57.1\%$) <.001 Score of 1 or 2 (minimal symptoms present in last 3 days)8,110 ($24.5\%$)2,707 ($26.8\%$)10,817 ($25.1\%$)6,131 ($24.6\%$)4,686 ($25.7\%$)10,817 ($25.1\%$) Score of 3,4,5 (moderate number of symptoms in last 3 days)4,034 ($12.2\%$)1,411 ($14.0\%$)5,445 ($12.6\%$)2,990 ($12.0\%$)2,455 ($13.5\%$)5,445 ($12.6\%$) Score 6 + (severe/all mood symptoms present in last 3 days)1,629 ($4.9\%$)626 ($6.2\%$)2,255 ($5.2\%$)1,232 ($4.9\%$)1,023 ($5.6\%$)2,255 ($5.2\%$)Pain Scale No pain10,711 ($32.4\%$)3,438 ($34.0\%$)14,149 ($32.8\%$)0.0057,834 ($31.4\%$)6,315 ($34.7\%$)14,149 ($32.8\%$) <.001 Less than daily pain3,291 ($10.0\%$)1,022 ($10.1\%$)4,313 ($10.0\%$)2,454 ($9.8\%$)1,859 ($10.2\%$)4,313 ($10.0\%$) Daily pain19,043 ($57.6\%$)5,640 ($55.8\%$)24,683 ($57.2\%$)14,639 ($58.7\%$)10,044 ($55.1\%$)24,683 ($57.2\%$) When controlling for age, sex, and number of chronic conditions, those receiving rehabilitation therapy services only (occupational therapy and/or physical therapy) were less likely to be readmitted to the hospital (3-month: OR = 0.78; $95\%$ CI = 0.73–0.83; 12-month: OR = 0.8; $95\%$ CI = 0.76–0.85) than individuals who received other home care services. Those receiving a combination of rehabilitation therapy and other home care services were also less likely to be readmitted to the hospital (3-month: OR = 0.85; $95\%$ CI = 0.81–0.89; 12-month: OR = 0.85; $95\%$ CI = 0.82–0.89) compared to those receiving other home care services (Table 3).Table 3The association between receipt of home care rehabilitation therapy services and hospital readmission Outcome: 3-month readmissionUnadjusted ModelBackwards Elimination Model*Model 2‡Odds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.780.73–0.830.770.72–0.820.780.73–0.83Home care rehabilitation therapy and other services0.850.80–0.890.810.77–0.850.850.81–0.89Outcome: 12-month readmissionUnadjusted ModelBackwards Elimination Model†Model 2Odds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.810.76–0.850.790.75–0.840.80.76–0.85Home care rehabilitation therapy and other services0.850.81–0.890.820.79–0.860.850.82–0.89* Backwards elimination model covariates (outcome 3-month readmission): sex, ADL Scale, Pain Scale, CPS, DRS, CHESS, bowel continence, falls, and number of chronic conditions†Backwards elimination model covariates (outcome 12-month readmission): sex, age, ADL, Pain Scale, CPS, DRS, CHESS, bowel incontinence, bladder incontinence, falls, number of chronic conditions‡Model 2: age sex, number of chronic conditions§ Each model contains covariates that align with the clinical characteristics component of the theoretical model discussed earlier; social characteristics were considered but not retained in the final models Among the cohort, of individuals experiencing an emergency department visit(s) within the 3-month and 12-month period, a higher proportion experienced moderate/severe health instability (as measured by the CHESS) (3-month: $30.8\%$ vs. $22.2\%$; 12-month: $27.5\%$ vs. $22.0\%$), a higher proportion experienced moderate/severe cognitive impairment (3 month: $15.4\%$ vs. $13.8\%$; 12-month: $14.8\%$ vs. $13.7\%$), and five or more co-occurring chronic conditions (3-month: $49.6\%$ vs. $42.9\%$; 12-month: $49.2\%$ vs. $39.0\%$) ($p \leq 0.001$) (Table 4).Table 4Distribution of baseline characteristics and embedded health subscales stratified by emergency department useVariable3 monthsP value12 MonthsP valueNo emergency department use $$n = 26$$,603 n(%)Emergency Department use $$n = 16$$,542 n(%)Total $$n = 43$$,145 n(%)No emergency department use $$n = 15$$,720 n(%)Emergency department use $$n = 27$$,427 n(%)Total $$n = 43$$,145 n(%)Home Care service group Other home care services only11,645 ($43.8\%$)8,187 ($49.5\%$)19,832 ($46.0\%$) <.0016,652 ($42.3\%$)13,180 ($48.1\%$)19,832 ($46.0\%$) <.001 Rehabilitation therapy only4,683 ($17.6\%$)2,398 ($14.5\%$)7,081 ($16.4\%$)2,762 ($17.6\%$)4,319 ($15.7\%$)7,081 ($16.4\%$) Home care rehabilitation therapy and other services10,275 ($38.6\%$)5,957 ($36.0\%$)16,232 ($37.6\%$)6,306 ($40.1\%$)9,926 ($36.2\%$)16,232 ($37.6\%$)Age < 551,320 ($5.0\%$)953 ($5.8\%$)2,273 ($5.3\%$)0.001796 ($5.1\%$)1,477 ($5.4\%$)2,273 ($5.3\%$) <.001 55–8417,378 ($65.3\%$)10,752 ($65.0\%$)28,130 ($65.2\%$)10,443 ($66.4\%$)17,687 ($64.5\%$)28,130 ($65.2\%$) 85 + 7,905 ($29.7\%$)4,837 ($29.2\%$)12,742 ($29.5\%$)4,481 ($28.5\%$)8,261 ($30.1\%$)12,742 ($29.5\%$)Sex Female16,985 ($63.8\%$)9,406 ($56.9\%$)26,391 ($61.2\%$) <.00110,221 ($65.0\%$)16,170 ($59.0\%$)26,391 ($61.2\%$) <.001Falls Frequency 015,131 ($56.9\%$)9,711 ($58.7\%$)24,842 ($57.6\%$) <.0019,015 ($57.3\%$)15,827 ($57.7\%$)24,842 ($57.6\%$) <.001 17,031 ($26.4\%$)3,867 ($23.4\%$)10,898 ($25.3\%$)4,204 ($26.7\%$)6,694 ($24.4\%$)10,898 ($25.3\%$) > 24,441 ($16.7\%$)2,964 ($17.9\%$)7,405 ($17.2\%$)2,501 ($15.9\%$)4,904 ($17.9\%$)7,405 ($17.2\%$)Diagnosis count 23,801 ($14.3\%$)2,040 ($12.3\%$)5,841 ($13.5\%$) <.0012,513 ($16.0\%$)3,328 ($12.1\%$)5,841 ($13.5\%$) <.001 35,581 ($21.0\%$)2,972 ($18.0\%$)8,553 ($19.8\%$)3,554 ($22.6\%$)4,999 ($18.2\%$)8,553 ($19.8\%$) 45,814 ($21.9\%$)3,322 ($20.1\%$)9,136 ($21.2\%$)3,524 ($22.4\%$)5,612 ($20.5\%$)9,136 ($21.2\%$) 5 + 11,407 ($42.9\%$)8,208 ($49.6\%$)19,615 ($45.5\%$)6,129 ($39.0\%$)13,486 ($49.2\%$)19,615 ($45.5\%$)Ontario Marginalization Index Low level of marginalization (Score = 1,2)8,047 ($30.2\%$)4,904 ($29.6\%$)12,951 ($30.0\%$) <.0014,784 ($30.4\%$)8,167 ($29.8\%$)12,951 ($30.0\%$)0.003 Medium level of marginalization (Score = 3)11,047 ($41.5\%$)7,114 ($43.0\%$)18,161 ($42.1\%$)6,588 ($41.9\%$)11,573 ($42.2\%$)18,161 ($42.1\%$) High level of marginalization (Score = 4,5)7,342 ($27.6\%$)4,382 ($26.5\%$)11,724 ($27.2\%$)4,265 ($27.1\%$)7,459 ($27.2\%$)11,724 ($27.2\%$)Marital Status Never married1,555 ($5.8\%$)963 ($5.8\%$)2,518 ($5.8\%$) <.001933 ($5.9\%$)1,585 ($5.8\%$)2,518 ($5.8\%$)0.01 Married11,633 ($43.7\%$)7,650 ($46.2\%$)19,283 ($44.7\%$)6,858 ($43.6\%$)12,425 ($45.3\%$)19,283 ($44.7\%$) Divorced, separated, widowed13,070 ($49.1\%$)7,696 ($46.5\%$)20,766 ($48.1\%$)7,713 ($49.1\%$)13,053 ($47.6\%$)20,766 ($48.1\%$) Other345 ($1.3\%$)233 ($1.4\%$)578 ($1.3\%$)Bladder continence Continent17,060 ($64.1\%$)10,505 ($63.5\%$)27,565 ($63.9\%$)0.00510,190 ($64.8\%$)17,375 ($63.4\%$)27,565 ($63.9\%$)0.009 Usually continent5,319 ($20.0\%$)3,201 ($19.4\%$)8,520 ($19.7\%$)3,069 ($19.5\%$)5,451 ($19.9\%$)8,520 ($19.7\%$) Usually incontinent4,184 ($15.7\%$)2,807 ($17.0\%$)6,991 ($16.2\%$)2,438 ($15.5\%$)4,553 ($16.6\%$)6,991 ($16.2\%$)Bowel continence Continent22,771 ($85.6\%$)13,558 ($82.0\%$)36,329 ($84.2\%$) <.00113,430 ($85.4\%$)22,899 ($83.5\%$)36,329 ($84.2\%$) < 0.001 Usually continent1,964 ($7.4\%$)1,477 ($8.9\%$)3,441 ($8.0\%$)1,129 ($7.2\%$)2,312 ($8.4\%$)3,441 ($8.0\%$) Usually incontinent1,838 ($6.9\%$)1,488 ($9.0\%$)3,326 ($7.7\%$)1,138 ($7.2\%$)2,188 ($8.0\%$)3,326 ($7.7\%$)22,771 ($85.6\%$)13,558 ($82.0\%$)36,329 ($84.2\%$)13,430 ($85.4\%$)22,899 ($83.5\%$)36,329 ($84.2\%$)ADL Hierarchy Scale Independent14,744 ($55.4\%$)8,421 ($50.9\%$)23,165 ($53.7\%$) <.0018,585 ($54.6\%$)14,580 ($53.2\%$)23,165 ($53.7\%$)0.013 Supervision/limited Assistance7,691 ($28.9\%$)4,990 ($30.2\%$)12,681 ($29.4\%$)4,513 ($28.7\%$)8,168 ($29.8\%$)12,681 ($29.4\%$) Moderate/Extensive Assistance4,168 ($15.7\%$)3,131 ($18.9\%$)7,299 ($16.9\%$)2,622 ($16.7\%$)4,677 ($17.1\%$)7,299 ($16.9\%$)IADL Involvement Scale Independent/Set-up assist606 ($2.3\%$)377 ($2.3\%$)983 ($2.3\%$)0.994340 ($2.2\%$)643 ($2.3\%$)983 ($2.3\%$)0.223 Moderate/Extensive Assistance25,997 ($97.7\%$)16,165 ($97.7\%$)42,162 ($97.7\%$)15,380 ($97.8\%$)26,782 ($97.7\%$)42,162 ($97.7\%$)CHESS Scale No instability2,941 ($11.1\%$)1,443 ($8.7\%$)4,384 ($10.2\%$) <.0011,679 ($10.7\%$)2,705 ($9.9\%$)4,384 ($10.2\%$) <.001 Minimal instability17,754 ($66.7\%$)9,997 ($60.4\%$)27,751 ($64.3\%$)10,582 ($67.3\%$)17,169 ($62.6\%$)27,751 ($64.3\%$) Moderate/severe instability5,908 ($22.2\%$)5,102 ($30.8\%$)11,010 ($25.5\%$)3,459 ($22.0\%$)7,551 ($27.5\%$)11,010 ($25.5\%$)Cognitive Performance Scale No cognitive impairment13,091 ($49.2\%$)7,603 ($46.0\%$)20,694 ($48.0\%$) <.0018,005 ($50.9\%$)12,689 ($46.3\%$)20,694 ($48.0\%$) <.001 Mild cognitive impairment9,843 ($37.0\%$)6,392 ($38.6\%$)16,235 ($37.6\%$)5,558 ($35.4\%$)10,677 ($38.9\%$)16,235 ($37.6\%$) Moderate/Severe cognitive impairment3,669 ($13.8\%$)2,547 ($15.4\%$)6,216 ($14.4\%$)2,157 ($13.7\%$)4,059 ($14.8\%$)6,216 ($14.4\%$)Depression Rating Scale Score of 0 (no depressive symptoms)15,756 ($59.2\%$)8,872 ($53.6\%$)24,628 ($57.1\%$) <.0019,423 ($59.9\%$)15,205 ($55.4\%$)24,628 ($57.1\%$) <.001 Score of 1 or 2 (minimal symptoms present in last 3 days)6,449 ($24.2\%$)4,368 ($26.4\%$)10,817 ($25.1\%$)3,758 ($23.9\%$)7,059 ($25.7\%$)10,817 ($25.1\%$) Score of 3,4,5 (moderate number of symptoms in last 3 days)3,143 ($11.8\%$)2,302 ($13.9\%$)5,445 ($12.6\%$)1,810 ($11.5\%$)3,635 ($13.3\%$)5,445 ($12.6\%$) Score 6 + (severe/all mood symptoms present in last 3 days)1,255 ($4.7\%$)1,000 ($6.0\%$)2,255 ($5.2\%$)729 ($4.6\%$)1,526 ($5.6\%$)2,255 ($5.2\%$)Pain Scale No pain8,666 ($32.6\%$)5,483 ($33.1\%$)14,149 ($32.8\%$)0.3284,963 ($31.6\%$)9,186 ($33.5\%$)14,149 ($32.8\%$) <.001 Less than daily pain2,643 ($9.9\%$)1,670 ($10.1\%$)4,313 ($10.0\%$)1,535 ($9.8\%$)2,778 ($10.1\%$)4,313 ($10.0\%$) Daily pain15,294 ($57.5\%$)9,389 ($56.8\%$)24,683 ($57.2\%$)9,222 ($58.7\%$)15,461 ($56.4\%$)24,683 ($57.2\%$)
When controlling for age, sex, and number of chronic conditions, in comparison to people receiving other home care services, those receiving rehabilitation therapy services only (occupational therapy and/or physical therapy) were less likely to use emergency department services (3-month: OR = 0.73; $95\%$ CI = 0.69–0.78; 12-month: OR = 0.79; $95\%$ CI = 0.75–0.83). Compared to other home care services, clients receiving a combination of home care rehabilitation therapy and other home care services were also less likely to use emergency department services (3-month: OR = 0.83; $95\%$ CI = 0.80–0.87; 12-month: 0.8; $95\%$ CI = 0.77–0.84) (Table 5). When controlling for these three covariates in the model examining the 3-month emergency department use outcome, the overall fit of the model was inadequate, however, the magnitude of the association between receipt of rehabilitation services and subsequent hospital utilization was the same in the unadjusted model and the backwards elimination model. The relationship between receipt of rehabilitation services was therefore still considered clinically relevant. These models were examined for multicollinearity, and it was not present. Table 5The association between receipt of rehabilitation therapy services and emergency department useOutcome: 3-month emergency department useUnadjusted ModelBackwards Elimination Model*Model 2‡Odds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.730.69–0.770.730.69–0.780.730.69–0.78Home care rehabilitation therapy and other services0.830.79–0.860.810.77–0.840.830.8–0.87Outcome: 12-month emergency department useUnadjusted ModelBackwards Elimination Model†Model 2Odds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOdds Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.790.75–0.840.780.74–0.830.790.75–0.83Home care rehabilitation therapy and other services0.790.76–0.830.790.75–0.820.80.77–0.84* Backwards elimination model covariates (outcome 3-month emergency department use): age, sex, ADL Scale, DRS, CHESS, bladder continence, bowel continence, falls, Ontario marginalization summary score, and number of chronic conditions†Backwards elimination model covariates (outcome 12-month emergency department use): sex, age, CPS, DRS, CHESS, bowel continence, falls, pain, number of chronic conditions‡Model 2-age, sex, and number of chronic conditions§ models contain covariates that align with the clinical and social characteristics components of the theoretical model discussed earlier The secondary analysis evaluated the association between receipt of rehabilitation therapy and the number of hospital admissions and emergency department visits (counts) within the 3-month and 12-month windows. During this time, a similar health utilization trend was observed for the therapy services. When controlling for age, sex, and number of chronic conditions, those who received rehabilitation therapy only were less likely to be admitted to the hospital (3-month Rate Ratio = 0.73; $95\%$ CI = 0.68–0.78; 12-month Rate Ratio = 0.79; $95\%$ CI = 0.75–0.83) and less likely to utilize emergency department services (3-month Rate Ratio = 0.69; $95\%$ CI = 0.66–0.73; 12-month Rate Ratio = 0.79; $95\%$ CI = 0.76–0.82) compared to those receiving other home care services only (Table 6).Table 6The association between home care rehabilitation therapy and healthcare utilizationHospital Readmission3 Months12 monthsRate Ratio$95\%$ Confidence IntervalRate Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.730.68- 0.780.790.75-0.83Home care rehabilitation therapy and other services0.820.78- 0.870.860.83–0.89Emergency Department Use3 Months12 monthsRate Ratio$95\%$ Confidence IntervalRate Ratio$95\%$ Confidence IntervalOther Home care servicesReferenceHome care rehabilitation therapy only0.690.66 -0.730.790.76 -0.82Home care rehabilitation therapy and other services0.820.79 -0.860.840.82 -0.87• healthcare utilization as a count variable-negative binomial regression* adjusted for: age, sex, and number of chronic conditions
## Discussion
In this study, we examined the relationship between the receipt of long-stay home care rehabilitation therapy and hospital readmission and emergency department use following an acute hospital discharge among persons with multimorbidity. This study offers an important contribution to the literature and suggests home care rehabilitation therapy is associated with lower hospital readmission and emergency department use in people with multimorbidity after an acute hospital discharge.
We found that persons receiving home care services, irrespective of the type of services, were more likely to require at least moderate assistance with instrumental activities of daily living, were older, and had a similar number of chronic conditions that they experienced. This finding is consistent with results across existing population-based home care studies, and highlights the important role home care services play in supporting older adults to live within their community [40, 41].
The profile of home care clients that received rehabilitation therapy in comparison to people that received other home care services may indicate that service referrals were made congruently with the therapists’ area of expertise. For example, the literature has consistently shown that people receiving rehabilitation therapy services tend to require higher levels of support with activities of daily living, have experienced fall(s), and experience cognitive impairment [42, 43]. In the current study, a higher proportion of home care clients receiving rehabilitation therapy experienced functional impairment, suggesting that home care rehabilitation therapists are providing services to this group, and the areas of functional decline are consistent with existing home care rehabilitation literature among other populations.
Our findings revealed that individuals receiving rehabilitation therapy services, whether alone or with other home care services, were less likely to be re-admitted to the hospital and less likely to use the emergency department services compared to those receiving other home care services only. This is consistent with the literature that has found home care rehabilitation was associated with a reduction in unplanned healthcare use by people who have experienced a stroke, older adults, and patients with musculoskeletal health conditions, and adds to the growing evidence highlighting the potential value of home care rehabilitation in reducing future unplanned healthcare use [11, 44–46]. One study explored the relationship between receipt of home care rehabilitation and health care utilization among older adults in a small geographic region within Ontario [44]. The authors found that people receiving physical therapy had the longest length of time before being re-hospitalized [44]. The current study considered multimorbidity across a broader chronic health condition profile that considered a range of 17 chronic conditions across cognitive, cardiorespiratory, and psychoemotional domains of health. The findings of the current study suggest rehabilitation therapists may help reduce subsequent healthcare utilization amongst a group of medically complex clients and their role can be leveraged to support hospital to home care transitions.
A recent observational study found that increased spending on hospital-based occupational therapy was the only healthcare service that reduced hospital readmissions among patients with a diagnosis of pneumonia, acute myocardial infarction, or heart failure [10]. The authors found that increased spending on occupational therapy in hospital lowered 30-day hospital readmissions. The authors hypothesized that this may be because occupational therapists focused on the immediate functional and social needs of the patients [10]. A recent study by Freburger et al. [ 47], revealed that receipt of acute inpatient rehabilitation services during an acute hospital admission for individuals with pneumonia or influenza was associated with reductions in hospital readmissions. The authors found that the inverse relationship between receipt of therapy services and 30-day hospital readmissions was stronger as the number of therapy visits increased. Specifically, only statistically significant reductions were observed among the group that received 6 + therapy visits (OR = 0.86; $95\%$CI:0.75–0.98) [47]. Another study examined the association between receipt of inpatient occupational therapy services, and the frequency and intensity of these services on 30-day readmission rates for individuals diagnosed with common cardiorespiratory conditions, and those requiring joint replacements [48]. Edelstein et al. [ 48] also found that those receiving a higher frequency of acute care occupational therapy services were $1\%$ less likely to be readmitted; however, these results should be interpreted with caution as the $95\%$ confidence intervals ranged from 0.99–1.00. Similarly, a systematic review identifying interventions aimed at promoting early hospital discharge and preventing hospital (re)admissions found that interventions delivered in the home were associated with reduced hospital length of stay and improved patient satisfaction; however, these were not rehabilitation specific [49]. Our results build on these findings by suggesting that rehabilitation therapy delivered in the home also reduces hospital readmissions and emergency department visits for individuals with multimorbidity. The results of the current study also highlight the need for further investigation into the types and duration of interventions delivered by trained rehabilitation therapists.
The findings of the current study add to the growing body of literature that demonstrates the value of rehabilitation therapy services in reducing unplanned hospital admissions and emergency department use [44–46, 50–53]. As summarized above, there is evidence in the literature across varied populations that occupational therapy and/or physical therapy aid in successful transitions to home with durable discharges from acute care facilities [10, 11, 45]. In this context, the term ‘durable discharge(s)’ is used to describe a successful and sustained transition from the hospital setting to home. Ontario health care is undergoing significant reform and is moving towards an integrated model of care delivery whereby coordinated services are easily navigated by both the patient and the provider [54]. Therefore, our results suggest health system planners should consider facilitating increased use of home care rehabilitation therapy as a means of reducing unplanned hospitalization and emergency department visits for people with multimorbidity who are transitioning home after an acute hospital stay. As the health system continues this transition, there will be potential to utilize integrated system-level data across health care sectors to further investigate the impact of rehabilitation on health care utilization outcomes during a period of policy change.
Another potential area of future research that would extend the findings of this study would be to conduct an economic analysis to investigate the cost–benefit of expenditures on rehabilitation therapy for the health system. The findings from this study shed light on the association between home care rehabilitation therapy and healthcare utilization after one of the most common health care transitions (hospital to home). Future research could use the findings from this study as the foundation for examining the relationship between home care rehabilitation and healthcare costs. Additionally, the relationship between receipt of rehabilitation therapy and other healthcare outcomes could be examined such as discharges to long-term care, discharges from home care services, functional changes, and mortality [11].
## Limitations
The selection of chronic conditions chosen for inclusion was limited to 17 and there is the possibility that some people with multimorbidity were not captured in this cohort. This selection does however consider the chronic conditions with the heaviest healthcare burden and is consistent with other ICES literature that utilized similar data [7, 14, 15, 17–19, 55–57]. Inconsistencies exist with respect to defining multimorbidity within the growing body of multimorbidity literature. Inconsistent definitions of the term ‘multimorbidity’ creates a significant barrier for comparisons at both the micro- and macro-levels. It was therefore important for the authors to maintain a consistent definition within the ICES data for two reasons: 1) it works towards contributing to the growing body of multimorbidity literature in a consistent way that can be compared to previous literature, and 2) it helps build and establish a consistent definition for future research.
This study also only considers long-stay home care clients. Short-stay clients were excluded from this study because full RAI-HC assessments are not completed for those on a short-stay caseload following an acute change in medical status. Long-stay home care clients align with the population of interest, those with multiple chronic conditions, because of the chronic nature of their diagnosis and prolonged health care interaction. Cook et al. [ 11], highlighted that long-stay home care clients are not often referred to home care rehabilitation and as such, this study may underestimate rehabilitation referrals and the association between receipt of rehabilitation therapy and health care utilization. This presents an opportunity for future research to consider the inclusion of short-stay home care clients as a means of capturing a more comprehensive representation of home care rehabilitation users. Another direction for future research in this area could be stratification by whether receipt of rehabilitation was a new service or a re-instatement of existing rehabilitation services. Future studies could also consider longitudinal analysis of interRAI data to capture competing interests such as alternative discharges from home care including long-term care admissions or deaths; similar to the work conducted by Cook et al. [ 11] Additionally, this study was limited by the variables collected across health administrative databases and therefore, there may be the potential for unmeasured and uncontrolled confounding. Data were also limited to what is captured within existing datasets. One particular area of data sparsity related to the receipt of rehabilitation services within the RAI-HC was with respect to the frequency, intensity, and type of therapeutic intervention. There is information related to the cumulative number of days, hours, and minutes of home care services that were provided in the previous week or since last assessment if it had been conducted less than seven days prior; however, there are gaps in data collection that extend beyond the previous week of services. Similarly, there were gaps with respect to receipt of rehabilitation therapy delivered within the acute care hospital setting. This information does not provide a comprehensive picture of the cumulative rehabilitation services delivered. Understanding receipt of home care services as a whole, may require further investigation into the services provided prior to discharge as well as between RAI-HC assessments that are beyond the seven-day timeframe that the assessment provides. Furthermore, understanding of supports that extend beyond the home care funded services, such as region-specific programming, consideration of the recently developed caregiver risk evaluation algorithm, duration of rehabilitation services, and system-level covariates, could be next steps in this research. Examination of these covariates may also provide further explanation of the enduring results related to receipt of rehabilitation observed at the 12-month mark in the current study.
## Conclusions
This study took a population-level approach to understanding the demographics of those with multimorbidity receiving home care rehabilitation therapy after acute care hospitalization, and the association between receipt of home care rehabilitation therapy and subsequent health care utilization. We found that there was an inverse relationship between receipt of home care rehabilitation and hospital admissions and emergency department visits over 3-month and 12-month periods following discharge from an acute care hospital. This work provides a platform to further examine rehabilitation specific interventions among those with multimorbidity and economic value of rehabilitation therapies, both in times of healthcare reform and health care stability.
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|
---
title: 'Quantitative muscle MRI displays clinically relevant myostructural abnormalities
in long-term ICU-survivors: a case–control study'
authors:
- R. Rehmann
- E. Enax-Krumova
- C. H. Meyer-Frießem
- L. Schlaffke
journal: BMC Medical Imaging
year: 2023
pmcid: PMC10024415
doi: 10.1186/s12880-023-00995-7
license: CC BY 4.0
---
# Quantitative muscle MRI displays clinically relevant myostructural abnormalities in long-term ICU-survivors: a case–control study
## Abstract
### Background
Long-term data on ICU-survivors reveal persisting sequalae and a reduced quality-of-life even after years. Major complaints are neuromuscular dysfunction due to Intensive care unit acquired weakness (ICUAW). Quantitative MRI (qMRI) protocols can quantify muscle alterations in contrast to standard qualitative MRI-protocols.
### Methods
Using qMRI, the aim of this study was to analyse persisting myostructural abnormalities in former ICU patients compared to controls and relate them to clinical assessments. The study was conducted as a cohort/case–control study. Nine former ICU-patients and matched controls were recruited (7 males; 54.8y ± 16.9; controls: 54.3y ± 11.1). MRI scans were performed on a 3T-MRI including a mDTI, T2 mapping and a mDixonquant sequence. Water T2 times, fat-fraction and mean values of the eigenvalue (λ1), mean diffusivity (MD), radial diffusivity (RD) and fractional anisotropy (FA) were obtained for six thigh and seven calf muscles bilaterally. Clinical assessment included strength testing, electrophysiologic studies and a questionnaire on quality-of-life (QoL). Study groups were compared using a multivariate general linear model. qMRI parameters were correlated to clinical assessments and QoL questionnaire using Pearson´s correlation.
### Results
qMRI parameters were significantly higher in the patients for fat-fraction ($p \leq 0.001$), water T2 time ($p \leq 0.001$), FA ($$p \leq 0.047$$), MD ($p \leq 0.001$) and RD ($p \leq 0.001$). Thighs and calves showed a different pattern with significantly higher water T2 times only in the calves. Correlation analysis showed a significant negative correlation of muscle strength (MRC sum score) with FA and T2-time. The results were related to impairment seen in QoL-questionnaires, clinical testing and electrophysiologic studies.
### Conclusion
qMRI parameters show chronic next to active muscle degeneration in ICU survivors even years after ICU therapy with ongoing clinical relevance. Therefore, qMRI opens new doors to characterize and monitor muscle changes of patients with ICUAW. Further, better understanding on the underlying mechanisms of the persisting complaints could contribute the development of personalized rehabilitation programs.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12880-023-00995-7.
## Background
Intensive care unit acquired weakness (ICUAW) is a general term that integrates the clinical and pathophysiological aspects of symmetric distal axonal neuropathy (CIP) and proximal myopathy (CIM) related to an intensive care therapy. ICUAW is common in intensive care patients (up to $82\%$ of patients) and is an independent risk factor for long-term disability and a reduced quality of life in ICU survivors [1–3]. CIM is thought to be the result of muscle protein breakdown and myofiber necrosis due to inflammatory systemic responses and inactivity [4–6]. In contrast to CIP, CIM is usually transient and resolves during rehabilitation. In CIP systemic toxins, inflammatory responses, endothelial changes in sepsis as well as vasoactive and other medication are prone to cause microvascular damage and toxic axonal degeneration that is often non-reversible and cause long-lasting clinical deficits [7]. Thus, ICUAW can be regarded as an organ failure of the peripheral neuromuscular system with a high impact on long-term disability [1, 2].
Long-term data of ICU survivors are rare. A 5-year follow-up revealed a reduced motor performance and quality of life [3, 8–10]. Thus, objective outcome measures are highly important to quantify the disease status of the patients. For the measurement of disability, especially in CIP—since it contributes mainly to long-lasting dysfunction—MRC grading, walking tests, electrophysiologic testing, quality of life questionnaires and patients functional status have been used [1, 2, 10–13].
Van Aerde et al. showed that even a slight reduction in MRC sum score could be associated with a high 5-year morbidity and mortality after ICU discharge and underline the importance to capture muscle degeneration early [14]. Imaging studies in ICUAW mostly relate to computed tomography (CT) and muscle ultrasound (US) where a reduction in muscle mass and a change in muscle composition can reflect muscle degeneration due to ICUAW [15–19]. While both modalities are easy to access and feasible to use for a comprehensive muscle status in the acute setting of an ICU therapy, they are unable quantify muscle status in terms of fatty infiltration and muscle architecture in the long-term evaluation of ICU survivors [2, 15, 20]. Diagnosing ICU related polyneuropathy is standardized with electrophysiologic testing (EMG). However, ongoing long term muscle degeneration and myoarchitectural changes cannot be captured with it. Diagnostic MRI is the current gold-standard to evaluate the extend of acute and chronic muscle damage in neuropathies and myopathies and quantitative MR imaging (qMRI) protocols are increasingly used in neuromuscular research [21, 22]. qMRI markers such as mDixon fat-fraction, muscle diffusion tensor imaging and water T2 are established objective outcome measures to grade muscle degeneration and correlate with clinical muscle function [23–25]. Using mDixon sequences fatty infiltration can be objectively quantified, T2 mapping allows to capture muscle oedema and inflammation via water T2 relaxation time and mDTI can reveal myofiber atrophy on a microstructural level [25–29]. The result pattern of qMRI outcome measures allows conclusions on pathomechanisms of muscle degeneration and its classification as acute or chronic. Thus, an increased water T2 time reflects active muscle degeneration due to tissue edema, a fatty muscle infiltration can be measured with mDixon and reflects chronic muscle damage, and muscle fiber atrophy can be captured with the mDTI sequences [25–27]. Until now, there are no studies on long-term ICU survivors with or without ICUAW regarding qMRI evaluation. The aim of this study was to comprehensively evaluate the quality and quantity of structural muscle differences in long-term ICU survivors with a qMRI protocol and to correlate them to clinical findings and quality of life questionnaires. Better understanding of the long-lasting myoarchitectural abnormalities may contribute to optimized treatment options in the rehabilitation phase.
## Study population
The subjects in the present study were part of a larger study (CRIT-Path study) with the aim to investigate the incidence of clinical and electrophysiological abnormalities in long-term ICU survivors. Subjects evaluated in the present study additionally underwent qMRI. Based on a medical patient management database screening, patients admitted to ICU between 2007 and 2017, (≥ 7d on ICU of an university hospital including ≥ 3d of invasive ventilation, at least 6mo-10y post-ICU, aged ≤ 85y) were contacted by letter [3]. Volunteers then called the study centre to authorise study participation (see Table 1 for details). Sex- and age-matched controls without history of ICU treatment, neuromuscular diseases (NMD) or injuries in lower extremity 12 months before study enrolment were recruited via advertisement. MRI exclusion criteria were metal implants in lower legs or back or electronical implants such as a cochlear implant or drug pumps. This prospective study had been approved by the local ethics committee of the Ruhr-University Bochum (No. 4905-14 3.0) and written informed consent for participation and publication was obtained from all participants prior to enrolment. Table 1Demographic dataNameAge (years)Height (cm)Weight (kg)BMIICU stay (days)Duration of ventilation (h)distance (ICU to examination in days)Anamnestic co-morbidities at study examinationCP 1771879426.8810951240Hypertension,Coronary artery disease, obstructive sleep apneaCP 2301908724.1015270771noneCP 3601688128.70831848712Hypertension,Coronary artery diseaseCP 46416012950.39152092422Type-II-diabetes. Hyper-tension. HypothyreoidismCP 55417813843.5619369965Hypertension, Atrial fibrillation. HypothyreoidismCP 6411796720.913058690Hypertension. Coronary artery diseaseCP 7471868323.99422406022noneCP 8731677025.1081273588Hypertension, Hyperthyroidism, Coronary artery diseaseCP 9771688128.70181802555Hypertension, HypothyreoidismICU data, comorbidities; patients are labelled as CP 1–9
## Clinical and electrophysiological assessments
Muscle strength was evaluated using the Medical Research Council (MRC; 0–60) by an experienced clinician. Grip strength was measured with a hand dynamometer on both hands [30]. Quality of life was measured with the EuroQOL5-Dimension questionnaire (EQ-5D-3L VAS). Subjects were assessed for any typical ICUAW symptoms like symmetrical proximal muscle weakness, distal dysesthesia, paraesthesia or pain (see Table 2 for details).Table 2Clinical dataNameICUAW symptoms (yes/no)Grip strength right (kg)Grip strength left (kg)MRCscoreTNCMAP (mV)SNSNAP (μV)Type of polyneuropathyEQ-5D-3L VASCP 1Y239542.20Axonal50CP 2N5456592.81.37Mixed axonal demyelinating90CP 3Y2329504.76.7Mixed axonal demyelinating50CP 4N20236000Axonal100CP 5Y24215634.2Mixed axonal demyelinating30CP 6Y1112527.18.7Demyelinating35CP 7Y3926588.815.3Small fiber neuropathy75CP 8Y2830608.59.5Axonal in electromyography90CP 9Y3228592.10.83Mixed axonal demyelinating50Grip strength was measured with a hand dynamometer in kilogramsMRC, Strength grade sum score according to Muscle Research Council: 0–60; TNCMAP, Compound muscle action potential of tibial nerve; SNSNAP, compound sensory nerve action potential of sural nerve; EQ-5D-3L VAS, EuroQoL 5-dimensional quality of life visual analogue scale ranging from 1 to 100 Electroneurography (ENG) was done unilaterally for the sural, the peroneal, the tibial and the ulnar nerve. For this study, we report tibial nerve compound motor action potential (TNCMAP) and sural nerve sensory action potential (SNSNAP). Polyneuropathy was graded as either axonal, demyelinating or combined based on the definition by England et al. [ 31] Electromyography (EMG) was performed unilaterally in the anterior tibialis and the vastus lateralis muscle and graded as abnormal in terms of acute, subacute or chronic neurogenic damage according to Mills [32].
## MRI acquisition and sequences
MR scans of both legs vertical to the femur and tibia bone were obtained using a Philips 3.0T Achieve MR system and a 16CH Torso XL coil. The participants lay in a feet-first supine position. Cushions were used to support participants’ knees and sandbags placed around their feet to prevent motion.
For the first MRI acquisition protocol (first four patients) the thigh region from hip to knee was split into three fields of view (FOV) along the z-axis with a 30 mm overlap, which each FOV comprised T1-weighted (T1w), T2-weighted (T2w), a diffusion-weighted spin-echo EPI (voxel size 3.0 × 3.0 × 6.0 mm3; TR/TE $\frac{5000}{57}$ ms; SPAIR/SPIR fat suppression; SENSE: 1.9; 17 gradient directions with b-values of 400 and 3 images with b-value of 0 [33] as well as one noise measurement (by turning of the RF and imaging gradients) with a total acquisition time of approximately 27 min for both thighs (9 min per FOV). An additional mDixonquant sequence (voxel size 1.5 × 1.5 × 6.0 mm3; TR/TE $\frac{210}{2.6}$, 3.36, 4.12, 4.88 ms; flip angle 8°, SENSE: 2) was acquired. After the image acquisition of the thigh-regions the data acquisition was paused and the TorsoXL coil was wrapped around the lower leg region; the calf region was split into two fields of view for additional 18 min scanning time for both calves.
The protocol for the remaining five patients consisted of a 4-point Dixon sequence (voxel size 1.5 × 1.5 × 6.0 mm3; TR/TE $\frac{210}{2.6}$, 3.36, 4.12, 4.88 ms; flip angle 8°, SENSE: 2), a multi‐echo spin‐echo (MESE) sequence for quantitative water mapping including 17 echoes and Cartesian k‐space sampling (voxel size 3.0 × 3.0 × 6.0 mm3; TR/TE $\frac{4598}{17}$x∆7.6 ms; flip angle $\frac{90}{180}$°, SENSE: 2), and a diffusion-weighted spin-echo EPI (voxel size 3.0 × 3.0 × 6.0 mm3; TR/TE $\frac{5000}{57}$ ms; SPAIR/SPIR fat suppression; SENSE: 1.9; 42 gradient directions with eight different b-values (0–600) [33]. A noise scan was obtained as described above. Here both, the thigh and the calf regions were both split into two fields of view each and the scanning time per stack was approximately 12 min.
## Data pre-processing
Data from the first protocol were analysed as described previously in Schlaffke et al. [ 34, 35] Data from the second protocol were pre-processed as described before using QMRITools (www.qmritools.com) [33] In brief, the diffusion data were denoised using a PCA method [36]. To correct for subject motion and eddy currents both legs were registered separately. Then the tensors were calculated by taking IVIM into account and using an iWLLS algorithm. A non-linear IVIM fit of the diffusion data was performed as described in Orton et al. [ 37]. Furthermore, the IVIM bias signal was removed from diffusion weighted data using all acquired b-values [38]. By using IVIM correction, an isotropic pseudo‐diffusion component was modelled in addition to the diffusion tensor, to effectively remove biases in mean diffusivity (MD) estimation.
However, if the pseudo‐diffusion process was anisotropic and aligned with the orientation of the muscle fibers, this would result in an increase in fractional anisotropy (FA) independently from the IVIM correction [33, 39, 40]. The IDEAL method was used for the *Dixon data* considering a singleT2* decay and resulting in a separated water and fat map [41]. The derived water maps were used for the manual segmentation. Considering different T2 relaxation times for the water and fat components the T2‐mapping data were processed using an extended phase graph (EPG) dictionary matching pattern method. Both water‐T2 relaxation time and transmit B1 (B1+) were fitted for each voxel using a dictionary method. The T2 of fat Rwas obtained according to Marty et al. [ 42].
## Muscle segmentation
Eight thigh muscles (vastus lateralis, vastus medialis, rectus femoris, semimembranosus, semitendinosus, biceps femoris, sartorius, and gracilis) and seven calf muscles (gastrocnemius medialis and lateralis, soleus, tibialis anterior, peroneus, extensor digitorum and tibialis posterior) were segmented manually avoiding subcutaneous fat and fascia on all slices of the reconstructed Dixon water images (3D-slicer 4.4.0, https://www.Slicer.org) [43].
The segmentations were then registered to T2 and DTI data to correct for small motions between sequences and image distortions using sequential rigid and b-spline transformations (elastix, https://elastix.lumc.nl) [44]. Average values within a muscle mask of water-T2 time (when available) and proton density fat fraction (FF) as well as the diffusion measures fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD) and axial diffusivity (λ1) were obtained. SNR was calculated as the local average signal divided by the local noise sigma [45].
## Outliers
Due to motion artifacts, image inhomogeneities and two patients aborted scanning, thigh data were obtained for $\frac{4}{9}$ patients. Calf MRI data could be obtained in all included patients.
## Statistical analysis
Water T2, FF, FA, MD, RD and λ1 were compared between CIP patients and matched controls in a general linear model with patient/control as fixed factors as well as the protocol as nuisance variable, for all leg muscles.
To evaluate correlations between clinical assessments and qMRI values mean water T2, FF, FA, MD, RD and λ1 of all thigh and calf muscles were correlated to grip strength, MRC, TNCMAP, SNSNAP, EQ-5D-3L VAS as well as level of symptoms using Pearson’s correlation coefficients. All statistical analyses were performed using IBM SPSS V28. The significance level for all tests was set at $p \leq 0.05.$
## Patent cohort
In total, 9 long-term ICU survivors (7 males; 54.8 ± 16.9y) as well as age and gender matched controls (7 males; 54.3y ± 11.14y) were included. Mean duration of ICU therapy was 26.2 ± 22.2 days. The mean interval between ICU therapy and testing was 5.4 ± 4.8y. Further demographic data, ICU data and comorbidities of patients can be found in Table 1. Clinical data including persisting symptoms, data on quality of life (EQ-5D-3L), grip-strength, muscle strength by MRC sum score, electrophysiological data (TNCMAP, SNSNAP) and polyneuropathy classification are listed in Table 2. In all included ICU-survivors a polyneuropathy was diagnosed.
## qMRI outcome measures in ICU survivors compared to controls
Water T2 mapping sequence was acquired for $\frac{5}{9}$ patients due to a change in examination protocol (see methods). Example images of the applied MRI sequences are shown in Fig. 1. The average qMRI values were significantly higher in all patient muscles compared to controls (multivariate general linear model: main effect: $p \leq 0.001$ for water T2, FF, MD, λ1, RD; p-value for FA was 0.047; see Table 3). The mean qMRI values of all muscles combined for patients and controls are displayed in Fig. 2. For complete qMRI data see Table S1.Fig. 1Example images of the applied MRI sequences: mDixon fat fraction (FF), fractional anisotropy (FA), mean diffusivity (MD) and water T2 maps for thigh and calf muscles of two representative patients, and a healthy control (HC)Table 3Multivariate general linear model results of qMRI parameters between controls (CON) and patients (CIP)q-MRI parametersAll musclesThigh musclesCalf musclesn muscles (PAT/CON)$\frac{98}{30028}$/$\frac{16070}{140}$T2ms∆ mean2.60.013.33p value < 0.001*0.957 < 0.001*n muscles (PAT/CON)$\frac{164}{30052}$/$\frac{160112}{140}$FF%∆ mean0.790.712.2p value < 0.001* < 0.001*0.022*FA∆ mean0.020.0250.01p value0.047*0.3520.429MD[10−3 mm2/s]∆ mean0.10.060.11p value < 0.001*0.001* < 0.001*λ1[10−3 mm2/s]∆ mean0.160.120.15p value < 0.001* < 0.001* < 0.001*RD[10−3 mm2/s]∆ mean0.070.030.1p value < 0.001*0.009* < 0.001*Significant results are highlighted with * and expressed in bold. Significance level was calculated as $p \leq 0.05.$ Δ-mean was calculated as: Mean of PAT value–Mean of CON value, for each parameterFF, fat fraction; MD, mean diffusivity; FA, fractional anisotropy; RD, radial diffusivity; T2, water T2-timeFig. 2Box-plot of qMRI values of FA, MD, RD, T2 and Fat Fraction for all muscles displayed with a min to max range. See also Table 3 for p-values. FF, fat fraction; MD, mean diffusivity; FA, fractional anisotropy; RD, radial diffusivity; T2, water T2-time. Pat = ICU survivors; Con = Controls When only thigh muscles were compared between ICU survivors and controls only FF, MD, λ1 and RD were significantly higher and FA and water T2 were not significant (multivariate general linear model with examination protocol as covariate—main effect: $p \leq 0.001$ for FF and λ1; for FA: $$p \leq 0.357$$; for MD: $$p \leq 0.001$$; for λ2: $$p \leq 0.002$$, for λ3: $$p \leq 0.038$$, for water T2: $$p \leq 0.957$$, for RD: $$p \leq 0.009$$; See also Table 3). The mean qMRI values (FA, MD, FF, water T2, RD) of thigh muscles combined for patients and controls are displayed in Fig. 3.Fig. 3Box-plot of qMRI values of FA, MD, RD, T2 and Fat Fraction for all thigh muscles displayed with a min to max range. See also Table 3 for p-values. FF, fat fraction; MD, mean diffusivity; FA, fractional anisotropy; RD, radial diffusivity; T2, water T2-time. Pat = ICU survivors; Con = Controls When calf muscles were compared between ICU survivors and controls water T2, FF, MD, λ1, RD were significantly higher (multivariate general linear model with examination protocol as covariate—main effect: $p \leq 0.001$ for MD, λ1, water T2, RD; for FF: $$p \leq 0.022$$; for FA: $$p \leq 0.429$$; see also Table 3). The mean qMRI values of calf muscles combined for patients and controls are displayed in Fig. 4.Fig. 4Box-plot of qMRI values of FA, MD, RD, T2 and Fat Fraction for calf muscles displayed with a min to max range. See also Table 3 for p-values. FF, fat fraction; MD, mean diffusivity; FA, fractional anisotropy; RD, radial diffusivity; T2, water T2-time. Pat = ICU survivors; Con = Controls Water T2 was significantly higher in calf muscles of ICU survivors compared to controls ($p \leq 0.001$). In contrast in thigh muscles there were no differences between ICU survivors and controls ($$p \leq 0.957$$; See Table 3, Figs. 2, 3 and 4).
Detailed qMRI values for each muscle separately can be found in Additional file 1: Table S1.
## Correlation between qMRI and clinical data
Correlations between clinical assessments and qMRI values in patients are displayed in Table 4. A significant negative correlation between MRC and FA (r = − 0.75, $$p \leq 0.02$$) as well as between MRC and water T2 (r = − 0.986, $$p \leq 0.002$$) could be observed for all muscles (see Fig. 5 for correlation plots). Furthermore, a significant negative correlation between water T2 and grip strength with significance for the left hand (r = − 0.987, $$p \leq 0.002$$) was revealed. Especially water T2 showed a strong negative linear correlation with MRC grade. FF showed a negative correlation with tibial nerve compound motor action potential (r = − 0.719, $$p \leq 0.029$$). No significant correlations could be found between other qMRI values and clinical as well as electrophysiological or quality of life assessments. Table 4Correlations of qMRI parameters with clinical parametersqMRIGrip strength rightGrip strength leftMRCTNCMAPSNSNAPEQ5DVASPainParesthesiaFFPearson−.276−.130.148−.719*−.553.100−.043.120p-value.472.739.703.029*.123.799.913.759n99999999MDPearson−.008−.337.204−.450−.284−.309−.131.078p-value.983.375.599.224.458.419.738.841n99999999FAPearson−.510−.132−.750*−.501−.515−.610−.491−.165p-value.161.734.020*.169.156.081.180.672n99999999RDPearson.133−.283.288−.329−.178−.314−.123.031p-value.733.460.453.387.647.410.752.938n99999999T2Pearson−.862−.987**−.986**.088.064−.648.142.486p-value.060.002*.002*.888.918.237.820.406n55555555Pearson correlation level; p-value and number of subjects (n) are presented. Significance level was set at $p \leq 0.05.$ Significant values are presented in bold and highlighted with *. MRC = Muscle Research Council strength grade (0–60)TNCMAP, tibial nerve compound action potential; SNSSNAP, Sensory neve compound action potential; FF, fat fraction; MD, mean diffusivity; FA, fractional anisotropy; RD, radial diffusivity; T2, water T2-timeFig. 5Correlation plots muscle strength by MRC (0–60) versus FA and MRC versus T2. FA, fractional anisotropy, FF, fat fraction; MRC, Medical Research Council
## Discussion
In this pilot study we show that qMRI values in leg muscles of patients after ICU treatment differ significantly from controls even years after ICU therapy and could reflect simultaneously muscle damage and chronic myostructural abnormalities [27–29, 46]. Water T2 time and FA correlate negatively with MRC sum score, indicating the clinical relevance of our findings.
An elevated FF, as observed in our cohort, relates to chronic muscle degeneration and shows higher sensitivity compared to MRC testing and qualitative MRI in terms of capturing muscle degeneration in myopathies [26, 47]. Fatty infiltration is a sign of previous muscle damage with fatty replacement of irreversible damaged muscle fibers [48]. A high FF in muscle supports the hypothesis that long-term motor dysfunction and muscle fatigue in patients is caused by a myostructural deficit [2, 3, 14, 49]. FF values were only slightly elevated in ICU survivors compared to controls and still in a “normal” range. Whereas FF describes fatty infiltration very accurately and is easy to capture as percent fat per muscle, changes in the other qMRI modalities DTI and water T2 need to be closely interpreted with the expected or known disease pathology.
Whereas CIM is transient and only reflects myostructural damage due to direct muscle injury in the acute phase of ICU therapy, CIP in contrast is long-lasting due to nerve injury and serves as the major cause of functional debilitation [50]. Neurogenic myofiber atrophy due to axonal loss is usually irreversible and has already been described in muscle biopsies of CIP patients [51].
Our electrophysiologic studies show predominantly axonal nerve damage in calf nerves of patients and clinical data underline that CIP usually affects distal limbs [51]. Since we confirmed a polyneuropathy in our patient cohort, consistent with CIP, our observed differences of qMRI parameters reflect neurogenic muscle damage. The observed higher FA in patient muscles shows a higher proportion of axial compared to radial diffusion. In neurogenic myofiber atrophy, myofibers do not lose their structural integrity initially but get atrophic. This atrophy leads to myofiber diameter reduction and consequently to an increase in FA [29, 52]. MD usually reflects the degree of overall diffusion as it equally integrates the eigenvalues λ1- λ3 as a simple mean value (MD = \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{\sum (\lambda 1, \lambda 2,\lambda 3)}{3}$$\end{document}∑(λ1,λ2,λ3)3) and is elevated in active muscle degeneration and inflammation whereas a reduction of MD is seen in muscle atrophy without an active degeneration. The relation of MD and FA values is usually fixed [29]. Thus a solely myofiber atrophy would lead to an increase in FA and a decrease in MD whereas an inflammatory edema would counterwise lead to a decrease in FA and an increase in MD (as well as RD, and λ2–3) [29]. Interestingly, in our patients, FA and MD are both significantly higher than in controls mostly pronounced in the calf muscles. A combination of a high FA and a high MD supports a parallel chronic myofiber atrophy and active myofiber degeneration.
Underlying active muscle fiber degeneration are revealed by a significantly elevated water T2 which is usually elevated in tissue with a higher content of fluid (e.g. inflammation, myofiber breakdown) [53–55].
Thus, we hypothesize that our observed combination of unanimous elevated MD, water T2, FA and FF reflect parallel active muscle degeneration and myofiber atrophy in chronic damaged muscle tissue due to ICUAW. Although FF is significantly increased between the two groups, the absolute value is still comparably low, so that an influence of the FF on the DTI parameters can be ruled out [56, 57]. This hypothesis is also underlined by a separate analysis of thigh and calf muscle qMRI values and by our clinical outcomes.
In thighs, compared to calves, chronic muscle degeneration is predominantly observed as MD is only mildly elevated compared to calves and water T2 is not elevated. In contrast, MD and water T2 are highly elevated in calves and reflect active muscle degeneration. Our data support that chronic axonal nerve damage in calves due to CIP leads to an ongoing myofiber damage and breakdown.
Correlations of FA and MD to clinical assessments have been described in myopathies before and the significant correlation of water T2 and FA to MRC values in our study additionally supports the relevance of qMRI values [57, 58]. Since up to now there are no qMRI studies in long-term ICU survivors the presented results and the discussed pathophysiology is derived from known relations between quantitative muscle MRI parameters and myofiber degeneration. Reviews on ICUAW and known long-term data on ICU-survivors highlight the impact of motor status on long term quality of life [59], since ICUAW and especially CIP affects peripheral nerves irreversibly and leads to ongoing neuromuscular complaints and a reduced ability to participate in daily life activities.
## Conclusion
We conclude that using qMRI we were able to quantify clinically relevant muscle differences in ICU survivors probably due to an ICUAW. These findings can help to characterize underlying mechanisms for ongoing neuromuscular complaints in long-term ICU-survivors. qMRI parameters show chronic next to active muscle degeneration in ICU survivors with diagnosed CIP according to the electrophysiological assessment within the study.
## Limitations
Since our recruitment was narrow and patients with long-term ICU data are not easy to recruit and are frequently affected with confounding diseases or metal implants most potential subjects did not meet our inclusion criteria. The acquisition protocols was changed during the study for protocol optimization and the implementation of T2-mapping. The protocol was integrated as a covariate in statistical analysis to correctly minimize the statistical impact on the results. Due to outliers in data acquisition only four thighs were available for statistical analysis. This may have confounded significance levels and differences might be induced in diffusion values between calves and thighs. FA was not elevated when thigh and calf muscles were analysed separately. Those different findings compared to the analysis of all muscles may be explained by methodological reasons and the small number of subjects.
## Supplementary Information
Additional file 1. Supplementary Table.
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|
---
title: Proteomics and transcriptomics profiling reveals distinct aspects of kidney
stone related genes in calculi rats
authors:
- Wang Zhu
- Deng Qiong
- Gu Yanli
- Li Min
- Zhang Ying
- Hu Qiyi
- Zhang Shenping
- Wang Xisheng
- Liang Hui
journal: BMC Genomics
year: 2023
pmcid: PMC10024419
doi: 10.1186/s12864-023-09222-7
license: CC BY 4.0
---
# Proteomics and transcriptomics profiling reveals distinct aspects of kidney stone related genes in calculi rats
## Abstract
### Backgrounds
Kidney stone also known as urolithiasis or nephrolithiasis, is one of the oldest diseases known to medicine, however, the gene expression changes and related kidney injury remains unclear.
### Methods
A calculi rat model was developed via ethylene glycol– and ammonium chloride–induction. Integrated proteomic and transcriptomic analysis was performed to characterize the distinct gene expression profiles in the kidney of calculi rat. Differential expressed genes (DEGs) were sub-clustered into distinct groups according to the consistency of transcriptome and proteome. Gene Ontology and KEGG pathway enrichment was performed to analyze the functions of each sub-group of DEGs. Immunohistochemistry was performed to validated the expression of identified proteins.
### Results
Five thousand eight hundred ninety-seven genes were quantified at both transcriptome and proteome levels, and six distinct gene clusters were identified, of which 14 genes were consistently dysregulated. Functional enrichment analysis showed that the calculi rat kidney was increased expression of injured & apoptotic markers and immune-molecules, and decreased expression of solute carriers & transporters and many metabolic related factors.
### Conclusions
The present proteotranscriptomic study provided a data resource and new insights for better understanding of the pathogenesis of nephrolithiasis, will hopefully facilitate the future development of new strategies for the recurrence prevention and treatment in patients with kidney stone disease.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12864-023-09222-7.
## Introduction
Kidney stone, also known as urolithiasis or nephrolithiasis, is one of the oldest diseases known to medicine, causing systemic symptoms, including endocrine disorders, metabolic syndrome, chronic kidney insufficiency [1], autoimmune diseases, osteoporosis [2], inflammatory diseases, hypertension and most recently ischemic strokes [3–6]. The prevalence of kidney stone disease is steadily increased worldwide in the recent decades. Previous literature using National Health and Nutrition Examination Survey (NHANES) data reported that the prevalence of kidney stone has continued to rise in the United States from $3.2\%$ in 1980 [7] to $5.2\%$ in 1994 [7, 8], $8.8\%$ in 2010 and $10.1\%$ in 2016 [9]. There is a high probability of recurrence of urinary stones, estimated to be up to $52\%$ within 10 years [10, 11].
Benefit from technologies development, a large number of genes and proteins have been identified, which are reported to be involved in the process of kidney stone formation. Several macromolecules, such as Spp1, the vitamin K-dependent protein matrix Gla protein (MGP), Bakunin [8], and Tamm-Horsfall proteins (THP) [9], have been identified in both the urine and kidney stone matrix affect the risk of kidney stone disease [10–12]. RNA sequencing studies demonstrated that a large number of coding or non-coding RNAs were dysregulated expression in the kidneys of calculi rats, which involved in complement and coagulation cascades, cytokine-cytokine receptor interactions, ECM-receptor interactions and histidine metabolism [12, 13]. Recently, we have identified a total of 1 141 proteins by TMT-labeled quantitative proteomics analysis, of which 699 were up-regulated and 442 were down-regulated in the calcium oxalate monohydrate (COM)-crystal treated HK-2 cells [14]. These proteins play role in modulating COM crystal initiation, provide us with amount of the possible signaling pathways, potential targets and interaction networks for understanding of the pathogenesis of kidney stones.
However, the aforementioned studies mostly focused on the transcripts or protein level dysregulations of the genes related to stone formation. Mounts of genes exhibited inconsistent expression patterns in mRNA level and protein level have been largely neglected. Accumulated studies indicated that CaOx induced endoplasmic reticulum (ER) stress mediated posttranslational protein modification also play a critical role in the gene expression related to kidney stone disease [15–17]. Achievements have been made to uncover the post-translational-related molecular mechanisms of nephrolithiasis, but more investigations are needed based on advances in technologies and bioinformatics.
## Experiment design and sample collection
All animal experiments were performed with adult male Sprague–Dawley (SD) rats (250–300 g), in accordance with the guidelines for the care and use of laboratory animals, and approved by the ethics committee of People’s Hospital of Longhua Shenzhen (LHRY-1907015). The rats were maintained and habituated in a standard 12-h light–dark cycle with ad libitum access to food and water in a temperature and humidity-controlled room, maintaining 22 °C ± 0.5 °C and a relative humidity of 40–$60\%$. SD rats were randomly divided into a control group and kidney stone group. The control group only received normal rat chow and sterile water for 14 days. The kidney stone group received drinking water with $1\%$ (v/v) ethylene glycol (EG, Sigma-Aldrich, Buchs, Switzerland) and $1\%$ (w/v) ammonium chloride 1 ml per day by gavage for 3 weeks. Bilateral kidneys of the rats were removed under $4\%$ isoflurane (CAS:26,675–46-7, RWD, Shenzhen, China) inhalation anesthesia for 3 min. The rats were then sacrificed via cervical dislocation after CO2 sedation. One kidney per rat was fixed in $4\%$ paraformaldehyde, dehydrated in ethanol solution, embedded into paraffin, sliced into 5-μm serial sections, stained with Hematoxylin–Eosin (HE) and von-Kossa’s staining, and observed to detect CaOx crystals using a polarizing microscope. The other kidney was applied for RNA and protein extraction.
## RNA preparation, cDNA synthesis and RNA sequencing
Total RNA was extracted using TRIzol method. RNA purity was checked using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer (Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq2500/X platform and $\frac{125}{150}$ bp paired-end reads were generated. The FPKM of all genes has been provided in Supplementary Table 1.
## Protein preparation and proteomics
The protein extraction and quality analyzation were performed according to previously reports [14]. In short, the sample was grinded by liquid nitrogen into cell powder and then transferred to a 5-mL centrifuge tube. After that, four volumes of lysis buffer (8 M urea, $1\%$ Protease Inhibitor Cocktail) was added to the cell powder, followed by sonication three times on ice using a high intensity ultrasonic processor (Scientz). The remaining debris was removed by centrifugation at 12,000 g at 4 °C for 10 min. Finally, the supernatant was collected and the protein concentration was determined with bicinchoninic acid (BCA) kit according to the manufacturer’s instructions.
Take equal amount protein of each sample, and adjust to equal volume with lysis buffer for digestion according to previous studies [18, 19]. The protein solution was reduced with 5 mM dithiothreitol for 30 min at 56 °C and alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. The protein sample was then diluted by adding 100 mM triethyl-ammonium bicarbonate buffer (TEAB) to urea concentration less than 2 M. Finally, trypsin was added at 1:50 trypsin-to-protein mass ratio for the first digestion overnight and 1:100 trypsin-to-protein mass ratio for a second 4 h-digestion.
The liquid chromatograph-mass spectrometer (LC–MS/MS) analysis was performed by PTM Biolabs Inc (Hangzhou, China) according to previous studies [19]. In short, the tryptic peptides were dissolved in solvent A ($0.1\%$ formic acid, $2\%$ acetonitrile/in water), directly loaded onto a home-made reversed-phase analytical column (25-cm length, $\frac{75}{100}$ μm i.d.). Peptides were separated with a gradient from 6 to $24\%$ solvent B ($0.1\%$ formic acid in acetonitrile) over 70 min, $24\%$ to $35\%$ in 14 min and climbing to $80\%$ in 3 min then holding at $80\%$ for the last 3 min, all at a constant flow rate of 450 nL/min on a nanoElute UHPLC system (Bruker Daltonics) [19].
The peptides were subjected to capillary source followed by the timsTOF Pro (Bruker Daltonics) mass spectrometry as reported previously [20]. The electrospray voltage applied was 1.60 kV; Precursors and fragments were analyzed at the TOF detector, with a MS/MS scan range from 100 to 1700 m/z; The timsTOF Pro was operated in parallel accumulation serial fragmentation (PASEF) mode; Precursors with charge states 0 to 5 were selected for fragmentation, and 10 PASEF-MS/MS scans were acquired per cycle; The dynamic exclusion was set to 30 s [20].
The secondary mass spectrometry data were retrieved using MaxQuant (v1.6.15.0). Tandem mass spectra were searched against the database of Rattus_norvegicus_10116_Ensembl_Rnor_6.0_20210527.fasta (including 17,063 sequences) concatenated with a reverse decoy database. The cleavage enzyme was specified as Trypsin/P. The maximum number of missed cleavages per peptide was set as 2. The mass tolerance for precursor ions in the first search was set to 20 parts per million (ppm) and 5 ppm in the main search, and the mass tolerance for fragment ions was set at 0.05 Da. The cysteine alkylation Carbamidomethyl (C) was specified as a fixed modification, and the oxidation on methionine (Met) residues, acetylation on proteins N-termini were specified as variable modifications.
The false discovery rate (FDR) was adjusted to < $1\%$ for both proteins and peptides. The identified protein should contain at least one unique peptide. The MS identification information has been described in Supplementary Table 2.
## Bioinformatics analysis
Differential expression analysis of the two groups was performed using the DESeq2 R package. The resulting P-values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted $P \leq 0.01$ found by DESeq2 were assigned as differentially expressed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG, www.kegg.jp/kegg/kegg1.html) pathway enrichment analysis of differentially expressed genes (DEGs) was implemented by the GOseq R package, in which gene length bias was corrected. GO terms with corrected $P \leq 0.05$ were considered significantly enriched by DEGs. We used KOBAS software to test the statistical enrichment of DEGs in KEGG pathways with the permission of Kanehisa Laboratories [21, 22].
## Histopathological analysis
Peroxidase immunohistochemistry of target proteins were performed using specific antibodies, anti-HAVCR1(Abcam, ab233720), Anti-C5(Abcam, ab275931), anti-AKR1B8(Thermofisher, PA5114302), anti-Spp1(Proteintech Group, 25,715–1-AP), anti-C3(Proteintech Group, 21,337–1-AP), anti-VILL(Novus, NBP2-86,054), anti-HAAO(Proteintech Group, 12,791–1-AP), anti-TEMT(Abcam, ab181854), anti-CSAD(Abcam, ab91016), anti-ALK(Abcam, ab203106), anti-MIOX(Abcam, ab154639), anti-GPX2(Abcam, ab137431), anti-ASRGL1(Proteintech, 11,400–1-AP) and anti-TUBB6(Proteintech, 66,362–1-Ig) following procedures as described previously [23]. The histogram profile and score of a cytoplasmic and nuclear stained immunohistochemistry image was determined by ImageJ program (1.8.0 version) with the IHC profiler plugin as described previously [24–26]. The quantified immunoscore was entered into an Excel spreadsheet and analyzed by GraphPad Prism 8.
## Model development and sample qualification
We developed the urolithiasis model by ethylene glycol and ammonium chloride-induced SD rats, followed by integrative RNA-seq transcriptomic and label-free proteomic experiments (Fig. 1A). Before the analysis, HE and von Kossa's staining was performed to detect CaOx deposits and tubulointerstitial damages of the rat kidney. In the experimental group ($$n = 3$$), a mount of CaOx deposits were found inside the proximal tubules, loops of Henle, distal tubules and collecting ducts. Considerable tubulointerstitial damages such as tubular atrophy, dilation, hyaline cast, tubular cell necrosis and interstitial inflammation were observed in renal tissue of calculi rats (Fig. 1B).Fig. 1Study design and validation of the calculi rat model. A Study design and workflow of rat kidney sample processing for proteomics and transcriptomics analysis. B Histochemical validation of the calcium crystals in the calculi rat model via HE staining and von Kossa’s staining. The arrows indicate the calculi oxalate crystals. Original magnification, × 40 We firstly showed the dispersion of the gene expression level distribution of samples in both treated and control groups. The overall gene expression level of different samples showed no significant differences (Fig. 2A). Pearson's correlation coefficient R was used to evaluate the correlation between different groups. Results showed stronger correlation between biological replicates than different groups, suggested significant difference of gene expression profiles between control and treat groups (Fig. 2B). For protein identification, we got 1 144 282 total spectrums, and then identified 6 135 proteins, of which 4 955 proteins quantifiable (Fig. 2C). Principal component analysis (PCA) indicated that better quantitative repeatability of biological replicates than different groups (Fig. 2D). These results demonstrated that the CaOx calculi rat model was developed successfully, and there was significant difference of gene expression profiles between control and treat groups. Fig. 2Sample qualification. A FPKM box diagram. The abscissa in the figure represents different samples. The ordinate represents the logarithm of the sample expression FPKM. The figure measures the expression level of each sample from the perspective of the overall dispersion of the expression amount. B The Pearson correlation of all samples. C Overview of protein identification. D Principal component analysis (PCA) to evaluate the repeatability of protein quantification
## Overview of transcriptomic analysis
We found that 191 genes were dysregulated in the kidney of calculi rats, of which 72 genes were up-regulated and 119 genes were down-regulated (Fig. 3A, Supplementary Table 3). Notably, the Gprin3 and Fgb were the most up-regulated genes in the kidney of calculi rats. Gprin3 is belonged to the G-protein-regulated inducer of neurite outgrowth (GPRIN) family, acting as a partner of β-arrestin-2, regulating dopamine receptor desensitization and playing roles in striatal physiology [27, 28]. Nevertheless, the biological roles of GPRIN3 in kidney stone formation is still unknown. Emerging kidney injury biomarkers such as Lcn2 and Havcr1 are also significantly increased expression in the kidney tissue of calculi rats. Spp1 also showed significantly increased expression in the kidney of calculi rats, which is an immunoregulatory molecule for immune cells, in particular, for neutrophils and macrophages and enhances T helper 1 inflammation [29]. A recent study demonstrated that Spp1 serum levels were correlated with kidney injury, and facilitated with AKI–induced acute lung injury (ALI) [30]. Therefore, our data indicates substantial kidney injury occurred at molecular and genetic level in the calculi rats. Fig. 3Overview of transcriptomic analysis. A Volcano plot of differential expressed genes in the kidney of calculi rat. B GO terms enrichment of the DEGs. C KEGG enrichment analysis of differential gene. D Statistics of pathway enrichment GO annotation and enrichment analysis were conducted to identify essential terms associated with the formation of calculi, as well as calculi-mediated kidney injury. The results showed that in the top 20 most enriched GO terms, 14 terms are related to the biological processes. Four cellular component terms, including extracellular space, extracellular exome, fibrinogen complex and platelet alpha granule were enriched. Two molecular function terms, cytokine activity and growth factor activity were enriched (Fig. 3B). In addition, KEGG enrichment analysis was conducted to evaluate the important signaling pathways of the differential expressed genes (DEGs). Results showed that the complement and coagulation cascades (KO:04,610) and cytokine-cytokine receptors interaction (KO:04,060) were most enriched (Fig. 3C, D). These findings suggested that the immunoregulatory features might have been significantly changed in the kidney of calculi rats.
## Proteomic profiling of the kidney in calculi rats
Among the 4 955 quantifiable proteins, we identified totally 352 differential expressed proteins (DEPs) in the kidney of calculi rat, of which 201 proteins were up-regulated and 151 proteins were down-regulated (Fig. 4A, B). The hierarchical clustering heatmap of the DEGs and DEPs was showed in Supplementary Fig. 1. Notably, the kidney injury-related molecules such as Havcr1 and Spp1, and apoptosis-related markers such as Niban1, Casp1, Casp3 and Casp8 were significantly up-regulated in the kidney of calculi rat compare to its normal control (Supplementary Table 4). Nevertheless, multiple important solute carriers (Slc12a6, Slc33a1, Slc15a2, Slc43a2, Slc8a1, Slc22a2, Slc22a1, Slc22a6, Slc22a8, Slc22a22, Slc6a8, Slc2a1, Slc37a4 and Slc34a1) and components of vacuolar ATPase (Atp6v1b1, Atp6v0a4, Atp6v1g3, Atp6v1c2) were decreased expression in the kidney of calculi rats (Supplementary Table 4), which indicated that decline of kidney function in calculi rats. We also found that the CD44 (Spp1 receptor) and a number of complements (C1qb, C1qc, C2, C3, C5, C6, C7, C8a, C8b and C9) were significantly increased in the kidney of calculi rats. It’s well known that Spp1:CD44 signaling is critical for GPCR-mediated chemotaxis of neutrophils and macrophage, which is required for the development of cell-mediated inflammatory responses [31, 32]. Our data suggested that complements-related immunoregulatory might play an important role in calculi-mediated kidney injury. Fig. 4Proteomic profiling of the kidney in calculi rats. A Volcano plot of differential expressed proteins. B Statistical chart of differential expressed proteins. C GO function classification. D Subcellular annotation of up-regulated proteins. E Subcellular annotation of down-regulated proteins GO category analysis was conducted to evaluate the critical terms of the DEPs involved in kidney stone formation. Results showed that most of the DEPs were involved in signal transduction mechanisms (51 proteins), and translation, ribosomal structure and biogenesis (31 proteins) (Fig. 4C). Interestingly, the proteins (44 proteins) related to signal transduction were significantly up-regulated, while proteins associated with translation, ribosomal structure and biogenesis (27 proteins) were down-regulated (Supplementary Fig. 2). Subcellular location analysis showed that most of the up-regulated proteins were located in the nucleus ($27\%$), and then cytoplasm ($24\%$) and extracellular ($24\%$) (Fig. 4D). However, most of the down-regulated proteins were located in the cytoplasm ($32\%$) and nucleus ($23\%$) (Fig. 4E). Functional enrichment analysis indicated that macrophage activation was the most enriched terms of biological process (Fig. 5A). Membrane attack complex was the most enriched cellular component (Fig. 5B), and Symporter activity, transporter activity and cytokine activity were the most enriched molecular function terms (Fig. 5C). KEGG pathway enrichment analysis showed that most DEPs were involved in ribosome, and complement and coagulation cascades (Fig. 5D).Fig. 5Functional enrichment analysis of differentially expressed proteins. A Biological process. B Cellular component. C Molecular function. D KEGG pathway. E Clustering analysis. Up: the differential expressed proteins were divided into four groups (Q1 to Q4) according to their expression level. Down: heatmap of the biological process terms of each subgroup. The color bar indicates the enrichment degree We then divided the DEPs into four parts according to its differential expression level, marked as Q1 to Q4, as showed in the Fig. 5E. For each group, we performed GO enrichment and cluster analysis to find the correlation between protein functions and differential expression levels. Q4 cluster includes 57 most up-regulated proteins, which was highly correlated with cell response to external stimulus and inflammatory response. The Q1 was a cluster includes 40 most down-regulated proteins mainly related to sodium-independent organic anion transport and inorganic anion transport (Fig. 5E).
## Identification of distinct gene expression profiles by integrated proteomic and transcriptomic analysis
With matched RNA-Seq and proteomics data described above, we set out to assess the integrated proteotranscriptomic analysis at several levels. First, we converted the protein ID into the corresponding transcripts ID, and then analyzed the data of the two omics according to the transcript ID. Results showed that 5 897genes were quantified at both transcriptome and proteome levels (Fig. 6A). RNA abundance and Protein abundance are only partially correlated, which is reflected in the $R = 0.44$ (Fig. 6B).Fig. 6Distinct gene expression profiles identification by integrated proteomic and transcriptomic analysis. A Wayne diagram for quantitative comparison of transcriptome and proteome. B Scatter plot of transcript and its corresponding protein expression. C Wayne diagram analysis of differentially expressed proteins and transcripts. D Cellular component of proteins in up-up group. E Biological process of proteins in up-up group. F Molecular function of proteins in up-up group. G KEGG analysis of proteins in up-up group. H Molecular function of proteins in down-down group. I KEGG analysis of proteins in down-down group We performed Wayne diagram to compare the proteins and transcripts according to the transcriptional ID (Fig. 6C), results showed that 7 genes were both upregulated at protein and transcripts level (up-up group), and 7 genes were both down-regulated at protein and transcripts level (down-down group). Particularly, the kidney injure-related factors Havcr1 and Spp1 were both significantly up-regulated at protein and transcript level. Notably, there were 140 genes down-regulated at protein level without significant change at transcript level (down-unchange group), and 190 genes upregulated at protein level with no significant change at transcript level (up-unchange group). A number of solute carriers and transporters including Slc22a22, Slco1a1, Slc21a4, Slc16a4, Slc1a1, Slc22a1, Slc34a1, Slc3a1, Slc22a6 and Slc34a3 were significantly decreased at protein level with no significant change at transcript level.
Further, we analyzed the functional enrichment of the genes consistently changed at both protein and transcript level. Result showed that the genes in the up-up group were most enriched in the extracellular region/space (Fig. 6D), phagocytosis and membrane invagination (Fig. 6E), and C5a anaphylatoxin chemotactic receptor binding (Fig. 6F). The most enriched KEGG signaling pathway was complement and coagulation cascades (Fig. 6G). Nevertheless, genes in the down-down group were most enriched in oxidoreductase activity (Fig. 6H) and involved in the metabolic pathways (Fig. 6I).
In addition, we also found that a number of genes related to protein poly-ADP-ribosylation (Parp10 and Parp14), glutamine catabolic process and glutamate biosynthetic process (Gls) and phagocytosis (Sirpa, Mfge8, Itgb2, Arhgap12, Elmo1 and Sh3bp1) were increased at protein level but unchanged at transcript level. Notably, several genes related to regulation of acute inflammatory response (C1qc, C1qb, Cqb2, Kng1, C8b, C8a, C6, Clu, C9) and immunoglobulin mediated immune response (RT1-Bb, Crp and Lnpp5d) were also up-regulated at protein level but no significant change at transcript level (Supplementary Fig. 3). Our data indicated that post-transcriptional regulation may also play critical roles in the metabolic process and immune response of crystal formation and kidney injury.
## Verification of the DEGs by IHC analysis
For validation, the common expressed genes at mRNA and protein levels were validated by immuno-histochemistry analysis. Spp1, Akr1b8 and Havcr1 were the top three most increased genes in the up-up group. We found that Spp1 was strongly stained in the proximal tubular cells and much higher in the tissue of treat group than that in normal control (Fig. 7). Havcr1 was significantly enhanced expression in the kidney of calculi rats (Fig. 7). Notably, the kidney tubule cell-released circulating Spp1 was correlated with kidney injury in patients [30], and Havcr1 was markedly upregulated to promote phagocytosis and inhibit innate immunity and inflammation via p85-PI3K-NFκB signaling in the proximal tubule cells after kidney injury [33]. Our data indicated that the calculi rats were suffering significant kidney injury during the crystal formation with markedly increased expression of Spp1 and Havcr1. We also found that the complements including C3 and C5 were common up-regulated and higher stained in the kidney of calculi rats (Fig. 7), which promoted phagocytosis, trigger inflammation and immune clearance, played central role in the activation of complement system [34]. Gpx2 belongs to the glutathione peroxidase family, plays a major role in protecting mammals against oxidative damage [35]. Thus, the up-regulation of Gpx2 might result from the oxidative damage in the kidney of calculi rats. Fig. 7Verification of the DEPs by IHC analysis. Original magnification, × 40. The histogram profile corresponds to the pixel intensity value vs. corresponding number counts of a pixel intensity Moreover, we also found that 7 genes were commonly down-regulated at transcript level and protein level and verified by immune-histochemistry analysis (Fig. 7). Of which Haao, Miox, Inmt, Haao, Csad and Alpl were involved in metabolic pathways, and the reduced expression of Akr1c12l1 was associated with oxidoreductase activity. These data confirmed and revealed that the metabolic network activity was significantly reduced in the kidney of calculi rats. Notably, a set of proteins were decreased at protein level with no significant change at transcript level, which potentially regulated by post-transcriptional modification. Our verification study showed that all the immune-histochemistry results were consistent with the findings from transcriptomic analysis.
## Discussions
Calcium oxalate urolith is accounted over 70 percent of all kinds of kidney stone [36], ranked the most common type of urolithiasis in patients worldwide [37, 38]. However, little is known about the mechanism of oxalate calculi crystals formation and calculi-related kidney injury. In present study, we demonstrated that by combining comprehensive RNA-seq data and high-resolution liquid chromatography-mass spectrometry analysis, achieved a better understanding of the profile of protein-coding genes in the kidney of calculi rats than previous efforts based on transcriptome or proteome guided denovo strategies.
We identified 14 protein-coding genes with consistent expression patterns at both protein and transcript levels in the kidney of calculi rat model. In the up-up group, the GO terms of ER lumen and phagocytosis were significantly enriched, which indicated enhanced ER stress and immune response in the kidney of calculi rats. The ER stress may lead to apoptosis, renal injury, and calcium oxalate crystal deposition in the renal of calculi rats via sigma-1 receptor related mitochondria dysfunction and reactive oxygen species (ROS) generation [39–42]. M2-macrophages associated phagocytosis could suppress kidney stone development through serval mechanisms, including NLRP3, miR-93-TLR4/IRF1, and miR-185-5p/CSF1 pathways [43, 44]. Therefore, C3, C5 and HAVCR1 related promotion of M2-like macrophage polarization and inhibition of inflammation could prevent intrarenal CaOx deposits, nucleation and kidney stone recurrence.
We found that the protein SPP1, C3 and HAVCR1 might also play critical roles in the ER stress and phagocytosis in the CaOx crystal formation process and its related kidney injury. In the down-down group, the protein-coding genes mainly enriched in NADP activity, oxidoreductase activity and metabolic pathways, which related to mitochondrial dysfunction and ROS overproduction, and led to cellular injury [45, 46]. Thus, MIOX, Akr1c12l1 and HAAO may act as negative modulators in the mitochondrial dysfunction related calcium oxalate crystal deposition. In addition, several components of vacuolar ATPases responsible for the translocation of H+ ions across membranes were also observed decreased expression at protein level in the kidney of calculi rats, suggested that decreased vacuolar ATPase activity may alter the cytoplasmic pH of the leading-edge environment of tubule cells.
We also found that a number of solute carriers and transporters (including Slc34a1 and Slc34a3) were decreased at protein level with no significant change at transcript level. For example, the Slc34a1 (NaPi-IIa) and Slc34a3 (NaPi-IIc) are responsible for filtration of phosphate from primary urine [47], play a crucial role of in calcium metabolism as well as phosphate balance in humans. The Slc22 transporter family are widely studied drag transporters, which regulate key metabolic pathways and optimize levels of numerous metabolites and signaling molecules, as well as uremic toxins associated with many chronic kidney diseases [48–50]. Thus, our results may reflect the functional changes in the tubule cells in the kidney of calculi rats.
Beyond the consistent upregulated C3 and C5, many complements related to regulation of acute inflammatory response were significantly increased at protein level with no change at transcript level in the kidney of calculi rats. Complement and coagulation cascades activation leads to chemotaxis and immune-complex clearance [51], is one of the most significantly enriched signaling pathways in the calcium oxalate crystal-induced ROS in kidney [52]. The deposition of locally produced and activated complement fragments can also drive severe inflammatory response in the kidney and result in complement-mediated inflammatory injury [51]. Notably, the forementioned solute carriers and transporters, as well as complements exhibited inconsistent expression pattern of transcripts and corresponding protein. Which indicated that the ER stress and its related post-transcriptional modification (PTM) might play a role in these protein coding genes transcriptional modulation. To date, over 100 types of mRNA related PTMs have been identified. Modifications at the 5’-cap and the 3’-end poly (A) tail of mRNAs play key roles in regulation, transcript stability, pre-mRNA splicing, polyadenylation, mRNA export, translation initiation and nuclear export, which are gaining increasing attention for their roles in cellular metabolism [53]. For example, the SLC34A1 protein expression level is regulated by specific chromatin architecture and SNPs elements [54], as well as natural antisense transcripts [55]. However, the realm of post-transcriptional gene regulation in kidney stone formation is still far from clear.
Although prevention the occurrence of new calcium stones and removing kidney stones with flexible ureteroscopy are possible today, there is no doubt that recurrence preventions are much more important and need to be further developed [56]. A better understanding of the mechanisms involved in stone formation are absolute prerequisites for kidney stone recurrence prevention. In present study, a series of proteins with distinct expression profiles related to metabolism and immune response has been identified to play critical roles in the kidney stone initiation. For example, C3 and C5 are main factors in complement and coagulation cascades related to the calcium oxalate crystal-induced ROS in kidney. C3 and C5 inhibitors might contribute to attenuate the complement-driven inflammation in kidney stone formation and recurrence. Additionally, beside the mechanism investigation facilitated pharmacological therapy development, individualized recurrence prevention procedures are important aspects for kidney stone prevention.
## Conclusions
*In* general, we characterized the calculi oxalate crystals-related gene expression profiles in the kidney by integrated proteomics and transcriptomics analysis. Our results showed that the calculi rat kidney was increased expression of injured & apoptotic markers and immune-molecules. On the other hand, the calculi rat kidney was decreased expression of solute carriers & transporters and many metabolic factors (Fig. 8). These effects jointly contribute to the formation of kidney stones and calculi-related kidney injury. The present proteotranscriptomic study has provided a data resource and new insights for better understanding of the pathogenesis of nephrolithiasis, will hopefully facilitate the future development of new strategies for the recurrence prevention and treatment in patients with kidney stone disease. The main limitation of this study could have been present due to the animal-based oxalate calculi model, which could not perfectly mimic the status of crystals in the kidney of patients. In the future, kidney organoids derived from human pluripotent stem cells might have great utility for kidney stone modeling. Fig. 8An overview of possible biological changes that might contribute to crystal formation in the kidney of calculi rats
## Supplementary Information
Additional file 1: Supplementary Table 1. The FPKM of genes analyzed in the kidney of rats. Additional file 2: Supplementary Table 2. The MS information of proteins identified in kidney of rats. Additional file 3: Supplementary Table 3. Details of DEGs in the kidney of calculi rats compared to normal control. Additional file 4: Supplementary Table 4. Details of DEPs in the kidney of calculi rats compared to normal control. Additional file 5: Supplementary Fig. 1. Cluster analysis of differentially expressed genes and protiens. ( A) Heatmap of DEGs. ( B)Heatmap of DEPs. Additional file 6: Supplementary Fig. 2. Functional enrichment analysis of differentially expressed protiens. ( A) Up GO. ( B) Down GO. ( C) UP KOG. ( D) Down KOG.Additional file 7: Supplementary Fig. 3. Heatmap of functional enrichment analysis of proteins under different regulatory relationships between transcriptome and proteome.
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|
---
title: Investigating the potential role of swertiamarin on insulin resistant and non-insulin
resistant granulosa cells of poly cystic ovarian syndrome patients
authors:
- Muskaan A. Belani
- Preeti Shah
- Manish Banker
- Sarita S. Gupta
journal: Journal of Ovarian Research
year: 2023
pmcid: PMC10024427
doi: 10.1186/s13048-023-01126-0
license: CC BY 4.0
---
# Investigating the potential role of swertiamarin on insulin resistant and non-insulin resistant granulosa cells of poly cystic ovarian syndrome patients
## Abstract
### Background and aim
Conventional drugs have limitations due to prevalence of contraindications in PCOS patients. To explore the potential effects of swertiamarin, on abrupted insulin and steroidogenic signaling in human luteinized granulosa cells from PCOS patients with or without insulin resistance.
### Experimental procedure
hLGCs from 8 controls and 16 PCOS patients were classified for insulin resistance based on down regulation of protein expression of insulin receptor-β (INSR- β) as shown in our previous paper. Cells were grouped as control, PCOS-IR and PCOS-NIR, treated with swertiamarin (66 µM) and metformin (1 mM). Expression of key molecules involved in insulin signaling, fat metabolism, IGF system and steroidogenesis were compared between groups.
### Results
Swertiamarin significantly ($P \leq 0.05$) reversed the expression of INSR-β, PI[3]K, p-Akt, PKC-ζ, PPARγ, ($P \leq 0.01$) IRS (Ser 307) and IGF system in PCOS-IR group and was equally potent to metformin. In the same group, candidate genes viz SREBP1c, FAS, ACC-1 and CPT-1 were down regulated by swertiamarin ($P \leq 0.001$) and metformin ($P \leq 0.001$). Significant upregulation was demonstrated in expression of StAR, CYP19A1, 17β-HSD and 3β-HSD when treated with swertiamarin ($P \leq 0.01$) and metformin ($P \leq 0.01$) in PCOS-IR followed by increase in 17β-HSD and 3β-HSD enzyme activity along with estradiol and progesterone secretions. However, swertiamarin did not reveal any effect on PCOS-NIR group as compared to metformin that significantly ($P \leq 0.01$) reversed all the parameters related to steroidogenesis and down regulated basal expression of insulin signaling genes.
### Conclusion
Swertiamarin, presents itself as a potential fertility drug in hLGCs from PCOS-IR patients.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13048-023-01126-0.
## Introduction
Polycystic ovarian syndrome (PCOS) is a multifaceted disease and an approach to decrease this has become a top priority for many health organizations [1]. The life style factors such as physical exercise, psychological stress, high carbohydrate diet and sedentary life that impact fertility are modifiable and are considered to be the first-line of treatment in PCOS [2–4]. Symptom oriented pharmacological intervention such as oral contraceptive to regulate menstrual cycles and decrease hirsutism, spironolactone that block androgen receptors, finasteride to block production of active form of testosterone and insulin sensitizers such as metformin, thiazilidinediones (TZD’s) and D-chiro Inositol are in use for improving insulin sensitivity and lipid levels accompanied by weight loss [1, 5, 6]. Psychosocial interventions have also proven to improve PCOS-related mental health issues [7].
Considering the role of insulin resistance (IR) in the interplay of metabolic and reproductive aberrations in infertility, insulin sensitizing drugs (ISD) are expected to have beneficial effects by restoring ovulatory menstrual cycles, reducing insulin resistance and thus being important therapeutic modality for PCOS [8, 9]. Insulin sensitizers such as the thiazolidinediones (TZD’s), D-chiro-Inositol, and metformin are postulated to improve insulin sensitivity and several other aspects of the syndrome, including reproductive abnormalities [10, 11]. TZD’s improve insulin sensitivity in PCOS through decrease in hepatic glucose production [11]. Studies with TZD’s have reported fetal growth restriction as a potential risk in animal experiments and high incidence of weight gain among the users that further hampers their use in obese women with PCOS [12, 13]. D-chiro-Inositol has been observed to show effect on serum hormone binding globulin levels without affecting testosterone, fasting glucose, fasting insulin, and ovulation rate [14]. Supplementation of insulin sensitizer myo-inositol has demonstrated positive effects in oocyte quality of PCOS patients and post-menopausal transitions [15–17].
However, it’s role in improving clinical pregnancy rates is under controversy and is not recommended for normo insulinemic lean or obese PCOS [11, 18, 19]. Thus, none of the ISD’s could increase a chance of having a live birth and are limited by the prevalence of contraindications in women with PCOS [1, 10, 20]. Moreover with limited understanding of their mechanism, the type of patients, whether only PCOS-IR or even PCOS-NIR would be benefitted by their effects remains to be identified [21].
A number of herbal medicines such as Vitex agnus-castus, Cimicifuga racemosa, Tribulus terrestris, Glycyrrhiza spp*., Paeonia lactiflora, *Cinnamomum cassia* and *Aloe vera* can improve ovarian function, androgen excess, obesity, insulin resistance, blood lipids and inflammation and exert beneficial effects in PCOS [1, 6, 22–24]. Enicostemma Littorale blume is a herbal plant that is known for its anti-diabetic effect but its bio-active molecules has been unexplored in the field of PCOS.
Enicostemma Littorale blume (EL) has been widely used by folks since ages for the treatment of diabetes. During last ten years our lab worked on EL and unravelled its hypo gycemic, hypo lipidemic, anti-inflammatory and insulin sensitizing potentials in different animal models [25–28]. Swertiamarin, being its principal compound has proved to be a potent insulin sensitizer in STZ-NA diabetic rat models and different cell lines [29, 30]. The present study focuses to investigate direct effect of bio-active molecule swertiamarin on reproductive endocrinology for the treatment of women with PCOS.
Certain obese and lean women with PCOS are observed to have normal insulin sensitivity, hence it would be interesting to discover potential bio actives for treating PCOS-IR and NIR, considering the side effects observed by conventional pharmaceutical drugs. [ 31]. PCOS patients were classified as IR and NIR based on a novel molecular marker insulin receptor – β (INSR-β) expression on luteinized granulosa cell as shown in our previous paper [32]. The effects of swertiamarin and metformin have been explored on granulosa cell death, insulin and steroidogenic signaling, genes involved in fatty acid metabolism and steroidogenic hormones in granulosa cells from PCOS-IR and PCOS-NIR.
## Materials and methods
The study was approved by the Institutional Ethics committee for human research (IECHR), Faculty of Science, The M. S. University of Baroda, Vadodara (Ethical Approval Number FS/IECHR/BC/SG2).
## Human follicular fluid
Human follicular fluid samples were collected after informed consent from patients undergoing IVF/ ICSI over the course of 12 months at Nova IVI Fertility Clinic, Ahmedabad, India from 2015 August TO 2016 April. All the controls and patients underwent controlled ovarian hyper stimulation (COH) using flexible antagonist protocol. Follicular fluid was sent in embryology laboratory for oocyte identification & oocytes were separated out for IVF/ICSI. The follicular fluid devoid of oocyte was collected for the experiments. All the controls and patients received a GnRH analog (GnRH-a) in combination with FSH or human menopausal gonadotropin (hMG), followed by administration of human chorionic gonadotropin (hCG). The follicular fluid was collected on the day of oocyte retrieval.
## Inclusion criteria
The diagnosis included donors, male factor infertility, tubal factor infertility and PCOS with an age ranging from 20–40 years.
## Exclusion criteria
Patients with endometriosis and poor ovarian response were excluded from the study.
## Human granulosa-luteal cell culture with bioactives
Luteinized granulosa cells from individual control and PCOS follicular fluid aspirates were isolated, analyzed for expression of INSR-β and segregated as control, PCOS-IR and PCOS-NIR as done before [32]. The cells from $$n = 8$$ control, $$n = 8$$ PCOS-IR and $$n = 8$$ PCOS-NIR were pooled in respective groups and cultured in at a density of 0.5 × 106 cells in 3 ml of DMEM/F12 culture medium supplemented with $10\%$ FBS and Penicillin-G/ Streptomycin (100 IU/ml/100 mg/ml) in a 6-well plate at 37° C and $5\%$ CO2 in a humidified incubator for 48 h. The cells were then incubated in serum free culture medium with or without swertiamarin (66 μM) and positive control- metformin (1 mM) for additional 72 h. The doses were selected based upon the studies in literature [30, 33, 34]. The viability of cells was analysed after 72 h by trypan blue exclusion dye method following which the supernatant was collected for hormone analysis and cells harvested for gene and protein expression and enzyme activity.
## Total RNA Extraction, RT-PCR and qRT-PCR
Total cellular RNA was isolated from the cultured luteinized granulosa cells and then reverse transcribed into first strand cDNA. qRT-PCR for lipogenic genes Sterol Regulatory Element Binding Protein (SREBP1c), Fatty acid synthase (FAS), Acetyl-CoA carboxylase 1 (ACC-1), Carnitine palmitoyl transferase I (CPT-1), Insulin like growth factor system (IGF-1, IGF-2, IGF-1R, IGF-2R) and gonadotropins receptors [follicle stimulating hormone receptor (FSH-R), luteinizing hormone receptor (LH-R)] study was performed on an Applied- Biosystem 7500-Real-Time PCR Sequence detection System using standard temperature cycling conditions. qRT-PCR for steroidogenic genes Steroidogenic Acute Regulatory protein (StAR), Cytochrome P450 side chain cleavage (CYP11A1), 3-beta hydroxy steroid dehydrogenase (3 β- HSD), Aromatase (CYP19A1), 17-beta hydroxy steroid dehydrogenase (17 β-HSD) was performed on Applied Biosystem 7500 FAST Real Time PCR. Sequence detection System by predesigned primers from TaqMan gene expression assays. All qRT-PCR results were normalized to the level of β-actin and 18S rRNA determined in parallel reaction mixtures to correct any differences in RNA input. Fold changes in qRT-PCR gene expression were analyzed using 7500 Real time PCR software V.2.0.6 and Data assist software (Applied Biosystems Inc.) which led to a possible estimation of the actual fold change. Negative RT was performed with untranscribed RNA. ( Supplementary files for Primer sequence).
## Western blot analysis
The cultured luteinized granulosa cells were harvested suspended in cell lysis buffer composed of 62.5 mM Tris–HCl, pH 6.8, 6 M urea, $10\%$ (v/v) glycerol, $2\%$ (w/v) SDS, $0.00125\%$ (w/v) bromophenol blue and freshly added $5\%$ (v/v) β-mercaptoethanol and subjected to sonication on ice. Total protein content was quantified using Bradford assay (Biorad Bradford Solution, USA). 20 µg protein was loaded on $10\%$ SDS–polyacrylamide gel electrophoresis under reducing conditions, along with pre-stained molecular weight markers. The separated proteins were electrophoretically transferred onto a nitrocellulose membrane and incubated overnight at 4 °C with appropriate antibody dilution (supplementary file for table). The samples were then incubated for 1 h at room temperature with a horseradish peroxidase-conjugated anti-rabbit or anti-goat or anti-mouse IgG and analyzed by Alliance 4.7 UVI Tec chemidoc.
## Hydroxysteroid dehydrogenase activity
17β-HSD and 3β-HSD activities were estimated in cultured granulosa cells following Shivanandappa &Venkatesh [35]. In brief, the assay system contained 0.1 M Tris–HCl (pH 7.8), 5 mM nicotinamide adenine dinucleotide (NAD), 1 mM estradiol/dehydroepiandrostenedione (DHEA), and 0.4 mM 2-p-iodophenyl-3-p-nitrophenyl-5-phenyl tetrazolium chloride (INT) and 50 µl of granulosa cell lysate containing enzyme in a total volume of 3 ml, which was incubated for 1 h at 37 °C. The reaction was terminated using 50 mM potassium phthalate buffer, and absorbance was measured at 490 nm.
## Hormone analysis
The steroid hormones were measured from the culture medium by enzyme-linked immunosorbent assay (Diametra; Italy), according to the manufacturer’s instructions. The standard curve for E2, P4 and T ranged from 0 to 2000 pg/mL, 0 to 40 ng/ml and 0 to 16 ng/ml respectively. The supernatants were diluted to 1: 1000 for E2 and 1: 250 for P4 in PBS to ensure that the final value fell within the detection range of the standard curve. Each sample was assayed in duplicate, and the E2 and P4 concentration was calculated by multiplying the end value by the dilution factor. The assay sensitivity range was 8.68 pg/ml for E2, 0.05 ng/ml for P4 and 0.07 ng/ml for testosterone.
## Statistical analysis
The results are presented as mean ± standard error mean. The data were statistically analyzed by employing one-way analysis of variance followed by Newman Keuls Multiple Comparison Test (GraphPad Prism; Graph Pad Software, Inc., La Jolla, CA). The minimum level of significance ($P \leq 0.05$) was considered. ( 1637 words).
## Swertiamarin increases hLGC viability from PCOS-IR only.
Treatment of swertiamarin significantly ($P \leq 0.001$) ameliorated cell death in PCOS-IR group but no difference was observed because of these treatments in PCOS-NIR group. However, metformin markedly increased the viability in PCOS IR as well as PCOS-NIR group (Fig. 1).Fig. 1Effect of swertiamarin and metformin on cell viability in PCOS-IR and PCOS-NIR. % granulosa cell viability was done by trypan blue exclusion dye method. The normalized expression values are represented as mean ± SEM of three independent experiments. * $P \leq 0.05$, ** $P \leq 0.01$ vs. C, ## $P \leq 0.01$ vs. PCOS-NIR, ### $P \leq 0.001$ vs. PCOS-IR, @@ $P \leq 0.01$ vs. PCOS-NIR swertiamarin $$n = 8$$ control, $$n = 8$$ PCOS-IR and $$n = 8$$ PCOS-NIR
## Divergent effects of swertiamarin on insulin signalling and lipid metabolism
The possible effect of swertiamarin on protein expression of candidate insulin signalling intermediates viz insulin receptor (INSR-β), phospho insulin receptor substrate [pIRS(ser307)], phosphatidylinositol 3-kinase (PI[3]K), phospho protein kinase B (pAkt), protein kinase C (PKC-ζ), extracellular regulatory kinase (ERK$\frac{1}{2}$), phospho P38 mitogen activated protein kinase (pP38MAPK) and Peroxisome proliferator-activated receptor gamma (PPAR-γ) in PCOS-IR and NIR hLGCs were analyzed by western blot. Swertiamarin treatment significantly ($P \leq 0.05$) reversed the down regulated expression of INSR-β, PI[3]K, p-Akt and PKC-ζ in PCOS-IR hLGCs. Increased expression of p-IRS(Ser 307) ($P \leq 0.01$) a hall mark of insulin resistance and PPARγ ($P \leq 0.05$) – an indicative of PCOS was also reduced remarkably because of the treatment with these bioactive compounds in PCOS-IR cells. Swertiamarin could significantly decrease protein expression of pP38 MAPK and p$\frac{44}{42}$ MAPK in hLGC’s from both PCOS-IR as well as PCOS-NIR. Moreover, swertiamarin with 66 μM was observed to be equally potent to metformin 1 mM in PCOS-IR, but, surprisingly, swertiamarin was unable to show any effect on the candidate insulin signalling from PCOS-NIR (Fig. 2, A, B, C and D). Swertiamarin ($P \leq 0.001$) significantly down regulated the candidate genes viz SREBP1c, FAS, ACC-1 and CPT-1 in PCOS-IR as compared to PCOS-NIR where it did not show any effect. Here, swertiamarin was observed to be equally potent to metformin. Metformin down regulated the basal expression of all the genes in PCOS- NIR hLGC’s (Fig. 2, E and F).Fig. 2Expression of genes and proteins involved in insulin signaling cascade in human luteinized granulosa cells isolated from control, PCOS-IR, PCOS-NIR and treated with swertiamarin and metformin. A Western blot image for INSR-β, pIRS(ser307), PI[3]K, pAkt, PKC-ζ, ERK$\frac{1}{2}$, pP38MAPK and PPAR γ from PCOS-IR. B *Densitometric analysis* for INSR-β, pIRS (ser307), PI[3]K, pAkt, PKC-ζ, ERK$\frac{1}{2}$, pP38MAPK and PPAR γ from PCOS-IR. C Western blot image for INSR-β, pIRS(ser307), PI[3]K, pAkt, PKC-ζ, ERK$\frac{1}{2}$, pP38MAPK and PPAR γ from PCOS-NIR. D *Densitometric analysis* for INSR-β, pIRS (ser307), PI[3]K, pAkt, PKC-ζ, ERK$\frac{1}{2}$, pP38MAPK and PPAR γ from PCOS-NIR. E mRNA expression of SREBP1c, FAS, ACC-1, CPT-1 genes involved in fatty acid metabolism by qRT-PCR PCOS-IR. F. mRNA expression of SREBP1c, FAS, ACC-1, CPT-1 genes involved in fatty acid metabolism by qRT-PCR PCOS-NIR. * $P \leq 0.05$, ** $P \leq 0.01$ vs. C, # $P \leq 0.05$, ## $P \leq 0.01$, ### $P \leq 0.001$ vs. PCOS-IR or PCOS-NIR, @ $P \leq 0.05$ vs. PCOS-IR + S, $$n = 8$$ control, $$n = 8$$ PCOS-IR and $$n = 8$$ PCOS-NIR
## Divergent effects of swertiamarin on IGF system
Swertiamarin treatment could significantly ($P \leq 0.05$) upregulate the gene expression of IGF-1 in PCOS-IR group. The bioactive molecule appreciably down regulated the expression of IGF-1R ($P \leq 0.01$), IGF-II ($P \leq 0.05$) and IGF-2R ($P \leq 0.01$) in hLGC’s from PCOS-IR. Although there was no alteration in the IGF system in PCOS NIR group by swertiamarin; metformin treatment dramatically reduced the basal levels of all the genes in this group (Fig. 3, A and B).Fig. 3Expression of genes and proteins involved in IGF system in human luteinized granulosa cells isolated from control, PCOS-IR, PCOS-NIR and treated with swertiamarin and metformin. A mRNA expression of genes involved in IGF system in PCOS-IR. B mRNA expression of genes involved in IGF system in PCOS-NIR * $P \leq 0.05$, ** $P \leq 0.01$ vs. C, # $P \leq 0.05$, ## $P \leq 0.01$, ### $P \leq 0.001$ vs. PCOS-IR or PCOS-NIR, @ $P \leq 0.05$ vs. PCOS-IR + S, $$n = 8$$ control, $$n = 8$$ PCOS-IR and $$n = 8$$ PCOS-NIR
## Swertiamarin re-establishes steroidogenesis in hLGC’s from PCOS-IR only
Swertiamarin ($P \leq 0.01$) significantly up regulated mRNA as well as protein expression of StAR, CYP19A1, 17β-HSD and 3β-HSD and was equally potent to metformin in PCOS-IR. CYP11A1 demonstrated a significant down regulation in mRNA as well as protein expression in both the groups. Swertiamarin did not show any effect in PCOS-NIR condition. However, the golden drug metformin did show significant reversal ($P \leq 0.01$) of all the genes and proteins in PCOS-NIR group (Fig. 4, A, B, C, D, E and F).Fig. 4Steroidogenic profile of human luteinized granulosa cells isolated from control, PCOS-IR and PCOS-NIR and treated with swertiamarin and metformin. A mRNA expression of StAR, CYP11A1, 3β-HSD, CYP19A1, 17β-HSD genes in PCOS-IR by qRT-FAST PCR B mRNA expression of StAR, CYP11A1, 3β-HSD, CYP19A1, 17β-HSD genes in PCOS-NIR by qRT-FAST PCR. C Protein expression of StAR, CYP11A1, 3β-HSD, CYP19A1, 17β-HSD by Western blot method from PCOS-IR. D *Densitometry analysis* using β-actin as endogenous control from PCOS = IR. E Protein expression of StAR, CYP11A1, 3β-HSD, CYP19A1, 17β-HSD by Western blot method from PCOS-NIR. F *Densitometry analysis* using β-actin as endogenous control from PCOS-NIR. * $P \leq 0.05$, ** $P \leq 0.01$, *** $P \leq 0.001$ vs. C, # $P \leq 0.05$, ## $P \leq 0.01$, ### $P \leq 0.001$ vs. PCOS-IR or PCOS-NIR, @ $P \leq 0.05$, @@ $P \leq 0.01$, @@@ $P \leq 0.001$ vs. PCOS-IR + S. $$n = 8$$ control and $$n = 8$$ PCOS-IR
## Swertiamarin reverts back enzyme activity and hormonal levels in hLGC’s from PCOS-IR only
The activities of the enzymes 17β-HSD and 3β-HSD were also up regulated by swertiamarin ($P \leq 0.01$). A significant increase in 3β-HSD and 17β-HSD was demonstrated by treatment with swertiamarin ($P \leq 0.01$, $P \leq 0.001$) and metformin ($P \leq 0.01$, $P \leq 0.001$) in PCOS- IR hLGCs with respect to control, whereas in PCOS-NIR group only metformin demonstrated a significant increase ($P \leq 0.01$) in 3β-HSD activity as well as 17β-HSD activity (Fig. 5, A and B). In vitro secretion of estradiol, progesterone and testosterone were analysed from the conditioned media of PCOS-IR and PCOS-NIR treated with swertiamarin and metformin. A significant increase was demonstrated by treatment with swertiamarin ($P \leq 0.001$) and metformin ($P \leq 0.001$) in PCOS-IR hLGCs with respect to untreated PCOS-IR, whereas in PCOS-NIR group only metformin demonstrated a significant increase ($P \leq 0.01$) in estradiol levels. Further metformin treated PCOS NIR cells could demonstrate significant rise ($P \leq 0.01$) in the levels of progesterone secretion relative to PCOS-NIR group. Testosterone levels were not detected in the cell culture supernatant. Swertiamarin could significantly down regulate expression of FSH-R and LH-R in PCOS-IR hLGCs and its effect was equally potent to metformin (Fig. 5, C and D).Fig. 5A Enzyme activity analysis of 3β-HSD and 17β-HSD in PCOS-IR and PCOS-NIR. B Analysis of steroid hormones estradiol, progesterone and testosterone concentration in follicular fluid aspirates devoid of luteinized granulosa cells by ELISA from PCOS-IR and PCOS-NIR. C and D) mRNA expression of FSH-R and LH-R genes in human luteinized granulosa cells by qRT-PCR from PCOS-IR and PCOS-NIR. * $P \leq 0.05$, ** $P \leq 0.01$, *** $P \leq 0.001$ vs. C, # $P \leq 0.05$, ## $P \leq 0.01$, ### $P \leq 0.001$ vs. PCOS-IR or PCOS-NIR, @ $P \leq 0.05$, @@ $P \leq 0.01$, @@@ $P \leq 0.001$ vs. PCOS-IR + S. $$n = 8$$ control and $$n = 8$$ PCOS-IR
## Discussion
Ever since the role of IR in the pathogenesis of PCOS has been established, a positive effect of insulin-sensitizing drugs in the treatment of PCOS has been demonstrated. In accordance with this, our study is the first one to report for the potential of swertiamarin, a bio active herbal insulin sensitizers for amelioration of IR and reestablishment of steroidogenesis in hLGC’s isolated from follicular fluid of PCOS-IR and PCOS-NIR patients using metformin as a positive control.
Insulin resistance in PCOS has been associated with increase in granulosa cell death [36]. The recovery of hLGC viability after incubation with swertiamarin in our study may be interpreted as indicative of reduced susceptibility of the PCOS-IR cells to undergo apoptosis as is observed with metformin in earlier studies [34]. However, swertiamarin did not show any effect on cell viability in hLGC from PCOS-NIR indicating the process of granulosa cell death in PCOS-NIR to be other than IR.
It is well known that ISD’s can modulate insulin signalling that can recruit its downstream docking proteins to activate several other signalling pathways in different cell types [18, 37]. Swertiamarin is well known for its anti-diabetic and anti-hyperlipidemic effects in various animal models, different cell lines and clinical trials with humans. Our results for the first time show a direct interaction of swertiamarin with key components of the classical insulin-signaling pathway thereby highlighting their ability to sensitize IR condition in hLGC’s from PCOS-IR. Moreover swertiamarin with a dose of 66 μM seemed to be a potent insulin sensitizing drug for reversing insulin sensitivity in PCOS-IR as compared to metformin 1 mM. Surprisingly metformin increased the basal expression of key signalling proteins in the insulin signalling pathway which might result in increased insulin sensitivity and decreased insulin levels to less than normal even in PCOS-NIR group in the long term.
The importance of P38 MAPK and ERK$\frac{1}{2}$ in PCOS granulosa cells has been demonstrated in studies indicating their over expression as a result of oxidative stress and inflammation leading to decrease in the expression of StAR and progesterone synthesis. [ 38–41]. Decrease in the protein expression of pP38 and ERK$\frac{1}{2}$ MAPK in treated hLGC’s from PCOS-IR as well as PCOS-NIR indicating reversal of oxidative stress and thus reversal of cell death. Our findings are in line with the literature where swertiamarin could enhance anti-oxidant defense system by suppressing oxidative stress and attenuate inflammation and apoptosis [30, 42].
*Lipogenic* genes being the regulators of the accumulation of lipids instead of cholesterol in granulosa cells, their profile was assessed. As insulin signalling is related to lipid metabolism and lipids are important for oocyte maintenance, we further observed the effect of swertiamarin on the lipogenic genes SREBP1c along with enzymes ACC-1, FAS and CPT-1 in PCOS-IR and PCOS-NIR condition. Our data indicated that SREBP-1c protein expression, in addition to the expression of lipogenic target genes ACC1 and FAS and fatty acid oxidation gene CPT-1 were suppressed by swertiamarin in hLGC’s from PCOS-IR supported by [30, 43]. The reversal of these processes explain the association of swertiamarin supplementations in decreasing lipid accumulation in granulosa cells seemed with an improvement in granulosa cell metabolism via the regulation of SREBP-1c, ACC-1, FAS and CPT-1 expression.
We further wanted to study the effect of the bio active on IGF (IGF-I, IGF-II, IGF-1R and IGF-2R) system whose involvement is appreciated in the development of preantral to preovulatory follicles, in the process of follicular atresia and is over expressed in diabetic condition [44–47]. In the present chapter, treatment of swertiamarin reversed the aberrant effects of PCOS on IGF-I, IGF-II, IGF-1R and IGF-2R in hLGC’s from PCOS-IR. Studies in literature with administration of metformin in diabetic patients have reported upregulation of IGF-1 gene and down regulation of its receptor thereby improving insulin sensitivity and glucose uptake [48–52].
As observed with the proteins involved in insulin signalling system, metformin reversed the expression of IGF-2R in hLGC’s from PCOS-NIR. These findings combined with no change in basal expression of these proteins and genes in control hLGC’s as well as PCOS-NIR hLGC’s strongly suggest that other than metformin, swertiamarin might function only during IR condition.
The process of steroidogenesis is very crucial for the development of oocyte, its fertilization and embryo implantation [53]. In PCOS condition irrespective of IR, gene and protein expression of steroidogenic factors and their corresponding steroid hormones are lowered [8, 53]. In the present study swertiamarin could revert back mRNA expression of gonadotropin receptors, mRNA and protein expression of StAR, CYP11A1, CYP19A1, 17β-HSD and 3β-HSD, their enzyme activity along with secretion of the corresponding steroid hormones estradiol and progesterone in hLGC’s from PCOS-IR thus improving the process of steroidogenesis. Metformin, the positive control of the study improves steroidogenesis by down regulating FSH-R and increasing the progesterone secretion by hLGC from PCOS women [8, 21]. Swertiamarin at a concentration of 66 μM proved to be a better drug in reversing steroidogenesis with the same potency as compared to metformin 1 mM in PCOS-IR. In the present study testosterone could not be detected in the cell culture supernatant. The finding was consistent with the literature explaining presence of the enzyme P450c17/CYp17, responsible for converting C21 steroids (progestrogens) to C19 steroids (androgens) solely in theca cells and not in granulosa cells [54].
An additional aim of this study was to determine whether swertiamarin and metformin could ameliorate decreased steroidogenesis in hLGC’s from PCOS-NIR. Strikingly, swertiamarin did not ameliorate steroidogenesis. On the basis of the findings that swertiamarin restored insulin sensitivity in PCOS-IR with no effect on PCOS-NIR, it is quite possible to speculate that swertiamarin could be mediating their effects on granulosa cell steroidogenesis only through insulin signaling. Metformin reversed back the decrease in steroidogenesis although with a less pronounced effect as compared to PCOS-IR. This effect could be attributed to the direct effect of metformin on steroidogenesis probably through a multipathway reaction with ERK$\frac{1}{2}$, pP38 MAPK or PI[3]K all involved in regulation of steroidogenesis [55–57]. Such direct effects of metformin have been supported by some clinical studies in which the insulin sensitizer increased the ovulation rate, and fertilization rates by having no effect on the basal insulin levels [13, 31, 58]. These findings indicate the possibility of some other factors in hLGC’s from PCOS-NIR yet unidentified and not associated with insulin signalling to have a role in restoring steroidogenesis by metformin. However few studies with metformin in clinical trials have reported increased ovulation rates with decreased basal insulin levels in normo insulinemic subjects indicating hypoglycaemia if prescribed to PCOS-NIR [31, 59]. Moreover as the actual treatment time for metformin to induce a clinical ovulation is 6 months, other drugs are preferred over metformin for rapidly inducing ovulation in PCOS [60]. D-chiro Inositol, an insulin sensitizer, has been observed to induce ovulation in non insulin resistant PCOS via modulating aromatase expression [61].
Collectively the results support the notion that, swertiamarin at 66 μM show effect as insulin sensitizers for alleviating IR condition. Swertiamarin had no effect on steroidogenesis in PCOS-NIR. Metformin did restore steroidogenesis in PCOS-NIR but it is important however to highlight that it decreases the basal insulin signalling parameters in PCOS-NIR group which might lead to adverse effects in the long-term health (Fig. 6). This calls for a proper diagnosis of IR condition in PCOS so that targeted therapy can be prescribed to achieve increased pregnancy rates with decreased time. Fig. 6Schematic representation of the summary
## Supplementary Information
Additional file 1.
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---
title: A novel clinical prediction model of severity based on red cell distribution
width, neutrophil-lymphocyte ratio and intra-abdominal pressure in acute pancreatitis
in pregnancy
authors:
- Wenyan Liao
- Guangwei Tao
- Guodong Chen
- Jun He
- Chunfen Yang
- Xiaohua Lei
- Shuo Qi
- Jiafeng Hou
- Yi Xie
- Can Feng
- Xinmiao Jiang
- Xin Deng
- Chengming Ding
journal: BMC Pregnancy and Childbirth
year: 2023
pmcid: PMC10024436
doi: 10.1186/s12884-023-05500-0
license: CC BY 4.0
---
# A novel clinical prediction model of severity based on red cell distribution width, neutrophil-lymphocyte ratio and intra-abdominal pressure in acute pancreatitis in pregnancy
## Abstract
### Background
Acute pancreatitis in pregnancy (APIP) with a high risk of death is extremely harmful to mother and fetus. There are few models specifically designed to assess the severity of APIP. Our study aimed to establish a clinical model for early prediction of severity of APIP.
### Methods
A retrospective study in a total of 188 patients with APIP was enrolled. The hematological indicators, IAP (intra-abdominal pressure) and clinical data were obtained for statistical analysis and prediction model construction.
### Results
According to univariate and multivariate logistic regression analysis, we found that red cell distribution width (RDW), neutrophil-lymphocyte ratio (NLR) and Intra-abdominal pressure (IAP) are prediction indexes of the severity in APIP (p-value < 0.05). Our novel clinical prediction model was created by based on the above three risk factors and showed superior predictive power in primary cohort (AUC = 0.895) and validation cohort (AUC = 0.863). A nomogram for severe acute pancreatitis in pregnancy (SAPIP) was created based on the three indicators. The nomogram was well-calibrated.
### Conclusion
RDW, NLR and IAP were the independent risk factors of APIP. Our clinical prediction model of severity in APIP based on RDW, NLR and IAP with predictive evaluation is accurate and effective.
## Introduction
Acute pancreatitis in pregnancy (APIP) is a severe disease that affects 1 in 1,000 to 12,000 pregnant women on average, which is more frequently than the general population [1]. Both the mother and the fetus might suffer substantial morbidity as a result of APIP. The APIP still has high rates of maternal and perinatal mortality, $3.3\%$ and 11.6–$18.7\%$, respectively [1].
Acute pancreatitis (AP) is an inflammatory disorder that can damage nearby and distant organs even result in the multiple organ dysfunction. According to the current Atlanta classification based on the presence and duration of organ failure [2012], acute pancreatitis is divided into three types mild acute pancreatitis (MAP), moderately severe acute pancreatitis (MSAP), and severe acute pancreatitis (SAP) [2]. Patients without organ dysfunction and local complications were MAP. Patients with temporary organ dysfunction (≤ 48 h) and/or localized or systemic accompanying diseases were classified as MSAP. SAP is persistent organ dysfunction (> 48 h). The risk of maternal and fetal death is highly associated with the severity of APIP [1, 3]. Reports indicate that SAP in pregnancy (SAPIP) with the maternal mortality rate as high as approximately 20–$40\%$ is serious hazardous to the health of pregnant women [4, 5]. Early recognition of APIP severity is critically important for prompt treatment to individual patients. The early stage of AP is usually referred to the first week after the disease onset [6]. Early prediction of the severity of APIP is very important for clinical treatment.
Elevated internal pressure (IAP) is common in critically ill patients. Intraperitoneal hypertension (IAH) has adverse effects on hemodynamics, respiration, and renal function, and may eventually lead to multiple organ failure [7]. It is reported that early recognition of IAH may help in early intervention improving outcomes of acute necrotizing pancreatitis [8]. However, to our knowledge, few studies have been conducted on IAP in APIP.
Currently, several prediction systems are usually used for AP patients. However, the prediction system is not specific for pregnant women, and some do not apply in pregnant women. At present, there is few scoring systems designed for patients with APIP in clinical practice [9]. Therefore, it is very urgent and important to establish a timely, simple and useful clinical prediction model to predict severe acute pancreatitis in pregnancy.
Red cell distribution width (RDW) is a parameter primarily reflecting the volume variability of red blood cells. Recent many studies indicated that RDW relates with the levels of many kinds of inflammatory cytokines in serum [10]. Moreover, RDW has been introduced as a novel inflammatory predictor in various diseases, such as functional bowel conditions [11], autoimmune diseases [12], rheumatoid arthritis [13], degenerative vertebral conditions [14], autoimmune hepatitis [15]. It is proved that RDW can be used to predict mortality and severity of patients with AP [16, 17]. However, few studies focused on the prediction of RDW in the severity of patients with APIP. On the other hand, neutrophil-lymphocyte ratio (NLR) has been introduced as a marker of inflammation in many inflammatory related diseases, such as inflammatory bowel disease [18], diabetes mellitus [19], thyroiditis [20], and AP where NLR can be used to predicting severity [21]. Up to date, it is not clear whether NLR and RDW can be used to construct predictive model of APIP severity. Therefore, we included RDW and NLR in our study, aiming to further determine their predictive role in APIP severity and whether a predictive model of APIP severity can be established.
In this study, 188 cases of APIP patients were retrospectively reviewed and classified into two groups by us. MAP and MSAP were included into non-SAPIP groups, and severe acute pancreatitis in pregnancy was included into SAPIP groups. we collected routine laboratory tests data within 48 h after the APIP onset, IAP and other clinical data to assess the predictive ability of these data on the severity of APIP, in order to construct a clinical prediction model of severity in APIP.
## Patients selection
The medical records of patients who were diagnosed with APIP were retrospectively collected at our hospital (The First Affiliated Hospital of University of South China) from January 2008 to December 2021. Patients meeting the following criteria were included: [1] *Definite diagnosis* of APIP; [2] All cases were first onset and were diagnosed within 48 h of onset. [ 3] All required information was completed. Exclusion criteria: [1] Pregnancy terminated within 24 h of admission; [2] Acute attack of chronic pancreatitis; [3] Patients complicated with other diseases, such as malignant tumor, comorbidities related to pregnancy or not related to pregnancy, sepsis, hemorrhagic disease (trauma, for example), other Inflammation-related disease, and so on; [4] Use of immunosuppressants, corticosteriod and other drugs; [5] Patients received red blood cells; [7] Patients from obstetrical emergencies as HELLP, preeclampsia, eclampsia etc.; [ 8] Incomplete information required. The flowchart prensented in Fig. 1. The Ethics Committee of our hospital approved our study, and our study was carried out following the Declaration of Helsinki.
Fig. 1The selection process for patients in a flow chart
## Data collection
We collected the following clinical parameters: age, gestational weeks, red cell distribution width (RDW), alanine aminotransferase (ALT), Albumin, triglyceride (TG), total cholesterol (TC), blood urea nitrogen (BUN), serum creatinine (Scr), Calcium, lactate dehydrogenase (LDH), intra-abdominal pressure (IAP), neutrophil–lymphocyte ratio (NLR). All clinical parameters were tested in the hospital and were collected within 48 h of admission. Among them, hematological test results were obtained on the day of admission, and IAP values were measured within 48 h after admission, the average values of the two highest IAP values were taken, and pressure was measured indirectly through the bladder [22].
## Definitions
We diagnosed the APIP patients via the 2012 revised version of the *Atlanta criteria* [6], the patients were diagnosed with APIP, if they met more than 2 pieces out of the following criteria: [1] characteristic abdominal pain of AP; [2] serum amylase or lipase was more than 3 times of the normal upper limit value; [3] characteristic results of acute pancreatitis from cross-sectional abdominal imaging. We graded the severity of APIP basing on 2012 revised version of the Atlanta criteria, and patients without organ dysfunction and local complications were mild acute pancreatitis (MAP). Patients with temporary organ disorder (≤ 48 h) and/or local or systemic other diseases caused by AP were moderately severe acute pancreatitis (MSAP). Patients with persistent organ dysfunction (> 48 h) were severe acute pancreatitis (SAP). MAP and MSAP were included into NSAPIP group, and severe pancreatitis in pregnancy was included into SAPIP group.
## Development and validation of the prediction model
Variables were screened by univariate logistic regression analysis. To establish our prediction model, we collected all associated factors to carry out the multivariate logistic regression analysis. We evaluated the accuracy of independent prediction factors in the predictive model of SAPIP by the receiver operating characteristic (ROC) curves. A nomogram for SAPIP was produced according to the multivariate logistic regression model. To validate the consistency and accuracy of the model in predicting severity of APIP patients, we applied the internal and external validation sets to assess the consistency, and used the calibration curves, ROC curves to check the accuracy of the prediction model in this study. In the end, decision analysis was applied to further assess the clinical applicability of the predictive model. Decision curve analysis (DCA) was carried out to evaluate the clinical utility of the prediction model. The flowing chart presents in Fig. 2.
Fig. 2Research flow chart
## Statistical analysis
We presented all the variables as mean ± standard deviation (SD) or median (range), as appropriate. Statistical analyses were conducted by GraphPad Prism 7.0 Software for Windows (GraphPad Software, La Jolla, CA, USA), Service Solutions SPSS Software 25.0 (SPSS, Chicago, IL, USA) and R statistical software (version 4.2.0; https://www.r-project.org/). Kolmogorov–Smirnov test was used for normality analysis of the study variables. Student’s t-test was applied to analyse normally distributed continuous variables, and the Mann-Whitney U test was utilized to analyse nonnormally distributed continuous variables. Univariate logistic regression analysis was performed to identify predictive factors of APIP. Predictive factors with p value less than 0.05 in univariate analysis were included in the multivariate analysis. Multivariate logistic regression analysis was conducted to identify independent predictive factors, and check the useful combination of factors that could predict APIP. All p values were two-sided, with statistical significance set at p values less than 0.05.
## Basic characteristics of the patients between training cohort and validation cohort
A total of 188 APIP patients were enrolled, and a 7:3 ratio of training cohort ($$n = 128$$) to validation cohort ($$n = 60$$) were randomly allocated. The detailed baseline characteristics of APIP patients in this investigation were presented in Table 1.
Table 1Baseline Characteristics of APIP Patients Between Training Cohort and Validation CohortVariableTraining cohort($$n = 128$$)Validation cohort($$n = 60$$)t/Z p Age(years)29(22–42)29(22–41)-0.7610.447Gestational weeks30.19 ± 4.2630.22 ± 3.07-0.0530.958RDW(%)14.1(10.9–19.1)13.55(11.1–18.2)-1.0010.317ALT(U/L)17.15(6.8-318.5)17.85(6.8-300.4)-0.1220.903Albumin(g/L)31.1(23.6–42)31.9(26.1–42.1)-1.8560.063BUN(mmol/L)5.35(2.2–18.6)6.2(2.5–17.9)-1.8220.068TG(mmol/L)3.58(1.28-131.57)3.69(1.6–57.6)-0.0160.987TC(mmol/L)6.22(2.2–32.8)5.66(2.68-31)-0.5220.602Scr(umol/L)69(26–282)72(27–262)-0.2950.768Calcium(mmol/L)1.95(1.2–2.9)1.99(1.25–2.9)-1.3140.189LDH(U/L)342(103–1435)340(177–1214)-0.0660.947IAP(mmHg)8.3(5.4–13.7)9.35(5.5–13.8)-0.9380.348NLR13.60 ± 3.6414.18 ± 3.35-1.0450.298
## Clinical characteristics between NSAPIP and SAPIP groups
As shown in Fig. 3, we compared and analysed the general conditions between the two groups. Significant differences in some variables were found between NSAPIP and SAPIP groups, including: RDW, TC, TG, Calcium, NLR, IAP($p \leq 0.05$). No significant differences in age, gestational weeks, ALT, albumin, BUN, Scr, and LDH were found between the two groups of patients.
Fig. 3Violin plots of clinical characteristics between NSAPIP and SAPIP Groups
## Univariate and multivariate logistic regression analysis
As shown in Table 2, the univariate logistic regression analysis indicated that the patients’ RDW [odds-ratio (OR) = 1.683, $$p \leq 0.000$$], TG (OR = 1.072, $$p \leq 0.007$$), TC(OR = 1.182, $$p \leq 0.017$$), IAP(OR = 1.827, $$p \leq 0.000$$), calcium(OR = 0.108, $$p \leq 0.001$$) and NLR(OR = 1.434, $$p \leq 0.000$$) were candidate factors related to the predicting severity of APIP. What’s more, the results of univariate logistic regression analysis indicated that age, ALT, albumin, BUN, Scr, and LDH were not severity predictors of APIP. This result was consistent with the result of variables comparison between NSAPIP and SAPIP groups. These factors which were not severity predictors of APIP were thus excluded from the multivariate logistic regression analysis. The results of the multivariate logistic regression analysis were showed as follows: RDW(OR = 1.450, $$p \leq 0.009$$), IAP(OR = 1.557,$$p \leq 0.000$$), NLR (OR = 1.228, $$p \leq 0.017$$). These results meant that RDW, IAP, NLR are independent prediction marker of severity in APIP patients. Moreover, we also constructed a forest plot of independent predictors of SAPIP with odds-ratio (Fig. 4).
Table 2Univariable and multivariate logistic regression analyses of factors for severity prediction of APIP in the training cohor (*$p \leq 0.05$)VariableUnivariate analysisMultivariate analysisOR$95\%$CI p OR$95\%$CI p Age0.9990.924–1.0790.975Gestational weeks0.9780.918–1.0430.504RDW1.6831.353–2.094 0.000* 1.4501.095–1.919 0.009* ALT1.0030.994–1.0130.499Albumin1.0580.963–1.1630.239BUN1.0880.956–1.2380.204TG1.0721.019–1.128 0.007* 1.0560.974–1.1440.185TC1.1821.030–1.355 0.017* 1.1220.971–1.2970.119Scr1.0040.997–1.0110.278Calcium0.1080.031–0.381 0.001* 0.2880.053–1.5690.150LDH1.0011.000-1.0030.059IAP1.8271.477–2.260 0.000* 1.5571.216–1.994 0.000* NLR1.4341.241–1.659 0.000* 1.2281.037–1.455 0.017* Fig. 4Forest plot of independent predictors of SAPIP with odds-ratio
## Model construction and validation
The ROC curve of severity prediction constructed based on the potential factors of RDW, IAP and NLR were presented in Fig. 5. The respective areas under the curve (AUC) of RDW and NLR were 0.754 [$95\%$ confidence interval (CI) 0.662–0.846], 0.788 ($95\%$ CI 0.709–0.866). The cut-off value of RDW for predicting the occurrence of SAPIP was $14.5\%$, the sensitivity was 0.76, and the specificity was 0.718, the cut-off value of NLR for predicting the occurrence of SAPIP was 12.228, the sensitivity was 0.88, and the specificity was 0.551. Meanwhile, the area under the curve (AUC) of IAP was 0.833 [$95\%$ confidence interval (CI) 0.761–0.906], the cut-off value of IAP for predicting the occurrence of SAPIP was 9.55 mmHg, the sensitivity was 0.70, and the specificity was 0.833.
Fig. 5ROC curves of NLR, RDW and IAP predict SAPIP The predictive model was created according to the independent prediction-related factors identified by the multivariate logistic regression analysis and was showed as follows: SAPIP risk = -11.618 + 0.206 × NLR + 0.371 × RDW + 0.443 × IAP. Then, the severity prediction model was visualized by a nomogram. As shown in Fig. 6, we constructed two kinds of nomogram.
Fig. 6A: line-segment static nomograms. Scores for each level of every variable on the nomogram were determined by a vertical dot-line from that factor to the point scale. Therefore, a total point was obtained by summing all the values. Finally, the risk of SAPIP for each patient could be estimated based on the total points. B: Line-segment dynamic nomograms. In R studio, we can click on different characteristics to see the probability of SAPIP in patients with different characteristics In order to further check the validity of the prediction model, ROC curves were used to assess the discriminative property. The AUC of the predictive model in the training cohort (internal validation) was 0.863 (Fig. 7A), and was 0.809 in the validation cohort (external validation) (Fig. 7B), indicating that the model has good discriminative ability. From the data in Fig. 8A and B, we knew that the calibration curves of internal (training cohort) and external (validation cohort) were very close to the 45°oblique line, showing that there was a great consistency between the predicted and actual results.
Fig. 7The ROC curves of the nomogram predicting SAPIP in the internal validation set (A) and external validation set (B) Fig. 8Calibration curve of poor prognosis prediction model in training cohort (A) and validation cohort (B) Finally, in order to evaluate the clinical usefulness and applicability of the model, we constructed a decision analysis curve. The decision curve shown in Fig. 9 indicated that the patients used this model can get more net benefit than the patients with complete intervention or no intervention at all. It means the model has potential clinical usefulness as a nomogram.
Fig. 9The decision curves of the nomogram predicting SAPIP
## Discussion
It is still difficult to early diagnose of SAP in the clinical treatment with AP patients [5, 23]. For APIP, early prediction of its severity is more difficult due to the complexity and specificity of pregnancy. At present, the measurement of hematuria amylase and enhanced CT scan are main techniques for AP diagnosis, but as every pancreatologist knows that the level of hematuria amylase is not in direct proportion to the severity of the disease [24, 25]. In addition, CT examination, especially enhanced CT scan, should be prudently selected for pregnant women, and uterine enlargement during pregnancy can lead to some changes in the anatomical position of abdominal organs, moreover, there are few specific predictive models for APIP, so it is relatively difficult to evaluate the severity of APIP. What’s more, APIP is a dangerous disease because of its rapid progression, once it escalates into SAP, it will be very harmful to both mother and fetus [26]. In a word, it is very useful to construct a new multi-factor clinical model to predict the severity of APIP, which will be helpful to deal with risk stratification and management of APIP.
RDW is a commonly used bio-marker to detect the severity of erythrocyte anisocytosis. The higher RDW indicates much more anisocytosis [27]. In addition, there are also many other studies showing that the abnormal elevation of RDW can predict the poor prognosis of patients with septic shock [28], acute myocardial infarction [29], and general trauma patients [30]. The predictive role of RDW in AP has received continuous attention [31, 32]. RDW has several advantages as a predictive marker. Firstly, RDW is a part of the blood routine test which is fairly inexpensive and is a routine test. Additionally, it can be accessed easily and its results can be obtained quickly. Some studies have proved that RDW is an independent risk factor related to the severity of AP [32, 33]. However, the predictive role of severity by RDW in APIP remains unclear. In our study, the higher level of RDW in SAPIP patients indicated that RDW maybe a potential predictor of SAPIP. After univariate and multivariate logistic regression analysis, we found RDW is an independent predictive factor of severity in APIP.
NLR is a widely-used marker of bodily inflammation, easily obtained from calculation of the parameters usually supplied in a full blood count report. NLR is the ratio of neutrophils to lymphocytes, which combines two different parts of the immune pathway and shows the balance between inflammatory activator neutrophils and inflammatory regulator lymphocytes. In addition, higher NLR values represent a more unbalanced inflammatory state [34]. There were some studies indicated that NLR is related to the severity of AP. Li et al. performed a retrospective study and found that NLR is the most significant biomarker of overall survival in the AP patient group [35]. Jeon et al. found that higher NLR value is closely associated with severe acute pancreatitis and organ failure [36]. In our study, we found the higher level of NLR in SAPIP patients indicated that NLR maybe a potential predictor of SAPIP. After univariate and multivariate logistic regression analysis, we determined that NLR is an independent predictive factor of severity in APIP.
Many studies have led to an increasing interest in the measurement of IAP as a indicator of prognostic or predictive severity in patients with acute pancreatitis [37–39]. Intra-abdominal hypertension (IAH) refers to a repeated pathological high IAP with the value of greater than or equal to 12 mmHg. It is commonly believed that IAH in AP with organ dysfunction indicates visceral oedema because of the inflammatory process [40]. More studies have been showed that it is important to monitor IAP in patients with AP as it can reflect severity and potential influence about management. Besides, studies also proved that IAP is related with organ dysfunction and mortality of pancreatitis [40, 41]. However, few studies have evaluate the value of IAP in APIP. Therefore, it remains unclear whether IAP can predict the severity of APIP. Our study determined that IAP is an independent severity predictor in APIP.
ROC curve analysis showed that RDW, IAP and NLR have a great predictive value in SAPIP. Moreover, in our study, a new predictive model consisting of three risk factors (RDW, IAP, NLR) was constructed. In addition, our new predictive model of severity in APIP based on the three risk factors (RDW, IAP and NLR) has good predictive value. Morever, we created a model-based prediction nomogram that provides a convenient metric for predicting the severity of APIP. The results can be obtained very quickly and accurately without replacing the numbers into the equation. Of note, the model showed great accuracy and consistency in both external and internal validation. The major advantage of our clinical model is that all variables can be obtained easily and quickly, providing a fast and reliable tool for clinical prediction of APIP. Yang’s team analyzed 190 cases of APIP and established the prediction model for moderately severe and severe acute pancreatitis based on lactate dehydrogenase, triglyceride, cholesterol, and albumin levels [42]; Sheng’s team constructed a nomogram for POF with APIP based on four indicators: lactate dehydrogenase, triglycerides, serum creatinine, and procalcitonin [43]. However, the above prediction models of APIP require more indicators, and some indicators cannot be obtained quickly, so patients’ conditions can’t be timely and accurately judged. Our blood indicators can be obtained from the blood routine, which takes only half an hour, and we can also quickly measure the IAP and get the value. About the clinical utility of the study findings, via this prediction model based on blood test and measurement of IAP, the doctors can early recognize APIP severity in the clinical work, which will be very helpful to deal with risk stratification and management of APIP.
Our study has several drawbacks. First off, our research was limited to a single-center retrospective design., so further multi-center studies are needed to support the results. Secondly, we only collected the data of 188 patients, further validation with a larger sample of data is required, and this is also our research plan to carry out in the future.
## Conclusion
In conclusion, we established and validated a new predictive nomogram model of severity according to RDW, IAP and NLR in APIP patients, which presents superior accuracy and accessibility. It is very useful to apply this model to stratify APIP patients for primary management and early intervention to improve prognosis.
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|
---
title: Are there socioeconomic inequalities in polypharmacy among older people? A
systematic review and meta-analysis
authors:
- Anum Iqbal
- Charlotte Richardson
- Zain Iqbal
- Hannah O’Keefe
- Barbara Hanratty
- Fiona E. Matthews
- Adam Todd
journal: BMC Geriatrics
year: 2023
pmcid: PMC10024437
doi: 10.1186/s12877-023-03835-z
license: CC BY 4.0
---
# Are there socioeconomic inequalities in polypharmacy among older people? A systematic review and meta-analysis
## Abstract
### Background
Socioeconomic status (SES) may influence prescribing, concordance and adherence to medication regimens. This review set out to investigate the association between polypharmacy and an individual’s socioeconomic status.
### Methods
A systematic review and meta-analyses of observational studies was conducted across four databases. Older people (≥ 55 years) from any healthcare setting and residing location were included. The search was conducted across four databases: Medline (OVID), Web of Science, Embase (OVID) and CINAHL. Observational studies from 1990 that reported polypharmacy according to SES were included. A random-effects model was undertaken comparing those with polypharmacy (≥ 5 medication usage) with no polypharmacy. Unadjusted odds ratios (ORs), $95\%$ confidence intervals (CIs) and standard errors (SE) were calculated for each study.
### Results
Fifty-four articles from 13,412 hits screened met the inclusion criteria. The measure of SES used were education (50 studies), income (18 studies), wealth (6 studies), occupation (4 studies), employment (7 studies), social class (5 studies), SES categories (2 studies) and deprivation (1 study). Thirteen studies were excluded from the meta-analysis. Lower SES was associated with higher polypharmacy usage: individuals of lower educational backgrounds displayed $21\%$ higher odds to be in receipt of polypharmacy when compared to those of higher education backgrounds. Similar findings were shown for occupation, income, social class, and socioeconomic categories.
### Conclusions
There are socioeconomic inequalities in polypharmacy among older people, with people of lower SES significantly having higher odds of polypharmacy. Future work could examine the reasons for these inequalities and explore the interplay between polypharmacy and multimorbidity.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-023-03835-z.
## Introduction
The burgeoning impact of polypharmacy, often defined as the use of five or more medications [1], has become an increasing challenge for healthcare professionals. With the growing usage of medication, the term hyper/excessive polypharmacy has also been used, which refers to people typically using ≥ 10 medication at any one time [2]. The increased use of multiple medications has raised some concerns, particularly across older people, as this population is more likely to develop adverse drug events, including drug-drug interactions, non-adherence and falls [3, 4].
Increasing numbers of people are experiencing polypharmacy and this challenge has become a global public health concern. In the United Kingdom (UK), for example, the number of people experiencing polypharmacy has quadrupled over a 20-year period [5], while an Australian based study [6] highlighted a $52\%$ increase in polypharmacy between 2006–2017.
In some contexts, the increasing trend of prescribing medication and the resulting polypharmacy is appropriate and necessary; multiple medications are often required to manage long-term conditions. As such, with rising multimorbidity and increasing life expectancy, polypharmacy may be clinically appropriate and thus reflective of treatment needs [7, 8]. However, there are situations where polypharmacy may be inappropriate and problematic; it is only possible to evaluate medication appropriateness by looking at individual patient preferences, circumstances, and contexts [9].
While previous studies have shown that certain patient-based factors, such as age, are associated with increased levels of polypharmacy, the role of socioeconomic factors such as education, income and occupation, is less clear. The literature suggests that such socioeconomic factors could play an important role in polypharmacy, with studies highlighting factors such as income [10–14] and employment [15–17] as possible contributors to the prevalence of polypharmacy. For example, a Swedish study [18] investigated the relationship between polypharmacy, socioeconomic status (SES) and inappropriate medication usage. The results showed that lower levels of education were associated with increased levels of polypharmacy and potential drug-drug interactions. Further to this, some authors have highlighted the rising concerns of low SES on the adverse impact on life expectancy, access to healthcare and multimorbidity. One study [19] has showed that low SES was associated with 2.1-year reduction in life expectancy for men and women aged 40–85 years. Such an impact is important given there is potential that those of lower SES being exposed to higher levels of polypharmacy and thus the associated harms.
Whilst there is significant academic interest on this topic, there is no single review and meta-analysis which draws together the current literature around polypharmacy and how prevalence may differ according to SES. Therefore, this systematic review and meta-analysis aimed to investigate the association between socioeconomic status and the prevalence of polypharmacy, in older people.
## Methods
This review was registered with accordance to The International Prospective Register of Systematic Reviews PROSPERO (CRD42021285455) and reported according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
## Data sources
A literature search was conducted across the following databases: Medline (OVID), Web of Science, Embase (OVID) and CINAHL, from inception to July 2021. The search was developed around three key terms: ‘polypharmacy’ ‘socioeconomic status’ and ‘ageing’ which captured the literature surrounding the key purpose of this review. The full search strategy can be found in Supplementary 1. Additional articles were identified through hand searching reference lists and forward citations of eligible articles.
## Study selection
Studies included in this review met the following criteria:Population: older people. In line with previous reviews [20] older people were defined as people aged ≥ 55 years. Studies required at least $50\%$ of participants to be over ≥ 55 years. Exposure: lower socioeconomic statusComparison: higher socioeconomic statusOutcome: receipt of polypharmacySetting: all settings were considered, irrespective of country, private or public healthcare systems. Study type: all observational study types including cohort and cross sectional.
To be eligible for inclusion, articles were required to be available as full text and published in English. After discussion with the review team, prescribing practices before 1990 were considered to be less relevant to address the review question, and hence articles published before 1990 were not eligible for inclusion.
## Selection criteria and screening
Records were uploaded to End Note and duplicates were removed. Rayyan QCRI was used for screening of titles and abstracts, which was conducted by one reviewer (AI). Two reviewers (CR,ZI) independently reviewed $25\%$ of extracted articles. Full text screening was conducted by one reviewer (AI) and checked by another reviewer (CR,ZI); any discrepancies were resolved through discussion and consensus (AT). The level of agreement between the review team was determined by a Kappa score – 0.85 showing excellent agreement.
## Data extraction and quality appraisal
The following information was extracted using a prepopulated data extraction form: first author, year, study data, participant characteristics, socioeconomic measure, main data extraction in relation to SES and polypharmacy. Data extraction was conducted by one reviewer (AI) and checked by the review team (CR,ZI); any discrepancies were resolved through discussion and consensus (AT). For the quality appraisal, one reviewer (AI) used the relevant critical appraisal tool from the Joanna Briggs Institute (JBI), which was checked (CR,ZI); any discrepancies were resolved through discussion and consensus (AT).
## Statistical methods
Random effects meta-analysis was performed to assess the association between a given socioeconomic factor and polypharmacy. Eligibility criteria for studies to be included in the meta-analysis were as follows: i) unadjusted raw data reporting polypharmacy rates for an individual socioeconomic factor ii) total participants/information to identify total number of participants displaying polypharmacy and no polypharmacy for the socioeconomic factor being investigated. Unadjusted odds ratios, confidence intervals and standard errors were then calculated independently by two reviewers (AI, FM). I2 was calculated to determine the degree of heterogeneity amongst the studies. Odd ratios were calculated by comparing the bottom $25\%$ of each study population to the remainder participants for each given socio-economic factor. Log odd ratios and SE were then entered into Revman 5.4 to generate forest plots.
## Literature search
Searches retrieved 20,064 citations. After de-duplication, 13,412 articles were screened for eligibility based on title and abstract. A further 187 articles were progressed to full text screening, which resulted in 54 articles meeting the inclusion criteria (Fig. 1); 13 articles were excluded from the meta-analysis. Fig. 1Study selection and exclusion according to the PRISMA flow diagram
## Study characteristics
The 54 included studies used a range of measures to assess SES factors. Fifty studies focussed on education [16, 18, 20–67], 18 studies on income [16, 23, 30, 32–34, 37, 41, 46–48, 50, 53, 61, 63, 64, 66, 68], 6 on wealth [22, 27, 45, 57, 67, 69], 4 studies on occupation [23, 44, 48, 57], 7 studies on employment [16, 17, 49, 58, 60, 63, 66], 5 studies on social class [17, 25, 36, 38, 60], 2 used SES categories [31, 70] and 1 used area-level deprivation [20]. Studies were conducted across a range of countries as follow: India [60, 66], Jordan [21], Netherlands [70], Sweden [18, 23, 39, 52], Spain [25], Belgium [26, 30], Pakistan [29, 68], UK [17, 20, 36, 38, 69], China [33, 44, 59, 65], Japan [46, 67], Singapore [40], Kuwait [42], Malaysia [43, 58], Poland [45], Togo [47], Saudi Arabia [55, 61], Taiwan [56], Vietnman [57]. Most studies were conducted within Brazil [28, 31, 32, 37, 41, 48, 50, 51, 53, 62, 63] and the US [16, 24, 27, 34, 35, 49, 54, 64]. One study included participants from across Europe and Israel [22]. Studies ranged in size from 59 [24] to 1,742,336 [52] participants. Full study characteristics can be found in Supplementary 2.
## Quality appraisal
The included 54 studies scored in the range 6–8, out of a possible 8 (S2). Articles often scored poorly on identifying and reporting confounders. The majority scored well on displaying inclusion criteria, using appropriate statistical analysis, and describing subjects and setting.
## Association between education and polypharmacy
Fifty studies [16, 18, 20–67] investigated the association between education and polypharmacy, 38 studies [18, 21–31, 33, 35, 37, 40–42, 44, 46–48, 50–55, 57–61, 63–67] were eligible for meta-analysis giving a pooled OR of 1.21 ($95\%$ CI 1.15–1.28; I2 = $94\%$) for receipt of polypharmacy in those of lower education when compared to higher education (Fig. 2).Fig. 2Forest plot showing the likelihood of polypharmacy according to education
## Association between income and polypharmacy
Eighteen studies [16, 23, 30, 32–34, 37, 41, 46–48, 50, 53, 61, 63, 64, 66, 68] investigated the association between income and polypharmacy, 12 studies [23, 30, 33, 37, 41, 46, 47, 50, 53, 61, 64, 66] were eligible for meta-analysis giving a pooled OR of 1.10 ($95\%$ CI 0.98–1.23; I2 = $46\%$) for receipt of polypharmacy in those of a low compared to high income (Fig. 3).Fig. 3Forest plot showing the likelihood of polypharmacy according to income, wealth, occupation, employment, social class and SES categories
## Association between wealth and polypharmacy
Six studies [22, 27, 45, 57, 67, 69] investigated the association between wealth and polypharmacy, 4 studies [22, 57, 67, 69] were eligible for meta-analysis, giving a pooled OR of 1.38 ($95\%$ CI 1.31–1.46; I2 = $0\%$) for receipt of polypharmacy in those of less wealthier backgrounds (Fig. 3).
## Association between occupation, employment, and polypharmacy
Four studies [23, 44, 48, 57] reported the association between occupation and polypharmacy, and 7 [16, 17, 49, 58, 60, 63, 66] reported the association between employment and polypharmacy. Three studies [23, 44, 57] assessing occupation and 5 studies [17, 58, 60, 63, 66] assessing employment were eligible for meta-analysis (Fig. 3). A pooled OR of 1.23 ($95\%$ CI 0.70 - 2.17; I2 = $92\%$) was calculated for those in receipt of polypharmacy from lower occupations. Similarly, the pooled OR was 1.34 ($95\%$ CI 0.85–2.13; I2 = $76\%$) for receipt of polypharmacy in unemployed, compared to employed, individuals.
## Association between social class, SES and polypharmacy
Five studies [17, 25, 36, 38, 60] reported the association between social class and polypharmacy, and 2 studies [31, 70] focused on SES and polypharmacy. Three studies [17, 25, 60] assessing social class were eligible for meta-analysis and the pooled OR was 1.31 ($95\%$ CI 1.21–1.42; I2 = $0\%$) for receipt of polypharmacy in those of lower social class compared to higher social class. Two studies [31, 70] assessing SES were eligible for meta-analysis and the pooled OR was 1.03 ($95\%$ CI 0.67–1.59; I2 = $97\%$) for receipt of polypharmacy for those of lower, compared to higher, SES.
## Main finding
This systematic review and meta-analysis found that, overall, polypharmacy is associated with lower socioeconomic status. In particular, pooled estimates revealed a significant association when using education as a marker of SES: those of lower educational backgrounds had $21\%$ higher odds to be in receipt of polypharmacy when compared to those of higher education. Significant associations were also observed when wealth and social class were used as SES measures. Similar trends were observed for income, occupation, employment and SES categories, although the results did not reach statistical significance. The majority of the studies included in this review used education as a marker of socioeconomic status, while fewer studies used socioeconomic measures such as occupation, income and social class.
## Comparison with other reviews
To the best of our knowledge this is the first systematic review and meta-analysis that has been carried out exploring the relationship between polypharmacy and socioeconomic status; focusing on an ageing population irrespective of co-morbidities or drug class. A previous review revealed that there were significant associations between socioeconomic factors, such as education and deprivation, and multimorbidity whereby people of lower socioeconomic status have a higher risk of multimorbidity [71]. This work, unlike our review, did not focus on polypharmacy or older people, but can be used as a possible justification of our findings. Given, older people with multimorbidity are more likely to use more medications, and people of lower SES have higher risk of developing multimorbidity, may help explain – at least in part – some of our findings. However, the interplay between multimorbidity and polypharmacy is likely to be complex and should be the subject of further investigation. For example, those of higher SES may still have high levels of multimorbidity but their social status has the potential influence to ensure that they can better manage their conditions, have access to better healthcare services, reduced waiting time to see healthcare professionals, all factors of which have the potential to influence medication usage. Some of the literature has touched on the aspect of healthcare access [72] and the so called ‘wealth health’ gradient, showcasing that socioeconomic status has a direct influence on healthy ageing. Others have also revealed the potential influence that patients have on the medication that is prescribed to them [73, 74]. Those of higher SES are often better at navigating healthcare systems (both public and private) and are potentially more able to obtain multiple clinical opinions for their concerns resulting in an increased likelihood of being prescribed the medications they want or believe they need. Prosser et al. [ 75] showcases prescribing of medication is often patient mediated, and thus more costly, beneficial treatment may be prescribed to those that are more proactive in their health, often those of higher SES.
Other reviews that have been conducted which investigate treatment adherence and subsequent factors have all revealed that socioeconomic status plays an integral role [76–79]. Whilst these reviews are not primarily focused on polypharmacy or the ageing population, they provide important information on medication usage: treatment adherence. The reviews have shown statistical significance whereby socioeconomic factors, such as lower income, unemployment, and lower education, are associated with medication non-adherence. It can therefore be suggested that non-adherence to medication could play an important role in deteriorating health and has a subsequent effect in the rise of multimorbidity and polypharmacy. It is also worth noting that, although we calculated unadjusted odds ratios for the meta-analysis, many of the studies included in this review did adjust for multimorbidity in their analysis and still yielded statistical significant results—with a higher odds of polypharmacy in older people of lower socioeconomic status.
## Individual socioeconomic factor results
To conceptualise socioeconomic status, this systematic review included studies employing different methods to assess socioeconomic status, including education, income, and employment.
With respect to education status, the overall findings of the review revealed that older people with lower educational backgrounds are of greater odds of polypharmacy. The literature suggests there are several reasons as to why this may be the case. Firstly, individuals with lower levels of education can be viewed as playing a less proactive role in preventive measures to improve/maintain their health, and thus are of greater risks of developing conditions that would likely result in them taking multiple medications [80, 81]. Secondly, some have suggested those of lower education are less likely to challenge healthcare professionals and be less involved with shared decision making [82, 83]. This, therefore, may have the potential for people to take additional medication without requiring a detailed explanation from their healthcare professional [84, 85]. Such patients are also seen to be less concerned in asking key questions regarding their medical care [86], thus it can be questioned whether they are truly aware of the potential additional harm that may be associated with taking multiple medications. However, other researchers have argued that people with lower levels of education may be less likely to approach healthcare professionals for medication and thus inevitably display lower levels of medication usage [87].
Previous work has shown that people entitled to free medications were more likely to display higher levels of polypharmacy [88]. In most instances, unemployed individuals, or individuals with lower income would be entitled to free prescription coverage and as there is no direct cost to the patient, they would be more likely to show higher medication usage. These findings can be used to support our results when assessing employment or income as a marker of socioeconomic status—that is unemployment or low income is associated with more polypharmacy. Out of pocket cost of medication, has a clear influence on the likelihood of individuals not wanting to take more medication. However, this can also be influenced by education attainment, and often people with higher income are more likely to have higher education attainment. As previously discussed, people with higher education attainment maybe more proactive in making decisions about their health and also be aware of the risks associated with polypharmacy.
## Strengths and limitations
This systematic review and meta-analysis showcased comprehensive findings in relation to the association of socioeconomic status on polypharmacy in older people. Whilst our approach was comprehensive and the methodology robust, we do acknowledge that our work has limitations. Firstly, the definition we used to conceptualise older people (≥ 55 years) was arbitrary – the appropriateness of which can be debated. It is important to acknowledge that our approach was in keeping with previous reviews in the field of older people and polypharmacy. For example, the work of Davies et al. was used to help establish our definition of older people; our initial scoping searches also supported using the ≥ 55 years definition, as this conservative approach enabled the inclusion of key literature, and ensured that articles were not excluded for being too broad in their inclusion criteria. Another advantage to this review was that included studies were from a variety of low, middle, and high-income countries. Whilst this is advantageous, it is important to acknowledge that studies undertaken in a variety of healthcare systems have been included, this also contributes to the large heterogeneity observed. In some cases, it was challenging to ascertain how studies assessed different socioeconomic factors; for example, the definition of ‘high’ education varied across studies. To account for these variations and differences, when conducting the meta-analysis, the decision was made to compare the lowest $25\%$ of each population (in terms of SES factor) to the remainder of the population. This approach also ensured that all participants within the included studies were included and factored into the meta-analysis.
## Future work
Whilst this review highlights that there are socioeconomic inequalities in polypharmacy – whereby people of lower SES are more likely to receive polypharmacy, the work does not explore the potential causes of this. It would be useful to understand how people of lower socioeconomic status engage with medication reviews, with such reviews having the potential to aid deprescribing decisions and possibly reduce polypharmacy. Previous work has shown that certain populations (e.g. ethnic minority communities), struggle to engage in medication review services [89, 90]. Whilst this review has demonstrated that there is socioeconomic inequalities in polypharmacy, it is important that policies be put in place to enable healthcare professionals to work towards reducing such inequalities and not exacerbate them further. At present, certain patient demographic (e.g. age) or medication-related factors (e.g. using a high risk medication) may trigger a medication review. Our work suggests that other factors, such as SES, could be used to trigger for medication related review services. Indeed, health inequalities have been at the forefront of healthcare policy formulation for many years, particularly in the UK, especially since the wide-spread appreciation of the existence of a ‘postcode lottery’ [91]. This concept suggests that healthcare standards and subsequently polypharmacy and medication utilisation can be influenced by an individual’s geographic location. For example, people living in the North of England are more likely to use an opioid analgesic, compared to people living the South of England [92]. If factors such as education/poor healthcare literacy play a critical role in polypharmacy it is important for healthcare professionals to understand the needs of their patients and factor these into consultations.
## Conclusion and implications
There are significant socioeconomic inequalities in polypharmacy among older people, whereby people with lower SES have higher odds of being in receipt of polypharmacy. This association was found using a range of markers of SES including education, and social class. Future work could examine the reasons for these inequalities and explore the interplay between polypharmacy and multimorbidity.
## Supplementary Information
Additional file 1. Additional file 2.
## Authors information
Not applicable.
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|
---
title: 'Association between neonatal near miss and infant development: the Ribeirão
Preto and São Luís birth cohorts (BRISA)'
authors:
- Liliana Yanet Gómez Aristizábal
- Paulo Ricardo Higassiaraguti Rocha
- Susana Cararo Confortin
- Vanda Maria Ferreira Simões
- Heloisa Bettiol
- Marco Antonio Barbieri
- Antônio Augusto Moura da Silva
journal: BMC Pediatrics
year: 2023
pmcid: PMC10024445
doi: 10.1186/s12887-023-03897-3
license: CC BY 4.0
---
# Association between neonatal near miss and infant development: the Ribeirão Preto and São Luís birth cohorts (BRISA)
## Abstract
### Aim
To analyze the association between neonatal near miss and infant development at two years.
### Methods
Data from two birth cohorts, one conducted in Ribeirão Preto (RP)/São Paulo and the other in São Luís (SL)/Maranhão, were used. The cognitive, motor and communication development of children was evaluated using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). The following criteria were used for the definition of NNM: birth weight < 1,500 g, 5-min Apgar score < 7, gestational age < 32 weeks, and report of congenital malformations. The relationship between neonatal near miss and development was assessed using the weighted propensity score from the Inverse Probability of Treatment Weighting (IPTW). A directed acyclic graph was built to select the adjustment variables.
### Results
A total of 1,050 mother-newborn dyads were evaluated in SL and 1,840 in RP. Regarding outcomes in SL and RP, respectively, $2.4\%$ and $17.3\%$ of the children were not competent in the cognitive domain, $12.1\%$ and $13.3\%$ in the receptive communication domain, $39.2\%$ and $47.1\%$ in the expressive communication domain, $20.7\%$ and $12.6\%$ in the fine motor domain, and $14.3\%$ and $13.8\%$ in the gross motor domain. The prevalence of neonatal near miss was $5.4\%$ in SL and $4.3\%$ in RP. Unadjusted analysis showed an association of neonatal near miss with fine motor development in SL and RP and with the cognitive, receptive communication, expressive communication, and gross motor domains only in RP. These associations remained after adjusted analysis.
### Conclusion
Neonatal near miss is a risk factor for developmental delays.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12887-023-03897-3.
## Introduction
The growth and development of children are understood as multifactorial constructs that are part of a continuous process characterized by changes in different domains – motor, cognitive/language and psychosocial – throughout the life cycle [1, 2]. Several factors that can compromise normal development have been identified. These factors are defined as a set of biological or environmental conditions that increase the chance of developmental delays in children. The larger the number of risk factors, the greater may be the developmental impairment [3]. Conditions associated with poverty such as reduced food intake and consumption of goods and services, as well as inadequate psychosocial stimulation and unfavorable perinatal conditions, have been indicated as risk factors for development [1].
According to the literature, in the presence of risk factors such as shorter gestational age, low birth weight, and unfavorable socioeconomic condition, the likelihood of a child having motor, psychological and cognitive delays is even greater [4]. Some of these factors are part of the criteria used to define neonatal near miss (NNM), which is a set of morbid events that almost result in the death of the newborn within the first 28 days of life. Although there is no consensus on the standard definition of NNM, some pragmatic criteria such as birth weight, gestational age, 5-min Apgar score, and congenital malformations have been used for its definition [5–7]. The advantage of using this indicator is that the number of survivors who suffered from serious illnesses can be three to six times greater than the number of deaths. Based on the concept of NNM, indicators that express the newborn’s risk (severe morbidity and mortality) can also be estimated, supporting both the calculation of resources required by health services and the assessment of the quality of the provided healthcare.
Improvements in neonatal care have led to an increase in infant survival and a reduction in perinatal and neonatal mortality rates. Nevertheless, there is concern that surviving babies may have a greater risk of long-term morbidity and of exhibiting delays in different developmental domains [8]. Within this context, studies have analyzed different predictors of child development such as birth weight, premature birth, intrauterine growth restriction, and 5- and 10-min Apgar scores. Important results were obtained that revealed changes in different behavioral domains in most cases, such as motor, cognitive and language behavior [8–11].
To our knowledge, there are no studies that evaluated the infant development of children classified as NNM. Therefore, the aim of the present study was to analyze the association between NNM and infant development. Furthermore, considering that the factors associated with NNM differ between the regions of Brazil, a fact that may lead to the formation of NNM groups with different characteristics depending on geographic location, we also investigated the occurrence of NNM in two Brazilian cities located in the northeastern and southeastern regions with different socioeconomic and demographic characteristics.
## Methods
This cohort study is part of the project entitled “Etiological factors of preterm birth and consequences of perinatal factors for child health: birth cohorts in two Brazilian cities (BRISA)”. Details of the method and baseline sampling of these studies have been published previously [12] and will be explained briefly.
Two birth cohorts were started in 2010, one in Ribeirão Preto (RP)/São Paulo and the other in São Luís (SL)/Maranhão. Each cohort consisted of two components: the first was started during prenatal care and the other at birth [12]. For the present study, the population-based component at birth was used.
In RP, all infants born from January to December 2010 to mothers residing in the city were included, with 7,752 ($95.7\%$) live births in the city that year. The 821 children who were part of the prenatal component of the study and who were born in 2010 are included in the analysis since they are also part of the birth cohort of that year. The first follow-up occurred from 2011 to 2013 and included 3,758 children in the second and third year of life [13]. All children of low or high birth weight (> 4.250 g) and one in three children of normal weight were invited to participate, which resulted in 3,758 children in the birth cohort assessed in RP from 12 to 36 months of age.
In SL, a systematic sample of $\frac{1}{3}$ of the births that occurred in 2010 per maternity hospital was evaluated, corresponding to 5,166 ($89.8\%$) live births. In $\frac{2013}{2012}$, an attempt was made to follow up the entire original cohort; 3,308 children evaluated in the second year of life were followed up [13].
## Outcome variable
Infant development was evaluated in the cognitive, motor (fine and gross), and communication (expressive and receptive) domains using Bayley Scale of Infant and Toddler Development Third Edition—screening (Bayley-III screening)), validated for children aged 1 to 42 months [14]. The instrument consists of 136 questions divided into five subscales: cognitive (sensorimotor development, exploration and manipulation of familiar objects, concept formation, memory, and other cognitive aspects); receptive communication (preverbal behaviors, development of vocabulary, ability to identify objects and images); expressive communication (pre-verbal communication – such as babbling, gesturing and development of vocabulary, naming objects and images—and morphosymptomatic development – use of two words, plural, verb tense); fine motor (prehension, perceptual-motor integration, motor planning, motor speed); gross motor (dynamic movement – locomotion, coordination, balance and motor planning – and static posture).
For evaluation, the age of preterm infants was corrected by subtracting from the chronological age of follow-up the number of weeks up to the gestational age of 40 weeks. Performance on the subscales was analyzed based on the cut-off point for age established by the scale itself as Competent, Emergent and Risk. In the present study the classifications were analyzed dichotomously as competent and emergent/risk.
## Exposure variable (neonatal near miss)
The classification proposed by Silva et al. [ 5] was used for the definition of NNM, which considers the presence of one or more of the following conditions as a criterion: birth weight < 1,500 g, 5-min Apgar score < 7, gestational age < 32 weeks, report of congenital malformations, and use of mechanical ventilation. The last parameter was not considered in the present study because of the lack of information in the cohorts. The criteria proposed by Silva et al. [ 5] were validated by Kale [15] in the absence of mechanical ventilation as a pragmatic criterion and showed a good response for the classification of NNM, thus supporting the choice of the present study to use only four of the five suggested indicators.
In both cohorts, birth weight and 5-min Apgar score were collected from the registry book and medical records at the maternity hospitals. Information on newborn malformation was collected by interview with the mothers within the first 24 h after delivery. Gestational age was calculated using two criteria: the date of the last menstrual period reported by the mother or an algorithm based on the date of the last menstrual period and obstetric ultrasound when available.
## Adjustment variables
The variables were obtained using validated and standardized questionnaires applied to mothers within the first 24 h after delivery. The following adjustment variables were evaluated: gender (male and female), maternal age (< 20, 20–34, and ≥ 35 years), maternal skin color (white, brown/mulatto/cabocla/brunette, and black), maternal education level (< 8, 9 to 11, and ≥ 12 years of schooling), and socioeconomic class (A/B, C, and D/E). The last variable was evaluated according to the Brazilian Economic Classification Criteria of the Brazilian Association of Research Companies (with classes AB being the most privileged and DE the least privileged) [16]. We also evaluated the type of delivery (cesarean or normal), the number of children (continuous – 1, 2, 3, 4, 5, 6 childrens), gestational hypertension (reported by the mother as yes or no), gestational diabetes (reported by the mother as yes or no), alcohol consumption during pregnancy (yes or no), and smoking during pregnancy (yes, if at least one cigarette per day, or no).
## Data analysis
A directed acyclic graph was created using the DAGitty program (Fig. 1) in order to identify a minimum set of adjustment variables. DAG is a visual and qualitative tool for selecting confounding variables identified from a theoretical causal model. The arrowheads inform a causal path between two variables, and it is possible, through pre-established rules, to identify a minimum set of variables for adjustment 12. After application of DAG’s rules, the minimum adjustment set of variables for analysis were selected: child sex, maternal age, maternal education level, socioeconomic class, parity, type of delivery, gestational hypertension, gestational diabetes, and alcohol and tobacco consumption during pregnancy. Fig. 1Directed acyclic graph (DAG) between near miss neonatal and infant development at two years Given the occurrence of losses to follow-up, all variables were compared between children who were seen in the second follow-up and those who were not using the chi-squared test. Weighting of the sample was thus performed by calculating the probability that the child would attend the second follow-up appointment using a logistic regression model. The inverse probability was then calculated in order to minimize spurious associations resulting from sample losses and was used for weighting the model estimates. Descriptive statistics with calculation of absolute frequency, percentage, mean, and standard deviation were used.
The relationship between NNM and the developmental domains (cognitive, expressive communication, receptive communication, and fine and gross motor) was assessed using propensity scores obtained by Inverse Probability of Treatment Weighting (IPTW). Statistical analysis was performed using the R 4.0.3 program (The R Foundation for Statistical Computing).
## Ethical aspects
All procedures were approved by the Ethics Committees of the University Hospital of the Federal University of Maranhão (UFMA), São Luís (protocol number $\frac{223}{2009}$) and of the Ribeirão Preto Medical School, University of São Paulo, Ribeirão Preto (protocol number 4.771⁄2008–30). All participants were invited and were only included in the present study after the free informed consent form was properly filled out and signed by the participant or legal representative.
## Results
A total of 1,050 mother-newborn dyads were evaluated in SL and 1,840 in RP. Regarding outcomes, $22.4\%$ (SL) and $17.3\%$ (RP) children were not competent in the cognitive domain, $12.1\%$ (SL) and $13.3\%$ (RP) were not competent in the receptive communication domain, $39.2\%$ (SL) and $47.1\%$ (RP) were not competent in the expressive communication domain, $20.7\%$ (SL) and $12.6\%$ (RP) were not competent in the fine motor domain, and $14.3\%$ (SL) and $13.8\%$ (RP) were not competent in the gross motor domain. The prevalence of NNM was $5.4\%$ in SL and $4.3\%$ in RP.
Table 1 shows the characteristics of the children. There was a predominance of boys ($52.9\%$ in SL and $50.2\%$ in RP). The mean birth weight was 3,133.0 g (± 603.9) and 3,070.9 g (± 587.1) in SL and RP, respectively. Table 1Characterization of the study population. Ribeirão Preto and São Luís birth cohorts (BRISA), Brazil, 2010-2012VariableSão LuísRibeirão PretonMean ± SDnMean ± SDMaternal age (years)25.2 ± 6.127.0 ± 6. 2Gestational age (weeks)38.0 ± 2.838.4 ± 2.4Birth weight (g)3,133.0 ± 603.93,070.9 ± 587.1Child age (months)17.44 ± 3.6520.62 ± 4.42Number of children1.7 ± 1.0-n%n%Neonatal near miss Yes575.4794.3 No99394.61,76195.7Low birth weight Yes151.4362.0 No1,03598.61,80498.0Gestational age <32 weeks Yes323.1432.3 No1,01897.01,79797.7Apgar score <5 Yes151.4150.8 No1,03598.61,82299.2Congenital malformation Yes70.7211.1 No1,04399.31,81998.9Child sex Female49547.191749.8 Male55552.992350.2Type of delivery Cesarean49547.11,03356.1 Normal55552.980743.9Cognitive Competent81577.61,54482.7 Not competent23522.432217.3Receptive communication Competent92387.91,61786.7 Not competent12712.124913.3Expressive communication Competent63860.898752.9 Not competent41239.287947.1Fine motor Competent23379.31,63187.4 Not competent21720.723512.6Gross motor Competent90885.71,60986.2 Not competent15214.325713.8Maternal skin color White17817.01,05957.6 Brown/mulatto/cabocla/brunette71067.658731.9 Black16215.419410.5Maternal educational level <8 years23722.640522.0 9 to 11 years67163.91,09259.4 >12 years14213.534318.6Maternal age <20 years20619.622312.1 20 to 34 years73970.41,33172.3 ≥35 years10510.02861.5Socioeconomic class A/B15414.774942.9 C62959.986849.7 D/E26725.41297.4Gestational hypertension Yes18617.726514.4 No86482.31,57585.6Gestational diabetes Yes282.61166.3 No1,03297.41,72493.7Alcohol use during pregnancy Yes14113.444324.1 No90986.61,39775.9Smoking during pregnancy Yes292.821311.6 No1,02197.21,62788.4 With respect to maternal characteristics, $67.6\%$ of mothers in SL had a brown/mulatto/cabocla/brunette skin color and $57.6\%$ of mothers in RP were white. Most mothers had 9 to 11 years of schooling ($63.9\%$ in SL and $59.4\%$ in RP), ranged in age from 20 to 34 years ($70.4\%$ in SL and $72.3\%$ in RP), and belonged to socioeconomic class C ($59.9\%$ in SL and $49.7\%$ in RP). Regarding the type of delivery, $47.1\%$ (SL) and $56.1\%$ (RP) had a cesarean section. Gestational hypertension was observed in $17.7\%$ of women in SL and $14.4\%$ in RP. Only $2.6\%$ and $6.3\%$ of mothers in SL and RP had gestational diabetes, respectively. The prevalence of alcohol consumption during pregnancy was $13.4\%$ in SL and $24.1\%$ in RP and that of smoking during pregnancy was $2.8\%$ in SL and $11.6\%$ in RP. The mean maternal age was 25.2 years (± 6.1) and 27.0 years (± 6.2) in SL and RP, respectively; the mean gestational age was 38.0 months (± 2.8) and 38.4 (± 2.4) months and the mean number of children was 1.7 (± 1.0).
Table 2 shows the results of unadjusted and adjusted analyses of the association between NNM and the outcomes. In unadjusted analysis, an association was observed between NNM and fine motor development in SL and RP. Neonatal near miss was associated with the cognitive, receptive communication, expressive communication, and gross motor domains only in RP. These associations remained in the adjusted analysis. Children with NNM from SL and RP were, respectively, 1.87 (OR: 1.87; $95\%$CI: 1.01 – 3.46) and 2.74 (OR: 2.74; $95\%$CI: 1.47 – 5.12) times more likely to be not competent in the fine motor domain when compared to competent children. Table 2Effect of neonatal near miss on infant development at two years. Ribeirão Preto and São Luís birth cohorts (BRISA), Brazil, 2010–2012São LuísRibeirão PretoUnadjusted analysisAdjusted analysis*Unadjusted analysisAdjusted analysis*OR ($95\%$CI)OR ($95\%$CI)OR ($95\%$CI)OR ($95\%$CI)Cognitive Near miss No1111 Yes1.70 (0.94—3.09)1.71 (0.92—3.20)3.94 (2.48—6.27)3.00 (1.68—5.35)Receptive communication Near miss No1111 Yes1.09 (0.50—2.40)1.40 (0.62—3.17)3.59 (2.21—5.85)3.10 (1.69—5.69)Expressive communication Near miss No1111 Yes1.20 (0.68—2.06)1.40 (0.64—2.05)2.37 (1.47—3.83)3.10 (1.53—4.91)Gross motor Near miss No1111 Yes1.47 (0.75—2.87)1.48 (0.72—3.03)4.74 (2.96—7.59)5.43 (3.04—9.69)Fine motor Near miss No1111 Yes1.99 (1.11—3.57)1.87 (1.01—3.46)3.01 (1.81—5.00)2.74 (1.47- 5.12)*Adjusted for socioeconomic class, maternal education level, maternal age, gestational hypertension, gestational diabetes, parity, child sex, type of delivery, alcohol use during pregnancy, and smoking during pregnancy Regarding the other domains that were associated with NNM only RP, children with the condition were approximately 3 times more likely to be not competent in the cognitive (OR: 3.00; $95\%$CI: 1.68 – 5.35), receptive communication (OR: 3.10; $95\%$CI: 1.69 – 5.69) and expressive communication (OR: 3.10; $95\%$CI: 1.53 – 4.91) domains, and 5 times more likely to be not competent in the gross motor domain (OR: 5.43; $95\%$CI: 3.04 – 96.9) when compared to their peers (Table 2).
## Discussion
In RP, the NNM group exhibited delays in all development scales compared to the non-NNM group. On the other hand, in SL, children in the NNM group showed worse performance than their peers only in fine motor development. These results suggest that the association of NNM morbidity with developmental delays in childhood may vary depending on the characteristics of the studied location.
The association between NNM and low performance in the developmental indicators observed in RP reinforces evidence showing significant delays in the development of very low birth weight (< 1,500 g) and preterm children (gestational age < 32 weeks) [17], as well as children with a 5-min Apgar score < 7 [10] and with congenital malformations [18]. Theanatomical alterations observed in children born with these characteristics, such as reduced whole brain volume [19], insular and temporal lobe and gray matter reductions [20], morphological alterations accompanied by reduced gray and white matter complexity [21], hippocampal, thalamus and cerebellar volume reductions [21–23], and altered functional connectivity in the brain [24], have been associated with behavioral delays. Nevertheless, deficits in the development of children with NNM cannot be explained solely by these structural alterations since stressful events experienced by this population early in life, for example, being more frequently submitted to medical interventions, can affect the organization of the central nervous system and can cause important physiological and behavioral changes in the child [25].
The discrepancy in the association between NNM and development observed between RP and SL can be explained in part by differences in the characteristics of the NNM groups between the two cities. Although the present results corroborate those of previous studies that did not find disparities in the frequency of NNM between different regions of Brazil [5], the factors associated with the occurrence of NNM can vary according to region [6, 26]. Rocha et al. [ 2022] showed that a low maternal educational level, living without a companion, gestational hypertension, smoking during pregnancy, and cesarean delivery were associated with NNM in RP but not in SL [27]. Thus, the particularities of each location tend to lead to the formation of NNM groups with different characteristics, a fact that would explain different outcomes in the developmental domains.
Exposure to alcohol and/or tobacco during pregnancy [28–30], cesarean delivery [27], high blood pressure [31], and gestational diabetes [32] are generally associated with long-term developmental delays [28]. Furthermore, cesarean delivery has been associated with changes in the intestinal microbiota, which plays an important role in child development and behavior [33–35].
In SL, an association between NNM and developmental delays was only observed for fine motor skills. These data suggest that, despite the adverse conditions experienced during the fetal period and at birth, children in the NNM group are able to recover and achieve expected patterns of typical development in the second year. On the other hand, the occurrence of typical behaviors within the expected time window in NNM children may have been due to the non-linearity and temporal variability that frequently characterize the development of children exposed to perinatal risk factors [21, 36]. However, it should be noted that, for all domains evaluated in the present study, the percentage of children classified as at risk of not being competent was higher in the NNM group. These results highlight the importance of following up these children over time, as studies have reported an increase in the difficulties of children born with adverse conditions, especially at school age.
Some limitations of this study must be mentioned, such as the use of the shortened version of the Bayley Scales for assessing infant development. However, this version allows to screen for possible developmental delays in studies involving a large number of participants. Another limitation is the difference in the mean age at which the children were evaluated in the two cities. However, in order to minimize the effect of age, we decided to use age-specific cut-off points for classification.
As strengths of this study, we mention the fact that the two birth cohorts were started in the same year and were followed up in two cities with contrasting socioeconomic and demographic characteristics. In addition, we highlight the originality of the present study that evaluated the development of NNM children in cohorts conducted in cities located in different regions of Brazil using the same methodology. Largen numbers in study is also stregth of the study.
In conclusion, NNM was associated with cognitive, motor and language delays in the second year of life in RP. However, in SL, a difference between the NNM and non-NNM groups was only observed for fine motor skills. These results suggest that, although the criteria used for the definition of NNM are known risk factors for child development, the characteristics of the groups resulting from the social and cultural differences between the cities studied seem to influence the relationship between NNM and infant development in the second year of life.
## Supplementary Information
Additional file 1.
## Authors’ information
LYGA. PhD, postdoctoral fellow at the Postgraduate Program in Public Health at the Federal University of Maranhão (UFMA). Has experience in Public Health, with emphasis on Epidemiology, working mainly on the following topics: people with disabilities, knowledge management, information and health of women and children, breastfeeding, violence against women and depression during and after pregnancy, sexual and reproductive health of migrant women.
PRHR. PhD, Postdoctoral fellow at the Faculty of Medicine of Ribeirão Preto, University of São Paulo (FMRPUSP). PhD in Child and Adolescent Health from the University of São Paulo. Master's in Human Development and Technologies from the Paulista State University. Graduation in Physical Education from Paulista State University. Research in Physical Education and Epidemiology, with emphasis on Motor Development and Motor Learning.
SCC. PhD, postdoctoral fellow at the Postgraduate Program in Public Health at the Federal University of Maranhão. Member of the Epifloripa research group and the Public Health Research Group at the Federal University of Maranhão. She works in research in Physical Education and Public Health, with an emphasis on Epidemiology and in the areas of Life Cycle, Sarcopenia and Health-related Physical Activity.
HB. PhD, associate Professor at the Department of Childcare and Pediatrics at FMRP-USP. She has experience in the field of Medicine, with an emphasis on Maternal and Child Health. His main areas of interest are: human growth, perinatal epidemiology, infant mortality, and the developmental origin of health and disease. She is involved in three birth cohort studies carried out in the last 40 years in Ribeirão Preto, SP, and São Luís, MA.
VMFS. PhD, professor at the Department of Public Health and the Postgraduate Program in Public Health at the Federal University of Maranhão. Has experience in Pediatrics, Collective Health and Maternal and Child Health, working mainly on the following topics: growth and development, epidemiological studies, cohort studies and chronic non-communicable diseases, child and adolescent health, newborns at risk, preterm birth, low birth weight, infant mortality.
MAB. Titular Professor of Pediatrics and coordinator of the Center for the Study of Child and Adolescent Health (NESCA) in the Department of Childcare and Pediatrics of the Faculty of Medicine of Ribeirão Preto, University of São Paulo. He has been involved in cohort studies carried out in Ribeirão Preto, SP, and São Luís, MA. His main areas of interest are: human growth, perinatal epidemiology, infant mortality, and the developmental origin of health and disease.
AAMS. PhD, professor at the Department of Public Health and the Postgraduate Program in Public Health at the Federal University of Maranhão. Has experience in Public Health, with emphasis on Epidemiology, working mainly on the following topics: low birth weight, infant mortality, prematurity, cesarean section and obesity. Participates in 5 birth cohort studies in Brazil and a cohort study of children with Congenital Zika Syndrome.
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|
---
title: Walkability and urban built environments—a systematic review of health impact
assessments (HIA)
authors:
- Joachim Westenhöfer
- Elham Nouri
- Merle Linn Reschke
- Fabian Seebach
- Johanna Buchcik
journal: BMC Public Health
year: 2023
pmcid: PMC10024446
doi: 10.1186/s12889-023-15394-4
license: CC BY 4.0
---
# Walkability and urban built environments—a systematic review of health impact assessments (HIA)
## Abstract
### Background
Urban environments are important determinants of human health. The term walkability summarizes features of the urban built environment that promote walking and other types of physical activity. While the beneficial effects of active and public transport have been well established, the health impact of other features of walkability are less well documented.
### Methods
We conducted a systematic review of health impact assessments (HIAs) of walkability. Studies were identified through PUBMED and Science Direct, from two German websites related to urban health and reference tracking. Finally, 40 studies were included in the present review. We applied qualitative thematic analysis to summarize the major results from these studies.
### Results
Most of the HIAs ($$n = 31$$) reported the improvement of health or health behaviour resulting from an investigated project or policy. However, three HIAs reported a lack of improvement or even a decrease of health status. In parallel, 13 HIAs reported a gain in economic value, whereas one reported a lack or loss of economic effects. Moreover, three HIAs reported on social effects and six HIAs gave additional recommendations for policies or the implementation of projects or HIAs.
### Conclusions
Most HIAs investigate the impact of increasing active or public transport. Other features of walkability are less well studied. With few exceptions, HIAs document beneficial impacts of improving walkability on a variety of health outcomes, including reductions of mortality and non-communicable diseases.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-023-15394-4.
## Background
Human health is influenced by a variety of determinants including factors related to the environment where people live [1]. Urbanization has become one of the global megatrends that characterize the current development of mankind. At the beginning of the 20th century, only about 10 percent of the world’s population were living in urban areas. In 2015, this percentage increased to about $54\%$, and is predicted to reach $60\%$ in 2030 and $66\%$ by 2050 [2].
Urbanization more often offers health advantages in comparison with rural areas, as the basic infrastructure relevant for health such as water, sanitation and housing are generally more developed. In addition, health services and facilities appear to be more available in cities than in rural areas. However, cities may also cause relative disadvantages for health, e.g. crowded living and stressing working conditions, higher rates of crime and violence, sedentary life styles, reduced physical activity, and, additionally, the urban food environment may contribute to the rise of non-communicable diseases [2].
However, many decisions that impact human health are made outside the health sector [3, 4]. For example, environmental changes resulting from the intensification of agriculture, industrialization and increasing energy use are considered as important sources of health problems [5]. Decisions about the quality of social services, housing, employment opportunities or public transport are among many others key influences on health [6], and are again usually made outside the health sector. This has led the WHO to extend the ideas of healthy public policy, already formulated in the Ottawa Charta of Health Promotion to the principle of “Health in All Policies” [7]. Yet, considering not only reduction of health risks, but also enhancing health promoting potentials in urban development and urban planning seems not to be implemented systematically on a large scale [6].
Health Impact Assessment (HIA) is an approach to bring health considerations into urban development and urban planning. HIA has been defined as “… a combination of procedures, methods and tools by which a policy, program or project may be judged as to its potential effects on the health of a population, and the distribution of those effects within the population” [4]. One aim is to produce recommendations for decision makers and stakeholders for “… maximizing the proposal’s positive health effects and minimizing its negative health effects” [8]. HIAs have been conducted in all regions of the world, and the majority of HIA practitioners expect an increased use in Australia, East Asia and Pacific, Europe and North America [9].
The impact of urban environment on physical activity has received some consideration during the last decades. Globally, physical inactivity has been accounted for the fourth leading cause of mortality, after high blood pressure, tobacco use and high blood glucose, contributing to 6 percent of worldwide deaths [10]. The Global Burden of Disease study is using a sophisticated hierarchical model of risk factors including physical inactivity as a level 2 risk factor [11]. Recently, results from this study indicated that, globally seen, close to 1 million deaths in 2019 were attributable to physical inactivity [11]. In addition, physical inactivity is considered as a major risk factor for non-communicable disease, particularly cardio-vascular diseases, diabetes mellitus type 2, and several types of cancer [12]. Moreover, physical activity contributes to the maintenance of healthy weight and to the prevention of overweight and obesity [12] which in turn is a major risk factor for the mentioned NCDs [13]. On the other hand, the beneficial effects of physical activity on all-cause mortality [14–16], the incidence of cardiovascular health, diabetes, several types of cancer [17], and mental health [18, 19] have been well documented.
In relation to physical activity two concepts for the urban environment have received considerable attention and generated some research: active transport and walkability. Active transport comprises walking or cycling for the purpose of reaching a destination such as school, workplace, or a shop [20]. Walkability summarizes attributes of the urban built environment that encourage and/or enable more walking [21–23]. The original concept of walkability was developed in the 1990s in US transportation research and has focused on walking for transportation [24]. As this concept was adopted by physical activity and public health researchers and practitioners, it was extended to include walking for transportation and recreational purposes as well as other types of physical activity, e.g. biking [24]. Hence, walkability has extended beyond walking to generally promoting physical activity in communities, urban neighbourhoods and larger urban areas [25]. A review [23] concludes that there is sufficient evidence that the proximity to potential destinations, aesthetic qualities – the attractiveness of the environment -, mixed land use, residential density within neighbourhoods, sidewalks and connectivity are attributes of the built environment that correlate with increased walking. Recently, a more comprehensive framework of walkability has been suggested [26, 27] that incorporates nine dimensions of the built environment, namely connectivity, diversity of land-use, residential density, traffic safety, surveillance (how well traveling in the street can be seen from surrounding houses and businesses), parking (less parking encourages more walking), experience (including e.g. aesthetics), greenspace and community (social interaction and participation).
The health impacts of active transport have been intensively studied and a systematic review provides strong evidence that active transport provides substantial net health benefits even if negative health impacts like accidents and exposure to air pollution are taken into account [14, 20].
To the best of our knowledge up to now no systematic review is available to summarize the evidence on the health impacts of walkability conceptualized as detailed above as characteristics of the urban built environment. We hypothesize that the walkability of urban environments may affect health outcomes via several pathways (see Fig. 1): walkability may result in more physical activity either by improving active transport or by encouraging recreational activities including deliberate exercising. In addition, green space improves health by encouraging more physical activity and by other effects, e.g. lower distress and better mental health [28] and the better walkability of the built environment could promote and result in improved social relationships [29] that in turn impact human health. Fig. 1Potential major pathways of the health impact of walkability, own presentation The objective of this paper is to conduct and report on a systematic review of health impact assessments (HIAs) of projects, policies or programmes that aim to improve the walkability of urban built environments.
Particularly, we aim at answering the following research questions related to such HIAs:Which types of projects, policies or programmes related to walkability in urban development have been investigated in HIAs?Which methods were used to assess the health impacts? *Which data* sources were used, and which analytical models were applied to assess health impacts?Which health impacts (e.g. changes in mortality, incidence or prevalence of diseases, quality of life) have been identified related to improvements of walkability?How and by whom are HIAs of walkability implemented in practice?
## Methods
This systematic review was designed based on the PRISMA 2020 guidelines [30]. We considered the following definition of walkability as the dividing line for identification of eligible articles in the current systematic review: Walkability summarizes attributes of the urban built environment that encourage and/or enable more walking or other types of physical activity in communities, urban neighbourhoods and larger urban areas.
In order to include a wide range of studies, no specific preference for a definition of Health Impact Assessment was considered.
## Data sources and search strategy
We searched PubMed and ScienceDirect databases with the purpose of incorporating international studies as well as the websites of two German associations “Stadt-und-Gesundheit” (City and Health; http://stadt-und-gesundheit.de) and “Akademie für Raumentwicklung in der Leibniz-Gemeinschaft” (ARL – Academy for Territorial Development in the Leibniz Association; https://www.arl-net.de) to identify research reports focusing on spatial planning particularly in the German context. The databases were searched thoroughly in November 2020, and an additional search of PubMed and Science Direct was also conducted in the end of 2021 to update the study pool. We operated the advanced search in PubMed and ScienceDirect using the search terms in Table 1. The two German databases did not offer an advanced search tool. Therefore, an adoption of the strategy for these two sources was necessary. This involved a title screening of all listed papers and reports. We included all papers that mentioned “Health Impact Assessment” (the German equivalent term “Gesundheitsfolgenabschätzung” respectively) or “Walkability” in the title. As the term Walkability was rarely used in the titles of the reports on the two German databases, we accepted the German terms for mobility/mobile, physical activity, walking (distance), transport (ation), walkable, and pedestrian as potential equivalents. For the same reason, we accepted papers with a title suggesting a health outcome related to walkability (e.g. increased walking). The details of identified articles and search terms for each of databases are provided in the Table 1. In addition, reference lists of papers included during the selection process were screened for additional papers that might be relevant. Table 1Search strategy and identified articles in each databaseDatabaseSearch termsHitsPubMed(Health Impact Assessment) AND (Walkability)24ScienceDirect"Health Impact Assessment" AND ‘’Walkability’’287Akademie für Raumentwicklung in der Leibniz-GemeinschaftTitles suggesting assessment of health impacts of walkability451Stadt-und-GesundheitTitles suggesting assessment of health impacts of walkability87
## Inclusion and exclusion criteria
Regarding selection criteria, any peer-reviewed publications evaluating a real or projected (modelled) health impact or health outcomes of a policy, programme or project that intended to change an aspect of walkability in an urban environment were eligible for the review, with the exception of short communications, published abstracts and conference contributions. Furthermore, the eligible articles were published in English or German and after the year 2010. We decided not to include grey literature because we believe that the peer-review process provides an important quality assurance and therefor enhances the credibility of the research findings. In addition, identification of relevant articles via databases of the peer-reviewed scientific literature seems more transparent and replicable than a comprehensive open search using a wide variety of search engines. Details of inclusion and exclusion criteria are available in Table 2.Table 2Inclusion and exclusion criteria for article selectionInclusion criteriaExclusion criteriaSubject:Subject:• Real or projected (modelled) health outcomes (e.g. mortality, incidence or prevalence of diseases) of º a policy, programme, or project that º intended to improve or actually improved a feature of the built environment related to walkability º within an urban area• Studies reporting only on the association between features of the built environment and health outcomes• Studies reporting on health outcomes or health impact of changes of active transport (AT) or physical activity (PA) in general without reference to specific changes of the built environment that could effect such changes of AT or PA• Studies addressing changes in specific settings (e.g. clinical setting as a hospital, school, or kindergarden) but not generally in an urban geographic areaType of publication:Type of publication:• Original publications (journal articles)• Book chapters• Review articles• Editorials, short communications, abstracts, and conference contributionsLanguage:Language:• English• German• Any language other than English or GermanYear of publication:Year of publication:• 2010—2021• Publication before 2010
## Screening, data extraction and analysis
Title screening was done by one reviewer. In case of any uncertainty, the decision was made after discussion with another author. For the remaining records, abstract screening was done independently by two reviewers. Any disagreement between the reviewers was resolved by discussion, in some rare cases by including additional authors of the present paper.
The final selection step was the review of the full text of remaining articles by two authors. Every step of the review process was discussed during regular meetings in order to clarify uncertainties and challenges related to selection process. In case of disagreement of authors on the eligibility of articles, a third reviewer conducted an additional review, and the final selection was approved by discussion.
During data extraction stage, two authors independently reviewed the full text and tabulated the extracted data. The third reviewer extracted the data of articles that were the subject of disagreement of the first two reviewers. Extracted data included author, year of publication, HIA definition and method that was used, operationalization of walkability (if any), which NCDs were considered in HIA, aim, setting, study population of the project, policy or program, dependent and independent variables, measuring instruments, statistical/analytical methods applied, and results. In addition, conductive conditions and resources, barriers and challenges, and recommendations were extracted, if mentioned. Regarding the HIA itself, we extracted, if mentioned, who initiated the HIA, who conducted the HIA, other actors involved in the HIA, and how HIA was integrated into existing planning instruments or processes. The extracted data were captured in an Excel-Sheet that is available as supplemental material.
We conducted a qualitative analysis and summary of the extracted data to answer our research questions. The development of categories and classification of study reports was done following the principles of inductive thematic analysis [31, 32], and was done in consensus of three authors (JW, JB, EN).
## Identification and selection of relevant studies
As shown in Fig. 2, a total of 946 records was identified. Database search resulted in 817 records. Additional 129 records were identified through reference tracking. 21 duplicate records were removed before title and abstract screening. Title screening excluded 682 records; abstract screening excluded additional 133 records. Since 8 reports could not be retrieved, the full text of 102 reports were assessed according to inclusion and exclusion criteria. 62 reports were excluded, most often because they did not address the effect of a policy, programme, or project on walkability (see Fig. 2). Finally, 40 study reports were included in the present review. Fig. 2Prisma flow chart of study identification and selection An overview of the included HIA reports is presented in Table 3. The complete data extraction sheet is available as supplementary material (Additional File 2).Table 3Source, place, aim of project, main results of included HIAsAuthor(s), YearPlace of ProjectAim of ProjectResultsAgarwal et al. 2021 [33]Patna, IndiaThe study quantifies the health benefits (reduction in mortalities) of a bicycle superhighway (BSH) in Patna, India1. A bicycle superhighway increases the share of bicycles from $32.3\%$ to $48.7\%$2. A significant rise in the number of cyclists which is a result of better bicycle infrastructure3. An increase in longer bicycle trips for higher-income groups4. For the whole population, the average cycling duration has increased by $48\%$ as a result of bicycle superhighway5. 755 lives per year can be saved or 1640 deaths prevented/ million person as the result of increase duration of cycling6. The monetized benefits turn out to 12.25 billion Indian rupee of saving by preventing 755 deaths annuallyAndersen et al. 2017 [34]Haraldsgade district, Copenhagen, DenmarkA multicomponent urban renewal project of approximately 35 million Euros including renovation of public housing and courtyards; adding streetlights; renovation or establishment of new urban green spaces, play grounds, and sport facilities; and opening of two civic centers offering social gatherings and sport activitiesAdolescents spent more time in the district and in PA in the district in 2012, after the urban renewal projectBadland et al. 2017 [35]Metropolitan Melbourne, Australia1) Identifying spatial walking-related urban planning policies used in selected Australian states and territories;2) creating spatial measures based on these policies; and3) examining which, if any, are associated with transport walking in an urban contextDwelling density and daily destinations predict walking tripsAssociation becomes stronger for larger neighbourhood areas: walking is influenced by attributes outside the immediate neighbourhoodBias and Abildso 2017 [36]Fairmont, West Virginia, USAAim of project:*Creating a* comprehensive bicycle and pedestrian “connectivity plan”Aim of HIA:The original HIA intended to capture broad feedback from the public around barriers to connectivity and physical activity including physical environment, safety, crime, etcAim of study:This study evaluates policy outcomes and other effects related to HIA after 21 months of the adoption of the HIA and Connectivity Plan1. Seven of the eleven specific recommendations can be tracked to specific outcomes in just over a year and a half after adoption of the HIA report in Fairmont2. The city is considering the creation of a Pedestrian Safety Board to further investigate recommendations around safe active commuting3. There are tentative plans to use the connectivity plan and HIA as the basis of an application for TIGER (Transportation Investment Generating Economic Recovery) fundingBranas et al. 2011 [37]Philadelphia, Pennsylvania, USAA program to clean, green, and maintain abandoned vacant lots in Philadelphia, Pennsylvania. This program involved removing trash and debris, grading the land, planting grass and trees to create a park-like setting, and installing low wooden post-and-rail fences1. Vacant lot greening was associated with consistent reductions in gun assaults and consistent reductions in vandalism2. Vacant lot greening was associated with residents’ reporting less stress and more exercise in select sections of the cityBuekers et al. 2015 [38]Flanders, BelgiumNew bicycle highways Antwerp–Mechelen and Leuven–Brussels, which were built near important traffic axes to provide the densely populated region with an alternative to car use1. Increased PA outweighed air polution and traffic incidents2. The benefit:cost ratio was mainly positiveBuregeya et al. 2020 [39]Quebec, CanadaAnalyzing a HIA tool’s impact on the revitalization of road infrastructure, parks and green spaces, and residential housingHIA acted in synergy with other policies or plans at the local level to foster actions favourable to health. For instance, the city’s active travel plan supported the HIA recommendations for additional cycling infrastructure. Furthermore, technical employees and elected local officials who advanced the inclusion of these recommendations in general gained a certain understanding of how transformed built environments contribute to liveable cities and a sustainable futureChapman et al. 2018 [40]New Plymouth and Hastings, New ZealandNew Zealand’s Model Communities Programme funded cycle paths, other walking and cycling facilities, cycle parking, ‘shared spaces’, media campaigns and events, such as ‘Share the Road’, and cycle-skills training1. Annual benefits for health in the intervention cities were estimated at 34.4 disability-adjusted life years (DALYs) and two lives saved2. The estimated benefit/cost ratio was 11:1 and $151.2 millionCoulson et al. 2011 [41]Dings neighbourhood, Bristol, south-west England, UKA ‘retro-fit’ model was applied, where pre-existing, residential streets were converted, and new features added. Also, a disused railway bed in neighbourhood was turned to a site for a short extension of the National Cycle NetworkSeveral aspects of the neighbourhood were perceived to have improved, including spatial aesthetics, lighting and streetscape planting. However, influence on physical activity was minimal due to safety related concerns, poor public transport provision, local residents’ parking behaviourFrank et al. 2019 [42]Vancouver, CanadaThe Comox *Greenway is* a major active transportation corridor aiming to improve conditions for bicyclists of all ages and abilities1. Participants near the greenway doubled their odds of achieving 20 min daily MVPA2. The odds of being sedentary for more than 9 h halved for nearby residents3. Physical activity benefits declined with increasing 100 to 500 m distance from the greenwayFrank et al. 2022 [43]San Diego county, California, USAPalomar Gateway District redevelopment plan calls for increased housing with an emphasis on multi-family housing; additional non-residential development with a more compact urban form (i.e. multi-story buildings with less surface parking) than current development; upgrading the ridership capacity and accessibility of the light rail service along with other local bus improvements; and expansion of current pedestrian and bicycle infrastructure1. Project increases physical activity from walking for transportation, park visitation, and reductions in type 2 diabetes and high blood pressure2. Potential for increased exposure to air pollution among children and teens3. The implementation of project is associated with a 9.6 percent reduction in type 2 diabetes and a 15.4 percent reduction in high blood pressure, better BMI, and general health status4. Transportation walking increased by $67.9\%$ for adults and $17.5\%$ for children and teens5. Increases in leisure walking, other leisure physical activity, moderate/vigorous PA; decreases in private automobile use, although these improvements were moderate (less than $10\%$)6. Higher park visitation for all ages7. The only health outcomes that worsened were asthma (a $10.9\%$ increase among children and a $17.8\%$ increase among teens) and the pedestrian/bicycle risk index ($2.0\%$ increase in risk)Goodman et al. 2014 [44]Cardiff, Kenilworth, Southampton,UKConnect2 initiative was established with the intention of building or improving walking and cycling routes at 79 sites across the United Kingdom1. Living nearer the infrastructure did not predict changes in activity levels at 1-year follow-up but did predict increases in activity at 2 years relative to those living farther away (15.3 additional minutes/week walking and cycling per km nearer; 12.5 additional minutes/week of total physical activity)2. The effects were larger among participants without car3. Individuals living near the infrastructure did not compensate for their increased walking and cycling by reducing their participation in other types of physical activityGotschi 2011 [45]Portland, Oregon, USATo assess how costs of Portland’s past and planned investments in bicycling relate to health and other benefits (cost/benefit analysis)Benefit–cost ratio for health care and fuel saving: 3.8—.13Benefit–cost ratio for value of statistical lifes: 53.3—20.2Benefit–cost ratio decreasesfor plans with higher investmentsGu et al. 2017 [46]New York, USA1. To evaluate the cost effectiveness of investments in bike lanes using New York City’s (NYC) fiscal year 2015 investment2. To provide a generalizable model, so that localities can estimate their return on bike lane investments1. 45.5 miles of bike lanes NYC constructed in 2015 at a cost of $8 109 511.47 may increase the probability of riding bikes by $9.32\%$2. The incremental cost-effectiveness ratio (ICER) was $1297/QALY gained (is considered very cost-effective)Guo and Gandavarapu 2010 [47]Dane County, Wisconsin, USAHelping public investment decision makers see the greatest return on their built environment investments by developing an analysis framework for identifying the most promising improvement strategies and assessing the attainable return on investment1. An investment of $450 million to make sidewalks available to all Dane County residents was estimated to yield a cost–benefit ratio of 1.87 over a 10-year life cycle2. Workers were likely to drive less and walk/bike more with increasing retail accessibility3. People who lived in neighborhoods with a higher percentage of high-income households drove more and walked less4. Land use mix measured within 1 mile of one’s residence was associated with decreased distance walked/biked5. Increased length of bike lane within $\frac{1}{4}$ mile of an individual’s residence has a significantly positive impact on non-motorized travelHoehner et al. 2012 [48]St. Louis, Missouri, USAPage Avenue project was a redevelopment plan including:1. Building a new grocery store,2. Commercial and mixed-income residential redevelopment,3. Infrastructure improvements1. Interdisciplinary teams are valuable but they require flexibility and organization2. Engaging community stakeholders and decision-makers prior to, during, and following the HIA is critical to a successful HIA3. HIA teams should not be too closely affiliated with decision-makersKaczynski and Sharratt 2010 [49]Williamsburg, Southwestern Ontario, CanadaQualitative analysis of perception of residents in a newly developed neighbourhood that was designed to increase walkabilityLand use diversity, safety, parks, aesthetics, sense of community are mentioned as facilitating factors for walking, lack of (old trees) is mentioned as a problemKing et al. 2010 [50]City in Colorado, USADiscussing the benefits and challenges of applying RE-AIM to evaluate built environment strategies and recommended modest adaptations to the model; afterwards applying the revised model to 2 prototypical built environment strategies aimed at promoting healthful eating and active livingThe 5 RE-AIM dimensions, with some modification of definitions, seem to be applicable to built environment interventions and provide added value given their usefulness in anticipating impact, planning for sustainability, and addressing unexpected or adverse consequencesKnuiman et al. 2014 [51]Perth, AustraliaStudying the influence of built environment characteristics (walkability) and its changes over time on transport walking1. Neighborhood walkability (especially land-use mix and street connectivity), local access to public transit stops, and variety in the types of local destinations are important determinants of walking for transportation2. Land-use mix had a greater and more significant relationship than did either street connectivity or residential density with transport walkingMacDonald et al. 2010 [52]Charlotte NC, USTo examine the cross-sectional associations between objective and perceived measures of the built environment; BMI; obesity pre- and post-LRT (light rail transit) construction1. More-positive perceptions of one’s neighborhood at baseline were associated with lower BMI; $15\%$ lower odds of obesity; $9\%$ higher odds of meeting weekly RPA through walking; and $11\%$ higher odds of meeting RPA levels of vigorous exercise2. The use of LRT to commute to work was associated with an average 1.18 reduction in BMI and $81\%$ reduced odds of becoming obese over timeMacDonald Gibson et al. 2015 [53]Raleigh, North Carolina, USADeveloping a computer simulation model for forecasting the health effects of urban features that promote walkingThe simulation model predicts that the plan would increase average daily time spent walking for transportation by 17 min. As a result, annual deaths from all causes are predicted to decrease by $5.5\%$. Annual new cases of diabetes, coronary heart disease, stroke, and hypertension are predicted to decline by $1.9\%$, $2.3\%$, $1.3\%$, and $1.6\%$, respectively. The present value of these health benefits is $21,000 per residentMansfield and Gibson 2016 [54]City Neighbourhoods in metropolitan areas, USADeveloping statistical models to estimate health impacts of alternative city planning scenarios (with and without infrastructure) to support active transportation (walking and cycling), and reduction of premature death without any referral to a specific disease. Application of mathematical models in hypothetical HIAs1. Increase in the number of walking and biking trips.2. Increased population density and percentage of rental units increased bike trips3. Overall increase in duration of walking and biking trips4. Built environment variables have small but significant effects on daily walking time but no significant effects on daily biking time5. Case study of Raleigh–Durham–Chapel Hill:5.a. The statistical model was able to predict observed transportation physical activity in the Raleigh–Durham–Chapel Hill region to within 0.5 MET-hours per day (equivalent to about 9 min of daily walking time) for $83\%$ of observations5.b. Across the Raleigh–Durham–Chapel Hill region, estimated 38 ($95\%$ CI 15–59) premature deaths potentially could be avoided if the entire population walked 37.4 min per week for transportation5.c. If changes to the built environment induced $14.5\%$ of drivers to commute by public transit, estimated 6.2 ($95\%$ CI 2.6–10.3) premature deaths could have been prevented in 2013Mansfield and Gibson 2015 [55]North Carolina, USATo demonstrate the use of DYNAMO-HIA for supporting health impact assessments of transportation infrastructure projects1. In the BRRC, DYNAMO-HIA estimates a significant reduction in premature all-cause mortality as well as significant preventive effects for hypertension, type 2 diabetes mellitus, and CHD2. In Sparta, significant reductions in premature mortality, cases of hypertension, and cases of type 2 diabetes mellitus are estimated; however, estimated effects on avoided cases of CHD are minimal3. In Winterville, DYNAMO-HIA estimates small, yet significant, reductions in premature mortality and cases of hypertension and minimal effects on type 2 diabetes and CHD4. Across all sites, no significant reductions in cases of stroke are estimatedMansfield et al. 2015 [56]Raleigh-Durham-Chapel Hill, North Carolina, USAModelling impact of 3 scenarios (base case, compact growth and increased sprawl) of urban development on air quality and mortality1. Compact development slightly decreases (-$0.2\%$) point estimates of regional annual average PM2.5 concentrations, while sprawling development slightly increases (+ $1\%$) concentrations2. Point estimates of health impacts are in opposite directions: compact development increases (+ $39\%$) and sprawling development decreases (-$33\%$) PM2.5 -attributable mortality3. Compactness increases local variation in PM2.5 concentrations and increases the severity of local air pollution hotspotsMueller et al. 2018 [57]Spain, UK, Belgium, Austria, Germany, Switzerland, ItalyEstimating the impact of cycling network expansions in seven European cities1. A cycling network of 315 km/100,000 persons lead to cycling mode share of $24.7\%$2. A cycling mode share of $24.7\%$ could prevent 10,000 premature deaths3. Benefits of increases in PA outweighed detriments of air pollution and traffic incidents4. Air pollution exposure have higher risk than fatal traffic incident5. Senario1 ($10\%$ expansion of cycling network) had the largest cost–benefit ratios6. S4 (expansion of all streets) produced greatest benefits among other scenarios with high increase of cycling and lower annual premature deathsMueller et al. 2020 [58]Barcelona, SpainImplementation of the Superblock Model in Barcelona/Spain. Superblocks are blocks of streets with pacified interior streets that are devoted to active transport and residential traffic1. Prevention of 667 annual premature deaths2. Greatest number of preventable deaths could be attributed to reductions in NO2, followed by noise, heat, and green space development3. Increased PA for an estimated 65,000 persons shifting car/motorcycle trips to public and active transport resulted in 36 preventable deaths4. An average increase in life expectancy for the adult population of almost 200 days5. Annual economic impact of 1.7 billion EURNicholas et al. 2019 [59]Los Angeles, California, USA1. To determine the health impacts of three future scenarios of travel behavior by mode2. To provide specific recommendations for how to conduct health impact assessments of local transportation plans1. The largest impacts were on cardiovascular disease through increases in physical activity2. Reductions in air pollution–related illnesses were more modest3. Traffic injuries and deaths increased across all scenarios but were greatly reduced through targeted roadway safety enhancements4. Both aspirational scenarios produce net savings ($79 million and $162 million, respectively), the conservative scenario produces net costs attributable to the high costs of traffic injuriesPanter et al. 2016 [60]Cambridge, UKThe Cambridgeshire Guided Busway comprised a new bus network and an adjacent 22-km traffic-free walking and cycling route in and around Cambridge1. Exposure to the busway was associated with a significantly greater likelihood of an increase in weekly cycle commuting time2. An increase in overall time spent in active commuting among the least active commuters at baselinePayton Foh et al. 2021 [61]Newark, New Jersey, USAOpening of park for recreational activities1. Self-reported neighborhood walkability was associated with increased walking ($$P \leq .01$$)2. Increased perception of neighborhood safety was associated with less walking ($$P \leq .01$$)3. Positive changes associated with improvements to the built environment may be limited by social conditions such as neighborhood violence4. Physical activity policies or interventions aimed at increasing access to open spaces must involve comprehensive, multi-pronged approaches that recognize the realities of the local social context to ensure their long-term successPerdue et al. 2012 [62]Oregon, USATo inform the debate, within a state legislature, about the value of state policy and provide information for local planning agencies to better incorporate health considerations into planning activities1. Increasing the cost of driving was not consistently found to reduce air pollution, increase physical activity, or reduce collisions2. Strengthening public transit was associated with increased levels of physical activity3. Altering the built environment was associated with increased physical activity and decreased air pollutionRoss et al. 2012 [63]Atlanta, Georgia, USAThe BeltLine project will transform a 22-mile loop of an abandoned railroad and surrounding property to 2100 acres of parks; 33 miles trails; 22 miles of transit, 6500 acres of redevelopment, 30,000 new jobs, plus sidewalk, streetscape, road, and intersection improvements1. Giving priority to the construction of trails and greenspace rather than residential and retail construction,2. Making health an explicit goal in project,3. Adding a public health professional to decision-making boards,4. Increasing the connectivity between the BeltLine and civic spaces,5. Ensuring affordable housingStevenson et al. 2016 [64]Melbourne, Australia; London UK; Boston, USA; Copenhagen, Denmark; Sao Paulo, Brasil; Delhi, India;Estimation of the population health effects arising from alternative land-use and transport policy initiatives (modelling compact city scenarios) in six cities using a health impact assessment framework1. Modelled compact city scenario resulted in health gains for all cities (for diabetes, cardiovascular disease, and respiratory disease) with overall health gains of 420–826 disability-adjusted life-years (DALYs) per 100 000 population due to modal shift towards walking and cycling2. For moderate to highly motorised cities, such as Melbourne, London, and Boston, the compact city scenario predicted a small increase in road trauma for cyclists and pedestrians (health loss of between 34 and 41 DALYs per 100 000 population)Thornton et al. 2013 [65]Baltimore, Maryland, USAHealth impact assessment (HIA) of a rezoning effort in Baltimore (TransForm Baltimore) by highlighting its' effects on multiple health outcomes, including physical activity, violent crime, and obesity1. Health was not an active goal for TransForm Baltimore for many city offcials and expert consultants2. Mixed-use developments are associated with increasing physical activity, decreasing obesity and obesity-related illness especially for socioeconomically advantaged populations3. Increased mixed-use developments can be associated with increased crime4. Higher density of alcohol sales outlets is associated with an increased risk of violent crime5. TransForm Baltimore would increase the percentage of city residents living in neighborhoods with pedestrian-oriented design from 1 to $24\%$6. The TransForm Baltimore HIA identified mixed-use development as an important mechanism for impacting health through zoning via potential impacts on physical activity, violent crime, and obesityTiwari et al. 2016 [66]3 Cities in IndiaAssess impact of improving non-motorized traffic infrastructure and public transport infrastructure on CO2 emissions and traffic fatalities1. Maximum reduction in CO2 emissions and highest improvement in safety and reduce traffic fatalities is achieved when both PT (Public transport) and NMT (non-motorized transport) infrastructure are improved2. Improving only PT infrastructure may have marginal effect on overall reduction of CO2 emissions and adverse effects on traffic safety3. NMT infrastructure is crucial for maintaining the travel mode shares in favor of PT and NMT in futureTully et al. 2013 [67]Belfast, Northern Ireland, UKConnswater Community Greenway: major inner city urban regeneration project:The project involves:1. A major urban regeneration project called The Connswater Community Greenway in Belfast includinga. Development of a 9 km linear park, and new cycle paths and walkwaysb. Improvement of the aesthetics of shared public spaces (i.e. planting trees/shrubs, erection of public art)c. Remediation of water courses to improve the natural diversity and reduce the risk of floodingd. Change perception of safety in the community through $\frac{24}{7}$ lighting, CCTV and the presence of park wardens2. A number of programmes to promote physical activity in the area (i.e. extension of neighbourhood walking groups, schools-based initiatives, community-based social marketing initiatives)This article is a protocol, therefore there are no outcomesVeerman et al. 2016 [68]Perth, AustraliaAnalysing the cost-effectiveness of extending the length of sidewalks in a neighbourhood to increase levels of walking and improve health1. Investing in the length of sidewalks is unlikely to be a cost-effective method of improving health at the existing levels of residential density in Perth2. 10 km of sidewalk in an average neighbourhood with 19,000 adult residents was estimated to cost A$4.2 million over 30 years and gain 24 HALYs over the lifetime of an average neighbourhood adult resident population3. The incremental cost-effectiveness ratio was A$176 000/HALY4. Increasing population densities improves cost-effectiveness by spreading the fixed costs of neighbourhood improvements over more people, and leading to greater overall benefit, which improves cost effectivenessWoodcock et al.2013 [69]England and Wales (London was excluded), UKEvaluation of health and environmental impacts of high walking and cycling transport scenarios1. Considerable reductions in disease burden2. The largest health gains were from changes in ischemic heart disease, stroke, and dementia, followed by reductions in injuries3. Largest benefits resulted from,in order, physical activity, road traffic injuries, and air pollutionZapata-Diomedi et al. 2016 [70]Brisbane, Perth and Adelaide, AustraliaDeriving scenarios from the literature for the association between built environment attributes and physical activity, then using a mathematical model to translate improvements in physical activity to health-adjusted life years (HALYs) and healthcare costs1. Density had no statistically significant estimation with gained HALYs2. No significant result on the impact of diversity on physical activity/HALYs3. Design has increasing impact on HALYs via connectivity (intersec-tions within area), availability of side-walks within neigh-bourhood, street lights4. Providing additional recreational destination increases HALYs5. Availability of bus stops increases walking for transport, gains HALYs6. Improvements in walking for transport (measured in standardised walkability index) have positive impact on HALYs7. Health care cost savings due to prevented physical activity-related diseases ranged between A$1300 to A$105,355 per 100,000 adults per year8. Additional health care costs of prolonged life years attributable to improvements in physical activity were nearly $50\%$ higher than the estimated health care costs savingsZapata-Diomedi et al. 2019 [71]Melbourn, AustraliaPlan Melbourne aims to influence housing supply within the existing urban area by industrial land redevelopment and new suburbs on the city’s fringePlan Melbourne addresses:1. transport congestion2. employment3. public transport4. services accessibility5. housing affordability6. environmental sustainability1. Altona North Developed vs Truganina:a. greater housing density (9 vs 3.5 dwellings per hectare),b. greater diversity of land uses (0.74 vs 0.53),c. destination accessibility train station, bus stop and supermarket, and 11 local living destination score, while, Truganina only had access to bus stops, and 8 local living destinations scored. higher Design number of intersections (54 vs 33 /sq.km),e. higher probability of transport walking ($48\%$ vs $26\%$),f. an Altona North Developed resident was estimated to walk 131 min per week for transport purposes, while a Truganina resident was estimated to walk 71 min per week2. The new development in Altona *North is* not changing the destination mix and density significantly3. There will be modest improvements in the probability of walking i.e.only $2\%$ as the result of new develpment in Altona North4. If the population [21,000] of greenfield neighbourhood (similar to Truganina) were exposed to the urban development form in a brownfield neighbourhood (similar to Altona North Developed) the decrease in incidence and mortality of physical inactivity-related chronic diseases would lead to 1600 HALYs and economic benefits of A$94 million5. Well-located higher density brownfield developments in established areas with existing amenities are likely to produce better health outcomes and economic benefits compared with continuing to house people in low density developments on the urban fringeZapata-Diomedi et al. 2018 [72]AustraliaCalculating monetised PA-related health benefits of walking and cyclingResults indicate that the value of PA-related health benefits associated with walking is A$0.98 ($95\%$ Uncertainty Interval (UI) 0.73 to 1.24) per kilometre. For cycling the benefits are worth A$0.62 ($95\%$ UI 0.46 to 0.79) per kilometre
## Location, type of projects, and type of HIAs
Most of the identified published HIAs addressed projects or policies in the USA ($$n = 18$$), followed by Australia ($$n = 6$$), UK ($$n = 5$$), and Canada ($$n = 3$$). Moreover, there were two reports from India, one from New Zealand, one that covered several European countries, one that covered several European and Non-European Countries, and another three reports each covering one European country. Particularly, Germany was only addressed in one HIA as one of several European countries (see Additional file 1: supplementary table S1 for details).
Most of the HIAs investigated the impact of improving or extending the infrastructure to facilitate active transport or public transport ($$n = 13$$). This refers to more concrete projects like extending cycling networks or better sidewalks. Among them, five HIAs were about improving bicycle and pedestrian infrastructure, and another five addressed bicycle infrastructure alone. Respectively one HIA studied improving public transport infrastructure alone, non-motorized transport plus public transport infrastructure, or extending sidewalks. Additional 11 HIAs assessed the impact of more general scenarios or policies to support active transport (see Additional file 1: suppl. table S2 for details).
Six HIAs examined the development of new suburbs; the redevelopment, revitalisation, or regeneration of a city or abandoned areas: Another five HIAs examined the redesign of urban neighbourhoods.
33 HIAs were clearly quantitative HIAs, that aimed at quantifying at least one primary health outcome, four HIAs were clearly qualitative HIAs and three reports included quantitative and qualitative reports (see Additional file 1: suppl. table S3 for details).
## Data sources and analysis
A variety of data sources was used as basis for HIAs, this included primary data collection, secondary use of existing data, measurement of built environment variables via geographic information systems (GIS), and analysis of existing reports, inventories, or other similar data (see Addirional file 1: suppl. table S5 for details). Primary data collection through surveys or questionnaires was used in eight HIAs, interviews and/or focus groups and group discussions in seven HIAs, structured observations and audits in three HIAs, and accelerometer measurement in one HIA.
The secondary use of existing surveys was applied in 14 HIAs. The secondary use of data from group discussions, interviews and travel diaries was each mentioned in one HIA respectively.
Literature reviews as basic data source were used in five HIAs, the measurement of built environment variables by GIS was applied in seven HIAs, and the analysis of existing reports, inventories or other types of data was applied in seven HIAs.
## Health impacts
Cardiovascular diseases were the health endpoint that was investigated most often, in 16 HIAs. This was followed by diabetes in 12 HIAs, Cancer (8 HIAs), mental illness (6 HIAs), premature death (5 HIAs), all-cause mortality (5 HIAs), respiratory diseases (5 HIAs), traffic accidents (4 HIAs) and obesity (4 HIAs) (see Additional file 1: suppl. table S4).
Most of the HIAs ($$n = 31$$) reported the improvement of health or health behaviour resulting from the investigated project or policy. However, three HIAs reported a lack of improvement or even a decrease of health status. In parallel, 13 HIAs reported a gain in economic value, whereas one reported a lack or loss of economic effects. Moreover, three HIAs reported on social effects and six HIAs gave additional recommendations for policies or the implementation of projects or HIAs (see Additional file 1: suppl. table S6 for details).
A closer look at those HIAs that reported negative health impacts or failed to find a positive impact yields the following results. One HIA that reported negative health impacts was on a comprehensive transit-oriented district redevelopment plan, that would result in increased exposure to air pollution and increased rate of asthma in children on one hand, but increased physical activity from walking and reductions in type 2 diabetes and high blood pressure on the other hand [43]. A HIA of different scenarios of urban development on air quality concluded that compact development increases particulate matter PM2.5 concentrations and PM2.5 attributable mortality [55]. Finally, a qualitative HIA using focus groups of a neighbourhood transformation project failed to find noticeable increases of physical activity [41].
Among the social effects reported, there was the potential for more alcohol outlets in mixed-use developments of a rezoning project in Baltimore, and associated increasing violent crime [65]. In contrast, greening of a vacant urban space was associated with reductions in gun assaults, vandalism and stress, and more safety [37]. Finally, in a newly developed neighbourhood that was designed to improve walkability, residents reported safety, aesthetics, and sense of community as factors that facilitated walking [49].
## Implementation of HIAs
Regarding the implementation of the HIAs, none of the reports mentioned explicitly who was doing the work, but it reasonable to assume that this was done by the listed authors.
Most of the studies (32 of 40) reported external funding of the HIA and few studies explicitly mentioned that the HIA was initiated by a particular institution.
Only two study reports mentioned other institutions or stakeholders that were actively involved in the HIA: the city where the project was located, the respective Metro company and the NIH in one study, a number of local organizations, the police department, the church priest and local farmers and ranchers in the other study.
None of the study reports mentioned whether or how the HIA was integrated or associated with other planning instruments or procedures.
## Discussion
The present review aims at summarizing the peer-reviewed literature on health impact assessments of walkability in urban development. The vast majority of studies reported beneficial health impacts particularly reductions of non-communicable diseases like cardiovascular diseases, diabetes, cancer, and mortality. As most HIAs examined the impact of projects, plans or scenarios that aimed at increasing active transport and/or public transport these results are in line with prior findings, because the beneficial consequences of active transport, walking and cycling have been well established [14, 20]. Negative health impacts were only reported in two HIAs and were related to increased exposure to air pollution that may result from more walking and cycling [43, 55]. However, it has been shown that beneficial impacts of walking and cycling clearly outweigh the potential negative impacts from air pollution and traffic accidents.
Walkability in a wider sense was studied in eleven HIAs, by either investigating the redevelopment of cities or abandoned areas, the development of new suburbs or the redesign of urban neighbourhoods. With one exception these HIAs reported beneficial health impacts. Social effects were rarely addressed in the included HIAs and differed fundamentally among the examined projects or plans. Mixed-use neighbourhoods could result in more alcohol outlets and more violent crime [65], whereas greening of vacant urban space could result in reduced violence and vandalism and increased perceived safety of the residents [37]. A newly developed neighbourhood designed to improve walkability was associated with improved perceived safety, aesthetics and sense of community [49].
In summary, the present review clearly shows that improving the walkability of neighbourhoods or cities yields positive health impacts and has only limited negative effects, depending on the project or policy. Moreover, improving infrastructure and opportunities for active transport and public transport play a major role for the beneficial effects. However, the research reports included in this review did not address aspects of inequality and equity, i.e. whether beneficial or harmful effects of projects, policies or programmes are equally distributed among different population groups. There is a need for future research to address the social inequalities of health impacts as well. Otherwise, it could happen that walkability is primarily improved in more affluent neighbourhoods, whereas more deprived neighbourhoods are even more neglected.
This review has some limitations that have to be considered when drawing conclusions. Most importantly, due to our search and selection strategy we may have missed several relevant HIA reports. First, we limited our search on peer-reviewed papers that were identified through two databases and two websites of German organizations and follow-up reference tracking. Thus, HIA reports in the grey literature were not included. It is likely that several HIA reports exist either unpublished or available only on local or regional websites. Indeed, this has been confirmed by a UK expert in health impact assessment (personal communication Dr. Fischer, Liverpool).
Secondly, we explicitly included walkability in our search terms which may cause restriction of the identified results. For example, the article by Mueller et al. [ 58] examines the health impacts of the superblock design in Barcelona, Spain. The superblock design is clearly improving the walkability of neighbourhood blocks as it “… aims to reclaim space for people, reduce motorized transport, promote sustainable mobility and active lifestyles, provide urban greening …” [58]. However, the whole article does not even mention the term “walkability”. Nevertheless, we hope that we have covered the most important HIAs related to walkability through our reference tracking which identified the mentioned article on the Barcelona superblocks.
Having the limitations in mind, the following conclusions seem justified. Health impact assessments related to the walkability of urban environments are more established in English speaking countries, including US, UK, Canada, Australia, and New Zealand. Other European countries, particularly Germany, clearly lack behind. Despite the 40 HIA reports that were included in our review, there is a need for more HIAs published in peer-reviewed journals, given the important role urban environments play in determining human health. Peer-reviewed publication would provide some basic quality assurance and hence increase the credibility of the findings, and in addition would help to identify relevant reports more easily through standard scientific literature data bases. This in turn could motivate more research on the health impacts of walkability and underline the importance of considering and improving walkability in urban planning processes. If not considerably more unpublished HIAs exist, we have to conclude that health impacts of urban planning and urban development need a considerable push in local administrations and planning agencies.
A major strength of this review is going beyond active and public transport; it also includes more general characteristics of the urban built environment which are subsumed under the concept of walkability.
## Conclusions
While the beneficial effects of active transport and public transport which are important components of walkability are well documented and established, the contribution of other features or components of walkability to health are less well understood. Particularly, there is a need to establish more quantitative associations of the different dimensions of walkability as suggested in the walkability framework [27] to increased physical activity, social interaction and perceived safety and stress. Such quantitative associations would allow to predict the health impact of urban design features more accurately.
Future research would benefit from explicitly mentioning the key terms like “health impact” or “walkability” in the title of their publications, and from providing more clear definitions of such constructs in their report. With regard to public health practice, reports should provide more detail about who initiated HIAs, who conducted them, who participated and how they were implemented in the planning processes. Finally, it is desirable, that access to existing HIAs is facilitated by publishing them in peer-review, preferably open access scientific journals. This could promote the consideration of health impacts in urban planning, particularly in countries and regions were this is not well-established practice.
## Supplementary Information
Additional file 1: Table S1. Geographic distribution of HIAs. Table S2. Types of projects, programs or policies that is evaluated using HIA. Table S3. Type of HIA. Table S4. Type of health endpoints. Table S5. Type of data source for the HIA. Table S6. Type of results.
Additional file 2: Data extraction sheet.
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|
---
title: 'Health economic evaluation of preventive digital public health interventions
using decision-analytic modelling: a systematized review'
authors:
- Oliver Lange
journal: BMC Health Services Research
year: 2023
pmcid: PMC10024449
doi: 10.1186/s12913-023-09280-3
license: CC BY 4.0
---
# Health economic evaluation of preventive digital public health interventions using decision-analytic modelling: a systematized review
## Abstract
### Background
Digital public health (DiPH) provides novel approaches for prevention, potentially leading to long-term health benefits in resource-limited health systems. However, cost-effectiveness of DiPH interventions is unclear. This systematized review investigates the use of decision-analytic modelling in health economic evaluations of DiPH primary prevention and health promotion interventions, focusing on intervention’s design, methods used, results, and reporting quality.
### Methods
PubMed, CINAHL, and Web of Science were searched for studies of decision-analytic economic evaluations of digital interventions in primary prevention or health promotion, published up to June 2022. Intervention characteristics and selected items were extracted based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). Incremental cost-effectiveness ratios (ICERs) were then extracted and price-adjusted to compare the economic evaluation results. Finally, the included studies’ reporting quality was assessed by building a score using CHEERS.
### Results
The database search (including search update) produced 2,273 hits. After removing duplicates, 1,434 titles and abstracts were screened. Of the 89 studies meeting the full-text search criteria, 14 were ultimately reviewed. The most common targets were physical activity (five studies) and weight loss (four). Digital applications include text messages, web-based inventions, app-based interventions, e-learning devices, and the promotion of smartphone apps. The mean ICER of the 12 studies using quality-adjusted life years (QALYs) is €20,955 per QALY (min. − €3,949; max. €114,211). The mean of reported CHEERS items per study is $81\%$ (min. $59\%$; max. $91\%$).
### Conclusions
This review only includes primary prevention and health promotion, and thus excludes other DiPH fields (e.g. secondary prevention). It also focuses on decision-analytic models, excluding study-based economic evaluations. Standard methods of economic evaluation could be adapted more to the specifics of DiPH by measuring the effectiveness of more current technologies through alternative methods, incorporating a societal perspective, and more clearly defining comparators. Nevertheless, the review demonstrates using common thresholds that the new field of DiPH shows potential for cost-effective preventive interventions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12913-023-09280-3.
## Introduction
Digital public health (DiPH), i.e. the use of digital means to address Public Health functions like health promotion or its governance [1, 2], is a new, expanding field. The potential benefits and advantages of DiPH could support the transition from cure to prevention, the empowerment of people and patients, and progress towards safer, cheaper, and more efficient health care management delivery [3].
Established concepts of digitalization in health care and public health, such as eHealth, mHealth, and digital health, target the individual level. By contrast, DiPH targets the population level [2]. While DiPH can be defined broadly to include health protection, health promotion, primary, secondary and tertiary prevention as well as cure [1], it can also be associated with a focus on disease prevention, health promotion [4], following the wide-spread view that ‘prevention is better than cure’ [5].
In this review, the term ‘digital’ is used in its broadest sense to refer to the use of information and communications technology. Examples of digital interventions are health apps, SMS reminders, web-based applications, and electronic devices. Based on the NICE Evidence Standards Framework for Digital Health Technologies [6], this study investigates interventions targeting preventive behaviour change, including changes in user behaviour related to health (e.g., smoking, alcohol consumption). It excludes interventions used to treat diagnosed conditions.
While the number of digital applications in (public) health is rising, it remains unclear whether they meet the ambition of providing cost-effective or even cost-saving care. Given that public health budgets are limited, coverage decision-makers need to assess not only effectiveness, but also the cost-effectiveness of new DiPH interventions to appraise whether they should be included into the reimbursement schemes. A formal method to do so is economic evaluation, which compares two or more interventions in terms of costs and consequences [7].
There are different types of economic evaluations. In this review, cost-effectiveness analysis (CEA) is understood to be a comparison of the relationship between costs and single or multiple health effects that are common to two or more alternatives [8]. Cost-utility analysis (CUA) includes the concept of utilities, using generic outcomes like disability-adjusted life years (DALYs) or quality-adjusted life years (QALYs). Other types include cost–benefit analysis, which also expresses costs and health effects in monetary units, or cost-minimisation analysis, which compares costs while health effects are assumed to be equal.
CEA and CUA can be study- or model-based: study-based economic evaluations generally elicit data through a relatively short-term concrete trial, whereas decision-analytic models combine data from different sources. There are advantages to economic evaluations using decision-analytic modelling: they allow synthesizing various input data, including different comparators, extrapolating costs and effects over time, and systematically accounting for the uncertainty of available evidence for the specific decision problem [9]. Therefore, model-based economic evaluations can forecast costs and health outcomes over a long time. Generating evidence about the costs and effects of preventive interventions is generally challenging, especially given the long time horizon over which effects manifest. This challenge is even greater for digital interventions, characterized by high innovation dynamics. Therefore, decision-analytic modelling may be particularly suited for assessing new DiPH interventions.
Economic evaluation allows decision-makers to compare new DiPH interventions with alternative uses of the limited resources. In cost-effectiveness analyses, one standard to do so is to calculate the incremental cost-effectiveness ratio (ICER) which can be used to measure, for example, the cost per kg weight loss. Analogously, in cost-utility analyses, it is the calculation of an incremental cost-utility ratio (ICUR), which based on the difference of costs divided by the difference of QALYs and can be exemplarily expressed as cost per QALY gained ([10], p. 41). Following the established definition [8], in the following sections this will be summarised under the term ICER. The ICER can then be compared to a threshold of cost-effectiveness, corresponding with the social willingness to pay for a QALY or the opportunity costs in terms of the cost-effectiveness of interventions that are replaced by the new DiPH interventions. While this allows for a theoretically sound economic assessment, it has to be noted that the choice of threshold value is a contested topic and there are varying thresholds in the literature (e.g. [11, 12]).
Widely used guidance for reporting economic evaluation are provided by the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) [13] which recommend including specific items into the title and abstract, introduction, methods (e.g. comparator, time horizon, choice of model), results (e.g. study parameters, characterizing uncertainty), and discussion. The CHEERS were originally co-published by 10 academic journals that frequently publish health economic evaluations [13, 14] and these standards are acknowledged as key reporting guidelines by the EQUATOR Network. Therefore, they are used in this review as methodological standard for reporting health economic evaluations.
To make evidence-based decisions in DiPH, decision-makers and researchers need an overview of the existing health economic evidence. Specific fields of digital health have already been studied. Investigated topics include preventive interventions and treatment of conditions and diseases such as diabetes [15] and depression [16]. The economic evaluation of digital interventions for primary prevention and health promotion has already been investigated for older people but not the broader population [17]. While the cost-effectiveness of internet-based interventions up to 2008 has been reviewed [18], the current review is updated and systematized. Digital prevention and health-promotion interventions have recently been investigated but without focus on cost-effectiveness [19].
Therefore, the aim of this systematized review is to identify and investigate the use of health economic evaluation using decision-analytic modelling to evaluate DiPH primary-prevention and health-promotion interventions, focusing on [1] which DiPH interventions have been evaluated by now, [2] the evaluation methods, [3] the results on the cost-effectiveness of DiPH, and [4] the studies’ reporting quality.
## Methods
The systematized review is reported in accordance with the PRISMA 2020 guidelines [20]. To find a homogenous and comparable group of studies, four main eligibility criteria were set: (i) model-based (not study-based) economic evaluation, in the expectation of long-term analysis and to enable investigation of unique study types; (ii) primary-prevention or health-promotion intervention, supporting the transition from cure to prevention; (iii) use of information and communications technology in the intervention excluding, simple phone calls; and (iv) original study.
PubMed, CINAHL, and Web of Science were searched for potentially relevant studies on 4 December 2020 and updated in June 2022. The search strategy linked the concepts of economic evaluation and digital (health) technologies. Based on the requirement of the CHEERS [13] item ‘Title’, that the title had to include a term of economic evaluation, titles must include one or more of various terms referring to economic evaluation (e.g. cost-effectiveness). Using the Boolean operator ‘AND’, the search included variations of ‘digital health’ and different digital technologies/applications (e.g. web-based) in the title or abstract (see Additional file 1). In addition, reference tracking was performed for known literature reviews.
The first step in the selection process used Microsoft Excel to eliminate duplicates followed by the title abstract screening. As the abstract may not reveal whether a study uses a model or a different method of economic evaluation title–abstract screening was based on broader criteria than in the full-text investigation. Specifically, studies were selected for full-text investigation if they:areported the quantitative results of a health economic evaluation or compared the costs of two alternative interventions;bfocused on a DiPH intervention involving information and communications technology; andcevaluated an intervention targeting primary prevention or health promotion.
Studies whose abstract was unavailable and whose title clearly did not comply with the criteria were excluded. Full texts were excluded if the study did not meet all of eligibility criteria (i)–(iv). Titles and abstracts were double-screened independently by OL and LB or by OL and WT. The two reviewers sought consensus in cases of disagreement; where agreement could not be reached, a senior researcher (WR) decided on inclusion. Two reviewers (OL and LB) independently investigated full texts and checked their eligibility. Disagreements were resolved through an iterative process by one reviewer (OL), who conducted extraction and quality reporting, supported by advice from WR on ambiguous cases.
With the search update OL performed title–abstract screening and full-text investigation and extraction following the same process and criteria of the original search.
The first step of assessing the identified studies focused on the interventions they evaluated. The following were extracted: the aim of the intervention (e.g. increasing physical activity); the primary digital component (e.g. smartphone application); any complementary web tools (to establish the complexity of the intervention); any requirement for face-to-face meetings; and more generic items like target population and location.
The second step assessed the studies’ evaluation methods. Selected items from the CHEERS and additional items of interest were operationalized and extracted to describe the characteristics and commonalities of included studies: type of economic evaluation, type of model, health outcome, time horizon, discount rate for outcome and costs, and measurement of effectiveness. Reported limitations of the studies and other relevant information in the discussion sections were also extracted and named as "self-reported limitations" in the results section of this review.
The third step assessed the studies’ results. Specifically, for every study comparing a digital intervention and a base-case scenario, the ICER was extracted. This allowed comparing the results of economic evaluations. Only ICERs resulting from the main investigation were considered, thus excluding ICERs for other scenarios (e.g. uncertainty analysis results). OECD purchasing power parities (PPP) were used to adjust ICERs to a common euro price [21]: after first converting each ICER by the PPP value of the corresponding year into US dollars, the US dollar prices were then converted to euros through multiplying by the ‘*Euro area* (19 countries)’ PPP value [21]. For comparability, only ICERs with QALY health outcomes were included.
Finally, to assess the studies’ reporting quality, every CHEERS item [22] was assessed for each economic evaluation, producing a total quality score. Items labelled ‘If applicable’ were not evaluated, thus ensuring that scores were always calculated in the same way. If an items required an additional reason, this had to be supplied for the item to be considered fully reported. If an CHEERS item was reported almost completely, it was assigned a score of 1; CHEERS items fulfilled partially or not scores of 0.5 or 0, respectively. The appendix was recognized as part of the study, but information from cited references was not included. For each study, the overall score was calculated as the sum of item values divided by the total number of CHEERS items.
## Results
The database search found 1,938 studies. After removing 774 duplicates, 1,161 titles and abstracts were screened. The 78 studies that fulfilled the criteria were investigated in a full-text search. In the updated search, 335 titles and abstracts articles were screened, and 11 full texts were investigated. Finally, data were extracted from the 14 studies that met the eligibility criteria. In Fig. 1, the modified PRISMA flow diagram [20] shows the review process. Fig. 1Modified flow diagram based on PRISMA 2020 [20]
## Studies that might appear to meet the inclusion criteria
PRIMSA 2020 [20] requires that details are provided for studies that might appear to meet the eligibility criteria but are excluded. This was the case for seven studies, covering:the application of a digital decision tool for economic evaluation of a non-digital intervention [23];a cost-minimization analysis that did not demonstrate at least equal health outcomes [24];an intervention using telephone counselling, which is not considered digital [25];an intervention to prevent suicide but not suicidal thoughts, which did not meet this review’s definition of prevention [26];an alcohol intervention with mainly therapeutic and secondary prevention elements, rather than primary prevention [27];a diabetes-prevention intervention targeting individuals with a weight-related risk factor (i.e. hypertension) – this constitutes secondary prevention as screening is required to detect the risk factor [28]; anda population of individuals with high risk of cardiovascular disease, based on either a history of the disease or a risk equation, and thus involving screening [29].
## Assessed DiPH interventions
Figure 2 shows the characteristics of assessed DiPH interventions (for details of individual studies, see Additional file 2). The interventions subject to economic evaluation pursued different goals: increase physical activity [30–34], lose weight [35–38], stop smoking [39–41], change health-related behaviours [42], and manage menstrual health [43]. Thus, most interventions aimed to prevent typical chronic diseases such as diabetes or heart disease. Fig. 2Assessed digital public health interventions The digital tools for pursuing these goals were also heterogeneous. Of the five physical-activity interventions, the first was particularly complex, combining text messages with complementary online exercises, a Facebook group, and face-to-face meetings to increase the physical activity of women with young children in Australia [30]. The second was web-based/internet intervention and targeted the Australian population aged over 15 [31]. The third physical-activity intervention delivered the same advice via a website and by mail, while also providing exercise videos; it targeted people over 50 [32]. The fourth intervention was a commercial smartphone app [33], while the fifth promoted existing apps to increase physical activity, targeting adults in New Zealand [34].
Of the four weight-loss interventions, the first promoted existing apps and targeted overweight or obese adults in New Zealand [37]. The second intervention was a mass-media campaign to promote existing smartphone apps for weight loss in New [38]. The third weight-loss intervention was purely app-based and targeted 15–64 year-olds in Italy [35]. The fourth employed e-learning devices and was assumed by the study to have a target population aged exactly 50 with BMI > 30 [36].
There were also three smoking-cessation interventions: two were based on text messages and respectively targeted smokers in the United Kingdom [39] and Spain [40], while the third was a web-based intervention with personalized feedback targeting smokers in the Netherlands [41]. Another intervention involved an online portal aiming to induce general behavioural change among young people starting their university studies [42]. Finally, one intervention entailed smartphone app-based menstrual management, aimed at preventing depression and dysmenorrhea. The economic evaluation of DiPH is thus no homogeneous field of investigation on which general conclusions can be drawn easily but incorporates a rich variety of very different interventions.
## Evaluation methods
Figure 3 shows the methods of economic evaluation. Studies typically compared the digital intervention with doing nothing or ‘business as usual’, and it was sometimes unclear what level of digital intervention the ‘usual’ scenario entailed or how widely used digital applications already are in the analysed healthcare system. Fig. 3Evaluation methods Most studies were conducted from a health care perspective [30–32, 34–39, 41], expressly identified as a health service, sector, or system perspective. Two studies additionally included societal costs (productivity losses [40, 43]), and one gave a ministerial perspective [42]. As some of the studies acknowledged, focusing on the health care perspective overlooks other costs such as productivity losses [34, 35] or greenhouse gas emissions [37].
The decision-analytic model in all the studies included CUA with QALYs (12 studies) or disability-adjusted life years (DALYs; two studies) as the health outcome. Three studies also conducted CEA, expressing the results as cost per smoking quitter [39], life years gained [42], or disease incidence [32]. Seven studies used Markov models; four employed multistate life table models ([34, 37, 38] used the same model); two used discrete event simulations; and one employed the OECD Strategic Public Health Planning for NCDs model, which forecasts the costs and health outcomes of different hypothetical public health measures up to 2050 [35].
Time horizon, which was expected to be long-term for decision-analytic studies on (chronic) disease prevention, was relatively short for two studies – two years [30] and five years [33] – but longer for all the others (e.g. lifetime perspective). Two studies used specific time horizons: one for women up to 45 years, the other for the years from 2019 to 2050. Although these analytic time horizons are long, the effects of the interventions were partially assumed to be short or are based on evidence of short-duration trials only. For example Mizdrak et al. [ 34] reported in their limitations that there was no evidence that the impact of the intervention persists for more than one year, and thus studies on long-term effects are needed.
Model-based economic evaluations include estimates of the effectiveness of interventions based on one (single study) or more studies (synthesis based). In this review, eight of the included models based their effectiveness estimates on a synthesis of studies (e.g. meta-analysis), whereas the other six based estimates on a single study. Several limitations were self-reported about the effectiveness studies used: For example, Cleghorn et al. [ 37] mentioned that effectiveness could be underestimated if data on smartphone app download rates are lacking, or if spillover effects within households and technological improvements over time are disregarded [37]. Further, evaluations of similar interventions (e.g. weight-loss apps) may find different levels of effectiveness [34, 37].
Various assumptions were made to forecast long-term effects with decision-analytic models. Self-reported limitations of these assumptions mainly concerned the under- or overestimation of effects due to the modelling. Therefore, effects could be underestimated if the impact of physical-activity interventions on weight loss is not considered or only certain diseases are modelled (e.g. disregarding mental health [34]). Moreover, if included diseases are assumed to be independent [34], the model does not reflect that an individual with one disease may have a greater risk of contracting another (e.g. cardiovascular disease increases the risk of diabetes). One study reported that dynamics in diseases (e.g. different health states within an activity level) were not considered – only inactive and active states [30]. Thus, with a small increase in physical activity, no change could occur. Further reported assumption limitations include failing to consider that the risk of contracting a disease could be irreversible or only decline a long time after behavioural change [31], or modelling future risk independently of past behaviour [32]. In another study modelling the promotion of existing apps, it was assumed that a temporary mass-media campaign for one year would stop, thus neglecting potential effects if it continued [37]. Even if decision-analytic modelling may appear as a well-suited approach to cope with the difficulties in the economic evaluation of DiPH, the existing models also reveal various methodological challenges.
## Results of health economic evaluations
Table 1 shows the included studies’ results, ordered by ICER. They are given in different currencies and price years. The calculated mean is €20,955 per QALY (studies using DALYs are not included). However, this value should be handled with caution and has low significance for decision-making, since it is based on a small number of heterogenous economic evaluations. The lowest ICER was found for a smoking-cessation study [41] resulting in cost-savings (− €3,949 per QALY). By contrast, the highest ICER was calculated for a weight-loss intervention [36] delivered by an e-learning device (€114,211 per QALY). Regarding intervention aim, those targeting smoking cessation had a lower ICER on average relative to physical-activity interventions. Jones et al. [ 38], Cleghorn et al. [ 37] and Mizdrak et al. [ 34] all evaluated interventions promoting existing apps, but the first study reported a much lower ICER than the other two. This difference may be explained by the use of different effectiveness studies and amounts of available evidence. Table 1Study results expressed in ICERAuthorsInterventionDeliveryICERCurrency perHealth-outcomeCountryPrice-yearICER€ per QALYNoteCheung et alSmoking cessationWeb-based-4,306.50aEuro per QALYNetherlands2016-3,949.43Price-year not identifiedLiterature search has ended in 2016Only lifetime horizon considered;aICER = 602.91 € / 0.14 QALYJones et alWeight lossPromoting apps --3348.07bNZ$ per QALYNew Zealand2011-1,757.40Price-year referred to Cleghorn et al.;bICER = -606,000 / 181 QALYGuerriero et alSmoking cessationText messages-1431,3448cGBP per QALYUnited Kingdom2009–2010(2010 used)-1,616,89Only weighed averagecICER = -41,509 / 29 QALYCobos-Campos et alSmoking cessationText messages1327Euro per QALYSpain20181,496.81Only Woman & Health system perspective consideredBurn et alPhysical activityText messages8,608AUS$ per QALYAustralia20144,458.14Rondina et alPhysical activityApp-based11,113CAD per QALYCanada20186,525.23Peels et alPhysical activityWeb-based10,100Euro per QALYNetherlands20119,423.44Only lifetime scenarioSong et alMenstrual managementApp-based-1,914,285dYen per QALYJapan201713,132.01dICER = -134,000 / 0.07 QALYKruger et alBehaviour change (mixed)Web-based22,844GBP per QALYUnited Kingdom201225,186.97Only the "Implementation at University of Sheffield"-scenarioCleghorn et alWeight lossPromoting apps79,700NZ$ per QALYNew Zealand201141,834.45Mizdrak et alPhysical activityPromoting apps81,000NZ$ per QALYNew Zealand201142,516.82Miners et alWeight lossE-learning device102,000GBP per QALYUnited Kingdom2009114,211.27Only Scenario Aa-dICER Calculated by article author on the basis of existing information in extracted study. All other ICERs were extracted directly from the study
## Reporting quality of health economic evaluations
Figure 4 shows the assessment of reported items on the CHEERS checklist. The mean of reported CHEERS items per study is $81\%$, with a minimum of $59\%$ and maximum of $91\%$. One reason for the high number of partially reported items is studies reporting, for example, the discount rate or time horizon but not stating why this is appropriate. Some studies do not provide all the required information in the abstract, or do not refer to common standards. Only a few items are unreported or only insufficiently reported. Fig. 4Reporting quality per study (in terms of item reported – yes, partly, or not) Figure 5 shows the assessed fulfilment of each CHEERS item. All studies reported a time horizon but few stated why the chosen horizon was appropriate. Moreover, for the estimation of resources and costs, sources were named without an accompanying explanation of why they were sufficient or how they were found. Fig. 5Reporting quality per CHEERS item (in terms of item reported – yes, partly, or not)
## Discussion
This review gives an overview of the use of decision-analytic modelling to evaluate digital interventions in primary prevention and health promotion. Of the 12 studies reporting outcomes in terms of QALYs, nine were found to have an ICER below €50,000 per QALY, while the overall mean was €20,955 per QALY. However, no inferences should be drawn on the cost-effectiveness of DiPH interventions: [1] the mean is based on a small sample of heterogeneous studies; and [2] the frequently cited threshold value of €50,000 per QALY is contested, and cost-effectiveness should always be assessed by the relevant health care decision-makers, comparing against other adopted or rejected interventions [11, 44]. Moreover, the results are far from conclusive, and it is not possible to determine which digital technology is the most cost-effective.
## General interpretation of results considering other evidence
Other reviews of economic evaluations with a similar research question do not provide detailed information on the cost-effectiveness of DiPH preventive interventions. In 2009, Tate et al. [ 45] reviewed the cost-effectiveness of internet-based interventions. They identified one health-promotion and one obesity-management intervention that could be classified as primary prevention, but these two studies did not use decision-analytic models. It can also be assumed that technological progress since 2009 has led to new digital interventions. In 2015 [46], a systematic review of economic evaluations focused on telemedicine, eHealth, and mHealth, considering public health fields such as secondary prevention (in the form of screening). However, no methods of evaluating primary-prevention or health-promotion interventions are included or analysed. In 2020, Ghani et al. [ 47] conducted an overview of the cost-effectiveness of mHealth for older adults, but did not include any primary-prevention interventions (target groups were patients or persons with diseases). One systematic review [48] investigated economic evaluations of eHealth for older adults. They identified Peels et al. [ 32] as the only study making a primary-prevention intervention – all other studies involved interventions against existing diseases.
In 2017, Iribarren et al. [ 49] reviewed economic evaluations of mHealth interventions in general: only a few identified studies involved typical primary-prevention or health-promotion interventions (targeting physical activity, vaccination, and obesity). Only two economic evaluations included in this review, [30] and [39], also feature in Iribarren et al. ’s synthesis of results. This could be explained by the latter review being conducted five years earlier and including only mHealth interventions, as well as it was not restricted to studies using decision-analytic modelling. While Law et al. [ 50] conducted a review of telehealth-delivered diet and exercise interventions, it included only studies of individuals with at least one health condition.
In the absence of a prior review of cost-effectiveness focused on DiPH preventive interventions, meaningful comparison of potential cost-effectiveness is only possible with individual economic evaluation studies.
## Limitations of the included evidence
Depending on the model used, assumptions made, and data collected, the included studies have several limitations, which are frequently reported in the studies. This section discusses the particularities of economically evaluating DiPH interventions, considering the use of effectiveness studies, selection of perspective, and choice of comparators. Limitations reported by the studies themselves were reported in the results section.
Economic modelling is based on effectiveness studies, which must be designed, conducted, evaluated, and published. It is well known that this scientific process takes time. However, digital applications are evolving rapidly, as illustrated by the evolution of cell phones from simple devices to smartphones equipped with various sensors and the ability to connect to servers or other devices. Yet this review has shown that many DiPH interventions still rely on web-based tools or text messages. At this stage, the evaluated interventions seem to lag far behind technical developments, with no just-in-time adaptive interventions identified, nor any built around sensors. This limits the value of effects estimates based on a single study and especially those based on synthesized results: meta-analyses may include old technology and, compared to the studies they analyse, have a greater risk of presenting outdated effectiveness estimates. To avoid this, other study designs could be used (e.g. n-of-1 trials [51]) that allow collecting data on effectiveness more quickly. No study applying such a design was identified in this review.
Only two identified studies offer a societal perspective, and both are limited to productivity losses. However, in the economic evaluation of prevention interventions (this also includes DiPH), all societal costs and benefits should be taken into account [52]. Moreover, given the importance of addressing climate change in health care and public health, there may be a need to extend the perspective beyond monetary costs to all members of society to include detrimental effects to the environment which cannot easily be monetarized. No conceptual framework to obtain such a broad perspective in economic evaluations was applied in the studies nor appears to be available in the health economic literature.
Another limitation of the evidence is that most investigated economic evaluations do not explicitly identify the comparator for the DiPH intervention. Although referring to ‘usual care’ or ‘business as usual’, they do not clearly describe the digital environment in the investigated country. It may be assumed, for example, that states with higher (vs. lower) levels of digitalization have higher public openness to digital health services, more scalable costs, and more effective interventions. This review’s findings reveal a tendency to name the digital environment but not describe it in detail. The mERA checklist could provide a solution to this problem by requiring the definition of comparators with respect to the digital environment (e.g. infrastructure, technology platform, and interoperability) [53].
## Limitations of the review processes
This review has several typical limitations. It only includes research literature written in English, and only three databases were searched. When unclear information was found, authors were not contacted to request clarification [20]. Only original studies were included. The terms “reports” and “studies” are taken to be synonyms, although PRISMA requires to distinguish between reports and studies. The search strategy could have been modified to add the term ‘decision-analytic’ to the concept of economic evaluation. The search strategy restricts the terms of economic evaluation to the title because CHEERS requires that authors state the type of economic evaluation in the article title. This search strategy was chosen because [1] the broad complementary search term to identify DiPH interventions had to be limited without losing evidence and [2] a large number of complementary exploratory searches indicated that combining this strategy with additional reference tracking is likely to identify all relevant decision-analytic economic evaluations.
The criterion that studies had to involve a primary-prevention or health-promotion intervention was sometimes difficult to apply. Smoking, drinking alcohol, obesity, and physical inactivity were considered as risk factors, not emerging diseases. However, studies whose target population was a (pure) patient group were excluded. While interventions targeting individuals at higher risk, as determined by a risk score, were considered in the screening phase of this review, these studies were subsequently excluded as secondary prevention. The requirement for studies to include a full economic evaluation also led to the exclusion of single individual studies, such as those that did not apply a model but only calculated alternative scenarios based on different input parameters (e.g. [54]). The search strategy also excluded all study-based economic evaluations, thereby missing possible insights from longitudinal studies or evaluations with a lifetime horizon. However, the above-mentioned advantages and need for comparability were considered to justify restricting the review to decision-analytic economic evaluations.
The mean ICER only takes the base-case results in each study into account. However, the base-case scenario in Kruger et al. [ 42] showed an ICER of £22,844 per QALY for a rollout restricted to one university, while an additionally evaluated rollout to other universities resulted in an ICER of £1,545 per QALY. This indicates the possibility that an intervention yielded a much lower ICER and was not considered in the results of this review because not all possible scenarios were included in an analysis in the synthesis. It should also be noted that the ICER can be misinterpreted (for example if it is high but incremental QALYs are low, or if it is negative), hence there are reasons to consider costs and outcomes separately [55]. Although prices were adjusted to PPP, no adjustment was made, as technological progress had not been considered. However, technologies can be expected to become more efficient or more widespread. Because of potentially falling IT costs, health care inflation rates were considered inappropriate, so it was deemed best to assume stable prices.
Regarding the quality assessment based on CHEERS items, a high score does not necessarily imply a high-quality evaluation as the instrument only assesses reporting transparency. Conversely, a low score does not necessarily indicate a low-quality study. CHEERS compliance was interpreted strictly, and missing reasons for choosing items resulted in a lower score. Moreover, referring to a source without presenting the respective content was not considered sufficient, thus decreasing the score. For example, merely referencing a very detailed RCT without describing why the intervention, comparator, and target population were selected could not be considered when assessing the fulfilment of an item. However, any reference to basic guidelines as a rationale for decisions (such as the discount rate) was sufficient. While the CHEERS consolidate other established checklists, more model-specific reporting guidelines could have been used instead (e.g. [56]). On balance, though, the widely known CHEERS were considered the most suitable yardstick for assessing quality. In light of the PRISMA statement, this review did not conduct subgroup or robustness analysis or certainty assessment because the sample was too small for these complementary analyses.
Although selected self-reported limitations were included in the results section, a comprehensive critique of the models is only possible within more detailed analyses. The heterogeneous nature of interventions assessed by the evaluations in this review precludes such analysis here.
## Implications of the results for practice and policy
This review suggests that some DiPH preventive interventions are potentially cost-effective. In particular, one reviewed smoking intervention yielded cost savings, with intervention costs lower than disease costs. DiPH interventions thus provide a new and potentially promising field of prevention that might, in some cases, even incur cost savings to health care payers.
However, they warrant economic evaluation. Decision-analytic modelling may be a particularly suited methodology to do so: first, it is comparatively easy to develop a large number of different versions that are more easily assessed by models than by clinical trials; and second, the effects of preventive interventions have a potentially long time horizon. However, this review also revealed multiple methodological difficulties of identifying appropriate estimates of effectiveness for DiPH technologies with short technology life cycles. There thus remains the need to generate sound evidence of effectiveness.
## Implications of the results for future research
This systematized review illustrates that the health economic evidence underscores the potential health effects and (disease) costs of DiPH prevention interventions. However, it also demonstrates that the evidence for cost-effectiveness in this field is still weak, highlighting the need for further studies to assess the cost-effectiveness of multiple forthcoming interventions.
Recent methodological studies identify specific challenges for evaluating DiPH, such as the plurality of outcomes, including not only individual health benefits but also a broader societal perspective, for example by measuring the carbon footprint of digital health interventions [57]. Equity impacts may also be particularly important in this field. None of the included studies fully accounted for these challenges.
While this systematized review provides an initial reference point of existing economic evaluations for digital primary prevention, further research is needed on what such evaluations should include and how to address the methodological challenges that were identified.
## Conclusion
Based on common thresholds, there are DiPH interventions which are potentially cost-effective. However, the economic evidence in this field remains weak. Also, the interventions identified in this review are too heterogeneous and digital technology life cycles are too short to draw general conclusions on the cost-effectiveness of DiPH.
## Supplementary Information
Additional file 1. Additional file 2.
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|
---
title: Honokiol acts as an AMPK complex agonist therapeutic in non-alcoholic fatty
liver disease and metabolic syndrome
authors:
- Ruifeng Tian
- Jinjie Yang
- Xiaoming Wang
- Shuaiyang Liu
- Ruixiang Dong
- Zhenya Wang
- Zifeng Yang
- Yingping Zhang
- Zhiwei Cai
- Hailong Yang
- Yufeng Hu
- Zhi-Gang She
- Hongliang Li
- Junjie Zhou
- Xiao-Jing Zhang
journal: Chinese Medicine
year: 2023
pmcid: PMC10024454
doi: 10.1186/s13020-023-00729-5
license: CC BY 4.0
---
# Honokiol acts as an AMPK complex agonist therapeutic in non-alcoholic fatty liver disease and metabolic syndrome
## Abstract
### Background
Non-alcoholic fatty liver (NAFLD) and its related metabolic syndrome have become major threats to human health, but there is still a need for effective and safe drugs to treat these conditions. Here we aimed to identify potential drug candidates for NAFLD and the underlying molecular mechanisms.
### Methods
A drug repositioning strategy was used to screen an FDA-approved drug library with approximately 3000 compounds in an in vitro hepatocyte model of lipid accumulation, with honokiol identified as an effective anti-NAFLD candidate. We systematically examined the therapeutic effect of honokiol in NAFLD and metabolic syndrome in multiple in vitro and in vivo models. Transcriptomic examination and biotin-streptavidin binding assays were used to explore the underlying molecular mechanisms, confirmed by rescue experiments.
### Results
Honokiol significantly inhibited metabolic syndrome and NAFLD progression as evidenced by improved hepatic steatosis, liver fibrosis, adipose inflammation, and insulin resistance. Mechanistically, the beneficial effects of honokiol were largely through AMPK activation. Rather than acting on the classical upstream regulators of AMPK, honokiol directly bound to the AMPKγ1 subunit to robustly activate AMPK signaling. Mutation of honokiol-binding sites of AMPKγ1 largely abolished the protective capacity of honokiol against NAFLD.
### Conclusion
These findings clearly demonstrate the beneficial effects of honokiol in multiple models and reveal a previously unappreciated signaling mechanism of honokiol in NAFLD and metabolic syndrome. This study also provides new insights into metabolic disease treatment by targeting AMPKγ1 subunit-mediated signaling activation.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s13020-023-00729-5.
## Background
Non-alcoholic fatty liver disease (NAFLD) is a collection of liver disorders ranging from simple steatosis (fatty liver) to non-alcoholic steatohepatitis (NASH) with necroinflammation and progressive fibrosis [1]. NAFLD is now an established and increasing cause of mortality and morbidity from liver disease, with in silico modeling predicting a significant increase in disease (and consequently economic) burden over the next decade, especially as the prevalence of obesity grows worldwide [2–5]. NAFLD can also have other, non-liver-related negative impacts, especially on cardiovascular health [6, 7]. However, there are still no clinically-approved drugs for NASH and discrepancies in the results from animal models and safety concerns limit the translation of laboratory findings to the clinic. Therefore, there is an urgent need for drug development in this area.
AMP-activated protein kinase (AMPK) is a sensor of cellular energy status, nutrient availability, and cellular injury implicated in the pathogenesis of cardiovascular disease, chronic metabolic disease, and cancer [8]. The AMPK complex is formed of mandatory heterotrimers consisting of the catalytic α subunit, the scaffold β subunit, and the regulatory γ subunit. Different subunit isoforms have specific spatiotemporal tissue and cellular expression patterns, so the pathophysiological regulation of AMPK is highly complex [9]. Activating AMPK has been shown to protect against NAFLD and metabolic syndrome, but chronic activation might also have serious adverse sequelae in the form of cardiac hypertrophy and cancer [10]. These effects may in part be due to AMPK activators mainly targeting the AMPK α and β subunits that possess non-substitutable capacity in regulating pathophysiological behaviors. However, AMPKγ is well conserved in eukaryotes and archaea [11], so it may be a good translational target. Encouragingly, a recent study reported that liver gain-of-function mutations in the AMPKγ1 subunit protected against hepatic steatosis [12]. However, there have been relatively few studies of AMPKγ1-targeting drugs, and their development and testing in NAFLD and metabolic syndrome requires further study.
FDA-approved drug libraries include drugs that have been shown to be clinically effective and drugs with known pharmacological activity included in pharmacopoeias [13]. Importantly, these drugs have been shown to be safe in the clinical setting. In our pharmacological screening, honokiol, an active ingredient found in the traditional Chinese herb magnolia [14], was one of the most effective drug candidates for NASH therapy. Honokiol belongs to a class of neolignan biphenols, and it has been shown to have anti-inflammatory, anti-infection, anti-oxidative, and anti-tumor effects [15–18]. Indeed, natural polyphenol is also reported to show antioxidative effect in liver [19]. Honokiol is also relatively non-toxic in experimental contexts, and several food safety authorities have evaluated honokiol as safe [20]. Previous reports have suggested that honokiol is beneficial in hepatocyte lipotoxicity and macrophage polarization [21–24], but it remains unclear whether honokiol could be a drug candidate for treating the spectrum of NASH and related metabolic syndrome diseases. Moreover, the molecular mechanisms underpinning honokiol's protective effect are still not fully understood.
The aim of this study was to screen for effective drugs targeting NASH and establish the underlying molecular mechanisms. To do so, we combined in vitro and in vivo modeling to systematically examine the protective effects of honokiol in NASH and the accompanying metabolic features. First, we screened an FDA-approved drug library in an in vitro hepatocyte model of lipid accumulation, in which honokiol exerted significant efficacy. To further evaluate the underlying therapeutic mechanisms, we administered honokiol to murine models of NAFLD induced by a high-fat diet (HFD) and NASH induced by choline-deficient, L-amino acid-defined (CDA)HFD or methionine-choline deficient (MCD) diets. Transcriptomic analysis revealed a role for AMPK activation in honokiol's mechanism of action, which was further validated using pharmacological and genetic approaches. While classical regulators of AMPK activation did not appear to be implicated in its honokiol-mediated regulation, docking analysis predicted that honokiol could directly bind to AMPKγ1, which was subsequently confirmed experimentally. We therefore show that honokiol exerts its protective effects through AMPK activation via a new activating mechanism. These findings represent a significant step towards the discovery of a new class of drugs that target AMPK to manage NAFLD and NASH.
## Cell lines and primary hepatocytes
Cell lines of L02 and HEK293T were obtained from the Chinese Academy of Sciences in Shanghai, China. L02 and HEK293T cells were cultivated in a Dulbecco's modified *Eagle medium* (DMEM) enriched with $10\%$ FBS and $1\%$ penicillin/streptomycin.
Two-step collagenase perfusion was used to acquire primary hepatocytes from 6- to 8-week male C57BL/6 J mice. Briefly, mice were sedated with $3\%$ pentobarbital sodium (90 mg/kg, #P3761, Sigma-Aldrich, St. Louis, MO). The anesthetized mice were sequentially perfused via the portal vein with Liver Perfusion Medium (#17701038, Thermo Fisher Scientific, Waltham, MA) and Liver Digestion Medium (#17701034, Thermo Fisher Scientific). The livers were then removed, chopped into small pieces, and passed through a 100 µm steel mesh. After two centrifugations at 50 × g for a duration of one-minute, primary mouse hepatocytes were isolated from a mixture of liver cells.
To recreate lipid accumulation in vitro, L02 cells or primary hepatocytes were cultured with a medium composed of 500 μM palmitic acid (PA) and 1 mM oleic acid (OA) and treated with honokiol (10 μM) or DMSO. 500 μM PA was used to construct a model of hepatocytes inflammation. To serve as a control, a $0.5\%$ BSA was employed. 10 μM compound C (CC) was used to inhibit AMPK phosphorylation.
PRKAA1/PRKAA2-deficient cell lines were generated using the CRISPR/Cas9 system as described in our previous work [25]. AMPKγ1 knockdown cell lines were generated by cloning a short hairpin sequence (GTCTTGTCCTCTAGGCATGCT) targeting human PRKAG1 into the pLKO.1 plasmid (#10878, Addgene, Watertown, MA). The hairpin sequence targeting PRKAG1 was designed using an online tool (http://rnaidesigner.thermofisher.com/rnaiexpress/design.do). The short hairpin RNA-expressing plasmid was combined with the packaging plasmids pMD2.G (#12259, Addgene) and psPAX2 (#12260, Addgene) at a ratio of 2:1:1 and co-transfected into HEK293T cells. Following transfection, the supernatants were harvested after 48 h and filtered through a 0.45 µm filter. L02 hepatocytes were then infected with the collected supernatants with the help of polybrene (2 mg/mL). To screen positive candidates, infected cells were killed with puromycin (1 μM).
## High-content screening from FDA-approved drug library
To identify potential effective drugs from the FDA-approved drug library (#L1300, Selleckchem, USA), we employed the human hepatocyte cell line L02. The FDA-approved drugs library was procured from Selleck. 10,000 hepatocytes were seeded per well in a 96-well plate. The day following plating, we exposed the cells to 0.5 mM/1 M PA/OA for 18 h while simultaneously administering the drugs. The drugs were administered at a concentration of 20 μM. Bodipy staining was employed to assess lipid accumulation, and the fluorescence intensity was then quantified using a high-content machine.
## Animal experiments
The male C57BL/6 J mice were provided free access to food and water in a temperature-controlled environment (23 ± 2 °C). C57BL/6 J mice were fed a high-fat diet (HFD) (#MD12032, Medicience, Jiangsu, China) starting at 8 weeks age to establish a NAFLD model. After HFD feeding for 12 weeks, mice were divided evenly into two groups, one of which received vehicle ($1\%$ carboxymethylcellulose sodium (CMC-NA), # 419273, Sigma-Aldrich) or 100 mg/kg honokiol (#BD8971-25 g, Bidepharm, Shanghai, China. The chemical purity was $98\%$) dissolved in $1\%$ b-CMC-NA by gavage every day. At 24 weeks after HFD feeding, mice were sacrificed and blood, liver, and white adipose tissues (WAT) samples were collected for further study.
To create a NASH model, C57BL/6 J mice were given a choline-deficient, L-amino acid-defined (CDA)HFD (A06071302, Research Diets, Inc., New Brunswick, NJ) or methionine-choline deficient (MCD) diet (TP3005G, Trophic Animal Feed High-Tech Co, Nantong, China) beginning at the age of 8 weeks. After feeding CDAHFD or MCD for 1 week, mice were separated evenly into two groups, which respectively received $1\%$ CMC-NA or 100 mg/kg honokiol dissolved in $1\%$ CMC-NA by gavage every day. After feeding 4 weeks for indicated diets, animals were sacrificed and their blood and liver samples were collected for further analysis.
To demonstrate the in vivo requirement for AMPK activation-mediated honokiol protection against NASH, C57BL/6 J mice were fed a CDAHFD for 1 week, and then assigned randomly to two groups, and treated with PBS or AMPK inhibitor compound C (CC, 10 mg/kg/every two days) in combination with $1\%$ CMC-NA or honokiol (100 mg/kg/every day) for another 3 weeks. After being fed for four weeks, mice were sacrificed and their blood and liver samples were taken for further analysis.
## Glucose and insulin tolerance tests
Glucose tolerance tests (GTT) were performed in mice after 22 weeks of HFD feeding or after 3 weeks of the CDAHFD diet. Following an 18 h fast, mice were intraperitoneally (i.p.) injected with 1 g/kg glucose. Subsequently, blood glucose levels were monitored at 0, 15-, 30-, 60-, and 120-min post-injection.
Following 23 weeks of high-fat diet feeding, insulin tolerance tests (ITT) were performed on the mice. 0.75 IU/kg insulin was injected intraperitoneally after a six-hour fast and their blood glucose levels were measured at intervals of 0, 15-, 30-, 60-, and 120-min post-injection.
## Serum biochemical analysis
Serum alanine transaminase (ALT), aspartate transaminase (AST), total cholesterol (TC), and triglycerides (TG) were detected to evaluate liver function and serum concentrations of lipids using an automatic biochemical analyzer (HITACHI 3110, Tokyo, Japan).
## Western blotting
Proteins were extracted from cells or mouse liver tissues using RIPA lysis buffer, which contained 50 mM Tris–HCl pH 8.0, 150 mM NaCl, 1 mM EDTA, $1\%$ NP-40, $0.5\%$ sodium deoxycholate and $0.1\%$ SDS. The extraction process included the use of complete protease inhibitor cocktail tablets (#04693132001, Roche, Basel, Switzerland) and phosphatase inhibitor (#4906837001, Roche). The concentration of samples was then determined with a BCA Protein Assay Kit (#23225, Thermo Fisher Scientific). Protein samples were fractionated via sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and then transferred to 0.45 µm PVDF membranes. Following the blocking the membranes with $5\%$ skimmed milk, primary antibodies were incubated overnight at 4℃, followed by 1 h incubation with secondary horseradish peroxidase (HRP)-conjugated antibodies at room temperature. Finally, the protein expression signals were detected in a ChemiDoc MP Imaging System (Bio-Rad, Hercules, CA). β-actin was used as an internal control for loading.
## Antibodies
Primary antibodies targeting ACC (#3676), p-ACC (#3661), AMPKα (#5832), p-AMPKα (#50081), mTOR (#2983), p-mTOR (#2971), CaMKK2 (#16,810), p-CaMKK2 (#12818), TAK1 (#4505), p-TAK1 (#4508), LKB1 (#3050), and p-LKB1 (#3055) were procured from Cell Signaling Technology (Danvers, MA). Primary antibodies targeting PP2C (#ab211660) were procured from Abcam (Cambridge, UK). Antibodies targeting actin were obtained from ABclonal (AC026, 1:3000; Wuhan, China). Antibodies targeting Flag (M185-3L) were obtained from MBL (Nagoya, Japan). Unless otherwise specified, the dilution of all primary antibodies was 1:1000. The secondary antibodies peroxidase AffiniPure goat anti-rabbit IgG (H + L) (111–035-003) and goat anti-mouse IgG (H + L) (115–035-003) were obtained from the Jackson Laboratory (Bar Harbor, ME). A 1:5000 dilution was used for secondary antibodies.
## Lipid droplet staining and detection
Cellular lipid droplets were measured by BODIPY (D3922, Thermo Fisher Scientific) staining. L02 or primary hepatocytes were fixed with $4\%$ paraformaldehyde for 20 min at room temperature after challenge with PA/OA for 12 h. Following a PBS wash, cells were subjected to BODIPY staining at room temperature for 15 min. Cellular nuclei were stained with 4',6-diamidino-2-phenylindole (DAPI) (S36939, Invitrogen, Waltham, MA). A confocal laser scanning microscope (TCS SP8X, Leica, Wetzlar, Germany) was used to acquire images.
## Cell viability determination
Primary hepatocytes were seeded in 96-well plates at 5000 cells per well and incubated with 10 μM of honokiol for 24 h. Next, 10 μL of Cell Counting Kit-8 reagent (Beyotime, China) was added to each well and incubated for an additional 4 h at 37 ℃. Absorbance at 450 nm was measured for each well to calculate cell viability.
## Detection of ATP, ADP, and AMP
After honokiol (100 mg/kg) administration 4 h, mice were anesthetized and their livers were quickly frozen and clamped to quantify AMP, ADP, and ATP levels. Utilizing 50 mg liver tissue from each mouse, ATP, ADP, and AMP were extracted and the detection parameters were previously specified [26].
## TG or TC detection
To determine hepatic lipid contents, cellular lipid was extracted from 50 mg of liver tissue using the Folch method as previously described [27]. The liver was tested for triglycerides (TG), total cholesterol (TC), and non-esterified fatty acids (NEFA) using Wako kits (Tokyo, Japan) as per the manufacturer’s instructions (#290–63701 for TG, #294–65801 for TC, #294–63601 for NEFA).
## Cellular respiration evaluation
The effect of honokiol on cellular respiration was assessed in primary mouse hepatocytes. Single cell suspensions of 0.1 M primary hepatocytes were prepared in DMEM with honokiol (10 μM) or DMSO. The oxygen consumption rate was documented after the successive administration of oligomycin (2.5 mM), FCCP (0.5 mM), rotenone (0.5 mM) and antimycin A (2.5 mM).
## Histopathological analysis
For histopathological analysis, HE staining (hematoxylin, G1004, Servicebio, Wuhan, China; eosin, BA-4024, Baso, Zhuhai, China) was examined on liver, heart or adipose tissues (AT). Liver lipid droplets were observed by oil red O (O0625, Sigma-Aldrich) staining using frozen liver tissues embedded in Tissue-Tek OCT Compound (4583, Sakura, Torrance, CA). Liver fibrosis was observed with picro-sirius red (26357–02, Hedebiotechnology, Beijing, China) staining. Histopathological images were acquired with a light microscope (ECLIPSE 80i, Nikon). The NAFLD activity scoring (NAS) system was used to quantify NAFLD in HE-stained liver sections [28]. Other histological images were quantified using Image-J.
## Immunohistochemistry
Immunohistochemistry staining of liver CD11b (BM3925, 1:12000 dilution; Boster; Wuhan, China) and WAT F$\frac{4}{80}$ (GB11027, 1:1600 dilution, Servicebio, Wuhan, China) were performed on paraffin embedded sections. To retrieve antigens, samples were boiled in a pressure cooker for 20 min in pH 9.0 EDTA buffer. After cooling, samples were placed in $3\%$ H2O2 for 20 min to quench endogenous peroxide activity. Following a wash with PBS, slides were blocked with $10\%$ BSA for 1 h at 37 ℃. Sections were incubated with the indicated primary antibodies overnight at 4℃. The next day, the sections were successively washed with PBS buffer for 5 min, followed by incubation with enhanced enzyme-labeled goat anti-rabbit IgG (Beijing ZSGB Biotech) at 37 degrees Celsius for one hour. Immunohistochemical staining was performed using the 3,30-diaminobenzidine (DAB) substrate kit (Beijing ZSGB Biotech) and counterstained with hematoxylin. The images were then taken with a light microscope. Histological images were quantified using Image-J.
## Synthesis of biotin-linked honokiol
A mixture of honokiol (270 mg, 1 eq) in dichloromethane (DCM) (5 mL) was added to biotin (378 mg, 1.5 eq), 4-dimethylaminopyridine (DMAP) (126 mg, 1 eq), and N, N’-dicyclohexylcarbodiimide (DCC) (522.9 mg, 2.5 eq). The reaction mixture was stirred at 25 °C for 12 h. LCMS showed a new peak of the desired mass. The mixture was poured into water (20 mL), extracted with EA (15 mL × 3), washed with saline (20 mL × 2), and concentrated under high vacuum to produce a residue. The residue was purified with a flash C18 column (20–$63\%$ ACN, $0.1\%$ TFA) and lyophilization to produce biotin-linked honokiol (80 mg, $95\%$ purity) as a white solid.
## Biotin-avidin binding assay
Plasmids expressing AMPKγ1, AMPKγ2, and the AMPKγ1-3A mutant were cloned into phage vectors. Cloned sequences were confirmed by Sanger sequencing. 293 T cells were seeded in 10 cm cell culture dishes. At $70\%$ confluence, 293 T cells were transfected with the indicated plasmids (12 μg). After 24 h, biotin-linked honokiol (40 μM) or biotin was added and incubated for another 4 h. The dish was washed with precooled PBS, and 1 ml immunoprecipitation buffer containing protease inhibitor cocktail tablets and phosphatase inhibitor tablets were added to lysis. To remove cellular debris, the lysates were centrifuged at 12,000 × g for 15 min at 4℃. Supernatants were incubated with Streptavidin Agarose Resins (#20353, Thermo Fisher Scientific) at 4 °C for 4 h followed by washing 5 times in cold immunoprecipitation wash buffer. The protein complex pull-down was degenerated in SDS loading buffer and subjected to western blot analysis using the indicated primary and corresponding secondary antibodies as described above.
## RNA-sequencing
The quality of extracted RNA samples was evaluated using the RNA 6000 Nano kit (#5067–1511, Agilent, Santa Clara, CA) after extraction with TRIzol reagent (#T9424, Sigma-Aldrich). In order to prepare the libraries, we used the MGIEasy RNA Library Prep Kit (#1000006384, MGI Tech Co., Ltd, Shenzhen, China).
For data analysis, sequences from cleaned reads were aligned to the Ensembl GRCm38 mouse genome with HISAT2, and SAMtools was used to sort and convert the mapped reads to BAM format. RAW counts and reads per kilobase per million (RPKM) values were calculated for each gene with StringTie. Normalized counts and differential expression between conditions were calculated with DESeq2 (v1.32.0). Differentially expressed genes (DEGs) were identified as those with |log2 (fold change) |≥ log2(1.5) and an adjusted P-value < 0.05. GSVA was carried out using the GSVA R package (v1.40.1) to assess pathway activity variation under different conditions. Gene sets with P-values < 0.05 were considered statistically significant.
## Statistical analysis
All data were analyzed using SPSS v26 and are expressed as the means ± SEM. For parametric data between two groups, a Student’s t-test was used to analyze differences. For parametric data for multiple comparisons, a one-way ANOVA was performed. Bonferroni's post hoc test was employed to analyze data that demonstrated significant results, while Tamhane's T2 (M) post hoc test was used for heteroscedastic data. For datasets with a skewed distribution, the Mann–Whitney U test and Kruskal–Wallis test were utilized for two and multiple group comparisons, respectively. P-values < 0.05 were considered significant.
## Screening of an FDA-approved drug library reveals honokiol is a candidate inhibitor of NAFLD
To effectively screen for drugs to prevent or treat NAFLD, we screened an FDA-approved drug library using L02 human hepatocytes in vitro. The therapeutic effect of drug candidate was further evaluated in multiple mice models (Fig. 1A). The cultured cells were exposed to palmitic/oil acid (PO) for 18 h to induce lipid accumulation, and compounds in the library were added at the same time with PO challenge. A high-content instrument was used to evaluate the lipid-lowering effect of the FDA library, and the top 10 candidates were further compared in independent experiments (Fig. 1B, C). Finally, honokiol was identified as an attractive potential candidate through a series of screenings of ~ 3000 drugs (Fig. 1D). Using a primary mouse hepatocyte model, we further confirmed the lipid-lowering and anti-inflammatory effects of honokiol with well-tolerable safety. BODIPY staining and TG and TC colorimetric assays showed that honokiol significantly decreased lipid droplet accumulation in the presence of PO stimulation (Fig. 1E, F). Cell viability assays indicated that current working concentrations had little to no effect on cells (Fig. 1G). Furthermore, transcriptomic analysis of primary hepatocytes showed robust inhibition of pathways and genes associated with inflammatory responses, and upregulated pathways and genes related with and fatty acid degradation (Fig. 1H, I).Fig. 1Screening candidate drugs in an FDA-approved library for lipid-lowering effects in hepatocytes and evaluating the therapeutic effects of honokiol in vitro. A Schematic of the screening strategy used with the FDA-approved drug library. B and C Representative image (B) and relative Alexa-488 intensity (C) of the top 10 candidates. $$n = 3$$ replicates. Student’s t-test was applied for statistical analysis. Scale bar 25 μm. D The molecular structure of honokiol. E Representative images (left) and quantification of fluorescence (right) generated by BODIPY staining of primary hepatocyte lipid droplets after treatment with different concentrations of honokiol. $$n = 3$$ replicates. Student’s t-test was applied for statistical analysis. Scale bar 10 μm. F Triglyceride (TG) and total cholesterol (TC) in primary hepatocytes challenged with PO stimulation for 12 h. $$n = 6$$ mice. Student’s t-test was applied for statistical analysis. G Relative cell viability of primary hepatocytes after treatment with 10μM honokiol for 24 h. $$n = 3$$ replicates. Student’s t-test was applied for statistical analysis. H GSVA enrichment of differentially regulated pathways involved in cell growth, lipid degradation, and immune responses in primary hepatocytes after treatment with PO for 12 h. K Heatmaps of gene expression associated with inflammation, and lipid degradation in primary hepatocytes after treatment with PO for 12 h
## Honokiol ameliorates high-fat diet (HFD)-induced NAFLD
To explore the potential clinical relevance of these findings, we evaluated the therapeutic effect of honokiol in HFD-fed mice. Mice were subjected to normal chow (NC) or a HFD for 12 weeks to initiate the NAFLD phenotype and were then treated with vehicle (carboxymethylcellulose, $1\%$ CMC) or honokiol (100 mg/kg/ every day) for an additional 12 weeks (Fig. 2A). The administration of honokiol significantly attenuated the increase in body weight, and there was a trend towards decreasing the liver weight gain induced by HFD (Fig. 2B, C). Histopathological analysis of liver tissue stained for lipids showed that honokiol treatment significantly decreased the size and contents of hepatic lipid droplets compared with vehicle treatment. In addition, honokiol significantly reduced the severity of fibrosis and inflammatory cell infiltration (Fig. 2D, E). The results of direct detection of liver TG, TC, and NEFA were consistent with the histological findings (Fig. 2F). Moreover, the liver injury markers (ALT and AST) and serum TC and TG were significantly reduced in the honokiol-treated group (Fig. 2G, H). There was no evidence of adverse effects on heart, kidney, or spleen function (Fig. 2I, J). Global transcriptome analysis of HFD mouse livers showed significant improvements in cell damage, inflammation, and lipid accumulation pathways in honokiol-treated mice (Fig. 2K, L). We conducted a further investigation into the lipid metabolism pathway impacted by honokiol treatment and discovered that honokiol usage significantly curbed several lipid metabolism pathways (Additional file 1: Fig. S1A).Fig. 2Honokiol ameliorates high fat diet (HFD)-induced non-alcoholic fatty liver disease. A Schematic showing HFD-induced NAFLD and evaluation of therapeutic effects of honokiol in vivo (100 mg/kg). B and C Body (B) and liver weight (C) of NC- or HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. One-way ANOVA was used for statistical analysis. D Representative images of indicated mouse liver sections stained with hematoxylin and eosin (HE), oil red O (ORO), picrosirius red (PSR), and immunohistochemistry (IHC) of CD11b-positive cells. $$n = 6$$ mice per group. Scale bar 50 μm. E Results of NAS (HE) and quantitative analysis of ORO, PSR, and CD11b shown in (D). $$n = 6$$ mice per group. Mann–Whitney U test was used for NAS, while Student’s t-test was applied to ORO, PSR, and CD11b data. F TG, TC, and non-esterified fatty acids (NEFA) in the livers of HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. G Serum ALT and AST activity in HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. H Serum TC and TG concentrations in HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. I Heart weights and heart histological staining of HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. Scale bar 50 μm. J Kidney and spleen weights of HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. K GSVA enrichment analysis related to inflammation, lipid metabolism, and fibrosis downregulated by honokiol treatment. $$n = 5$$ mice per group. L Heatmap of gene expression profiles involved in cell damage and death, inflammation, and lipid metabolism. $$n = 5$$ mice per group
## Honokiol ameliorates HFD-induced obesity and insulin resistance
We further analyzed the effect of honokiol on HFD-induced metabolic disorder. The white adipose tissue (WAT) and subcutaneous adipose tissue (SAT) weighed significantly less in honokiol-treated groups (Fig. 3A, B), with significantly decreased area and size of adipocytes and inflammatory cell infiltration in the WAT of honokiol-treated mice than controls on histopathological examination (Fig. 3C, D). Glucose tolerance testing (GTT) revealed a significant improvement after honokiol administration (Fig. 3E), as did insulin tolerance testing (ITT) (Fig. 3F). Furthermore, serum insulin concentrations also markedly decreased in honokiol-treated mice (Fig. 3G). Systematic transcriptome analysis of HFD mouse white adipose tissue showed significant therapeutic effects of honokiol with respect to cell damage, inflammation, and lipid metabolism pathways and associated gene expression (Fig. 3H, I). These results suggest that honokiol improves systemic metabolism. Fig. 3Honokiol ameliorates high fat diet (HFD)-induced obesity and insulin resistance. A and B Weights of white adipose tissue (WAT) (A) and subcutaneous adipose tissue (SAT) (B) from NC- or HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. One-way ANOVA was used for statistical analysis. C Representative images and quantification of HE staining of WAT from HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. Scale bar 50 μm. D Representative images and quantification of IHC staining of F$\frac{4}{80}$-positive cells in WAT sections. CLS, crown-like structure. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. Scale bar 50 μm. E Blood glucose concentrations during glucose tolerance testing (GTT) of HFD-fed mice treated with honokiol or vehicle at 22 weeks. $$n = 6$$ mice per group at each time point. Student’s t-test was applied for statistical analysis. Area under the GTT curve shown right. F Blood glucose concentrations during insulin tolerance testing (ITT) of HFD-fed mice treated with honokiol or vehicle at 23 weeks. $$n = 6$$ mice per group at each time point. Student’s t-test was applied for statistical analysis. Area under the ITT curve shown right. G Serum insulin concentration of HFD-fed mice treated with honokiol or vehicle after 12 weeks of their respective diets. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. H GSVA pathway enrichment analysis related to cell death, inflammation, and fatty acid degradation differentially regulated by honokiol treatment. $$n = 5$$ mice per group. I Heatmap of gene expression profiles involved in cell damage and death, inflammation, and fatty acid degradation
## Honokiol blocks severe inflammation and fibrosis in mouse NASH models
To further evaluate whether honokiol could block NASH progression, we applied a more severe NASH model induced by a choline-deficient, l-amino acid-defined high fat diet (CDAHFD). Compared with HFD models, CDAHFD-induced NASH models more closely resemble human NASH pathology in terms of ballooning and fibrosis, making them especially suitable for pharmacological interventions [29]. To initiate NASH progression, mice were subjected to CDAHFD for one week, followed by oral gavage with honokiol at 100 mg/kg every day along with CDAHFD feeding for another three weeks (Fig. 4A). Honokiol administration significantly decreased body weight, liver weight, and liver to body weight (LW/BW) ratio in CDAHFD-fed mice but had negligible impact in NC-fed mice (Fig. 4B, C). Both GTT and fasting blood glucose levels significantly improved on honokiol treatment (Fig. 4D, E). Moreover, hepatic lipid accumulation, fibrosis, and inflammatory cell infiltration were all significantly mitigated by honokiol administration (Fig. 4F–H). Consistent with the above effects, ALT, AST, and TG levels were all significantly lower in the honokiol-treated group (Fig. 4I).Fig. 4Honokiol protects against mouse NASH. A Schematic of the CDAHFD-induced NASH model and evaluating the therapeutic effects of honokiol in vivo (100 mg/kg). B Body weights of NC- or CDAHFD-fed mice treated with honokiol or vehicle three weeks after subjecting them to their respective diets for one week. $$n = 6$$ mice per group. One-way ANOVA was used for statistical analysis. C Liver weights and ratio of liver weight to body weight (LW/BW) of NC- or CDAHFD-fed mice treated with honokiol or CMC three weeks after subjecting them to their respective diets for one week. $$n = 6$$ mice per group. One-way ANOVA assay was used for statistical analysis. D Blood glucose concentrations during GTT and the AUC of GTT of CDAHFD-fed mice treated with vehicle or honokiol. E Fasting blood glucose (FBG) concentrations of CDAHFD-fed mice treated with vehicle or honokiol. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. F TG, TC and NEFA levels in the livers of CDAHFD-fed mice treated with honokiol or vehicle. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. G Representative images of indicated mouse liver sections stained with HE, ORO, PSR, and IHC for CD11b-positive cells. $$n = 6$$ mice per group. Scale bar 50 μm. H Results of NAS (HE) and quantitative analysis of ORO, PSR, and CD11b shown in (G). $$n = 6$$ mice per group. The Mann–Whitney U test was used for NAS and Student’s t-test was applied to ORO, PSR, and CD11b data. I Serum ALT and AST activity and serum TG concentrations in CDAHFD-fed mice treated with honokiol or vehicles. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. J Schematic of the MCD-induced NASH model and evaluating the therapeutic effects of honokiol in vivo (100 mg/kg). K TG, TC. and NEFA levels in the livers of MCD-fed mice treated with honokiol or vehicle. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. L Representative images of the indicated mouse liver sections stained with HE, ORO, PSR, and IHC for CD11b-positive cells. $$n = 6$$ mice per group. Scale bar 50 μm. M Results of NAS (HE) and quantitative analysis of ORO, PSR, and CD11b data shown in (L). $$n = 6$$ mice per group. For statistical analysis, the Mann–Whitney U test was used for NAS and Student’s t-test was applied to ORO, PSR, and CD11b. N Serum ALT and AST activity of MCD-fed mice treated with honokiol or vehicle. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. O GSVA pathway enrichment analysis related to cell damage and death, inflammation, lipid metabolism, and fibrosis differentially regulated by honokiol treatment. $$n = 5$$ mice per group. P Heatmaps of gene expression profiles involved in cell damage and death, lipid metabolism, inflammation, and fibrosis To further clarify the beneficial effect of honokiol on averting NASH progression, we established the methionine and choline deficient diet (MCD)-induced NASH model. In this NASH model, mice were subjected to MCD for one week followed by oral gavage of honokiol at 100 mg/kg every day along with MCD feeding for another three weeks (Fig. 4J). In MCD-fed NASH mice, liver lipid accumulation, fibrosis, and inflammatory cell infiltrates were all significantly improved by honokiol administration (Fig. 4K–M). Serum ALT and AST were consistently and significantly lower in the honokiol-treated group (Fig. 4N). Global transcriptomic profiling of liver tissue revealed significant differences in cell damage, inflammation, lipid metabolism, and fibrosis pathways between vehicle and honokiol-treated mice fed by CDAHFD (Fig. 4O, P).
## Honokiol significantly activates AMPK in vitro and in vivo
To explore the molecular mechanisms underlying the observed beneficial effects of honokiol, we interrogated the transcriptomic data obtained from our in vitro and in vivo models. Combined transcriptomic analysis suggested that activated AMPK signaling was one of the common pathways upregulated by honokiol (Fig. 5A). Moreover, correlation analysis also indicated that AMPK signaling was negatively related to cell death, inflammatory responses, and lipid metabolism in fatty liver settings (Fig. 5B). In line with the transcriptomics assay, Western blotting revealed that honokiol treatment activated AMPK in vitro (Fig. 5C). Furthermore, honokiol consistently activated AMPK and inhibited mTOR in both liver and WAT in the indicated mouse models (Fig. 5D–G). These results robustly demonstrate AMPK activation by honokiol and collectively point to AMPK activation as the molecular mechanism mediating its anti-NASH protective effects. Fig. 5The AMPK signaling pathway is activated in vitro and in vivo. A The AMPK signaling pathway was one of the four common signaling pathway upregulated by honokiol in PHCs and in the livers of HFD- and CDAHFD-fed mice. B The AMPK signaling was negatively associated with cell death-associated genes, immune response-associated genes, and lipid metabolism-associated genes in HFD-fed mouse livers. C Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-mTOR, mTOR, and CPT1α in PHCs stimulated with palmitic acid (PA) for 18 h at the indicated concentrations. $$n = 3$$ replicates. D Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-mTOR, mTOR, and CPT1α in the livers of HFD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group. E Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-mTOR, mTOR, and CPT1α in the WAT of HFD-fed mice treated with CMC or honokiol. $$n = 5$$ mice per group. F Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-mTOR, mTOR, and CPT1α in the livers of CDAHFD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group. G Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-mTOR, mTOR, and CPT1α in the livers of MCD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group
## AMPK activation is essential for honokiol-mediated liver protection
To further demonstrate whether AMPKα activation is essential for the protective effects of honokiol, we cotreated primary hepatocytes with the AMPK activation inhibitor compound C (CC) and honokiol. CC significantly reduced the induction of AMPK activity and largely diminished the lipid-lowering effect by honokiol in primary hepatocytes (Fig. 6A–C). To further confirm the requirement of AMPK activation genetically, we generated PRKAA1 and PRKAA2 (expressing AMPKα1 and AMPKα2) double knockout (DKO) hepatocytes (Fig. 6D). Of note, AMPKα-DKO completely abolished the lipid-lowering effect of honokiol in hepatocytes (Fig. 6E, F). Furthermore, transcriptomic profiling firmly validated the reversal of pathways nvolved in lipid metabolism and inflammation by CC treatment (Fig. 6G). The collective pharmacological and genetic evidence suggest that AMPK activation is a necessary component for honokiol to exert a protective effect in vitro. Fig. 6AMPK activation is required for honokiol-mediated beneficial effects in vitro. A Western blots of p-AMPKα, AMPKα, p-ACC, and ACC in primary hepatocytes challenged with PA for 18 h under the indicated conditions. $$n = 3$$ replicates B Representative images (left) of BODIPY staining and quantification (right) of lipid droplets in primary hepatocytes challenged with PO under the indicated conditions. $$n = 3$$ replicates. One-way ANOVA was used for statistical analysis. Scale bar 10 μm. C TG and TC of primary hepatocytes challenged with PO stimulation for 18 h under the indicated conditions. $$n = 6$$ mice per group. One-way ANOVA was used for statistical analysis. D Western blots of p-AMPKα, AMPKα, p-ACC, and ACC in WT or PRKAA$\frac{1}{2}$ (encoding AMPKα$\frac{1}{2}$) double-knockout (DKO) hepatocytes challenged with PA for 18 h under the indicated conditions. E Representative images (left) of BODIPY staining and quantification (right) of lipid droplets in WT or PRKAA$\frac{1}{2}$ DKO L02 hepatocytes challenged with PO for 12 h under the indicated conditions. $$n = 3$$ replicates. One-way ANOVA was used for statistical analysis. Scale bar 10 μm. F TG and TC of WT or PRKAA$\frac{1}{2}$ DKO hepatocytes challenged with PO stimulation for 18 h under the indicated conditions. G Dot plot representing pairwise GSVA comparisons of transcriptomic data from primary hepatocytes challenged with PA for 18 h under the indicated conditions We next explored whether AMPK activation is vital to honokiol-mediated protective effects in vivo. We subjected CDAHFD-induced NASH mice to combined administration of CC (10 mg/kg/i.p. every other day) and honokiol (100 mg/kg every day, intragastric gavage) (Fig. 7A). CC effectively diminished honokiol-induced AMPK activation in the liver (Fig. 7B). CC treatment also abrogated the observed improvements in glucose intolerance by honokiol (Fig. 7C). The beneficial effects of honokiol on liver lipid accumulation, fibrosis, and inflammatory cell infiltration were also significantly reversed (Fig. 7D–F). CC treatment also reduced honokiol-induced improvements in serum markers of liver function and lipid metabolism (Fig. 7G, H). Finally, transcriptomic data systematically showed CC-induced reversal of the pathways and gene expression profiles involved in lipid metabolism, inflammation, fibrosis, and cell damage (Fig. 7I, J). All the evidence led to the conclusion that AMPK activation is critical component of honokiol-mediated hepatic protection. Fig. 7AMPK activation is required for honokiol-mediated beneficial effects in vivo. A Schematic of the experimental procedure used with mice fed a CDAHFD diet and treated with vehicle or honokiol (100 mg/kg) in the absence or presence of compound C (CC, 10 mg/kg, every other day, i.p.). B Western blots of p-AMPKα, AMPKα, p-ACC, and ACC of mice in the indicated groups. $$n = 3$$ mice per group. C The blood glucose concentration during GTT of CDAHFD-fed mice in the indicated groups. $$n = 6$$ mice per group. One-way ANOVA was applied for statistical analysis. P-values of the red color represent the comparison of PBS-vehicle vs PBS-honokiol, while black represents CC-vehicle vs CC-honokiol. D Liver contents of TG, TC, and NEFA of CDAHFD-fed mice in the indicated groups. $$n = 6$$ mice per group. The Kruskal–Wallis test was applied for statistical analysis. E Representative images of the indicated mouse liver sections stained with HE, ORO, PSR, and IHC for CD11b-positive cells. $$n = 6$$ mice per group. Scale bar, 50 μm. F Results of NAS (HE) and quantitative analysis of ORO, PSR, and CD11b data shown in (E). $$n = 6$$ mice per group. For statistical analysis, the Kruskal–Wallis test was used for NAS and one-way ANOVA was applied to ORO, PSR, and CD11b data. G Serum ALT and AST activity of CDAHFD-fed mice shown in the indicated groups. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. H Serum TC concentrations in CDAHFD-fed mice treated with the indicated groups. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. I Dot plot representing pairwise GSVA comparisons of transcriptomic data from CDAHFD-fed mice shown in the indicated groups. J Heatmap of transcriptomic data from CDAHFD-fed mice shown in the indicated groups
## Honokiol activates AMPK by directly binding to AMPKγ1 and acts as an AMPK complex agonist
To further explore the exact molecular mechanisms underpinning honokiol’s activation of AMPK signaling, we examined the impact of honokiol on well-established upstream regulators of AMPK. However, honokiol showed negligible influence on the expression and phosphorylation of liver kinase B1 (LKB1), transforming growth factor beta-activated kinase 1 (TAK1), calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2), and no decrease in the negative regulator protein phosphatases 2C (PP2C) in hepatocytes or fatty livers compared with blank controls (Fig. 8A–D). As the AMPK complex directly senses cellular energy status, we hypothesized that these negative findings might be related to the direct influence of honokiol on cellular respiration and subsequent changes in cellular ATP, ADP, and AMP. However, the oxygen consumption rate (OCR) of hepatocytes was not significantly altered upon treatment with honokiol (Fig. 8E). Similarly, liver ATP, ADP, and AMP levels were not significantly altered by honokiol in fatty livers in diet-induced mouse models (Fig. 8F–H). This evidence collectively suggests the possibility of a previously unknown mechanism through which honokiol activates AMPK.Fig. 8AMPK activation is independent of classical pathways. A Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-LKB1, LKB1, p-CAMKK2, CAMKK2, p-TAK1, TAK1, and PP2C of primary hepatocytes subjected to PA stimulation at the indicated conditions. $$n = 3$$ replicates. B Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-LKB1, LKB1, p-CAMKK2, CAMKK2, p-TAK1, TAK1, and PP2C in the livers of HFD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group. C Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-LKB1, LKB1, p-CAMKK2, CAMKK2, p-TAK1, TAK1, and PP2C in the livers of CDAHFD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group. D Western blots of p-AMPKα, AMPKα, p-ACC, ACC, p-LKB1, LKB1, p-CAMKK2, CAMKK2, p-TAK1, TAK1, and PP2C in the livers of MCD-fed mice treated with vehicle or honokiol. $$n = 5$$ mice per group. E Representative image of the oxygen consumption rate (OCR) of primary hepatocytes subjected to DMSO or honokiol at the indicated conditions. $$n = 3$$ replicates. Omy, oligomycin, F1F0 ATP synthase inhibitor. FCCP, mitochondrial uncoupler. Rot, retenone, complex I inhibitor. AA, antimycin A, complex II inhibitor. F ATP, ADP, and AMP in the livers of HFD-fed mice treated with vehicle or honokiol. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. G ATP, ADP, and AMP in the livers of CDAHFD-fed mice treated with vehicle or honokiol. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis. H ATP, ADP, and AMP in the livers of MCD-fed mice treated with vehicle or honokiol. $$n = 6$$ mice per group. Student’s t-test was applied for statistical analysis *Docking analysis* suggested that honokiol might directly bind to the AMPKγ1 subunit [30], potentially explaining how honokiol activates AMPK. To test this prediction, we chemically linked biotin to honokiol and constructed plasmids expressing AMPKγ1 (Fig. 9A). Since AMPKγ2 is predominantly expressed in the liver, we also created an AMPKγ2 plasmid (Fig. 9B). Streptavidin–biotin binding assays showed that honokiol significantly interacted with AMPKγ1 but not with AMPKγ2 (Fig. 9C, D). Honokiol binds AMPKγ1 at histidine [151], arginine [152], and lysine [243], since mutation of these 3 amino acid sites largely abolished the interaction between honokiol and AMPKγ1 (Fig. 9E). Importantly, AMPKγ1 knockdown significantly reduced AMPK activation by honokiol, and consequently the lipid-lowering effects of honokiol (Fig. 9F, G). This suggests that AMPKγ1 is necessary for the full activation of AMPK by honokiol. Moreover, in shPRKAG1 cells, rescue of honokiol-induced AMPK activation and its subsequent protective effect only occurred in cells supplemented with WT-AMPKγ1, instead of the AMPKγ1-3A mutant (Fig. 9H, I). Our research suggests that honokiol could potentially be an AMPK complex agonist through direct binding to AMPKγ1 (Fig. 9J).Fig. 9AMPK activation is independent of classical pathways. A The structure of honokiol and the synthesis of biotin-linked honokiol. B Schematic showing the construction of plasmids expressing 3X-FLAG-PRKAG1, 3X-FLAG-PRKAG2, and 3X-FLAG-PRKAG1-3A, respectively. C Representative gels showing the binding of honokiol to Flag-AMPKγ1 in 293 T cells. $$n = 3$$ replicates. D Representative gels showing the binding of honokiol to Flag-labeled AMPKγ1 and Flag-labeled AMPKγ2. $$n = 3$$ replicates. E Representative gels showing the binding of honokiol to Flag-labeled AMPKγ1 and Flag-labeled AMPKγ1-3A. $$n = 3$$ replicates. F Western blots of p-AMPKα, AMPKα, p-ACC, and ACC of shRNA hepatocytes or shPRKAG1 hepatocytes at the indicated conditions. $$n = 3$$ replicates. G Representative images (left) of BODIPY staining and quantification (right) of lipid droplets in shRNA hepatocytes or shPRKAG1 hepatocytes challenged with PO for 12 h under the indicated conditions. $$n = 3$$ replicates. One-way ANOVA was used for statistical analysis. Scale bar 10 μm. H Western blots of p-AMPKα, AMPKα, p-ACC, and ACC of shRNA hepatocytes transfected with empty vector and shPRKAG1 hepatocytes transfected with Flag-PRKAG1 or Flag-PRKAG1-3A plasmids and challenged with PA for 18 h under the indicated conditions. $$n = 3$$ replicates. I Representative images (left) of BODIPY staining and quantification (right) of lipid droplets in shPRKAG1 L02 hepatocytes transfected with empty vector, Flag-PRKAG1, or Flag-PRKAG1-3A plasmids and challenged with PO for 12 h under the indicated conditions. $$n = 3$$ replicates. Scale bar 10 μm. J Schematic showing the mechanism of honokiol-mediated activation of the AMPK complex and beneficial effects of honokiol in ameliorating obesity and NASH progression
## Discussion
NAFLD and related metabolic syndrome have become major disease burdens worldwide, and approved pharmacological interventions for these conditions are lacking. The failure of promising drug candidates in phase II or phase III clinical trials over the last three years further emphasizes the urgent need for new, effective, and safe agents for NASH therapy. FDA-approved drug libraries provide a highly efficient strategy for anti-NASH drug screening and development, benefiting from the well-tested safety and pharmacology of the included compounds. Here we identified honokiol as an AMPK agonist that ameliorated NASH and metabolic syndrome. Intriguingly, honokiol did not inhibit lipid accumulation, inflammation, and cell damage via classical upstream AMPK activation pathways, but instead through direct interaction with AMPKγ1 and subsequent phosphorylation.
Honokiol is a pleiotropic compound found in magnolia plants, and it is used in traditional Chinese medicine for the treatment of several diseases. Previous studies have reported that honokiol is useful for the treatment of tumors, sepsis-associated acute lung injury, neurodegenerative diseases, and cardiomyopathy [31]. The main beneficial effects of honokiol are related to its ability to induce apoptosis, reduce inflammation, and scavenge harmful oxidizing agents. Moreover, previous studies have also reported that honokiol can ameliorate hepatocyte lipotoxicity and macrophage polarization in the liver [21–24]. It also has a reported anti-obesity effect [32, 33]. However, previous studies have not fully established the detailed molecular events and gene expression profiles related to the phenotypes induced by honokiol treatment. Based on the results of our present study, honokiol represents a promising drug candidate for metabolic disorder-related diseases via a previously unappreciated molecular mechanism. Notably, there might be other mechanisms, except from AMPK signaling, also involved in the beneficial effects of honokiol against NAFLD progression. In our experiments, both AMPK signaling and Retrograde endocannabinoid signaling are enriched, the latter having been associated with neuron diseases [34] but its implication in NAFLD yet to be investigated. It is essential to conduct further research to determine if honokiol can ameliorate NAFLD and metabolic syndrome through other pathways, such as retrograde endocannabinoid signaling.
Hepatic and adipose tissue lipid accumulation results from an imbalance between lipid production and utilization [35]. As noted above, our multi-transcriptomic analysis further showed that honokiol protected against obesity/NAFLD/NASH by promoting fatty acid oxidation (FAO), a key metabolic pathway for fatty acids. Further analysis of signaling pathways regulating FAO revealed AMPK activation. AMPK is a master regulator of nutrient metabolism, including lipid synthesis and degradation [36]. AMPK activation has been shown to protect against NASH [37–42], obesity [43], and type 2 diabetes [44, 45]. Notably, AMPK activation in the intestine by nicotine could aggravate NASH by increasing intestinal ceramide formation [46]. We confirmed activation of AMPK and its targets by honokiol [21, 24, 33], consistent with its protective effects against NAFLD and NASH in vitro and in vivo. Through both genetic and pharmacological methods, we demonstrated that the protective effect of honokiol in NAFLD/NASH depends on AMPK activation. Transcriptomic analysis suggested that compound C (CC) treatment significantly reversed the gene expression profile regulated by honokiol administration.
As reported previously, AMPK activation is tightly regulated by upstream kinases and phosphatases [8]. However, we found that honokiol did not rely on its classical regulators for activation of AMPK. Molecular docking analysis and biotin-avidin affinity capture of honokiol and AMPK complex indicated that honokiol could directly bind to the AMPKγ1 subunit, thus activating the AMPK complex. After point mutation of predicted binding sites, enrichment of the AMPKγ1-3A mutant by honokiol largely decreased. These experimental results further suggest honokiol can bind to AMPKγ1 to activate the AMPK complex. In PRKAG1 (gene expressing AMPKγ1)-knockdown hepatocytes, we found a marked reduction in honokiol-induced AMPK activation, an effect that could be rescued by supplementation with AMPKγ1 but not with the AMPKγ1-3A mutant. These findings suggest direct targeting AMPKγ1 by honokiol to activate the AMPK complex. Consistent with this, liver-specific gain-of-function mutations in AMPKγ1 or direct targeting of AMPKγ1 with small molecules have shown protective effects against NASH [12, 47, 48] and liver glucose production [49], highlighting the importance of AMPKγ1 in the subsequent protective effects of AMPK activation. These results suggest that honokiol appears to act as an AMPK complex agonist that can be applied to several AMPK inactivation-mediated pathologies.
Hepatic and adipose tissue lipid accumulation occurs through an imbalance between lipid production and utilization [35]. Our multi-transcriptomic analysis showed that honokiol benefited obesity/NAFLD/NASH by promoting FAO, which is a key metabolic pathway for fatty acids. Further analysis of signaling pathways regulating FAO revealed AMPK activation. AMPK is a master regulator of nutrient metabolism, including lipid synthesis and degradation [36]. Inactivation of AMPK is a major hallmark of metabolic diseases [25, 41, 50–53], while activating AMPK has been shown to protect against NASH [37–42], obesity [43] and type 2 diabetes [44, 45]. However, excessive activation of AMPK might lead to unwanted side-effects, for instance AMPK activation in the intestine by nicotine from tobacco could aggravate NASH by increasing intestinal ceramide formation [46]. Furthermore, long-term administration of pan-AMPK agonists is causally related to cardiac hypertrophy [54]. In the present study, using genetic and pharmacological methods, we demonstrated that the protective effect of honokiol on NASH and its related metabolic diseases is dependent on AMPK activation. More importantly, we did not observe any side effects of honokiol on the cardiovascular system, which might be due to the specific regulation of AMPK by honokiol.
## Conclusions
In summary, here we introduce a treatment that may be suitable for the entire spectrum of NASH and metabolic syndrome. Honokiol effectively prevented lipid accumulation, cell damage, and immune responses both in vitro and in vivo. In-depth analysis of the molecular mechanisms regulating FAO uncovered significant activation of AMPK, which was required for honokiol’s mechanism of action in pharmacological and genetic studies. Of note, honokiol-mediated activation of the AMPK complex did not rely on its classical regulators, instead acting as an AMPK complex agonist via directly binding to AMPKγ1 subunit. Thus, our findings add new insight that targeting AMPKγ1 with small molecular agents could be a potential treatment for obesity, NAFLD, and NASH without adverse effects.
## Supplementary Information
Additional file 1: Fig. S1. Lipid metabolism pathway influenced by honokiol treatment. ( A) Heatmaps of gene expression associated with multiple lipid metabolism pathways of liver from HFD-fed mice.
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|
---
title: Retrospective study on the therapeutic efficacy of zinc acetate hydrate administration
to patients with hypozincemia-induced dysgeusia
authors:
- Tomoaki Shintani
- Kouji Ohta
- Toshinori Ando
- Yasutaka Hayashido
- Souichi Yanamoto
- Mikihito Kajiya
- Hideki Shiba
journal: BMC Oral Health
year: 2023
pmcid: PMC10024455
doi: 10.1186/s12903-023-02866-7
license: CC BY 4.0
---
# Retrospective study on the therapeutic efficacy of zinc acetate hydrate administration to patients with hypozincemia-induced dysgeusia
## Abstract
### Background
Dysgeusia is a relatively early symptom of zinc deficiency, and zinc replacement is effective in treating dysgeusia. The administration of zinc acetate hydrate (ZAH) was approved in 2017 for patients with hypozincemia in Japan. This retrospective study was conducted to explore the efficacy and safety of ZAH administration in patients with hypozincemia-induced dysgeusia.
### Methods
Patients with hypozincemia-induced dysgeusia who visited our hospital from May 2013 to December 2019 were included in this study. ZAH (zinc content; 50 mg/day) was administered to 42 patients for 24 weeks. The taste test was performed using the filter paper disk method, and the total cognitive thresholds of the left and right chorda tympani regions were used. Changes in taste function, serum zinc and copper levels, and copper/zinc ratio were analyzed. A total of 28 patients who received polaprezinc (PPZ, zinc content; 34 mg/day) for 24 weeks, who were prescribed until ZAH was approved, were registered as controls.
### Results
Serum zinc levels at 12 and 24 weeks after ZAH or PPZ administration were higher than those before administration. These levels were significantly higher in the ZAH-treated group than in the PPZ-treated group. However, serum copper levels did not significantly change before and after administration. In the taste test, the taste thresholds for the acidity and salty at 12 and 24 weeks after ZAH administration were significantly decreased compared to before administration. In contrast, in the PPZ group, the taste thresholds for the acidity and salty were significantly decreased 24 weeks after administration.
### Conclusions
ZAH (50 mg/day) administration was effective in improving the gustatory sensitivity of patients with dysgeusia and hypozincemia 12 weeks after administration without affecting the serum copper level. ZAH was also more effective than PPZ.
## Background
Zinc (Zn), an essential trace element, is related to enzymatic activities and exerts several physiological roles in the body [1–3]. Zn serves as a cofactor in DNA polymerase and in Zn-finger proteins that are addicted to DNA and regulates protein synthesis [4, 5]. Zn deficiency is diagnosed on the basis of clinical symptoms due to lower serum Zn levels [6]. The symptoms of Zn deficiency include loss of appetite, stunting, skin symptoms, hair loss, gonadal dysfunction, dysgeusia, delayed wound healing, and immune decline [7]. Moreover, previous studies have reported Zn deficiency in diseases such as hepatitis C, liver cirrhosis, diabetes, inflammatory bowel disease, and chronic renal diseases [8, 9]. Other studies have also shown that Zn deficiency elevates the concentration of inflammatory cytokines and oxidative stress and induces the apoptosis of endothelial cells [10–12]. Zn supplementation was found to decrease the concentrations of plasma C-reactive protein and interleukin (IL)-6 in elderly subjects [13]. Consequently, Zn replacement therapy is recommended as a treatment for patients with Zn deficiency [14].
In recent years, the number of patients visiting a hospital with a primary complaint of dysgeusia has been increasing annually in Japan [15]. The Japanese survey conducted in 2003 reported that the number of patients with dysgeusia was 240,000 per year [16]. The causes of dysgeusia include drugs and stress, but the most common cause is Zn deficiency [17, 18]. Particularly, *Zn is* present in the squamous epithelium containing the taste buds of the tongue papilla in a high concentration, and the taste buds contain several Zn enzymes such as alkaline phosphatase, acid phosphatase, and cyclic AMP phosphodiesterase [19]. Kinomoto et al. reported about the closure of the parakeratinized epithelium in the taste buds of the fungiform papillae and the circumvallate papillae in rats with Zn deficiency [20]. The Zn supplementation polaprezinc (PPZ), which has been approved in Japan as an antiulcer agent, had a therapeutic effect in patients with idiopathic taste disorders, including those with hypozincemia [19]. Animal experiments showed that the administered PPZ was distributed to the lingual epithelium and restored Zn concentration in Zn-deficient rats, resulting in improvement of the proliferation of taste bud cells [21]. To date, no Zn drugs for the treatment of Zn deficiency have been approved as a therapeutic agent. In 2017, the administration of zinc acetate hydrate (ZAH), which contains a higher amount of Zn than that in PPZ and is used for the treatment of rare inherited disorder Wilson’s disease that causes copper (Cu) accumulation in vital organs, was approved for treating patients with hypozincemia in Japan [22, 23]. ZAH has demonstrated efficacy in patients with cirrhosis, in which Zn deficiency is common [22]. A recent study has shown that ZAH is more effective than PPZ in elevating serum Zn levels in patients under maintenance hemodialysis [24]. It has also been shown that ZAH administration to patients with inflammatory bowel disease with Zn deficiency may normalize serum Zn levels and improve disease activity, particularly in patients with Crohn’s disease [25].
Zn and Cu, which are divalent metal cations, are known to antagonize the absorption of each other in the intestinal tract [26]. Thus, Zn supplementation can easily cause a decrease in serum Cu concentration. In fact, it has been reported that pancytopenia due to hypocupremia was observed in three patients under maintenance hemodialysis with hypozincemia who received ZAH for 4–7 months [27]. Another study also reported that serum Cu levels were significantly decreased at 3 months after PPZ administration in patients under hemodialysis [24]. Nevertheless, one study showed that there is no relationship between serum Zn concentration and dysgeusia and that the Cu/Zn ratio is a more important diagnostic marker for dysgeusia [28].
Reports on the administration of ZAH to patients with Zn-deficient dysgeusia are insufficient. Thus, in this study, we retrospectively investigated the effects of ZAH administrations on taste and changes in serum Zn and Cu levels and Cu/Zn ratio in patients with hypozincemia-induced dysgeusia. Patients who received PPZ for the same period were used as controls. Furthermore, patients who received ZAH were divided into the improved and non-improved groups on the basis of the results of the taste test, and their clinical characteristics were compared.
## Subjects
In total, 70 outpatients who visited the study hospital with a complaint of taste disorder between May 2013 and February 2021, with zinc deficiency (serum zinc level < 80 µg / dL) and a total taste test score of 28 or higher on the filter paper disc method shown below were diagnosed with hypozincemia-induced dysgeusia and were included in this study. Of these 70 patients, 42 received 50 mg (element Zn)/day of ZAH (Nobelzin® tablet; Nobelpharma Co., Ltd., Tokyo, Japan) and were treated daily for 24 weeks. The remaining 28 patients received 150 mg/day of PPZ (Promac® granules; Zeria Pharmaceutical Co., Ltd., Tokyo, Japan), which contained approximately 34 mg/day of Zn, and were treated daily for 24 weeks. Both drugs were taken orally after breakfast and dinner. PPZ was prescribed to patients with dysgeusia and hypozincemia before April 2017. Following its approval for treatment in Japan in April 2017, ZAH, but not PPZ, was prescribed to patients.
## Paper filter disk method
The paper filter disk method was performed to test the recognition of the four flavors sweet, salty, sour, and bitter using the kit (Taste Disc®; Sanwa Chemistry Institute, Nagoya, Japan) [29]. Gustatory sensitivity was investigated for two regions of the bilateral location in the chorda tympani nerve. Unless the patient recognized the taste, the test was continued with solutions of higher concentrations until the correct response was given. A scoring system was used, ranging from 1 to 6, where scores 1 and 5 represent the lowest and highest measurable thresholds, respectively. Score 6 indicates that no flavor was experienced even at the highest concentration. The sum of the scores on both sides for each flavor was taken as a total taste score. The paper filter disk method was conducted at the first visit and 12 and 24 weeks after the drug administration. Patients whose total taste scores at 12 and 24 weeks after the administration of the Zn preparation were lower than those before its administration, i.e., at the first visit, were defined as those who exhibited improvement (improved group). Patients whose total taste scores after the administration of Zn preparation were higher or unchanged compared to that before its administration were defined as those who did not exhibit improvement (non-improved group). All taste tests were performed by the same dentist at the outpatient dentistry clinic.
## Measurement of serum Zn and Cu levels
All patients attended the clinic in the morning due to the circadian rhythm of serum zinc levels. Serum Zn and Cu levels were measured by colorimetry (Zn in our laboratory, Cu; LSI Medience Corporation, Tokyo, Japan). The reference values for serum Zn and Cu were 80–130 and 68–128 µg/dL, respectively [30, 31].
## Cu/Zn ratio
The Cu/Zn ratio was examined because Yanagisawa et al. reported that this ratio was a useful diagnostic marker for taste disorders, and a value of 1.1 may be a threshold level for detecting taste disorders [28].
## Statistical analysis
Statistical analysis was conducted using BellCurve for Excel (Social Survey Research Information Co., Ltd., Tokyo, Japan). Data were expressed as mean ± SD (standard deviation). For continuous variables, changes in Zn, Cu, Cu/Zn ratio and taste over time were compared using paired t test, whereas other comparisons were performed using two-sample t test. The χ2 test was used for analyzing categorical variables. In all analyses, P values of < 0.05 were considered to be statistically significant.
## Ethical considerations
We obtained approval for this study from the research ethics board of Hiroshima University (approval number: epidemiology—1485). The study protocol was posted on the websites. Patients opted out from the study if they did not wish to give consent. The informed consent was waived.
## Subject characteristics
Seventy patients diagnosed with taste disorders with hypozincemia entered into this study. Table 1 summarizes the baseline characteristics of these patients. The ZAH/PPZ group consisted of 14 men ($33\%$)/28 women ($67\%$) or 13 men ($46\%$)/15 women ($54\%$), and the mean age of subjects in the ZAH/PPZ group was $\frac{73.2}{68.4}$ years (range, 61–$\frac{88}{55}$–98 years), respectively. The mean body weight of subjects in the ZAH/PPZ group was $\frac{63.9}{65.9}$ kg (range, 58.4–$\frac{75.5}{53.8}$–78.1 kg). The mean serum Zn level was $\frac{68.3}{73.2}$ µg/dL (range, 58–$\frac{76}{63}$–78 µg/dL), and the mean serum Cu level was $\frac{102.7}{108.7}$ µg/dL (range, 72–$\frac{130}{76}$–135 µg/dL) in the ZAH/PPZ group, respectively. The mean Cu/Zn ratio in the ZAH group was 1.50 (range, 1.0–1.8), and that in the PPZ group was 1.62 (range, 0.9–1.9). In the ZAH group, there were 35 patients ($83\%$) with a Cu/Zn ratio of ≥ 1.1, whereas in the PPZ group, there were 19 patients ($73\%$). The taste scores (ZAH group vs PPZ group) were 9.3 ± 1.8 vs 9.4 ± 2.1 for sweetness, 9.3 ± 1.9 vs 9.3 ± 2.5 for saltiness, 10.0 ± 2.3 vs 10.2 ± 2.0 for acidity, 9.4 ± 2.2 vs 10.0 ± 1.8 for bitterness, and 37.9 ± 5.2 vs 38.8 ± 4.6 for total. Thus, no significant differences were observed between the ZAH and PPZ groups. Table 1Baseline characteristics of subjectsParameterZAH ($$n = 42$$)PPZ ($$n = 28$$)P valueSex (M / F)14 / 2813 / 150.139aAge (years)73.2 ± 11.368.4 ± 12.50.188bBody weight (kg)63.93 ± 13.7465.92 ± 11.210.276bZn (µg / dL)c68.3 ± 8.773.2 ± 10.00.109bCu (µg / dL)d102.7 ± 29.4108.7 ± 29.40.234bCu / Zn ratio1.50 ± 0.481.62 ± 0.760.271bCu / Zn ratio (< 1.1 / ≧1.1)7 / 357 / 19e0.36aTaste scoresf Sweet9.3 ± 1.89.4 ± 2.10.96b Salty9.3 ± 1.99.3 ± 2.50.84b Sour10.0 ± 2.310.2 ± 2.00.96b Bitter9.4 ± 2.210.0 ± 1.80.2b Totalg37.9 ± 5.238.8 ± 4.60.5bValues are expressed as means ± standard deviationZn zinc, Cu copper, ZAH zinc acetate hydrate, PPZ Polaprezinca Test used for analysis: X2 testb Test used for analysis: two-sample t testc The reference value is 80–130 µg/dLd The reference value is 68–128 µg/dLe 2 patients have no Cu dataf The scores of the bilateral location in the chorda tympani nerve using the paper filter disk methodg Sum of the above 4 flavors
## Changes in serum Zn and Cu levels
The changes in the concentrations of serum Zn and Cu after drug administration in both groups are shown in Fig. 1A, B and C. The serum Zn levels in the ZAH group significantly increased at 12 and 24 weeks after drug administration ($P \leq 0.05$, Fig. 1A), whereas those in the PPZ group significantly increased only at 24 weeks ($P \leq 0.05$, Fig. 1A) although the observed increase was very low. In terms of the change in serum zinc levels before and after administration of zinc preparations, there was a significant increase in the group that received PPZ for 24 weeks compared to the group for 12 weeks ($P \leq 0.05$, Fig. 1B). The serum Zn levels at weeks 12 and 24 were significantly higher in the ZAH group than in the PPZ group ($P \leq 0.05$, Fig. 1A). The serum Cu levels showed no significant differences between before and after the administration of Zn supplements (Fig. 1C).Fig. 1Changes in serum Zn, Cu levels and Cu/Zn ratio in patients receiving ZAH or PPZ. Serum Zn levels increased after receiving oral Zn supplements. These levels were significantly higher in the ZAH-treated group than in the PPZ-treated group (A). The mean change in serum zinc levels was greater in the group that received the zinc supplements for 24 weeks than in the group that received it for 12 weeks (B). No change was observed in serum Cu levels after the oral administration of Zn supplements (C). After the administration, the Cu/Zn ratio was lower in both groups than that before the administration (D). Each bar represents mean ± standard deviation. * $P \leq 0.05$ (vs week 0, paired t test)
## Changes in the Cu/Zn ratio
The Cu/Zn ratio was significantly lower at 12 and 24 weeks after ZAH administration than that before its administration ($P \leq 0.05$, Fig. 1D). In contrast, the Cu/Zn ratio was significantly lower at 24 weeks after PPZ administration than that before its administration ($P \leq 0.05$, Fig. 1D).
## Evaluation of taste
The taste threshold was examined using the paper filter disk method at 12 and 24 weeks after drug administration to evaluate whether gustatory sensitivity had improved compared to the first visit. In the ZAH group, the taste thresholds for the acidity and salty at 12 and 24 weeks after administration were significantly decreased compared to before administration ($P \leq 0.05$, Fig. 2A). In contrast, in the PPZ group, the taste thresholds for the acidity and salty were significantly decreased 24 weeks after administration ($P \leq 0.05$, Fig. 2B). In the total taste thresholds for the four tastes (sweet, salty, sour and bitter), the ZAH-administered group showed a significant improvement at 12 and 24 weeks after the administration compared to before administration, but the PPZ-administered group showed a significant improvement only at the 24 weeks ($P \leq 0.05$, Fig. 2C).Fig. 2Changes in taste threshold in patients receiving ZAH or PPZ. At 12 and 24 weeks after ZAH administration, the taste scores of acidity and salty were significantly lower than those before administration (A). At 12 weeks after PPZ administration, the taste scores of acidity and salty were significantly lower than those before administration (B). In total of 4 tastes (sweet, salty, sour and bitter), a significant decrease in taste threshold was observed in the ZAH-administered group at 12 and 24 weeks after administration, and in the PPZ-administered group at 24 weeks after administration (C). Each bar represents mean ± standard deviation. * $P \leq 0.05$ (vs week 0, paired t test)
## Clinical characteristics of ZAH-treated patients
We next compared the clinical parameters of subjects in the improved and non-improved groups 24 weeks after ZAH administration (Table 2). The patients in the non-improved group were significantly younger than those in the non-improved group (age, 66.8 ± 11.2 vs 75.0 ± 11.8 years, $P \leq 0.05$). The serum Zn levels were significantly higher in the improved group than in the non-improved group (106.9 ± 9.0 vs 84.3 ± 7.8 µg/dL, $P \leq 0.05$). There was no difference in the Cu/Zn ratio between the two groups. However, the number of patients with a Cu/Zn ratio of < 1.1 was significantly higher in the improved group than in the non-improved group ($\frac{23}{26}$ vs $\frac{9}{16}$, $P \leq 0.05$). Other clinical parameters did not differ between the two groups. Table 2Clinical characteristics of patients receiving ZAH at 24 weeksParameterImproveda ($$n = 26$$)Non-improvedb ($$n = 16$$)P valueSex (M / F)10 / 165 / 110.75cAge (years)66.8 ± 11.275.0 ± 11.8 < 0.05dZn (µg / dL)e106.9 ± 9.070.3 ± 7.80.109dCu (µg / dL)f104.7 ± 37.1101.3 ± 25.50.87dCu / Zn ratio1.02 ± 0.251.32 ± 0.510.161dCu / Zn ratio (< 1.1 / ≧1.1)23 / 39 / 7 < 0.05dWhite blood cell (/mm3)5610 ± 12786206 ± 18940.54dRed blood cell (× 104/mm3)489 ± 97501 ± 870.82dHemoglobin (g/dL)13.2 ± 3.913.5 ± 3.30.58dAST (IU/L)26.0 ± 9.723.4 ± 6.60.35dALT (IU/L)27.3 ± 10.724.1 ± 10.40.34dAlbumin (g/dL)4.3 ± 0.64.2 ± 0.70.72dCreatinine (mg/dL)0.73 ± 0.10.74 ± 0.10.65dBUN (mg/dL)12.6 ± 4.612.5 ± 2.50.97dTriglyceride (mg/dL)173.9 ± 115.3204.9 ± 165.60.52dValues are expressed as means ± standard deviationZn zinc, Cu copper, ZAH zinc acetate hydratea Patients whose total taste scores at 12 or 24 weeks after the administration of the Zn preparation were lower than those before its administration, were defined as improved groupb Patients whose total taste scores after the administration of Zn preparation were higher or unchanged compared to that before its administration were defined as non-improved groupc Test used for analysis: X2 testd Test used for analysis: two-sample t teste The reference value is 80–130 µg/dLf The reference value is 68–128 µg/dL
## Adverse events of ZAH administration
Approximately 24 weeks after ZAH administration, two patients complained of gastric discomfort, which disappeared without additional intervention after the completion of treatment. By contrast, there were no adverse events in the PPZ group.
## Discussion
We investigated the efficacy of ZAH administration in patients with hypozincemia-induced dysgeusia in comparison with PPZ administration. Moreover, we examined the relationship between Cu/Zn ratio and taste test results to confirm the usefulness of the Cu/Zn ratio as a diagnostic marker for dysgeusia.
Serum Zn levels were significantly higher in ZAH-treated patients 12 weeks after administration than those before administration. However, in PPZ-treated patients, these levels were significantly higher 24 weeks after the administration. Because the amount of Zn contained in ZAH was 50 mg/day, which was higher than that in PPZ (34 mg/day), the serum Zn level in patients taking ZAH increased more rapidly than that in patients taking PPZ. It was anticipated that ZAH, which was originally developed as a treatment for patients with Wilson’s disease with hypercupremia, would decrease serum Cu levels [32, 33]. However, in the present study, there was no significant decrease in serum Cu level until 24 weeks after the administration of 50 mg/day of ZAH for hypozincemia. Zn and Cu, which are absorbed through divalent metal transporter-1 (DMT1) on the intestinal epithelial cells, are known to antagonize the absorption of each other [34]. Previous studies have reported that Zn supplementation causes anemia, peripheral and optic neuropathy, and myelopathy when serum *Cu is* deficient [35, 36]. The Japanese Urology group demonstrated that serum Cu levels in the ZAH group were significantly lower than those in the PPZ group at 3 and 6 months after administration in patients under maintenance hemodialysis [24], despite using the same concentration of ZAH as that used in our study. Another study showed that when patients with cirrhosis complicated by hypozincemia were treated with ZAH (Zn content; 100 mg/day), the primary reason for the discontinuation of treatment was hypocupremia [37]. We speculated that *Cu is* reduced more in patients with impaired kidney function. It has been reported that a positive correlation exists between the serum Cu/Zn ratio and the salty taste recognition threshold or the awareness of dysgeusia [28]. In the present study, we investigated the Cu/Zn ratio and taste test results using not only salty taste but also four types of taste qualities. The mean Cu/Zn ratios before the administration of ZAH or PPZ were respectively 1.50 or 1.62, which were higher than the cut-off value of 1.1 for dysgeusia described by the previous study [28].
Taste evaluation after Zn replacement therapy was performed using the paper filter disk method. In the ZAH-administered group, a significant decrease in the taste threshold was observed at 12 weeks after administration, whereas in the PPZ-administered group, it was observed at 24 weeks. It was suggested that an increase in serum zinc concentration was associated with an improvement in taste. The administration of the zinc preparation decreased the threshold for salty and acidity among the four tastes but did not change the sweetness and bitterness. It has been reported that the serum Zn concentration was associated with salty taste recognition threshold [28]. It is also believed that the improvement in acidity is due to the reduction of tongue inflammation due to the anti-inflammatory effect of zinc preparations [13].
Finally, we compared the clinical parameters of patients in the improved and non-improved groups who received ZAH for 24 weeks. Our analyses revealed that the patients in the improved group were significantly younger than those in the non-improved group. The serum Zn concentration was higher in the improvement group than in the non-improved group. The Cu/Zn ratio was < 1.1 in $88\%$ of patients in the improvement group compared with $56\%$ in the non-improved group, which was significantly lower. The difference in the amount of Zn absorbed from the digestive tract between the two groups might have influenced the efficacy of ZAH. In the taste test, we evaluated four taste qualities and verified that the cut-off value for taste abnormality was 1.1. Based on the results, we believe that the Cu/Zn ratio cut-off value of 1.1 is reliable for dysgeusia. Previous reports showed that Zn can function as an anti-inflammatory agent in elderly subjects [13, 38]. As several patients with dysgeusia also suffer from glossitis, Zn administration can be expected to improve painful glossitis [39].
The present study was conducted at a single center and had a small sample size. In addition to zinc deficiency, other causes of taste disorders include pharmaceutical (chelating agents, vitamins and minerals), systemic (hypothyroidism, diabetes, neurological diseases such as Parkinson's and Alzheimer's), oral (Sjogren's syndrome and xerostomia) and psychogenic (clinically difficult to diagnose) causes. In addition, there is often more than one cause of the disorder in more than one case. In other words, although this study focused on patients with zinc deficiency taste disorder, it must be considered that other causes may be involved. Furthermore, ZAH and PPZ differ also in components other than Zn content. Thus, the present study has some limitations, and regional or selection bias might have affected the results. Prospective clinical studies with uniform patient backgrounds and drugs used should be conducted in the future.
## Conclusions
Serum Zn levels increased without reducing serum copper levels in patients with hypozincemia-induced dysgeusia treated with ZAH (50 mg/day) and taste improvement was observed in about $60\%$ patients at 24 weeks of ZAH administration, suggesting Zn replacement using ZAH in patients with dysgeusia and lower serum Zn levels (< 80 µg/dL) might be considered as a new therapeutic approach.
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|
---
title: Is there a placental microbiota? A critical review and re-analysis of published
placental microbiota datasets
authors:
- Jonathan J. Panzer
- Roberto Romero
- Jonathan M. Greenberg
- Andrew D. Winters
- Jose Galaz
- Nardhy Gomez-Lopez
- Kevin R. Theis
journal: BMC Microbiology
year: 2023
pmcid: PMC10024458
doi: 10.1186/s12866-023-02764-6
license: CC BY 4.0
---
# Is there a placental microbiota? A critical review and re-analysis of published placental microbiota datasets
## Abstract
The existence of a placental microbiota is debated. The human placenta has historically been considered sterile and microbial colonization was associated with adverse pregnancy outcomes. Yet, recent DNA sequencing investigations reported a microbiota in typical human term placentas. However, this detected microbiota could represent background DNA or delivery-associated contamination. Using fifteen publicly available 16S rRNA gene datasets, existing data were uniformly re-analyzed with DADA2 to maximize comparability. While Amplicon Sequence Variants (ASVs) identified as Lactobacillus, a typical vaginal bacterium, were highly abundant and prevalent across studies, this prevalence disappeared after applying likely DNA contaminant removal to placentas from term cesarean deliveries. A six-study sub-analysis targeting the 16S rRNA gene V4 hypervariable region demonstrated that bacterial profiles of placental samples and technical controls share principal bacterial ASVs and that placental samples clustered primarily by study origin and mode of delivery. Contemporary DNA-based evidence does not support the existence of a placental microbiota.
Importance Early-gestational microbial influences on human development are unclear. By applying DNA sequencing technologies to placental tissue, bacterial DNA signals were observed, leading some to conclude that a live bacterial placental microbiome exists in typical term pregnancy. However, the low-biomass nature of the proposed microbiome and high sensitivity of current DNA sequencing technologies indicate that the signal may alternatively derive from environmental or delivery-associated bacterial DNA contamination. Here we address these alternatives with a re-analysis of 16S rRNA gene sequencing data from 15 publicly available placental datasets. After identical DADA2 pipeline processing of the raw data, subanalyses were performed to control for mode of delivery and environmental DNA contamination. Both environment and mode of delivery profoundly influenced the bacterial DNA signal from term-delivered placentas. Aside from these contamination-associated signals, consistency was lacking across studies. Thus, placentas delivered at term are unlikely to be the original source of observed bacterial DNA signals.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12866-023-02764-6.
## Introduction
The womb has historically been considered sterile throughout typical pregnancy [1–3]; yet, the detection of microorganisms, especially bacteria, in some placentas from complicated pregnancies is an established phenomenon [4–7]. For instance, there are demonstrated associations between bacterial colonization of the placenta and preterm labor [5, 8–15], preterm prelabor rupture of membranes (PPROM) [11, 12], histological chorioamnionitis [8, 14, 16], and clinical chorioamnionitis [12–14, 16]. Therefore, research has largely focused on the presence [9, 17–20] and types of bacteria [21–26] associated with the human placenta in the context of spontaneous preterm birth and other pregnancy complications.
However, in 2014, bacterial DNA-based evidence was presented for a universal low-biomass placental microbiota even among placentas from term pregnancies [27]. Since placental colonization by bacteria suggests that fetal colonization is also feasible, this study revitalized the in utero colonization hypothesis, which maintains that commensal bacteria residing in the placenta and/or amniotic fluid colonize the developing fetus during gestation [3, 28–34]. The in utero colonization hypothesis stands in stark contrast to the traditional sterile womb hypothesis, which posits fetal sterility until colonization at delivery or following rupture of the amniotic membranes [1–3, 35–37, 39–41]. Since publication of this seminal study in 2014, many other studies have similarly utilized DNA sequencing techniques to investigate the existence of a placental microbiota in term pregnancies [28, 29, 34, 36, 37, 39–65]. Yet, the existence of a placental microbiota remains under debate eight years later [66–69].
The debate over the existence of a placental microbiota is fueled by several issues. First, the placenta cannot be readily sampled in utero. Thus, attempts at characterizing a placental microbiota have entailed collection of placental samples following either a vaginal or cesarean delivery. While both delivery methods can introduce bacterial contamination [36, 38, 40, 42, 51, 70], in the form of vaginal and skin bacteria, respectively, vaginal delivery likely exposes the placenta to bacterial contamination to an extent that would overwhelm any weak bacterial DNA signal legitimately present in placental tissue in utero [40, 42, 56]. Furthermore, since bacterial infection is a well-known cause of preterm labor and birth [13, 71–74], infection of the placenta cannot be ruled out as a source of any bacterial DNA signals in preterm placental tissues. Thus, to establish that a placental microbiota exists, it must be documented in placentas from term cesarean deliveries to minimize misinterpretation of potential infection-related or delivery-associated contamination [3, 29, 75].
Second, a lack of robust technical controls has made it difficult to determine if reagent or environmental DNA contamination might be the source of bacterial DNA signals attributed to placentas rather than a resident placental microbiota [27, 29, 46, 53, 54, 57–61, 63–65], given that such a theoretically sparse bacterial community could easily be obfuscated by background DNA contamination in laboratories, kits, and reagents [39, 76–79]. Indeed, several recent studies have shown that the bacterial loads [41] and profiles of placentas from term cesarean deliveries do not exceed or differ from those of technical controls [41, 42]. This issue pertains not only to DNA sequencing of placental tissue, but also to many other internal organs such as the lung [80–82], liver [83], brain [84], or even the blood [85]. Technical controls and sterile collection conditions are therefore essential for the verification of any low-biomass microbiota.
Finally, a lack of consistency in the analytical pipelines used to process the DNA sequence data has resulted in additional debate, including how sequences should be grouped or split into taxonomic units [77, 86, 87]. Specifically, too coarse or too fine a taxonomic resolution could either potentially reveal a shared bacterial DNA signal between placental tissues and technical controls or a signal unique to the placenta, respectively.
Ultimately, if there is a placental microbiota it should exist in a majority of, if not all, placentas from women delivering at term without complications, and there should be some degree of consistency in the bacterial taxa residing in placentas across studies. For example, the human vaginal microbiota worldwide is consistently predominated by various species of Lactobacillus and, in a smaller proportion of women, higher diversity bacterial communities exist, which consist of nevertheless predictable genera such as Prevotella, Sneathia, Megasphaera, Atopobium, Mobiluncus, Streptococcus, and Gardnerella [88, 89]. Yet, among investigators proposing the existence of a placental microbiota, there are conflicting reports regarding its predominant bacterial members [27–29, 44, 49, 54, 58, 62–65] and, when complementary culture results are available, placental samples are often culture negative or the bacteria recovered are discrepant with the DNA sequencing results [19, 28, 40, 41, 44, 56, 90–95].
Given these current conflicting conclusions regarding the existence of a placental microbiota, here we performed a critical review and re-analysis of fifteen publicly available 16S rRNA gene sequencing datasets from human placental microbiota studies for which sample distinguishing metadata were available [29, 36, 37, 39–44, 50, 53, 57, 96]. In this re-analysis we standardized the analytical process to enable assessment of taxonomic consistency in placental bacterial profiles across studies conducted by different laboratories across the world (Fig. S1).
Three primary analyses were performed. The first analysis was a comparison of the bacterial profiles of placental samples to technical controls for studies which included at least six controls for background DNA contamination [97] since this environmental contamination could be a source of the purported placental bacterial DNA signal. Ideally, a valid placental microbiota would be expected to exhibit a bacterial DNA signal distinct from that of kit reagents or surrounding laboratory environments. The second analysis was restricted to placentas from term cesarean deliveries so as to avoid potential bacterial contamination of placentas that could occur during vaginal delivery [36, 42, 51, 98]. If there were a placental microbiota, the bacterial DNA signals should be clear and consistent across placentas from term cesarean deliveries. This analysis was therefore performed using data from the six studies for which placental samples could be restricted to those from term cesarean deliveries. The third and final analysis was restricted to studies that targeted the V4 hypervariable region of the 16S rRNA gene to control for any variation which might arise due to variation in targeted 16S rRNA gene hypervariable regions across studies or the DNA sequence processing algorithms used. A valid placental microbiota would be expected to be independent of study identity and mode of delivery.
Collectively, these analyses did not support the presence of a placental microbiota in typical term pregnancies. Observed bacterial signals were products of mode of delivery and background DNA contamination. Although there may be a true, consistent, extremely low biomass bacterial signal beyond the limits of detection by contemporary 16S rRNA gene sequencing, it remains to be demonstrated that the placenta harbors a legitimate bacterial DNA signal or a viable microbiota in typical human pregnancy.
## Overview of studies included in this re-analysis
Fifteen studies (Fig. 1; Table 1) were included in this re-analysis of investigations of the existence of a placental microbiota. Seven included sequence data from the V4 hypervariable region of the 16S rRNA gene [29, 39, 41, 43, 44, 50, 96], allowing for direct comparisons of sequence data across six of those studies [29, 39, 41, 44, 50, 96]; one study could not be included in the direct comparison due to short read lengths of sequences in the publicly available dataset [43]. Three of the remaining studies included sequence data from the V1-V2 16S rRNA gene hypervariable region [36-38] two studies sequenced the V6-V8 region [40, 53], and one study each sequenced the V3-V4 [58], V4-V5 [57], and V5-V7 [42] regions. All fifteen studies included at least one placental sample from a term cesarean delivery, but only eight included more than one term cesarean delivered placenta and sufficient background technical controls [i.e., $$n = 6$$ [97]] to infer likely DNA contaminants using the R package DECONTAM [36, 39–43, 50, 96] (Fig. 2). Two of these studies lacked available metadata to discriminate placental samples by gestational age at delivery [42, 50], leaving a total of six studies [36, 39–41, 43, 96] for assessing uniformity of bacterial profiles among term cesarean delivered placentas across studies while accounting for potential background DNA contamination (Fig. 2). Notably, five of these six studies concluded that there was no evidence for a placental microbiota in uncomplicated term pregnancies [36, 39–41, 96] (Fig. 2). In contrast, the four studies which did not include sequence data from background technical controls concluded that a placental microbiota does exist [29, 53, 57, 58] (Fig. 2).Table 1Overview of placental microbiota studies that were based on 16S rRNA gene sequencing data and that were included in this critical review and re-analysis. The presented study characteristics include the: name of the first author(s); geographical location at which subjects were sampled; specific 16S rRNA gene hypervariable region that was targeted for sequencing; number of placentas sampled by mode of delivery and whether delivery was before (i.e., preterm) or after (i.e. term) 37 weeks; number of technical controls included to address potential background DNA contamination; and whether we were able to categorize placental samples based on mode of delivery, gestational age at delivery (i.e., before or after 37 weeks), and whether DECONTAM analysis could be performed to identify background DNA contaminants (i.e., N ≥ 6 technical controls included in the study) [97]. Square brackets indicate that available sample metadata did not allow for placentas to be grouped by gestational age at delivery. StudyGeographicLocation16S rRNA gene hypervariable regionCesareanVaginalTechnical ControlsAbility to group byAbility to run DECONTAMTermPretermTermPretermDeliveryGestational Agede Goffau Part IbCambridge, UKV1-V28000047XXXLauderPhiladelphia, PA, USAV1-V2105039XXXLeibyPhiladelphia, PA, USAV1-V2151915103XXXOlomuLansing, MI, USAV447a000131XXXParnellSt. Louis, MO, USAV434023021XXXTheiscDetroit, MI, USAV429a00043XXXTheis, WintersDetroit, MI, USAV4281421612XXXSterpuStockholm, SwedenV6-V850a02606XXXDinsdaleSouth Hedland, AustraliaV4[19][31]8XXTangShanghai, ChinaV3-V415a0000XXLiuKunming, ChinaV4-V54203600XXGomez-ArangoBrisbane, AustraliaV6-V816a02000XXYoungeDurham, NC, USAV45a5000XLeonLondon, England, UKV5-V7[136][120]21XSeferovicHouston, TX, USAV42680182a Indicates that placental samples were delivered without laborb *Analyzed data* are from the Cohort 1 component of the studyc *Analyzed data* are from the nested PCR component of the studyFig. 1Study inclusion flowchartFour searches were performed on PubMed to identify studies for inclusion in the re-analysis. Filtering criteria were: primary research article, 16S rRNA gene sequencing data, placentas obtained from term deliveries, sequencing data accessible with published accession number, and sufficient metadata available to parse sequencing data into individual samples. Fig. 2Conclusions of thirteen studies evaluating the existence of a placental microbiota, which included data from multiple placentas delivered via cesarean section at termThe studies are principally separated and contrasted depending upon whether they included technical controls to account for potential background DNA contamination.
## Lactobacillus ASVs are the most consistently identified ASVs across placental microbiota studies
After processing the raw 16S rRNA gene sequence data from placental samples from all 15 studies through the same DADA2 analytical pipeline, the most prominent bacterial ASVs, as defined by mean relative abundance, across studies were classified as Lactobacillus, Escherichia/Shigella, Staphylococcus, Streptococcus, and Pseudomonas. Lactobacillus ASVs were among the top five ranked ASVs in eight of the 15 studies [37, 39, 42, 50, 53, 57, 96], making Lactobacillus the most consistently detected genus in placental samples across studies with publicly available 16S rRNA gene sequencing data.
The detection of Lactobacillus ASVs was not exclusive to the targeted sequencing of specifically any one 16S rRNA gene hypervariable region; Lactobacillus ASVs were found among the top five ASVs in the dataset of at least one study targeting the V1-V2, V4, V4-V5, V5-V7, or V6-V8 hypervariable region(s) of the 16S rRNA gene. *Other* genera which were not 16S rRNA gene hypervariable region specific and were detected in the top five ranked ASVs in more than one dataset, but in no more than four, included Staphylococcus [40, 44, 54, 96], Streptococcus [38, 40, 42], and Pseudomonas [50, 54, 57]. In contrast, Escherichia/Shigella ASVs were exclusively among the top five ranked ASVs in datasets of studies that targeted the V4 hypervariable region of the 16S rRNA gene for sequencing ($\frac{3}{7}$ such datasets) [29, 39, 96].
## Lactobacillus ASVs in placental samples are typically contaminants introduced through vaginal delivery and/or background DNA contamination
While it can be difficult to identify the definitive source of a particular ASV in placental samples, the difference in Lactobacillus predominance between vaginally delivered placentas and cesarean delivered placentas is striking. Lactobacillus ASVs were among the top five ASVs in five of seven datasets which included placentas from vaginal deliveries before running the R package DECONTAM, and three of four datasets which included placentas from vaginal deliveries after running DECONTAM. Consider, for instance, the Lauder et al. [ 37] and Leiby et al. [ 38] datasets. While all samples in the Lauder et al. dataset [37] had Lactobacillus ASVs, the percentage of Lactobacillus normalized reads in cesarean delivered placental samples was $23\%$ compared to $46\%$ in vaginally delivered placental samples. In the Leiby et al. [ 38] dataset only four of 23 ($17\%$) cesarean delivered placentas had any Lactobacillus ASVs, and they made up only $2\%$ of the total reads from their respective samples. In contrast, 35 of 116 ($30\%$) placentas from vaginal deliveries had Lactobacillus ASVs, and they made up $22\%$ of the total reads from those 35 samples.
Lactobacillus ASVs were among the top five ranked ASVs in three [39, 57, 96] of the six datasets which could be restricted to placental samples from cesarean term deliveries (Table 2). Yet, after removing potential background DNA contaminants using DECONTAM, only the Olomu et al. [ 39] dataset still retained a Lactobacillus ASV in the top five ranked ASVs (Table 2). Notably, the authors of that study identified the origin of Lactobacillus in placental samples as well-to-well DNA contamination from vaginal to placental samples during 16S rRNA gene sequence library generation. Table 2Summary of prominent bacterial ASVs in term cesarean delivered placental samples before and after removal of background DNA contaminants using the R package DECONTAM. The top five ASVs as determined by mean relative abundance across placental samples after DADA2 processing are provided for studies which could be restricted to term cesarean delivered placental samples. Asterisks indicate ASV sequence genus level classifications which were assigned by NCBI BLAST with the highest percent identity in excess of $95\%$. The Liu et al. [ 57] dataset could not be assessed post-DECONTAM since no technical controls were included in the study. Study16S rRNA gene hypervariable regionASV1ASV2ASV3ASV4ASV5Before DECONTAMOlomuV4Escherichia/ShigellaLactobacillusLactobacillusFinegoldiaFenollariaParnellV4LeptospiraLeptospiraLeptospiraCutibacteriumLeptospiraTheisV4AchromobacterDelftiaPhyllobacteriumClostridium sensu stricto 5StenotrophomonasTheis, WintersV4LactobacillusStaphylococcusEscherichia/ShigellaSerratia*StreptococcusLiuV4-V5PseudomonasPantoea*Pantoea*LactobacillusPantoea*SterpuV6-V8CutibacteriumStaphylococcusStreptococcusStreptococcusStreptococcusAfter DECONTAMOlomuV4FenollariaAcinetobacterLactobacillusCampylobacterPeptoniphilusParnellV4LeptospiraLeptospiraLeptospiraCutibacteriumLeptospiraTheisV4AchromobacterAlcaligenaceaeEscherichia/ShigellaAchromobacterAlcaligenaceaeTheis, WintersV4CorynebacteriumSerratia*MycoplasmaCorynebacteriumFusobacteriumSterpuV6-V8StaphylococcusGardnerellaStaphylococcusStaphylococcusStreptococcusASV1–5 are rank designations based on percent relative abundanceLactobacillus ASVs are bolded to emphasize prevalence and lack thereof after likely-contaminant removal Furthermore, Lactobacillus ASVs were also more prominent in samples of placental tissues of maternal origin, such as the decidua or basal plate, than placental tissues of fetal origin, such as the amnion, chorion, or villous tree. After separating placental sample data from non-labor term cesarean deliveries by fetal and maternal origin, with the exception of the Olomu et al. [ 39] study, Lactobacillus ASVs were absent from placental samples of fetal origin (Table S1). In contrast, among samples of maternal origin from the Theis, Winters et al. dataset [96], Lactobacillus was the most relatively abundant ASV even after removal of likely DNA contaminants with DECONTAM, and in the Lauder et al. [ 37] study, only the maternal side of the single cesarean delivered placenta had a high predominance of Lactobacillus.
## The bacterial ASV profiles of placental samples and background technical controls cluster based on study origin
Beta diversity between placental samples and technical controls was visualized through Principal Coordinates Analysis (PCoA) for each study in the re-analysis to assess the extent of influence of background DNA contamination on the bacterial ASV profiles of placental samples (Fig. 3). A majority of placental samples cluster with their respective technical controls across the studies. Specifically, in five of eleven studies, technical controls covered the entire bacterial profile spectrum of placental samples (Fig. 3A-E), and in the remaining six studies which included technical controls, the bacterial profiles of most placental samples largely clustered with those of technical controls (Fig. 3F-K). Placental samples in the latter group which did not cluster with technical controls were characterized by a predominance of Lactobacillus (Fig. 3F-H), Cutibacterium (Fig. 3I,K), Gardnerella (Fig. 3F), Pseudomonas (Fig. 3F), Ureaplasma (Fig. 3G), Mesorhizobium (Fig. 3I), Prevotella (Fig. 3J), Actinomyces (Fig. 3J), Streptococcus (Fig. 3J), Veillonella (Fig. 3J), and Staphylococcus (Fig. 3K). Notably, the bacterial profiles of most placental samples from term cesarean deliveries were not significantly different from those of technical controls in either dispersion or structure (Table S2). In cases where the structure of the bacterial profiles differed between placental samples and technical controls, but the dispersion of the bacterial profiles did not, it was only the bacterial profiles of the exterior surfaces of the placenta which differed from those of controls. In these cases, the bacterial profiles of the exterior surfaces of placental samples were characterized by Cupriavidus, Serratia, Corynebacterium, and Staphylococcus (Table S2), the latter two of which are well-known commensal bacteria of the human skin [99].Fig. 3Principal Coordinates Analyses of the beta diversity of bacterial DNA profiles between placental samples and technical controls in published placental microbiota studiesStudies were included if technical controls were sequenced and made publicly available to account for background DNA contamination. Beta diversity between placental (red open circles) and technical control (black open circles) samples is illustrated by study in PCoA plots based on the Bray-Curtis dissimilarity index. Genus level classifications of the top ten ASVs in placental samples and technical controls by total reads are plotted at their weighted average positions (grey diamonds). Asterisks indicate ASV sequence genus level classifications which were assigned by NCBI BLAST with the highest percent identity in excess of $95\%$.
## The bacterial ASV profiles of vaginally delivered placental samples also cluster with their respective vaginal samples across studies
Six studies [29, 37, 39, 50, 57] in the re-analysis included vaginal or vaginal-rectal swab samples as a complement to placental samples; four of these studies also included technical controls [37, 39, 50]. While most technical controls did not cluster with vaginal samples, placental samples typically clustered with vaginal samples and/or technical controls (Fig. 4A-D), or if technical controls were not included in the study, with vaginal samples (Fig. 4E-F). Notably, nine Lactobacillus ASVs were shared between the top ranked ASVs of placental and vaginal swab samples across five studies [37, 39, 50, 57] (Fig. 4).Fig. 4Principal Coordinates Analyses of the beta diversity of the bacterial DNA profiles of placental and vaginal/vaginal-rectal samples in placental microbiota studiesPrior published studies were included if vaginal or vaginal-rectal samples were sequenced and made publicly available alongside placental samples. The top ten ASVs shared between placental samples and technical controls, and the top ten ASVs in vaginal samples are plotted at their weighted average positions in the ordination space (grey diamonds) and their genus level classifications are noted. Agglomerated genus level classifications were plotted for the Liu dataset instead of ASVs since no ASVs were greater than $1\%$ mean relative abundance across placental samples. Asterisks indicate ASV sequence genus level classifications which were assigned by NCBI BLAST with the highest percent identity in excess of $95\%$.
## Placental and technical control samples co-cluster by study and placental samples additionally cluster by mode of delivery
In order to fully utilize the capacity for ASVs to be directly compared across placental microbiota studies, taxonomy and ASV count tables were merged based on the exact ASV sequence data for six [29, 39, 41, 44, 50, 96] of seven studies [29, 39, 41, 43, 44, 96] which sequenced the V4 hypervariable region of the 16S rRNA gene using the PCR primers 515F and 805R. Principal Coordinates Analysis illustrated that placental and technical control samples formed distinct clusters based on study origin (Fig. 5A; NPMANOVA using Bray-Curtis; placental samples: $F = 16.0$, $$P \leq 0.001$$; technical controls: $F = 4.64$, $$P \leq 0.001$$). The only exception was the Theis, Winters et al. dataset [96], which encompassed the bacterial profiles of placental samples from the other studies. This was likely due to the inclusion of samples in Theis, Winters et al. [ 96] from multiple regions of the placenta (i.e., amnion, amnion-chorion interface, subchorion, villous tree, and basal plate) as well as placentas from term and preterm vaginal and cesarean deliveries (Fig. 5A). When stratifying by study and thereby taking study origin into account, placental and technical control samples did not exhibit distinct bacterial DNA profiles (Fig. 5A; NPMANOVA: $$n = 775$$, $F = 6.66$; $$P \leq 0.512$$). When technical controls were excluded from the PCoA, discrete clustering of placental samples by study origin was still apparent (Fig. 5B). Furthermore, the bacterial DNA profiles of placental samples were clearly affected by mode of delivery across studies (Fig. 5C; NPMANOVA: $$n = 690$$, $F = 23.9$, $$P \leq 0.001$$). Unsurprisingly, common vaginal bacteria such as Lactobacillus, Ureaplasma, and Gardnerella were predominant in the profiles of placental samples from vaginal deliveries (Fig. 5C).Fig. 5Placental and technical control samples cluster by study origin, mode of delivery, and gestational age at deliveryA) Beta diversity between placental (open circles) and technical control samples (open triangles) from studies which sequenced the V4 hypervariable region of the 16S rRNA gene is visualized through principal coordinate analysis (PCoA) based on the Bray-Curtis dissimilarity index. B) Beta diversity of placental samples without technical control samples from each study. C) Placental samples from the same six studies were characterized by mode of delivery and gestational age at delivery. Weighted average positions of ASVs greater than $1\%$ were plotted as grey diamonds and labelled with genus level classifications. Asterisks indicate ASV sequence genus level classifications which were assigned by NCBI BLAST with the highest percent identity in excess of $95\%$.
## Bacterial profiles of placental and technical control samples characterized using the V4 hypervariable region of the 16S rRNA gene share prominent ASVs
While placental samples from each study exhibited characteristic patterns of predominant ASVs, some ASVs such as ASV2533-Escherichia/Shigella, ASV6218-Lactobacillus, and ASV6216-Lactobacillus were predominant in the datasets of several studies (Fig. 6A-B, E). However, across studies, nearly every ASV that was consistently predominant in the bacterial DNA profiles of placental samples, was also consistently predominant in the profiles of the technical control samples from the same dataset (Fig. 6). For instance, in two studies, all ASVs with a mean relative abundance greater than $1\%$ in placental samples were also greater than $2\%$ mean relative abundance in technical control samples (Fig. 6B-C). In a third study, all ASVs other than ASV5229-Cutibacterium were also greater than $2\%$ mean relative abundance across technical control samples (Fig. 6D). These data collectively indicate that prominent placental ASVs were likely derived from background DNA contamination captured by the technical control samples. Fig. 6Heatmaps of the bacterial DNA profiles of placental and technical control samples from studies which sequenced the V4 hypervariable region of the 16S rRNA gene demonstrating a high degree of overlap between these two sample typesASVs are listed by study if they had a mean relative abundance greater than $1\%$ across placental samples (green bar). Red asterisks indicate ASVs which had a mean relative abundance greater than $2\%$ across all technical control samples (purple bar) from that study. Regular asterisks indicate ASV sequence genus level classifications which were assigned by NCBI BLAST with the highest percent identity in excess of $95\%$.
## Principal findings of the study
In this re-analysis of fifteen placental microbiota studies, of the ASVs which were ranked in the top five ASVs for relative abundance in any one study, Lactobacillus ASVs were clearly the most prevalent across studies. Yet, Lactobacillus ASV prevalence was explained by background DNA contamination, contamination from the birth canal during vaginal delivery, or well-to-well contamination from vaginal samples during the sequence library build process. Overall, the bacterial DNA profiles of placental samples were highly similar to those of technical controls in their respective studies. Indeed, a secondary analysis of the six studies which targeted the V4 hypervariable region of the 16S rRNA gene for sequencing, showed that the bacterial DNA signal of both placental and technical control samples clustered by study of origin rather than by sample type. In addition, the top two ASVs in placental samples from each of the six studies in the secondary analysis were also the top ranked ASVs in technical controls from the corresponding study. Considered in isolation, placental samples clustered by mode of delivery, suggesting that the process of delivery greatly affected the bacterial DNA profiles of placentas. Therefore, placental samples included in this re-analysis do not provide evidence of a consistent bacterial DNA signal in typical term pregnancy independent of mode of delivery. Instead, the modest consistency in bacterial DNA signals identified across studies was associated with general background DNA contamination or contamination introduced during vaginal delivery.
## The findings of this study in the context of prior reports
Currently, the extent of bacterial presence within the placenta is under debate. There have been numerous reviews, commentaries, and editorials, which have sought to synthesize and resolve conflicting results regarding the existence of a placental microbiota [3, 30, 32, 33, 66–68, 77, 100–144]. Although there has been disagreement about the existence of a placental microbiota in typical human pregnancy, there is a consensus that any given body site, including the placenta, can be at least transiently infected by microorganisms. Several reviews have emphasized that microorganisms in placental tissue would not be able to survive for long durations given the structure of the placenta and the immunobiological response of the host [3, 110]. In contrast, some have proposed that microorganisms could survive intracellularly within the basal plate of the placenta and thus effectively evade the host immune system [68, 145]. Many reviews addressing prior microbiota datasets have been challenged to draw conclusions given the multiple confounding factors which could significantly influence results: the specific 16S rRNA gene hypervariable region targeted for sequencing, brand and lot number of the DNA extraction kits, gestational age at delivery and sampling, mode of delivery of the placenta, inadequate metadata for deposited sequence data, and a general lack of technical controls to account for background DNA contamination. Regardless, many have viewed the current evidence for placental and/or in utero colonization as theoretically tenuous given the existence of germ-free mammals [146, 147] and the strong potential for background bacterial DNA to influence DNA sequencing surveys of low microbial biomass samples [36, 37, 39–41, 91, 112]. Finally, similar to the results presented here in this re-analysis, the prevalence of Lactobacillus across placental samples in prior studies has been acknowledged, yet so too has been the high variability in the bacterial taxa reported within placental tissues across studies. Indeed, variability is high even across studies of similar cohorts from the same research groups [27, 41, 44, 63–65, 96].
## Mode of delivery must be taken into account when investigating the existence and structure of a placental bacterial DNA signal
Eight studies [27, 38, 43, 44, 52, 54, 57, 59] concluded that the bacterial DNA signals in placentas from cesarean deliveries were not significantly different from those in placentas delivered vaginally. Yet, five other studies [36, 42, 51, 96, 98] have reported that the bacterial DNA signals in placentas from vaginal and cesarean deliveries significantly differ. The latter studies have reported increased prevalence and relative abundance of Lactobacillus and other vaginally associated taxa in placentas from vaginal deliveries. Additionally, even the rupture of membranes, a prerequisite for labor and vaginal delivery, provides microorganisms access to the amniotic cavity [148] and thus the placenta, with prolonged access leading to microbial invasion and infection [149, 150]. Notably, bacterial culture of placentas from vaginal deliveries have significantly higher positivity rates [18, 96], higher total colony counts [40], and a higher prevalence of bacterial colonies from Lactobacillus and Gardnerella, both of which are typical residents of the human vagina [88]. In contrast, placentas from cesarean deliveries consistently yield bacteria which typically predominate on the skin, such as Propionibacterium, Staphylococcus, and Streptococcus [40, 99].
Importantly, through robust analysis of the entire bacterial DNA signal from hundreds of placental samples, this re-analysis clearly highlights the influence of mode of delivery on the bacterial DNA signal from placental samples by demonstrating mode of delivery-associated clustering across six studies. Furthermore, it is apparent that removing the exterior layers (i.e., amnion, chorion, and basal plate) of a placenta delivered vaginally is not sufficient to eliminate delivery associated DNA contamination of the sample since the diversity and structure of bacterial DNA profiles from the inner layers (i.e., subchorion, villous tree) of the placenta differed significantly between cesarean and vaginal deliveries. Evidence in the literature combined with this re-analysis warrants careful consideration of mode of delivery and even time since rupture of membranes [52, 149, 150] when investigating the bacterial DNA signal from placental samples.
## Background bacterial DNA limits analysis of bacterial 16S rRNA gene signal from the placenta
Theoretically, a low bacterial biomass community is detectable using 16S rRNA gene sequencing when its concentration is at least 10–100 CFU/mL [151]. However, discerning a true tissue-derived low bacterial DNA signal from other potential sources is exceedingly difficult. This re-analysis, along with eight other studies [36, 37, 39–41, 91, 96], found that placental samples and technical controls share highly abundant bacterial taxa when 16S rRNA gene sequencing is used. Since technical controls represent the environment and reagents to which the placenta is exposed post-delivery, it follows that a majority of the bacterial DNA signal from placental samples is also acquired from those environments and reagents. While a placental tissue limit of bacterial detection through DNA sequencing is yet to be determined, other low-bacterial-biomass sample types such as oral rinse, bronchoalveolar lavage fluid, and exhaled breath condensate were predominated by stochastic noise below 104 16S rRNA gene copies per sample [152]. Even the bacterial DNA signal from a pure culture of *Salmonella bongori* serially diluted to a final concentration of 103 CFU/mL was mostly contamination [78]. If these limits are comparable to those in placental tissue, then stochastic noise and background DNA contamination would predominate the bacterial DNA signal from placental tissue leaving any true DNA signal well beyond the detection limits of 16S rRNA gene sequencing. Therefore, it follows that 16S rRNA gene sequencing by itself without additional verification is inadequate to make a clear assessment of the existence of a placental microbiota.
## Prior reports of 16S rRNA gene sequencing on placentas from term pregnancies
With the prior considerations in mind, out of the 40 studies which performed 16S rRNA gene sequencing on placental samples, 32 included at least some term deliveries. However, only 16 focused exclusively on placentas from term deliveries [28, 37, 39–41, 43, 49, 53, 54, 56–58, 62–65]. Additionally, only nine of these studies focused exclusively on placentas from cesarean deliveries [28, 39, 41, 49, 56, 58, 62, 64, 65] and only three included technical controls and their DNA sequencing data thus accounting for gestational age, mode of delivery, and background DNA contamination [39, 41, 49]. Two of three concluded that there was no evidence for a placental microbiota in the context of term cesarean delivery [39, 41].
Many studies have reported evidence for a low biomass placental microbiota [27, 29, 30, 43–47, 49, 50, 52–54, 57, 58, 60, 61, 63–65, 92, 93, 145, 153] but only nine of these studies exclusively sampled placentas from term deliveries [43, 49, 53, 54, 57, 58, 63–65]. Predominant bacterial taxa reported in these studies included Pseudomonas [54, 64, 65], Lactobacillus [49, 54], Bacteroidales [64], Enterococcus [63], Mesorhizobium [43], Prevotella [58], unclassified Proteobacteria [57], Ralstonia [43], and Streptococcus [54]. Two studies from this term delivery subset, which sampled multiple regions of the placenta, observed gradients of Lactobacillus relative abundance across levels of the placenta, but in opposite directions [43, 49].
In contrast, five studies did not find evidence for a microbiota in placentas from term deliveries since neither the placental bacterial DNA signal from 16S rRNA gene sequencing [37, 39–41] nor the bacterial load as determined by quantitative real-time PCR [37, 39–41, 56] were significantly different from technical controls. One study even noted that no operational taxonomic units greater than $1\%$ relative abundance in placental samples, were less than $1\%$ in technical control samples, emphasizing the overlap between the two sample types [37]. Three of these studies [40, 41, 56] also attempted to culture viable bacteria from placental tissue, but were rarely successful. In cases where culture was successful, viable bacteria often conflicted with the DNA sequencing results suggesting that cultured bacteria were likely contaminants [40, 41].
## Non-viable or viable bacterial DNA could feasibly be filtered from maternal blood by the placenta leading to a placental bacterial DNA signal
The placenta is a transient internal organ with functions that include promotion of gas exchange, nutrient and waste transport, maternal immunoglobulin transport, and secretion of hormones critical for fetal growth and development [154]. These exchanges and transfers occur due to diffusion gradients between fetal and maternal blood, the latter of which bathes the chorionic villi in the intervillous space of the placenta [108]. This maternal blood, which cannot be fully drained from the placenta before biopsy or sampling, can undoubtedly contain bacterial particles or even the remnants of a low-grade bacterial infection [56, 112, 155]. Because of its structure, the placenta functions as a filter and retains these particles or bacteria, dead or alive. A bacterial DNA signal due to this filtering process would be extremely weak and transient. In addition, the bacterial taxa identified would be highly variable since they do not correspond to a specific niche, which is consistent with the findings of this re-analysis.
## Infection is a potential source for the placental bacterial DNA signal
Instead of in utero colonization, it is more likely that the bacterial DNA signal coming from a subset of placental samples is caused by infection. It is curious to note that specific bacteria are associated with stronger bacterial DNA signals and inflammation in placental tissue resulting in adverse pregnancy outcomes including preterm birth and/or preterm prelabor rupture of membranes (PPROM) [52, 55, 98]. Spontaneous preterm birth has been shown to increase bacterial load [55] and the relative abundances of several taxa in placental samples including but not limited to Ureaplasma [26, 36, 38, 42, 51, 52, 156, 157], Fusobacterium [51, 52], Mycoplasma [42, 51, 52], Streptococcus [36, 51], Burkholderia [27], Escherichia/Shigella [55], Gardnerella [51], Gemella [52], and Pseudomonas [50]. Ureaplasma urealyticum, Mycoplasma hominis, Bacteroides spp., Gardenerella spp., Mobiluncus spp., various enterococci, and *Streptococcus agalactiae* (also known as Group B *Streptococcus or* GBS) are frequently associated with histologic acute chorioamnionitis as well as uterine infection [16, 26, 108, 157]. GBS is also a major cause of early onset neonatal sepsis and has been commonly isolated at autopsy in addition to E. coli, and Enterococcus [16, 158]. While metagenomics sequencing could identify genes with pathogenic potential to determine pathogenicity of bacteria (bacterial DNA) detected in the placenta, it is difficult to infer pathogenicity exclusively from 16S rRNA gene sequencing data. Nevertheless, the DNA of the notoriously pathogenic bacterial genera detailed above were all found in placental tissue, suggesting an invasive phenotype rather than commensal colonization.
## Recommendations for future studies
In order to establish the existence of a viable placental microbiota several criteria need to be met, which have been detailed previously [36, 41]. Studies which aim to assess the viability of a bacterial DNA signal in a purported low biomass sample type should start with the null hypothesis that the entire DNA signal results from contamination and subsequently attempt to reject it with experimental evidence [159]. Therefore, any study evaluating a potential microbiota of the placenta should attempt to demonstrate viability through both culture and DNA sequencing. Placentas should come from term cesarean deliveries without labor to obviate contamination during vaginal delivery and subjects should be screened to ensure that only healthy women are sampled (i.e., no history of antenatal infection, pre-eclampsia, recent antibiotic use, signs of infection or inflammation). Additionally, future studies need to include ample sequenced technical controls in order to identify and account for sources of contamination, which will inevitably exist no matter how rigorous and/or sterile the protocol [75]. Universal sources of contamination include environmental DNA, reagents used to process samples and build sequence libraries, and even the sequencing instruments themselves. Further, biological replicates from the same placenta should also be included to ascertain the consistency of any bacterial DNA signal. Moreover, it should not be assumed that all remaining sequences are legitimate after post-hoc contaminant removal by algorithms such as DECONTAM, especially in low microbial biomass environments such as the placenta.
Since 16S rRNA gene sequencing limits of detection have not yet been thoroughly interrogated in placental tissue, serial dilutions of spiked-in live bacteria or cell-free DNA should be included in a portion of tissue samples to demonstrate the feasibility of recovering the bacterial DNA signal from placental tissue. When multiplexing samples, unique dual index primer sets should be used to eliminate the possibility of barcode hopping which is another source of sample “contamination” [160, 161], and before sequencing, low biomass samples should be segregated from higher biomass samples to avoid well-to-well contamination [39, 162]. Furthermore, 16S rRNA gene sequencing results should be complemented with shotgun metagenomics sequencing to allow for strain level microbiome analyses that can more effectively link DNA detected in placental tissues to its source. With strain-level resolution, bacterial DNA signals can be identified as being unique to the placenta in an individually-specific manner within a study and thus suggestive of a placental microbiota, or shared across the placental samples in a study indicating universal sample contamination. If multiple sequencing methods or other investigative methods such as culture are utilized, concordance should be sought among the data from these multiple methods to determine the legitimacy of potentially credible microbial signals. Finally, in conjunction with publishing, all sequence data and accompanying detailed metadata should be submitted to a public database and code for any data analyses or manipulations should be made publicly available so that others can replicate and verify the results.
## Strengths of this study
Broad searches of the available literature were utilized to ensure that all publicly available 16S rRNA gene sequencing data of placental samples (with associated metadata to partition pooled sample data) were incorporated into the re-analysis, which re-examined the data with in-depth comparisons of term placental samples to technical controls. This allowed for the detection of background DNA contamination in the bacterial DNA signal from placental tissue. In addition, potential confounding variables such as mode of delivery, gestational age at delivery, and 16S rRNA gene target hypervariable region were controlled for whenever possible. By utilizing DADA2 to process the sequence data, variation and biases due to post-sequencing processing were eliminated. This enabled ASV-to-ASV comparisons for six studies which targeted the same 16S rRNA gene hypervariable region using the same PCR primers, a first in the placental microbiota field.
## Limitations of this study
The quality and public availability of data and metadata were the primary limiting factors of this re-analysis. Unfortunately, the availability of metadata or even the data themselves is a pervasive issue in the microbiome field [163–165]. While study cohort statistics were well reported overall, detailed metadata for each subject are required in order to perform a proper re-analysis. Ideally, any study investigating the existence of a viable placental microbiota would, at a minimum, include associated metadata by subject for potential confounders (e.g., gestational age at delivery, and mode of delivery).
Additionally, the impacts of individual low abundance ASVs (i.e., less than $1\%$ mean relative abundance) were not evaluated. While some of these ASVs could potentially represent DNA signals from viable bacteria, most were likely stochastic environmental DNA contamination. Finally, while the R package DECONTAM was used to remove likely contaminants by comparing the prevalence of ASVs in biological samples and technical controls, this tool would be unable to identify contaminants introduced during sampling, delivery, or to identify taxa which were truly present in a sample but also happened to be present in most control samples. In addition, the contaminant identification accuracy of DECONTAM also diminishes when used on low biomass samples such as placental samples where the majority of the sequences are likely contaminants [75, 166].
## Conclusion
As we extend the boundaries of DNA sequencing technologies we need to tread carefully, especially in purported low-biomass sites such as the placenta. Although DNA sequencing is required to capture bacteria that are unable to grow under typical culture conditions, the limitations of current DNA sequencing technology make detection of a legitimate signal or determination of viability unattainable at such low levels [76, 78]. DNA sequencing in and of itself is insufficient to demonstrate the existence of a microbiome in an organ previously believed to be sterile since the presence of DNA merely alludes to former viability not necessarily concurrent with the time of sampling.
Only after demonstrating a valid, viable bacterial DNA signal from term cesarean deliveries, through sterile protocol, with technical controls, and associated culture positive data, can we evaluate the degree to which the maternal immune system tolerates these bacteria without eliciting a deleterious immune response and whether their presence resembles commensal existence or infection. Finally, the placental microbiota may or may not exist, but it is quite clear that attempts to maintain sterility and avoid contamination have not been successful since the vast majority of sequencing reads from placental samples can be attributed to multiple modes of contamination. Therefore, sequencing methodologies require significant improvement before a placental microbiota can be established as 16S rRNA gene sequencing appears to lack the ability to discriminate between a markedly low biomass microbiota and background DNA contamination at present.
## Study inclusion criteria
Searches for “human placental microbiome”, “placenta microbiota”, “placental microbiomes”, and “placenta 16S” were queried on PubMed with a cutoff date of $\frac{6}{16}$/21 to identify studies addressing the existence of a placental microbiota or lack thereof. Additionally we included our recent preprint [96] in this pool of studies. Of the 387 unique studies identified, 58 performed primary research and 41 implemented 16S rRNA gene sequencing on placental samples (Fig. 1). Therefore, we focused on 16S rRNA gene sequencing data. 16S rRNA gene sequencing is a well-characterized way of detecting and classifying bacterial communities within biological samples [167–169], and it is potentially sensitive enough to detect the typically low number of 16S rRNA bacterial gene copies hypothesized to be in the placenta [34, 170]. 33 of the 41 studies which implemented 16S rRNA gene sequencing included at least one placental sample from an uncomplicated delivery at term [27–29, 36, 37, 39–45, 47, 49–65, 96, 153]. However, only 15 of these 33 studies included publicly available 16S rRNA gene sequence data (i.e., sequencing files uploaded to a public database with a published and accurate accession number with sufficient metadata to partition pooled sample data) [29, 36, 37, 39–44, 50, 53, 57, 58, 96]. Thus, the re-analysis ultimately included 15 studies.
## Processing of 16S rRNA gene sequence data using DADA2
Fastq files of the 16S rRNA gene sequence data from samples included in each study were downloaded from publicly accessible databases. If a study included fastq files that contained sequence data from multiple samples, the data were demultiplexed using QIIME2 (version 2020.2) [171] and SED (GNU Sed 4.7), a stream editor for text processing [172].
Sequence datasets from each study were individually processed using the Differential Abundance Denoising Algorithm (DADA2), which is an R package designed to partition 16S rRNA gene sequences into distinct Amplicon Sequence Variants (ASVs) and to taxonomically classify the resultant ASVs [173]. R version 3.6.1 [174] was used for DADA2 processing and all downstream analyses. Processing followed the 1.16 DADA2 guidelines (https://benjjneb.github.io/dada2/tutorial.html), except when stated otherwise. Samples that had an average sequence quality score which dipped below 30 before the expected trim length cutoffs were removed from the dataset. Trim length cutoffs were set to maximize the read length and number of passing samples while still removing poor quality base calls from the ends of reads. Reads were then filtered using the filterAndTrim() function with multithread set to TRUE to enable parallel computation and decrease real time spent computing. Error rates of base calling in the filtered sequences were inferred from the data using the learnErrors() function with multithread set to TRUE. Using the inferred error rates, sequences were partitioned into ASVs with pool and multithread set to TRUE. If the dada() function failed to complete partitioning after 7 days for a particular study’s dataset, which occurred for only one study [36], pool was set to FALSE for sample independent sequence partitioning.
If forward and reverse sequences were not yet merged, they were merged using the mergePairs() function. In cases where the forward and reverse reads were already merged in publicly available data files, the DADA2 merging step was omitted and the code adjusted for merged input sequences. Merged sequences with lengths greater or less than 20 nucleotides from the expected amplified region were removed from the data set since they were most likely due to non-specific merging of forward and reverse reads resulting in extra-long or extra-short reads. Chimeric sequences were detected and removed using the removeBimeraDenovo() function with multithread set to FALSE. This employs the default consensus method instead of the pooled method. The consensus method determines chimeric sequences in each sample and then compares detected chimeric sequences across samples for a consensus. Taxonomy was assigned to sequences using the Silva 16S rRNA gene bacterial database (v 138) [168, 175]. Species assignments were added, when possible.
For each study, merged datasets of ASV counts and taxonomic classifications were filtered using functions from the R package dplyr [176] to remove ASVs that were classified as mitochondrial, chloroplast, or not of bacterial origin. ASVs not classified at the phylum level and samples which did not have at least 100 sequence reads after filtering were removed from the data set.
## Removal of likely DNA contaminants through the prevalence-based method of the R package DECONTAM
To control for background DNA contamination, the R package DECONTAM was used to identify and remove sequences which were more prevalent in technical controls than in placental samples. For likely sequence contaminant removal, only studies which included at least six technical controls [36, 37, 39–43, 50, 96] were included based on the recommendation of the authors of DECONTAM [97]. Technical controls included air swabs, blank DNA extraction kits, and template-free PCR reactions. The DECONTAM prevalence-based function isNotContaminant(), recommended for use with low-biomass samples, was used to remove ASVs based on chi-square tests of ASV presence-absence tables [97]. Thresholds were study specific with the goal of excluding most of the low prevalence likely-contaminant ASVs while retaining high prevalence ASVs not likely to be contaminants. Despite using these stringent study specific thresholds, the results were unchanged if the default threshold of 0.5 was used instead.
## Normalization of 16S rRNA gene sequence datasets within and across studies
All datasets were normalized using the function rarefy_even_depth() from the R package phyloseq (1.30.0) [177]. Following the normalization process, samples whose sequence libraries did not have at least 100 reads were excluded. The remaining samples were subsampled without replacement (i.e., the same sequence was never reselected when subsampling) to the minimum number of sequences per sample within a study. RNGseed was set to 1 to fix the seed for reproducible random number generation. This normalization approach was utilized since 16S rRNA gene read counts can vary by five orders of magnitude among samples in a single study. Given this degree of variability, normalization to the same sequence depth is justified and required for accurate comparisons of 16S rRNA gene profiles among samples [178].
## Data visualization
Heatmaps illustrating the relative abundances of ASVs were prepared using the ComplexHeatmap R package (version 2.2.0) [179]. Samples were grouped by sample type and ASVs were ordered based on ASV mean relative abundances within samples.
The function vegdist() from the R package vegan (version 2.5–6) [180] was used to create Bray-Curtis dissimilarity matrices which were then used as the basis for Principal Coordinates Analysis plots that were generated using the pco() function from the R package ecodist (version 2.0.7) [181]. The Bray-Curtis index was used because it takes into account both the composition and structure of 16S rRNA gene sequence bacterial profiles [182]. The Lingoes method was used to correct for negative eigenvalues so that dissimilarity between samples could be completely explained in Euclidean space [183].
All code to produce the published figures from the raw data is included in the supplementary materials in an R markdown file available at https://github.com/jp589/Placental_Microbiota_Reanalysis.
## Statistical analysis
Homogeneity of 16S rRNA gene sequence profiles between placental samples and technical controls was tested using betadisper() from the R package vegan (version 2.5–6) [180]. Differences in 16S rRNA gene profile structure between placental samples by sampling level and technical controls were evaluated using the function pairwise.adonis() from the R package pairwiseAdonis (version 0.4) [184].
All code to recapitulate these analyses are included in an R markdown file available at https://github.com/jp589/Placental_Microbiota_Reanalysis.
## Supplementary Information
Additional file 1.
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|
---
title: 'Assessing Healthy Aging Score and Its Association With All-Cause Mortality:
Findings From the China Health and Retirement Longitudinal Study'
authors:
- Zihang Zeng
- Xuerui Li
- Wenzhe Yang
- Jiao Wang
- Yun Zhu
- Xiuying Qi
- Weili Xu
journal: Innovation in Aging
year: 2023
pmcid: PMC10024481
doi: 10.1093/geroni/igad006
license: CC BY 4.0
---
# Assessing Healthy Aging Score and Its Association With All-Cause Mortality: Findings From the China Health and Retirement Longitudinal Study
## Body
By 2020, the number of people aged 60 years and older has exceeded 1 billion globally. Over the next 30 years, this number is projected to more than double, reaching 2.1 billion (World Health Organization [WHO], 2022). Despite the increasing longevity of older people, the late-life quality among older adults is poor due to diseases and disabilities (Bryant et al., 2019). How to delay the aging process and improve the quality of life to achieve healthy aging is receiving considerable attention (Vogt et al., 2015).
The WHO [2015] has recently redefined the conceptual framework of healthy aging, showing that healthy aging is not limited to identifying a disease-free status. Healthy aging is supposed to be a multidimensional concept that encompasses disease, individual intrinsic capacity (IC, combination of the physical and mental abilities), and extrinsic environmental support (ES, friendliness of the environment), as well as the interaction between them (Davern et al., 2020; WHO, 2015).
The development of a healthy aging indicator for predicting health-related outcomes or mortality has gained interest during the last few years, but there is still a lack of consensus on the assessment of healthy aging. Most studies focused on the IC dimension solely. Among others, clinical biomarkers (e.g., systolic blood pressure, forced vital capacity, and fasting glucose) have been assessed on a scale of 0–2 and summed up to a healthy aging score (HAS), with a higher score indicating unhealthier. These studies indicated that higher HAS was related to higher mortality and risk of disability (Dieteren et al., 2020; O’Connell et al., 2019; Zhang et al., 2021). Other studies focused on the physiological function, integrating different items which indicated physical function, mobility, cognitive function, sensory, and psychology to an HAS, and showed that the HAS was an ideal predictor for death (Camozzato et al., 2014; Daskalopoulou et al., 2019; de la Fuente et al., 2018; Gao et al., 2022; Locquet et al., 2022; Prince et al., 2021). For the ES dimension of healthy aging, previous studies have been limited to sociological factors such as social engagement and social networks (Jaspers et al., 2017; Manierre, 2019; Nosraty et al., 2015). Though the HAS containing these sociological items were observed to be statistically associated with death, the validity of these items was unclear. Besides, the role of surrounding environmental characteristics in healthy aging has been ignored. Several studies explored the association between death and multidimensional healthy aging including disease status, IC-related indicators (i.e., cognitive and physical function), and ES-related indicators (social support and engagement; Kim et al., 2019, 2021; Manierre, 2019; Nosraty et al., 2015; Stickel et al., 2020). However, most of them used a qualitative method to dichotomize healthy aging with yes and no, which was inconsistent with the continuous process of function change (WHO, 2015). Although these studies provide evidence that better performance on the healthy aging dimension is associated with lower mortality, the items used to construct the HAS were empirically motivated and lacked validation (Zamudio-Rodriguez et al., 2021). Moreover, few studies have comprehensively assessed healthy aging from different healthy domains. Open questions remain in the multidimensional construction of healthy aging.
In the present study, we aimed to (a) construct an optimal HAS by selecting the best items in three dimensions associated with death, including IC, ES, and chronic disease (CD); (b) examine the association of HAS with all-cause mortality among older adults; and (c) identify potential interactions of HAS with demographic and lifestyle factors on mortality.
## Abstract
### Background and Objectives
To construct a comprehensive healthy aging score (HAS) and explore its association with all-cause mortality and its potential interactions with other demographics on mortality.
### Research Design and Methods
This study included 5,409 participants aged ≥60 years from the China Health and Retirement Longitudinal Study. An HAS was constructed based on three dimensions of healthy aging including intrinsic capacity (IC), environmental support (ES), and chronic disease (CD), which were assessed at baseline, and categorized by tertiles (poor, moderate, and high). Participants were followed up biennially for all-cause mortality through the death registration or family interview from 2011 to 2018. Data were analyzed using Cox regression, Laplace regression, and receiver-operating characteristic analysis.
### Results
During 7 years of follow-up, 877 ($16.21\%$) participants died. An HAS was constructed based on the cognition, mobility, and instrumental activity of daily living in the IC dimension; housing in the ES dimension; and hypertension, diabetes, chronic lung disease, stroke, and cancer in the CD dimension, which was associated with death. HAS seems a good predictor of all-cause mortality, with an area under the curve of 0.749. The hazard ratios and $95\%$ confidence intervals for all-cause mortality related to moderate and poor HAS (vs high HAS) were 1.26 (1.01–1.56) and 2.38 (1.94–2.91), respectively. The median survival time was 2.46 years shorter in participants with poor HAS than those with high HAS. There were significant additive interactions of HAS with age, sex, and marital status on death.
### Discussion and Implications
Poor HAS may increase mortality and shorten survival, especially among older, male, and single adults.
## Study Population
The China Health and Retirement Longitudinal Study (CHARLS) is an ongoing prospective cohort study that aims to set up a high-quality database representing Chinese households and individuals aged 45 and above. The CHARLS provides a wide range of information from socioeconomic status to health conditions, which fosters interdisciplinary research on aging (Zhao et al., 2014). The baseline survey was conducted between June 2011 and March 2012, and the participants were followed up biennially until 2018 through a face-to-face computer-assisted personal interview (Zhao et al., 2014). More details regarding CHARLS are described elsewhere (Zhao et al., 2014). Among 7,681 participants who were aged ≥60 years, 2,100 with missing data on healthy aging dimensions at baseline and 172 lost during the follow-up were excluded, leaving 5,409 participants for the current study (Supplementary Figure 1).
Informed consent was obtained from all participants. The study was approved by the Institutional Review Board of Peking University. CHARLS data can be requested at http://charls.pku.edu.cn/.
## Data Collection
Data on age, sex, education, marital status, current residence, annual per capita household expenditure, alcohol consumption, smoking status, and healthy aging information (including IC, ES, and CD) were obtained from the questionnaire survey, and participants’ height and weight were measured in the medical examination at baseline.
Education was categorized as no formal education (illiterate), junior high school and below, and senior high school and above. Marital status was grouped into married and single (including separated, divorced, or widowed). The current residence status was dichotomized into rural versus urban. The annual per capita household expenditure was grouped into tertiles (low, moderate, and high). Alcohol consumption was grouped into nondrinking or drinking (including former and current). Smoking status was grouped into never smoking, former smoking, and current smoking. Body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, was classified into underweight (<18.50 kg/m2), normal (18.50–23.99 kg/m2), overweight (24.00–27.99 kg/m2), and obese (≥28.00 kg/m2).
Data on death, incident falls, and hospitalization were obtained at the biennial follow-up. Because the exact date of death was not available, the midpoint of the two follow-up visits was defined as the time of death.
## Assessment of Healthy Aging Score
For the assessment of the IC dimension, we used an international scale of healthy aging measurement developed by the Ageing Trajectories of Health—Longitudinal Opportunities and *Synergies consortium* (Sanchez-Niubo et al., 2021), which was constructed by item response theory approach based on data from 16 international cohorts comprising more than 340,000 individuals. The items included in this scale were in accordance with IC of WHO’s concept (WHO, 2015), covering the biopsychosocial aspects of health and function. In our study, they were harmonized and combined into 30 dichotomous items (presence or absence), covering 7 domains, including cognition (5 items), psychological symptoms (1 item), vitality (3 items), sensory (4 items), mobility (7 items), activity of daily living (ADL, 5 items), and instrumental activity of daily living (IADL, 5 items). A detailed list of items is shown in Supplementary Table 1.
We took a framework of environmental indicators (Davern et al., 2020) as a guide, which was proposed based on WHO’s Age-Friendly Cities and Communities Guide (WHO, 2007) and extensive research evidence, to assess the ES dimension of healthy aging. The following 16 dichotomous items (presence or absence) in 6 domains, including outdoor spaces and buildings (4 items), housing (5 items), communications and information (2 items), community support (3 items), transportation (1 item), and social participation (1 item), were used to measure the ES dimension in the present study. A detailed list of items is shown in Supplementary Table 2.
For the CD dimension, the conditions of chronic noncommunicable diseases were assessed because they were more common in older adults. Fourteen diseases were surveyed by the question “Have you been diagnosed with these diseases by a doctor?”, including hypertension, dyslipidemia, diabetes or high blood sugar, cancer or malignant tumor, chronic lung diseases, liver disease, heart attack, stroke, kidney disease, stomach or other digestive diseases, emotional or psychiatric problems, memory-related disease, arthritis or rheumatism, and asthma. A detailed list of items is shown in Supplementary Table 3.
To construct the HAS, we used “risk score” based on selected variables, which has been commonly utilized in other studies (Gao et al., 2020; Kivipelto et al., 2006). This strategy may decrease the multicollinearity among many variables and ensure practical utilization. Based on a two-step strategy, the outcome-related risk factors were first assessed individually, then those factors that were statistically associated with the outcome were further included in the multivariable regression model. Specifically, in our study, for IC and ES dimensions with two subcomponents (domain and item): [1] univariable analysis was used to examine whether the potential items were significantly associated with mortality, and [2] multivariable Cox regression model was employed to further assess the association between each domain and mortality (summing up corresponding significant items [$p \leq .05$]). For the CD dimension with only one subcomponent (disease), a two-step process of univariate and multivariate Cox regression analysis was performed to select mortality-related diseases. The scores of IC, ES, and CD dimension were calculated by summing items that were ultimately selected. An HAS was constructed with the equation of 3 × (β1 × IC score + β2 × ES score + β3 × CD score)/(β1 + β2 + β3) according to the previously reported weighted method (Fan et al., 2020), in which the β coefficient, that is, ln(HR), was based on the relationship of IC score, ES score, and CD score (reverse scoring) with death from the Cox regression model. Then an HAS was converted to a T-score with a mean of 50 and a standard deviation (SD) of 10, where the higher the score, the healthier a person is deemed to be.
## Statistical Analyses
Characteristics of participants by different HAS groups were compared using one-way analysis of variance for continuous variables and Chi-square tests for categorical variables.
The normal approximation to the Poisson distribution was used for calculating the mortality with $95\%$ confidence intervals (CIs; Rothman & Boice, 1979). Cox regression model was used to estimate hazard ratios (HRs) and $95\%$ CIs of death related to IC score, ES score, CD score, and HAS. The median difference ($95\%$ CI) of survival time in relation to HAS was estimated using Laplace regression. Receiver-operating characteristic (ROC) curve and the area under the curve (AUC) analyses were conducted to assess the predictive ability of three dimensions (including IC, ES, and CD) and HAS for death. DeLong’s test was used to compare two AUCs (DeLong et al., 1988). Potential additive interactions of HAS with demographic or lifestyle characteristics on the study outcome were examined based on the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP), and the synergy index (SI; Hosmer & Lemeshow, 1992). The formulas are as follows: RERI = HR11 − HR01 − HR10 + 1, AP = RERI/HR11, and SI = (HR11 − 1)/[(HR01 − 1) + (HR10 − 1)], where HR11 indicates the HR for death associated with having both exposures, HR01 or HR10 indicates the HR for death associated with one of the exposures alone. When the $95\%$ CI of RERI or AP does not contain 0 or S does not contain 1, there is a significantly additive interaction. The models were basic-adjusted for age, sex, and education, and further multiadjusted for marital status, current residence, annual per capita household expenditure, alcohol consumption, smoking status, and BMI.
In supplementary analyses, the association between the HAS and incident falls or hospitalization was assessed. And the sensitivity analyses were performed by using multiple imputation of chained equation (Markov chain Monte Carlo) to impute missing values on sex ($$n = 3$$), education ($$n = 2$$), and BMI ($$n = 748$$). The level of statistical significance was set at a p value less than.05. All statistical analyses were performed using Stata/SE 16.0 for Windows (StataCorp, College Station, TX) and R software version 4.1.2 (The R Foundation, Vienna, Austria).
## Characteristics of the Study Population at Baseline
Among the 5,409 participants, the mean age was 67.74 ± 6.45 years and $48\%$ were females. Participants in the high HAS group were younger, more likely to be male, more educated, married, living in urban, with high annual per capita household expenditures, drinkers, smokers, and had lower BMI (Table 1). In addition, we have compared the characteristics between participants and dropouts (Supplementary Table 4). Compared to the participants, the dropouts were older, more likely to be female, less educated, single, with poor annual per capita household expenditure, nondrinkers, nonsmokers, and had lower BMI and higher mortality.
**Table 1.**
| Characteristics | Poor HAS (n = 1,856) | Moderate HAS (n = 1,787) | High HAS (n = 1,766) | p Value |
| --- | --- | --- | --- | --- |
| Age, years | 69.52 ± 7.12 | 67.53 ± 6.19 | 66.08 ± 5.41 | <.001 |
| 60–69 | 1,015 (54.69) | 1,187 (66.42) | 1,350 (76.44) | <.001 |
| ≥70 | 841 (45.31) | 600 (33.58) | 416 (23.56) | |
| Sex | | | | <.001 |
| Male | 760 (40.97) | 952 (53.30) | 1,075 (60.91) | |
| Female | 1,095 (59.03) | 834 (46.70) | 690 (39.09) | |
| Education | | | | <.001 |
| Illiterate | 882 (47.52) | 583 (32.66) | 338 (19.14) | |
| Junior high school and below | 745 (40.14) | 849 (47.56) | 874 (49.49) | |
| Senior high school and above | 229 (12.34) | 353 (19.78) | 554 (31.37) | |
| Marital status | | | | <.001 |
| Married | 1,386 (74.68) | 1,435 (80.30) | 1,530 (86.64) | |
| Single | 470 (25.32) | 352 (19.70) | 236 (13.36) | |
| Current residence | | | | <.001 |
| Rural | 1,213 (65.36) | 1,109 (62.06) | 904 (51.19) | |
| Urban | 643 (34.64) | 678 (37.94) | 862 (48.81) | |
| Annual per capita household expenditure | 3,396 ± 4,100 | 3,881 ± 4,109 | 4,247 ± 4,497 | <.001 |
| Low | 728 (39.22) | 593 (33.18) | 484 (27.41) | <.001 |
| Moderate | 618 (33.30) | 599 (33.52) | 587 (33.24) | |
| High | 510 (27.48) | 595 (33.30) | 695 (39.35) | |
| Alcohol consumption | | | | <.001 |
| No | 1,456 (78.45) | 1,193 (67.76) | 1,084 (61.38) | |
| Yes | 400 (21.55) | 594 (33.24) | 682 (38.62) | |
| Smoking status | | | | <.001 |
| Never smoking | 1,141 (61.48) | 988(55.29) | 925 (52.38) | |
| Former smoking | 258 (13.90) | 223 (12.48) | 188 (10.65) | |
| Current smoking | 457 (24.62) | 576 (32.23) | 653 (36.97) | |
| Body mass index, kg/m2 | 23.31 ± 4.32 | 22.94 ± 3.90 | 22.76 ± 3.68 | <.001 |
| Underweight | 161 (10.13) | 164 (10.60) | 118 (7.74) | <.001 |
| Normal | 798 (50.22) | 836 (54.04) | 905 (59.34) | |
| Overweight | 427 (26.87) | 395 (25.53) | 410 (26.89) | |
| Obese | 203 (12.78) | 152 (9.83) | 92 (6.03) | |
## Construction of HAS and Its Relationship With All-Cause Mortality
During the 7-year follow-up, 877 ($16\%$) participants died (baseline characteristics in Supplementary Table 5). The mortality per 1,000 person-years was 26.12 ($95\%$ CI: 24.39–27.85). The relationships (univariable analysis) between each item in the three dimensions and the risk of all-cause mortality are shown in Supplementary Tables 6–8. Multivariable Cox regression analyses identified the following independent predictors for mortality: cognition (four items), mobility (seven items), and IADL domains (five items) in the IC dimension; housing domain (three items) in the ES dimension; and hypertension, diabetes, chronic lung disease, stroke, and cancer in the CD dimension (Supplementary Tables 8–10).
Likewise, each dimension score was calculated by summing corresponding domains, and the relationships of three dimensions with death were assessed. The results of the multiadjusted Cox regression model showed that the IC score (HR = 0.90, $95\%$ CI: 0.88–0.92), ES score (HR = 0.91, $95\%$ CI: 0.82–1.00), and CD score (reverse; HR = 0.72, $95\%$ CI: 0.66–0.79), as continuous variables, were associated with death in a dose-dependent fashion. Compared with the high IC score group, the HRs ($95\%$ CI) of death were 1.42 (1.11–1.82) for the moderate group and 2.21 (1.72–2.84) for the poor group. Moreover, compared to participants without CD, those with one disease (HR = 1.57, $95\%$ CI: 1.34–1.84) or with two and more diseases (HR = 2.16, $95\%$ CI: 1.73–2.69) had a higher risk of death (Table 2).
**Table 2.**
| Scorea | Mortality (95% CI) | HR (95% CI)b | HR (95% CI)c |
| --- | --- | --- | --- |
| IC score | 25.40 (23.57–27.23) | 0.88 (0.86–0.90) | 0.90 (0.88–0.92) |
| Categorical IC score | Categorical IC score | Categorical IC score | Categorical IC score |
| High | 13.50 (10.71–16.29) | Reference | Reference |
| Moderate | 22.16 (19.59–24.73) | 1.47 (1.18–1.84) | 1.42 (1.11–1.82) |
| Poor | 38.06 (34.15–41.97) | 2.50 (2.00–3.13) | 2.21 (1.72–2.84) |
| ES score | 25.40 (23.57–27.23) | 0.85 (0.78–0.92) | 0.91 (0.82–1.00)d |
| Categorical ES score | Categorical ES score | Categorical ES score | Categorical ES score |
| High | 21.09 (17.90–24.28) | Reference | Reference |
| Moderate | 23.88 (21.29–26.48) | 1.01 (0.85–1.20) | 0.89 (0.73–1.82) |
| Poor | 32.66 (28.59–36.73) | 1.37 (1.14–1.64) | 1.16 (0.93–1.44) |
| CD score (reverse) | 25.40 (23.57–27.23) | 0.65 (0.59–0.71) | 0.72 (0.66–0.79) |
| Categorical CD score | Categorical CD score | Categorical CD score | Categorical CD score |
| 0 | 18.80 (16.69–20.91) | Reference | Reference |
| 1 | 31.09 (27.66–34.51) | 1.50 (1.30–1.74) | 1.57 (1.34–1.84) |
| ≥2 | 42.80 (35.14–50.45) | 2.37 (1.96–2.86) | 2.16 (1.73–2.69) |
Further, an HAS was constructed by the equation of 3 × [ln(0.90) × IC + ln(0.91) × ES + ln(0.72) × CD]/[ln(0.90) + ln(0.91) + ln(0.72)]. The HAS (as a continuous variable) was associated with death in a dose-dependent fashion in both basic (HR = 0.955, $95\%$ CI: 0.949–0.961) and multiadjusted (HR = 0.956, $95\%$ CI: 0.949–0.962) Cox regression models. Compared to participants with high HAS, the risks of death were increased by $26\%$ (HR = 1.26, $95\%$ CI: 1.01–1.56) among those with moderate HAS and by $138\%$ (HR = 2.38, $95\%$ CI: 1.94–2.91) among those with poor HAS after multivariable adjustment (Table 3).
**Table 3.**
| HASa | Mortality (95% CI) | Cox Regression Model | Cox Regression Model.1 | Laplace Regression | Laplace Regression.1 |
| --- | --- | --- | --- | --- | --- |
| HASa | Mortality (95% CI) | HR (95% CI)b | HR (95% CI)c | 50th PDs (95% CI)b | 50th PDs (95% CI)c |
| Continuous | 25.40 (23.57–27.23) | 0.955 (0.949–0.961) | 0.956 (0.949–0.962) | 0.13 (0.11–0.15) | 0.13 (0.11–0.16) |
| Categorical | Categorical | Categorical | Categorical | Categorical | Categorical |
| High | 14.80 (12.56–17.04) | Reference | Reference | Reference | Reference |
| Moderate | 21.50 (18.81–24.19) | 1.24 (1.02–1.52) | 1.26 (1.01–1.56) | −0.56 (−1.14 to 0.02) | −0.55 (−1.16 to 0.06) |
| Poor | 42.77 (38.88–46.66) | 2.45 (2.04–2.95) | 2.38 (1.94–2.91) | −2.61 (−3.26 to −1.96) | −2.46 (−3.04 to −1.88) |
The results of Laplace regression analyses show that the multiadjusted median time of death was 9.65 years for participants with poor HAS, 11.55 years for those with moderate HAS, and 12.11 years for those with high HAS (Supplementary Figure 2). The HAS (as a continuous variable) was associated with death in a dose-dependent fashion in both basic (50th percentile difference [PDs] = 0.13, $95\%$ CI: 0.11–0.15) and multiadjusted (50th PDs = 0.13, $95\%$ CI: 0.11–0.16) Laplace regression models. The 50th PDs ($95\%$ CI) of death for the participants with poor HAS were 2.46 (1.88–3.04) years earlier than those with high HAS after multiadjusted (Table 3).
In the ROC analysis, the AUCs and $95\%$ CIs for the IC score, ES score, CD score, and HAS model were 0.737 (0.717–0.757), 0.716 (0.696–0.737), 0.728 (0.708–0.748), and 0.749 (0.729–0.768), respectively. The HAS model indicated the best prediction ability of death among the four models ($p \leq .05$; Figure 1 and Supplementary Table 11).
**Figure 1.:** *Comparison of areas under the ROC curve (AUC) for IC score, ES score, CD score (reverse scoring), and HAS. BMI = body mass index; CD = chronic disease; ES = environmental support; HAS = healthy aging score; IC = intrinsic capacity; ROC = receiver-operating characteristic. . All scores were standardized. Adjusted for age, sex, education, marital status, current residence, annual per capita household expenditure, alcohol consumption, smoking status, and BMI. aCompared to HAS, p < .05.*
## Interactions of HAS With Demographic and Lifestyle Characteristics
The multivariable Cox regression analyses were repeated for the combinations of HAS categories and different demographic and lifestyle factors (Supplementary Table 12) and the results showed that there were significant additive interactions of HAS with age, sex, and marital status in relation to death. Copresence of both poor HAS and aged ≥70 years greatly increased the HR ($95\%$ CI) for death up to 5.05 ($95\%$ CI: 4.11–6.21; Figure 2A), with significant additive interaction (RERI [$95\%$ CI]: 1.64 [0.81–2.46]; AP [$95\%$ CI]: 0.32 [0.19–0.46]; S [$95\%$ CI]: 1.68 [1.28–2.20]). Similarly, the copresence of both poor HAS and male increased the HR ($95\%$ CI) up to 3.63 ($95\%$ CI: 2.75–4.78) for death (Figure 2B), with significant additive interaction (RERI [$95\%$ CI]: 0.82 [0.16–1.49]; AP [$95\%$ CI]: 0.23 [0.07–0.39]; S [$95\%$ CI]: 1.46 [1.06–2.00]). Besides, compared to the participants who were in moderate/high HAS and married groups, the risks of death were increased by $174\%$ (HR = 2.74, $95\%$ CI: 2.18–3.44) among those with poor HAS and single (Figure 2C), with significant additive interaction (RERI [$95\%$ CI]: 0.65 [0.04–1.26]; AP [$95\%$ CI]: 0.24 [0.05–0.43]; S [$95\%$ CI]: 1.60 [1.02–2.52]). There were no significant interactions of HAS with education, current residence, annual per capita household expenditure, alcohol consumption, smoking status, or BMI on death.
**Figure 2.:** *Additive interaction between HAS and age, sex, and marital status for the risk of all-cause mortality: (A) age and HAS; (B) sex and HAS; (C) marital status and HAS. Categorical HAS (tertiles): poor group (9.54–47.17); moderate/high group (47.18–65.60). Adjusted for age, sex, education, marital status, current residence, annual per capita household expenditure, alcohol consumption, smoking status, and BMI, if applicable. BMI = body mass index; CI = confidence interval; HAS = healthy aging score; HR = hazard ratio.*
## Supplementary Analysis
We assessed the association of HAS with incident falls or hospitalization during follow-up. Multiadjusted Cox regression showed that each point increase of HAS was associated with a lower risk of falls and hospitalization, HRs ($95\%$ CIs) were 0.979 (0.973–0.985) and 0.972 (0.966–0.977), respectively. Compared to the high HAS group, the risks were increased by $41\%$ (HR = 1.41, $95\%$ CI: 1.22–1.62) for falls and by $81\%$ (HR = 1.81, $95\%$ CI: 1.58–2.08) for hospitalization among poor HAS group (Supplementary Tables 13 and 14).
The multivariable-adjusted Cox regression for the association between HAS and mortality was reconstructed in the complete data set after imputation for missing covariates and the results did not alter much compared to those from the initial analysis (Supplementary Table 15).
## Discussion
In this national cohort study among older adults aged ≥60 years, we found that (a) cognition, mobility, and IADL domains in the IC dimension, housing domain in the ES dimension, as well as hypertension, diabetes, chronic lung disease, stroke, and cancer in the CD dimension were related to death. ( b) HAS composed of IC and ES and CD was an optimal predictor of all-cause mortality, and poor HAS was related to increased all-cause mortality and premature death by more than 2 years. ( c) There were significant interactions among older, male, single, and poor HAS on death.
The development of measurement for health status across populations and over time has long been a focus in the study of aging (Caballero et al., 2017). In the past decade, exploring the relationship between healthy aging and mortality in older adults has provoked widespread interest. However, the measures and operational definitions for healthy aging were inconsistent. Many studies assessed the IC dimension by integrating the clinical biomarker (e.g., systolic blood pressure, forced vital capacity, and fasting glucose) and consistently indicated that a poor IC score was associated with death (Dieteren et al., 2020; O’Connell et al., 2019; Zhang et al., 2021). Some studies have constructed IC scores including physical function (Camozzato et al., 2014; de la Fuente et al., 2018), mobility (Locquet et al., 2022), cognition function (Daskalopoulou et al., 2019), sensory (Gao et al., 2022), and psychology (Prince et al., 2021). As a result, the IC scores were reported to be a good predictor of death (Daskalopoulou et al., 2019), and a high level of IC score was related to a lower risk of death (Camozzato et al., 2014; Daskalopoulou et al., 2019; de la Fuente et al., 2018; Gao et al., 2022; Locquet et al., 2022; Prince et al., 2021). Studies on the ES dimension were mostly limited to the social component such as social networks (Santini et al., 2015), social support (Chen et al., 2021), and self-perceived support (Holt-Lunstad et al., 2010), which suggested that the poor social factors were risk factors for mortality. Moreover, older people have one or more diseases commonly, which is highly related to unhealthy aging and mortality (Boyd & Fortin, 2010; Omran, 2005). Similarly, in our study, the higher IC scores and higher ES scores were dose-dependently associated with a lower risk of death, and the absence of diseases was related to decrease mortality.
Considering that healthy aging is a multidimensional concept, several studies have assessed healthy aging from the combinations of different dimensions. A few cohort studies (Kim et al., 2019, 2021; Menec, 2003) have revealed that older people who achieved health, including the IC dimension (freedom from disability, high physical and cognitive function), ES dimension (active social engagement), and absence of major disease, might presage lower all-cause mortality. However, the dimensions in these studies were not integrated into one healthy aging indicator. Another two cohort studies (Jaspers et al., 2017; Manierre, 2019) constructed an HAS encompassing IC dimension (mental health, physical and cognitive function), ES dimension (social support and engagement), and CD, with a result that higher HAS was related to lower risk of mortality. The findings of those studies are in agreement with our results, but few studies have assessed the association between items within dimensions of healthy aging and death. However, combining a number of unscreened items into one indicator would lead to multicollinearity. Following the construction strategy of the “risk score,” we constructed a practical HAS covering multidimensional but relevant factors to maximize the prediction of outcomes. As a result, we found that cognition, mobility, and IADL domains in the IC dimension, housing domain in the ES dimension, as well as hypertension, diabetes, chronic lung disease, stroke, and cancer in the CD dimension were good predictors for death. The AUC of the HAS was larger than each dimension of HAS alone with statistically significant, which may illustrate the importance of including all health domains. In addition, the predictive power of the HAS for mortality was comparable to other studies (AUC ranged from 0.673 to 0.780; Daskalopoulou et al., 2019; Sanders et al., 2014; Swindell et al., 2010). Moreover, we found that poor HAS was associated with a higher risk of death and shortened survival. To further evaluate HAS, we assessed the predictivity of HAS for other age-related outcomes and found that poor HAS was associated with an increased risk of falls or hospitalization.
We found that there was an additive interaction between HAS and age on death. The presence of both poor HAS and being relatively older (aged ≥70 years vs 60–69 years) substantially increased mortality. Although increasing age is a risk factor for death due to function decline, older adults with poor health might not have sufficient physiological reserves to resist the intrinsic decaying effects of aging (Rowe & Kahn, 1987), leading to a fast progression to death. Our findings suggested the importance of maintaining good health in younger old age. Besides, additive interactions were observed between HAS and sex on mortality. Older male adults with poor HAS may have higher mortality. Interestingly, we also found that male participants had higher HAS compared to females (51.60 vs 48.30, $p \leq .001$), as in another study (Jaspers et al., 2017). This may be related to a health-survival paradox, which describes that females live longer than males but the extended life expectancy is unhealthy (Case & Paxson, 2005). Several explanations have been proposed: (a) the favorable effects of estrogen on serum lipids (Waldron & Johnston, 1976), (b) the compensatory effect of the second X chromosome (Austad, 2006), and (c) accompanied by more risk-taking behavior (Galdas et al., 2005) and more severe forms of diseases among males (Case & Paxson, 2005). As a result, unhealthy states may widen this gender gap and further increase the risk of death. Moreover, marital status seemed to be a potential effect modifier for the HAS–death association. In many studies, marital status is also a well-known predictor of health, and previous studies have shown that married people have longer survival and lower mortality (Kaplan & Kronick, 2006; Tani et al., 2018). The explanation for this is that older adults who are separated, divorced, or widowed might experience psychological and emotional stress and suffer from the loss of family support. This may have pervasive and perpetuating effects on health, increasing social vulnerability, depression, loneliness, and even social isolation (Kojima et al., 2020). It could be possible that single older adults might have more unhealthy behaviors, that is, smoking and excessive drinking compared to those with partners (Keenan et al., 2017), resulting in an elevated mortality.
The mechanisms of observed associations were complex and may have several explanations. *In* general, the decline in cognitive (Qiu et al., 2019) and physical (McPhee et al., 2016) functionalities are associated with accelerated aging, which further leads to impairment of IADL (Stickel et al., 2020) and mobility limitations (Schrack et al., 2013). Once these domains above of older adults can be developed and maintained, they may achieve healthy aging (WHO, 2015). People with higher cognition may be more capable of living a healthy lifestyle (Kochan et al., 2017). Besides, maintenance of cognitive function is likely to reduce the death rate by preventing the progression of dementia (Ojagbemi et al., 2016). Taken together, IC involving physical and psychosocial domains is considered as a residual biological reserve of the individual to resist age-related decline in physiological above mentioned (Cesari et al., 2018), thus reducing the risk of death. A review (Garin et al., 2014) has reported that a comfortable living environment was related to good physical and mental health, which may improve the life quality in later life and contribute to longevity. Furthermore, due to the protracted course of CD, self-management is a widely used strategy that can improve health status specifically in physical function (Jonker et al., 2009) and chronic pain (Reid et al., 2008). All in all, high HAS with strong IC, enough ES, and well-controlled CD may help postpone the onset of death in late life.
A notable strength of the study is the high-quality, nationally representative prospective cohort study with a relatively large sample. Furthermore, a comprehensive HAS was constructed based on weights of three dimensions of healthy aging including IC, ES, and CD, which incorporated the proposals from some operational definitions and the existing theoretical framework of healthy aging and addressed the knowledge gap. A novel aspect of the present study is the inclusion of potential interactions between HAS and other demographic and lifestyle factors. Nevertheless, our study involved some limitations. First, some confounding factors such as physical activity and nutrition status were not available. Second, information bias may be inevitable due to self-reported disease status. Third, the exact death date was unclear, but we assessed it by using the midpoint of two follow-up visits to minimize survival time errors. Finally, caution is required when generalizing our findings to other populations due to our findings derived from a relatively healthier population.
In conclusion, our study provides evidence that a low level of HAS encompassing IC, ES, and CD dimension is related to increased all-cause mortality and shortens survival. Our findings highlight the importance of maintaining the IC, improving the ES, and well-controlling CDs to live longer with healthy aging, especially among older, male, and single adults. Further studies are warranted to focus on the dynamic change of healthy aging over time, within individuals and between populations, and establish age- or sex-specific strategies.
## Funding
This work was supported by National Natural Science Foundation of China (No. 82003533), the Chinese Nutrition Society-ZD Tizhi and Health Fund (No. CNS-ZD2020-82), the Swedish Research Council (Nos. 2017-00981 and 2021-01647), the Swedish Council for Health Working Life and Welfare [2021-01826], and Karolinska Institutet Research Foundation [2020-01660].
## Conflict of Interest
The authors declared no conflict of interest.
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|
---
title: Metabolomics combined with clinical analysis explores metabolic changes and
potential serum metabolite biomarkers of antineutrophil cytoplasmic antibody-associated
vasculitis with renal impairment
authors:
- Siyang Liu
- Qing Xu
- Yiru Wang
- Yongman Lv
- Qing quan Liu
journal: PeerJ
year: 2023
pmcid: PMC10024486
doi: 10.7717/peerj.15051
license: CC BY 4.0
---
# Metabolomics combined with clinical analysis explores metabolic changes and potential serum metabolite biomarkers of antineutrophil cytoplasmic antibody-associated vasculitis with renal impairment
## Abstract
### Background
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an autoimmune systemic disease, and the majority of AAV patients have renal involvement presenting as rapid progressive glomerulonephritis (GN). Currently, the clinically available AAV markers are limited, and some of the newly reported markers are still in the nascent stage. The particular mechanism of the level changes of various markers and their association with the pathogenesis of AAV are not well defined. With the help of metabolomics analysis, this study aims to explore metabolic changes in AAV patients with renal involvement and lay the foundation for the discovery of novel biomarkers for AAV-related kidney damage.
### Methods
We performed liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based on serum samples from patients with AAV ($$n = 33$$) and healthy controls ($$n = 33$$) in order to characterize the serum metabolic profiling. The principal component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) were used to identify the differential metabolites. Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) analysis were further conducted to identify the potential diagnostic biomarker. A receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic performance of the identified potential biomarker.
### Results
A total of 455 metabolites were detected by LC-MS analysis. PCA and OPLS-DA demonstrated a significant difference between AAV patients with renal involvement and healthy controls, and 135 differentially expressed metabolites were selected, with 121 upregulated and 14 downregulated. Ninety-two metabolic pathways were annotated and enriched based on the KEGG database. N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid, and the combination of 1-methylhistidine and Asp-phe could accurately distinguish AAV patients with renal involvement from healthy controls. And 1-methylhistidine was found to be significantly associated with the progression and prognosis of AAV with renal impairment. Amino acid metabolism exhibits significant alternations in AAV with renal involvement.
### Conclusion
This study identified metabolomic differences between AAV patients with renal involvement and non-AAV individuals. Metabolites that could accurately distinguish patients with AAV renal impairment from healthy controls in this study, and metabolites that were significantly associated with disease progression and prognosis were screened out. Overall, this study provides information on changes in metabolites and metabolic pathways for future studies of AAV-related kidney damage and lays a foundation for the exploration of new biomarkers of AAV-related kidney damage.
## Introduction
Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is an autoimmune systemic disease that can affect organs such as the kidneys, heart, lungs, and digestive tract, characterized by an inflammatory reaction in the wall of small vessels and fibrinoid necrosis seen in the pathological tissue, mainly invading small vessels (Brogan & Eleftheriou, 2018; Nakazawa et al., 2019). AAV can be divided into granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic GPA (EGPA) based on clinical phenotype, or PR3-ANCA disease vs MPO-ANCA disease based on ANCA specificity. The kidney is the most affected organ in AAV. Studies have shown that over $75\%$ of AAV patients have renal involvement, which is one of the leading causes of mortality in AAV patients, and its prognosis is closely related to the renal function of the patient at the time of diagnosis (Geetha & Jefferson, 2020; Sinico, Di Toma & Radice, 2013). The typical renal presentation is rapidly progressive glomerulonephritis with decreased renal function accompanied by proteinuria, microscopic hematuria, and hypertension for days to months. Patients are often diagnosed in the stage of renal failure due to the insidious onset and rapid progression (Binda, Moroni & Messa, 2018). Therefore, biomarkers for AAV diagnosis, progression monitoring, and prognosis predicting for AAV with renal involvement are urgently needed to improve patients’ therapeutic outcomes.
In recent years, the classical marker ANCA has been studied intensively. ANCA is currently used as the most important biomarker for AAV diagnosis. However, up to $10\%$ of patients with AAV still test clinically negative for ANCA. In contrast, false positive results are found in the general population and are associated with infections, malignancies, and autoimmune gastrointestinal and kidney diseases (Bossuyt et al., 2017; Houben et al., 2016; McAdoo et al., 2012). The value of ANCA in monitoring progression and predicting relapse is also controversial. Some studies indicate that ANCA may have a role in predicting the recurrence of AAV in individuals with renal or pulmonary involvement, although its function in predicting granulomatous disease is limited (Fussner et al., 2016; Kemna et al., 2015). But there were also quite a few studies that support the view that ANCA is weakly correlated with disease activity (Finkielman et al., 2007; Tomasson et al., 2012).
Many new biomarkers for the diagnosis, progression detection, and prognostic analysis of AAV have been reported successively. Recent studies have shown that the activation of the complement bypass pathway plays an important role in the pathogenesis of AAV, and some members of the complement system such as C3a, C5a, soluble C5b-9, Bb and complement factor H can also act as biomarkers (Chen et al., 2015; Gou et al., 2013a; Gou et al., 2013b; Wu et al., 2019; Yuan et al., 2012). LAMP-2 is a promising biomarker with a detection rate of up to $90\%$ in untreated AAV patients and is frequently undetectable in the absence of clinical disease activity, indicating that it is associated with disease activity. However, there is no feasible detection method for LAMP-2 for clinical application, which limits its further verification and application (Kain et al., 2008; Kain et al., 1995; Kain et al., 2012; Peschel et al., 2014). Similarly, anti-PLG antibodies were found to be elevated in serum in patients with AAV, and anti-PLG levels correlated with disease activity and renal involvement while being limited by the lack of suitable detection methods (Berden et al., 2010; Hao et al., 2014). Some biomarkers associated with inflammation, including HMGB1, B-cell activating factor (BAFF), soluble urokinase plasminogen activation receptor (suPAR), and urinary biomarkers such as monocyte chemoattractant protein-1(MCP-1), sCD163 and Gremlin are also reported (Droguett et al., 2019; Huang et al., 2020; O’Reilly et al., 2016; Tam et al., 2004; Wang et al., 2013; Xin et al., 2014).
However, currently, the clinically available AAV markers are limited, and some of the newly reported markers are still in the nascent stage. The particular mechanism of the level changes of various markers and their association with the pathogenesis of AAV are not well defined, and a significant number of clinical investigations are still required to verify these findings. Increasing research findings have suggested that metabolic alterations play an important role in autoimmune diseases by providing energy and specific biosynthetic precursors to regulate the growth, differentiation, survival, and activation of immune cells (Colamatteo et al., 2019; O’Neill & Hardie, 2013; Stathopoulou, Nikoleri & Bertsias, 2019). Yet, there have been few studies to date focusing on the metabolic changes in AAV with renal involvement (Geetha et al., 2022). The high-throughput, high-resolution phenotyping enabled by metabolomics has been increasingly applied in nephrology research for the analysis of disease mechanisms and promising biomarkers (Kalim & Rhee, 2017). We anticipate that the application of the metabolomic technique in AAV with renal involvement will provide us with windows of opportunities to explore promising biomarkers for diagnosis, progression monitoring, and prognosis assessment, and screen intervention sites available for clinical treatment.
## Clinical samples
This study was approved by the Medical Ethics Committee of Tongji Hospital of Huazhong University of Science and Technology (TJ-IRB20220159). The Medical Ethics Committee granted an exemption from the requirement for informed consent because the serum samples we collected were the samples left over from the participants’ routine blood tests, and the study would not affect the rights or health of participants.
Thirty-three patients with AAV in the department of nephrology at Tongji Hospital from June 2015 to July 2017 were recruited in this study, and they were followed up until December 2019. All of them were newly diagnosed with AAV-related renal impairment and had not received any immunosuppressive therapy prior to sampling. An equal number of healthy controls from the health management center of Tongji Hospital were enrolled in this study. Plasma samples were collected and stored at −80 °C for experimental use. The patients were tested positive for ANCA antibodies by immunofluorescence and enzyme-linked immunosorbent assay, and their clinical diagnoses were confirmed as AAV with renal involvement. All of the patients’ symptoms met the criteria of the 2012 Chapel Hill Consensus Conference definition for AAV (Jennette et al., 2013). Patients with metabolic syndrome, malignancy, diabetes, hyperthyroidism and hyperlipidemia were excluded because these diseases had great effects on patient’s serum metabolic profile, which would have interfered with the results of this study. We also excluded patients with other kidney diseases, other autoimmune diseases and patients taking immunosuppressive drugs. Because they have similar clinical presentation or pathogenesis to AAV patients with renal involvement, there may be a lot of overlap in metabolic changes, which may mask the specific metabolic changes of AAV patients with renal involvement. The healthy controls were selected based on gender matching to eliminate gender differences from the results.
## Sample preparation
Frozen samples were taken out from the −80 °C refrigerator and thawed at 4 °C. Taking 100 µl of each plasma into an EP tube and adding 300 µL of methanol. The mixtures were vortexed for 3 min and then centrifuged at 12,000 r/min for 10 min at 4 °C. The supernatants were finally transferred to the injection bottle for LC-MS/MS analysis. Equal volumes of the separated samples were utilized to generate the pooled plasma sample, which was used to assist quality control, ensure the high-quality of data collected in batches by the high-resolution mass spectrometer, and assess the repeatability of the LC-MS/MS system.
## LC-MS/MS analysis
We adopted broadly targeted metabolome technology to analyze the metabolomes of plasma samples from AAV patients with renal involvement and healthy controls. The data acquisition instrumentation system mainly consisted of Ultra Performance Liquid Chromatography (UPLC) (Shim-pack UFLC SHIMADZU CBM30A; Shimadzu, Kyoto, Japan) and tandem mass spectrometry (MS/MS) (4500 QTRAP; Applied Biosystems, Foster City, CA, USA). An ACQUITY UPLC HSS T3 column (2.1 mm i.d. × 100 mm, 1.8 µm; Waters) was used in UPLC to analyze the metabolomes of interest. And the quantification of metabolites was carried out using the multiple reaction monitoring mode of triple quadrupole mass spectrometry. The samples were placed in an autosampler maintained at 40 °C, and then 2 µl samples were injected for LC-MS/MS analysis.
## Data analysis and visualization
Firstly, the software Analyst 1.6.1 (https://sciex.com/products/software/analyst-software) was used to process mass spectrometry data. The raw data of LC-MS/MS were qualitatively analyzed based on the metware database and the public database of metabolite information and quantitatively analyzed by the software MultiaQuant.
Next, the qualitative and quantitative data were analyzed and visualized using PCA, OPLS-DA, volcano plots, and heatmaps to understand metabolic differences between groups and screen for differential expressed metabolites (DEMs). The KEGG database was used to annotate the differential metabolites and identify metabolic pathways associated with them. The analyses mentioned above were achieved with the R Programming Language (R Core Team, 2021) (base package; MetaboAnalystR; ComplexHeatmap).
Lastly, subjects were divided into 4 combinations by disease and health, male and female, and each combination was randomly split into training and validation sets in a 2:1 ratio. We applied R (glmnet) and R (xgboost) to perform Least Absolute Shrinkage and Selection Operator (LASSO) and eXtreme Gradient Boosting (XGBoost) in the training set to screen variables and obtain the best combination for diagnosing biomarkers with the use of a regression model. Unpaired t-test and receiver operating characteristic (ROC) curves were used to estimate the selected biomarker combination in both the training and validation sets. Data were expressed as mean ± standard deviation (mean ± SD), and $P \leq 0.05$ were considered statistically significant. GraphPad Prism software (Graph Software, San Diego, CA, USA) was used to generate graphs.
## Demographic and clinical characteristics of subjects
The clinical characteristics of the study cohort are summarized in Table 1. The two groups have equal numbers of male and female subjects. The AAV group has much higher values of BUN and creatinine (mean 16.44 mmol/L and 476.16 mmol/L respectively) than the healthy group (mean 8.17 mmol/L and 75.90 mmol/L respectively). The eGFR of the AAV group (mean 19.25 ml/min/1.73 m2) significantly declined compared with the healthy group (mean 103.38 ml/min/1.73 m2). There is a statistical difference in age between the two groups. However, given the average ages are similar (48.88 years for the AAV group and 40.25 years for the healthy group), it is anticipated that the difference would not affect the findings or interpretation of the data.
**Table 1**
| Unnamed: 0 | Patients (N = 33) | Controls (N = 33) | P values |
| --- | --- | --- | --- |
| Male/Female | 16/17 | 16/17 | |
| Age (years) | 49.09 ± 15.84 | 40.21 ± 9.80 | 0.008 |
| BUN (mmol/L) | 16.49 ± 7.88 | 8.04 ± 13.48 | 0.003 |
| Creatinine (mmol/L) | 451.30 ± 316.08 | 75.06 ± 14.94 | <0.001 |
| Total cholesterol (mmol/L) | 4.07 ± 1.31 | 4.12 ± 0.54 | 0.856 |
| eGFR (ml/min/1.73 m2) | 20.81 ± 20.92 | 102.96 ± 21.14 | <0.001 |
## Metabolome profiling analysis of serum samples obtained from AAV patients and healthy controls
Broadly targeted metabolome technology was used to comparatively analyze the serum samples collected from the AAV patients with renal involvement ($$n = 33$$) and the healthy controls ($$n = 33$$). A total of 455 metabolites were detected and quantified. The curves of the total ion current of the quality control samples were highly overlapping, that is, the retention time and peak intensity were consistent, indicating that the instrument had high stability, which was an important guarantee for the repeatability and reliability of the data (Fig. S1).
Unsupervised PCA preliminarily demonstrated a significant separation between AAV samples and controls PC1 ($23.38\%$) and PC2 ($6.3\%$) (Fig. 1A). Similarly, the two groups were separated in a three-dimensional PCA score plot (Fig. 1B). PCA analysis indicated that metabolic alterations did occur in the serum of AAV patients with renal involvement. The variable importance in the project (VIP) was calculated using OPLS-DA to further demonstrate the differences between the two groups. The model had an R2Y value of 0.966, which meant that it explained $96.6\%$ of the variance observed within the data, and a Q2Y value of 0.886, showing that the model was highly predictive. The OPLS-DA score plot also showed a significant difference between the disease and control groups (Fig. 1C). Then we performed 200 permutation verification experiments on this OPLS-DA model, R2′ and Q2′ were found to be smaller than R2 and Q2 of the original model (Fig. 1D), indicating that the model was meaningful and could be used to screen for differential metabolites based on the VIP values.
**Figure 1:** *PCA and OPLS-DA demonstrated a significant metabolic difference between patients with AAV and healthy controls.(A) Plane score plot of the PCA analysis; (B) 3D score plot of PCA analysis; (C) OPLS-DA score plot of OPLS-DA model; (D) permutation test of OPLS-DA model.*
## Differentially expressed metabolites were statistically characterized to capture metabolic changes
We obtained VIP values from the OPLS-DA analysis and calculated fold change values for each metabolite. Metabolites with VIP ≥ 1 and metabolites with fold change ≥2 or ≤0.5 were generally considered significant. Based on this criterion, 135 DEMs were selected, with 121 upregulated and 14 downregulated. The DEMs mainly consisted of amino acid, nucleotide, organic acid, bile acids eicosanoid, and their derivatives. The results were visualized in a volcano plot as shown in Fig. 2A and the DEMs were clustered and shown by a heatmap diagram in Fig. 2B. The top 10 upregulated DEMs and the top 10 downregulated DEMs were displayed in Fig. 2C and the top 20 DEMs ranked according to VIP values were presented in Fig. 2D.
**Figure 2:** *Differentially expressed metabolites (DEMs) were statistically characterized under VIP and fold change double screening and visualized to capture metabolic changes.(A) Volcano plot under VIP + Fold Change double screening condition; (B) heatmap overview of all DEMs and samples clustered by cluster analysis; (C) bar plots of top 10 upregulated DEMs and top 10 downregulated DEMs; (D) the top 20 DEMs with the largest VIP values in OPLS-DA model.*
## Characterizing altered metabolic pathway in AAV patients with renal involvement by KEGG annotation and metabolic set enrichment analysis
The KEGG database was used to annotate pathways for DEMs, and 92 pathways were engaged, mostly by amino acid metabolism and nucleotide metabolism. We then used metabolic set enrichment analysis to identify metabolic pathway sets with distinct biological functions. As shown in Fig. 3, pyrimidine metabolism, cysteine and methionine metabolism, tryptophan metabolism, glyoxylate and dicarboxylate metabolism, and D-glutamine and D-glutamate metabolism were significantly enriched.
**Figure 3:** *Metabolite set enrichment analysis diagram.The metabolite sets with top 50 P-values were displayed.*
## Identifying DEMs that could accurately distinguish AAV patients with renal involvement from healthy controls
ROC analyses of individual metabolites were performed to verify plasma metabolites with high selectivity and specificity in identifying AAV patients with renal involvement. N-acetyl-L-leucine, Acetyl-DL-Valine, and 5-hydroxyindole-3-acetic acid exhibited remarkable diagnostic capacity with very high AUC values of 1, higher than 0.987 for creatinine (Fig. 4A). The significantly different expression levels of these DEMs and creatinine between patients and healthy controls were visualized as violin plots in Fig. 4B. Furthermore, subjects were divided into four combinations by disease and health, male and female, and each combination was randomly split into training and validation sets in a 2:1 ratio. LASSO, XGBoost, and logistic regression were combined to calculate the best diagnostic regression model in the training set. As shown in Table 2, the optimal logistic regression model (AIC = 6.00) was formed from two candidate biomarkers, 1-methylhistidine and Asp-phe. The model was evaluated with the training and validation sets separately, and both showed extremely high sensitivity and specificity in diagnosis with an AUC value of 1 (Fig. 5A). And these two metabolites differed significantly between the AAV renal involvement group and the healthy control group in both training and validation datasets (Fig. 5C).
**Figure 4:** *N-acetyl-L-leucine, Acetyl-DL-Valine, and 5-hydroxyindole-3-acetic acid could accurately distinguish AAV patients with renal involvement from healthy controls.(A) ROC curves of N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid and creatinine; (B) violin plots of N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid, and creatinine.*
## Screening DEMs associated with the progression and prognosis of AAV patients with renal involvement
We set the endpoint event as entry to end-stage renal disease or death and followed patients until December 2019. AAV patients with renal involvement were divided into two groups (the events group and no-events group) based on the occurrence of endpoint events at the end of follow-up. An independent sample T-test was performed and we found that 1-methylhistidine, N-acetyl-L-leucine, 2-dimethylamino guanosine, N-acetylalanine, cytidine, and adenosine O-ribose were expressed differently between the events group and the no-events group, while ANCA showed no statistical difference between the two groups (Fig. 6A). Spearman correlation coefficient analysis among DEMs selected above, BVAS scores, ANCA, age, gender, and clinical characteristics reflecting the degree of renal injury, including eGFR, creatinine, and BUN (Table 3). ANCA showed no correlation with gender, age, creatinine, BUN, eGFR and BVAS, which indicated that ANCA cannot assess disease progression. All the selected DEMs exhibited no statistically significant difference in age and gender, so the interferences of age or gender could be eliminated. All the selected DEMs were significantly related to creatinine, which might result from the accumulation of metabolites due to impaired renal function. However, these DEMs are also related to BVAS, so AAV also plays an important role in their metabolic changes. To draw Kaplan–Meier survival curves with end-point events, patients were divided into two groups according to their creatinine levels, and those whose creatinine levels were higher than 442, indicating that they entered the stage of renal failure, were in group 2 (Fig. 6B). The renal survival time of patients with high creatinine was significantly shorter than that of patients with low creatinine ($$P \leq 0.0015$$), consistent with the general consensus. We also divided patients into two groups based on the 1-methylhistidine median level of 1.55, and made Kaplan–Meier survival curves (Fig. 6C). Patients with plasma 1-methylhistidine levels higher than 1.55 had significantly shorter renal survival times than patients with low 1-methylhistidine levels ($$P \leq 0.046$$). 1-methylhistidine was significantly associated with the progression and prognosis in patients with AAV-associated renal impairment, and further study of its role in this disease may contribute to the discovery of new biomarkers or therapeutic targets.
## Discussion
Renal damage is one of the main causes of death in AAV patients, and its prognosis is closely related to the patient’s renal function at the time of diagnosis. However, patients are often diagnosed in the stage of renal failure due to the insidious onset and rapid progression. As an important serum biomarker for the diagnosis and treatment of AAV, the role of ANCA in assessing disease activity and prognosis prediction remains controversial (Finkielman et al., 2007; Tomasson et al., 2012). Biomarkers that can monitor the progression and predict the prognosis of AAV with renal involvement are urgently needed to improve patients’ therapeutic outcomes.
With the help of metabolomics analysis, we obtained information on changes in serum metabolites and related metabolic pathways in patients with AAV renal impairment. In this study, we detected 455 metabolites based on broadly targeted metabolomic techniques and successfully identified metabolic differences between AAV with renal involvement groups and healthy controls by PCA and OPLS-DA analysis. 135 metabolites were identified as the DEMs in AAV with renal involvement groups, which were involved in 92 altered metabolic pathways.
Based on clinical data, we identified some metabolites that could accurately distinguish patients with AAV renal impairment from healthy controls in this study, as well as metabolites that were significantly associated with disease progression and prognosis. ROC curve analyses revealed that N-acetyl-L-leucine, Acetyl-DL-Valine, 5-hydroxyindole-3-acetic acid and the combination of 1-methylhistidine and Asp-phe have the highest sensitivity and specificity to distinguish patients with AAV renal impairment from healthy controls. These metabolites have the potential to be new diagnostic markers and need to be verified by further studies. N-acetyl-L-leucine is a derivate of the essential amino acid leucine, and it is often used to treat vestibular diseases and improve ataxia as a drug that could regulate vestibular function (Günther et al., 2015; Tighilet et al., 2015). However, its role in AAV with renal involvement or autoimmune disease has not been reported. N-acetyl-L-leucine and acetyl-DL-valine are derivates of the branched-chain amino acid leucine and valine; their upregulation in AAV with renal involvement might reflect the upregulation of branched-chain amino acid. Previous study of branched-chain amino acid suggest that high concentrations of branched-chain amino acid can damage circulating blood cells and contribute to the pro-inflammatory and oxidative status observed in several pathophysiological conditions (Zhenyukh et al., 2017). Asp-*Phe is* also a derivative of an amino acid. From the result of KEGG annotation in this study, we can find that amino acid metabolism is highly positive in the AAV with renal involvement group, which might be related to the effects of amino acids in promoting protein synthesis and lymphocyte proliferation during the active phase of vasculitis (Coras, Murillo-Saich & Guma, 2020).
**Table 3**
| rs | Gender | Age | creatinine | BUN | eGFR | BVAS |
| --- | --- | --- | --- | --- | --- | --- |
| ANCA | 0.058 | −0.081 | −0.188 | 0.224 | −0.225 | −0.344 |
| 1-methylhistidine | −0.146 | 0.244 | 0.597** | 0.496** | −0.519** | 0.564** |
| N-acetyl-L-leucine | −0.064 | −0.055 | 0.606** | 0.369* | −0.379* | 0.375* |
| N-acetylalanine | 0.07 | 0.135 | 0.595** | 0.312 | −0.369* | 0.576** |
| adenosine O-ribose | 0.032 | 0.21 | 0.570** | 0.393* | −0.438* | 0.454** |
| 2-dimethylamino guanosine | 0.115 | 0.118 | 0.634** | 0.209 | −0.282 | 0.495** |
| cytidine | −0.025 | 0.276 | 0.548** | 0.193 | −0.208 | 0.465** |
1-methylhistidine was found to be significantly associated with the progression and prognosis of AAV patients with renal involvement. 1-methylhistidine significantly increased in patients with prognoses of end-stage renal disease or death and was positively related to the renal survival times of patients. 1-methylhistidine is a metabolic byproduct of anserine (beta-alanyl-L-1-methyl-histidine), a carnosine analog (Hu et al., 2019). Carnosine and its analog have been recognized to play a powerfully protective role in oxidative and nitrosative stress and have the potential to inhibit multiple mechanisms of injury after hypoxia–ischemia (Bellia et al., 2011). Oxidative and nitrosative stress and hypoxia-ischemia injury are key links in the development of AAV with renal involvement, so the significant increase of 1-methylhistidine might indicate that carnosine and its analog participate in antagonizing AAV with renal involvement. Whether 1-methylhistidine has the value of being a prognostic biomarker and the role of its related metabolic pathway changes in AAV renal damage deserves further study.
KEGG annotation and metabolite set enrichment analysis demonstrate that amino acid metabolism, including cysteine and methionine metabolism, tryptophan metabolism, and D-glutamine and D-glutamate metabolism, change tremendously in the AAV with renal involvement group. It is acknowledged that amino acid catabolism is an important node in controlling immune response (Grohmann & Bronte, 2010; Murray, 2016). Cysteine and methionine metabolism and D-glutamine and D-glutamate metabolism are associated with oxidative stress, inflammation, and specific immunity (Go & Jones, 2011; Jain et al., 2009; Wang & Green, 2012). The metabolism of tryptophan has also been linked to inflammatory reactions and immune regulation (Günther et al., 2020). In this study, kynurenic acid and kynurenine upregulated significantly while serotonin and N-hydroxy tryptamine showed a significant downregulation, which indicated the activation of the tryptophan-kynurenine pathway in AAV patients with renal involvement. Some studies suggest that the tryptophan-kynurenine pathway plays a protective role by counter regulating the immune response during inflammation (Bauer et al., 2005; Günther et al., 2020; Wang et al., 2006), while other research shows that the tryptophan-kynurenine pathway could promote the renal damage progression in AAV (Barth et al., 2009). Therefore, the activation of the tryptophan-kynurenine pathway is a key link in the development of renal damage in AAV. Investigating the mechanisms of the tryptophan-kynurenine pathway in AAV with renal involvement may facilitate the discovery of therapeutic targets and improve the therapeutic outcomes of AAV patients with renal involvement.
This study has several constraints. Although we included all untreated patients with a first diagnosis of AAV, the majority of patients in this study had renal insufficiency due to the insidious onset and rapid progression of AAV. Metabolic changes in this study were the result of the combined action of AAV and renal insufficiency, and the accumulation of metabolites caused by renal insufficiency had a great influence on the outcome. Due to the absence of two controls, patients with AAV but without renal impairment and patients with renal impairment but without AAV, we could not distinguish between metabolic changes caused by renal insufficiency and those caused by AAV. Therefore, the results of this study are only applicable to the cases of AAV with renal impairment. But we believe that our results still have some reference value for researchers who want to conduct AAV related research. Firstly, a recent study used metabolomics analysis to investigate the metabolic differences between the active and the remission phase of 10 AAV patients with renal impairment. They found that amino acid metabolism and nucleotide synthesis were significantly higher in the active phase samples, which was consistent with our results (Geetha et al., 2022). Secondly, our results showed that the major metabolic change in the AAV patients with kidney damage was in amino acid metabolism pathway. The essential amino acids and their metabolites elevated significantly or not significant altered in the AAV patients in our study. However, it is widely acknowledged that plasma essential AAs (EAAs), notably branched-chain AAs (BCAAs), decrease in patients with chronic renal failure (Canepa et al., 2002; Divino Filho et al., 1997; Suvanapha et al., 1991). Laidlaw’s study found that valine, tyrosine, arginine, serine, BCAA, and total essential amino acids significantly decreased in renal failure patients than healthy control (Laidlaw et al., 1994). A new study showed that plasma concentrations of lysine, methionine, threonine, tryptophan, valine, alanine, asparagine, glutamine, serine, and tyrosine were all lower in renal failure patients before hemodialysis compared to controls (Post et al., 2022). Therefore, we think that the changes in amino acid metabolism in this study are more related to AAV. Thirdly, there were 11 differential metabolites (no essential amino acids) reducing in AAV patients with renal involvement in our study, which could not be attributed to the accumulation of metabolites resulted from impaired renal function and was likely to be associated with AAV. Collectively, we believe that AAV was the key factor of metabolic change in this study. We will include patients with AAV but no renal impairment and patients with renal impairment but no AAV to distinguish the metabolic changes caused by renal failure and AAV respectively, and further investigate the possible mechanism of metabolic changes in AAV patients with renal involvement in our future studies.
The second limitation was that despite two-years sample collection timespan, the sample size for biomarker screening was remained very limited because of the low prevalence of AAV. We are still collecting samples and will expand the sample size in our future studies to further verify our findings. Finally, this study was a single-center study, and the majority of the patients were from Hubei Province, China, therefore ethnic differences, diet and geographical factors may not have been avoided.
## Conclusions
Our metabolomic analysis of serum samples demonstrates that metabolic alterations do occur in AAV patients with renal damage. In this study, amino acid metabolism was found to be the most significantly altered metabolic pathway in AAV patients with renal impairment. We also identified some metabolites that could accurately distinguish patients with AAV renal impairment from healthy controls in this study, as well as metabolites that were significantly associated with disease progression and prognosis. Overall, this study provides information on changes in metabolites and metabolic pathways for future studies of AAV-related kidney damage and lays a foundation for the exploration of new biomarkers of AAV-related kidney damage.
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|
---
title: Prevalence and factors associated with periodontal disease in patients with
diabetes mellitus attending Kiruddu National Referral Hospital, Uganda
authors:
- Haruna Muhmood Kiryowa
- Ian Guyton Munabi
- William Buwembo
- Charles Mugisha Rwenyonyi
- Mark Kaddumukasa
- Erisa Mwaka Sabakaki
journal: The Pan African Medical Journal
year: 2022
pmcid: PMC10024556
doi: 10.11604/pamj.2022.43.202.35971
license: CC BY 4.0
---
# Prevalence and factors associated with periodontal disease in patients with diabetes mellitus attending Kiruddu National Referral Hospital, Uganda
## Abstract
### Introduction
patients with diabetes mellitus present with high rates of periodontal disease. Severity and extent of periodontal disease may be directly associated with poor glycemic control. The burden of periodontal disease in patients with diabetes mellitus in *Uganda is* not documented. This study set out to determine the prevalence and factors associated with periodontal disease in patients with diabetes mellitus attending a national referral hospital in Uganda.
### Methods
this was a cross-sectional study involving 264 patients with diabetes mellitus. Data were collected using a pretested questionnaire to assess factors associated with periodontal disease. This was followed by an oral examination to determine the community periodontal index (CPI) and clinical attachment loss (CAL). Laboratory tests included glycated hemoglobin and fasting blood sugar. Factors associated with periodontal diseases were evaluated using logistic regression analysis.
### Results
of the 264 participants, $68.9\%$ were females. The average age was 48.9 (SD = 11.0) years. Majority of the participants ($32.6\%$) had diabetes mellitus for 2 to 5 years with oral hypoglycemic drugs being the most commonly ($55.7\%$) used medication. The overall prevalence of periodontal disease was $85\%$. Univariate analysis revealed that prevalence of periodontal disease was associated with male sex, lower level of education, smoking, oral hygiene practices, poor glycemic control and combined diabetic medication. However, based on multivariate model, this prevalence was only significantly associated with lower level of education: aOR: 10.77 $95\%$ CI 1.04-226.38, $$p \leq 0.05.$$
### Conclusion
periodontal disease is highly prevalent in patients with diabetes mellitus in Uganda, especially those with a lower level of education. All diabetic patients should be screened and managed for periodontal disease. Oral health interventions should also be packaged and presented in a simple language to allow easy comprehension by even the less educated population.
## Introduction
The rate at which the prevalence of diabetes mellitus is increasing has become a concern to mankind [1]. Available data show that about $6.8\%$ of the population worldwide is affected by the disease [2]. A systematic review, Mukamusoni et al. [ 3] reported an average prevalence of $13.7\%$ in the African population. In Uganda, approximately $10.3\%$ of the population have clinically diagnosed diabetes mellitus [4]. Periodontal disease has been identified as one of the early signs of diabetes mellitus [5]. About $95\%$ of patients with diabetes mellitus have some degree of periodontal destruction [6]. Asthana et al. [ 7] revealed that periodontal disease is more prevalent and severe in patients with diabetes mellitus than their counterparts. Periodontal disease may initiate insulin resistance thereby worsening glycemic control [7], which in turn, may lead to an increased extent and severity of periodontal disease [8]. Despite the fact that about $84\%$ of patients with diabetes mellitus in Uganda have poor glycemic control [9], the association between periodontal disease and diabetes mellitus has not been investigated. The present study was aimed at determining the prevalence and factors associated with periodontal disease in patients with diabetes mellitus attending Kiruddu National Referral Hospital in Uganda.
## Methods
Study design and setting: this was a cross-sectional study conducted between December 2020 and February 2022 in the Outpatient Department of Kiruddu National Referral Hospital. The hospital is a public facility located in Kampala, capital city of Uganda. The diabetic clinic is one of the 14 specialized out-patients clinics in Kiruddu Hospital. The hospital medical records indicate that the diabetic clinic attends to about twenty new patients on daily basis.
Study population: study participants comprised of adult patients with a confirmed diagnosis of diabetes mellitus who provided written informed consent before participating in the study. Individuals who were pregnant, or had a history of periodontal treatment in the last 6 months were excluded from the study. The sample size was estimated using Kish *Leslie formula* where $$p \leq 0.437$$, $e = 0.05$ and Z value = 1.645 with statistical power of $90\%$. The study participants ($$n = 267$$) were selected using simple random sampling.
Data collection: data were collected using a pre tested questionnaire administered by a trained research assistant. The weight in kilograms and height in meters of the participants were determined using a weighing scale (KINLEE, China) and a measuring tape (BYLOO, China), respectively. The measurements were used to calculate the Body Mass Index (BMI). An automated glucometer (Contour Plus, Switzerland) was used to determine the fasting blood sugar while HbA1c levels were determined using an automated analyser (LabonaCheck™ A1cHbA1c, CERAGEM MEDISYS INC, South Korea). An oral examination was carried out by a trained dental surgeon using a mouth mirror and a calibrated periodontal probe (Koushen, China) to determine the Community Periodontal Index (CPI) and the Clinical Attachment Loss (CAL). The data were recorded on the World Health Organization Oral Health Assessment form for adults. The CPI values were recorded as 0, normal; 1 and 2, gingivitis; 3, pockets < 4 mm (mild periodontitis) and 4, pockets > 4 mm (severe periodontitis).
Definitions: the outcome variable was periodontal disease. Periodontal disease was defined as presence of either gingivitis or periodontitis. The independent variables included age, sex, level of education, oral hygiene practices, history of active smoking, glycemic control and type of diabetic medication. Level of education was classified into no formal education, primary, secondary and tertiary. Oral hygiene practices were categorized into inadequate (brushing teeth less than twice a day) and adequate (brushing teeth more than twice a day).
Statistical analysis: data were entered in Microsoft Excel, cleaned and exported to R (version 4.10) for analysis. Participants were divided into two groups, according to the presence or absence of periodontal disease. Continuous variables were presented as means and standard deviations while categorical variables were presented as percentages. Univariate analysis was used to determine the association between periodontal disease and the independent variables. All independent variables were employed in the multivariate regression analysis because of their potential role in pathogenesis of periodontal disease. P values < 0.05 were considered as statistically significant.
Ethical considerations: the study protocol was approved by Makerere University School of Biomedical Sciences Research Ethics Committee (Ref. no. SBS-899) and Uganda National Council of Science and Technology (Ref. no. HS1853ES). The participants gave written informed consent before their enrolment in the study in accordance with Helsinki Declaration [10]. All data were assigned special codes and stored in a password-protected computer. No identifying information of the participants was included in data collection.
Source of funding: this study was sponsored by the Government of Uganda through the Makerere University Research and Innovation Fund (MAK-RIF 1).
## Results
General characteristics of the study population: of the 267 participants, 3 were excluded from the study, for reasons of being completely edentulous ($$n = 1$$), failing venipuncture ($$n = 1$$) and withdrawing informed consent ($$n = 1$$), leaving 264 for the analysis (Figure 1). Most participants ($68.9\%$) were females (Table 1). The average age was 48.9 (SD = 11.0) years. Most participants ($78.0\%$) had attained either primary or secondary education and $14.0\%$ had no formal education. Majority of the participants ($93.6\%$) reported brushing their teeth once ($40.6\%$) or twice ($52.6\%$) a day (Table 1). Only $3.8\%$ reported active smoking. About $97.3\%$ of the participants were earning less than one million Uganda shillings per month. About half ($51.6\%$) of the participants had lived with diabetes mellitus for at least 6 years. The most ($55.7\%$) common form of diabetic medication was oral hypoglycemics while a combination of oral hypoglycemics and injectable insulin was used by $31.4\%$ of the participants. The average body mass index (BMI) was 29.5 (SD=5.78) and mean HbA1c was 8.84 (SD=2.57) (Table 1).
**Figure 1:** *flow chart showing participant recruitment* TABLE_PLACEHOLDER:Table 1 Prevalence of periodontal disease: most participants ($85.2\%$) had periodontal disease, which was categorized as gingivitis ($25\%$), mild periodontitis ($31.8\%$) and severe periodontitis ($28.4\%$) (Table 1).
Factors associated with periodontal disease: based on univariate analysis prevalence of periodontal disease was associated with male sex, level of education, oral hygiene practices, poor glycemic control and combined diabetic medication. From multivariate regression, this association was only significant for lower level of education (aOR: 10.77 $95\%$ CI 1.04-226.38, $$p \leq 0.05$$; Table 2).
**Table 2**
| Dependent: periodontal disease | Attributes | Periodontal disease | Normal | OR (univariable) | aOR (multivariable) |
| --- | --- | --- | --- | --- | --- |
| Sex | Male | 72 (87.8) | 10 (12.2) | - | - |
| | Female | 153 (84.1) | 29 (15.9) | 1.36 (0.65-3.08, p=0.43) | 1.68 (0.72-4.26, p=0.25) |
| Age | Mean (SD) | 49.6 (10.5) | 44.4 (12.4) | 0.96 (0.92-0.99, p<0.01) | 0.96 (0.93-1.00, p=0.03) |
| Level of education | No formal education | 36 (97.3) | 1 (2.7) | - | - |
| | Primary | 106 (86.2) | 17 (13.8) | 5.77 (1.12-105.86, p=0.09) | 5.99 (1.13-110.90, p=0.09) |
| | Secondary | 67 (80.7) | 16 (19.3) | 8.60 (1.65-158.29, p=0.04) | 7.54 (1.36-142.02, p=0.06) |
| | Tertiary | 16 (76.2) | 5 (23.8) | 11.25 (1.64-225.01, p=0.03) | 10.77 (1.40-226.38, p=0.04) |
| Smoking | Yes | 9 (90.0) | 1 (10.0) | - | - |
| | No | 216 (85.0) | 38 (15.0) | 1.58 (0.29-29.62, p=0.67) | 1.03 (0.15-20.68, p=0.98) |
| HbA1c | Mean (SD) | 8.9 (2.6) | 8.4 (2.6) | 0.93 (0.80-1.06, p=0.30) | 0.89 (0.76-1.03, p=0.12) |
| BMI | Mean (SD) | 29.4 (5.9) | 30.2 (5.2) | 1.02 (0.96-1.08, p=0.47) | 1.01 (0.94-1.07, p=0.81) |
| Oral hygiene | Inadequate | 97 (86.6) | 15 (13.4) | - | - |
| | Adequate | 128 (84.2) | 24 (15.8) | 1.21 (0.61-2.48, p=0.59) | 1.04 (0.50-2.22, p=0.92) |
| Diabetic medication | Both | 73 (88.0) | 10 (12.0) | - | - |
| | Insulin | 26 (74.3) | 9 (25.7) | 2.53 (0.91-6.97, p=0.07) | 2.57 (0.86-7.64, p=0.09) |
| | Oral | 126 (86.3) | 20 (13.7) | 1.16 (0.52-2.71, p=0.72) | 1.13 (0.49-2.73, p=0.78) |
## Discussion
This study set out to investigate the prevalence and factors associated with periodontal disease in adult patients with diabetes mellitus. There was a high prevalence of periodontal disease, which was significantly associated with lower level of education. The prevalence of periodontal disease in this study was $85.2\%$, with $60.2\%$ of participants having periodontitis and $25\%$, gingivitis. This prevalence is higher than the $67.8\%$ that was reported in a recent metanalysis involving 3092 patients with diabetes mellitus and 23,494 controls [11]. It was similarly higher than the $45.9\%$ that was reported by Nand et al. [ 12] in a rural Indian population. This difference in prevalence could be attributed to the poor glycemic control in the majority of our participants. Poor glycemic control is one of the factors that has been associated with a high prevalence of periodontal disease in patients with diabetes mellitus [13]. This is true for our study where the mean HbA1c in both groups was greater than 7.0 indicating that most of the participants had poor glycemic control. Inadequate oral hygiene, especially in patients with diabetes mellitus may also have contributed to the increase prevalence of periodontal disease in our study population [14]. Our findings indicate that participants who practiced inadequate oral hygiene had a four percent chance of having periodontal disease compared to those who practiced adequate oral hygiene. Kabali et al. also reported a low prevalence of periodontal disease in participants who practiced good oral hygiene [15]. Poor oral hygiene practices accelerate accumulation of plaque, an important reservoir for the periodonto-pathogenic microorganisms.
Males in this study had a sixty-eight percent higher chance of having periodontal disease than females. This is consistent with reports from several other studies that have highlighted the role of male sex as a risk factor for periodontal disease [16,17]. Males usually exhibit relatively poor oral hygiene practices when compared to females [18]. Male participants with poor oral hygiene are more prone to development of periodontal disease than their female counterparts [19]. Older age and smoking have also been identified as risk factors for periodontal disease in male sex [17]. Smoking whether in males or females alters oral microbiome as well causes direct tissue destruction [20]. Though our findings indicate that the chances of having periodontal disease were three percent more in active smokers than in the non-smokers, the number of participants who reported active smoking was small. In addition, the gender differences in smoking status were not evaluated. It is thus difficult to conclude that smoking was an independent risk factor for development of periodontal disease in the male sex.
This study investigated the association between age and periodontal disease. We note that for every unit decrease in age, there was a $4\%$ decrease in odds of having periodontal disease. Eriksson et al. have reported ageing to be associated with an increased incidence of periodontal disease [21]. On the other hand, Pranckeviciene et al. reported that periodontal disease was more prevalent in the younger patients with diabetes mellitus [22]. However, that particular study was conducted in participants with type one diabetes mellitus whose early exposure to chronic glycaemia predisposed them to higher risks of developing periodontitis [23]. Much as our findings suggest that participants with periodontal disease were slightly older than those without periodontal disease, there was no significant variations in the mean age of the two populations. Periodontal disease has been reported to be more common in the older individuals. Though the risk factors for periodontal disease in the elderly are no different from the younger population, it is important that older people are keeping their teeth longer hence prolonging their exposure to periodontopathogenic bacteria. In addition, older individuals may present with a number of systemic conditions which have a direct or indirect link to the pathophysiology of periodontal disease.
In this study, participants who had no formal education were six times more likely to have periodontal disease than those who had attained primary education. The odds for a person with no formal education having periodontal disease increased for every additional increase in level of education. This was statistically significant (p value = 0.04). Our findings are consistent with Masriadi et al. who reported low level of education to be a risk factor for periodontal disease [24]. The level of education and income status are one of the measures of socioeconomic status. Individuals with low level of education are more likely to be poor and unable to afford periodontal treatments which are in most cases expensive, thereby increasing chances of having periodontal disease [25]. Socioeconomic factors like primary education and low social class have been reported to be associated with a greater prevalence of periodontal disease in the adult population [26]. This is true for our study where $97.3\%$ of the participants were earning less than one million Uganda shillings per month which is less than 350 US Dollars per month (Table 1).
Participants using combined therapy of oral hypoglycemics and insulin in this study were twice more likely to have periodontal disease than those who were using insulin alone, and the chances were thirteen percent when compared to those who were using oral hypoglycemics. We opine that this may be related to the association between type of anti-diabetic medication and glycemic control [27,28].
This study had some limitations. It employed a cross-sectional study design to determine the factors associated with periodontal disease. A case-control design would have been the ideal design for this study. The study was also conducted during the COVID-19 epidemic. This might have affected the results of this study.
## Conclusion
Periodontal disease is highly prevalent in patients with diabetes mellitus in Uganda, especially those with a lower level of education. All diabetic patients should be screened and managed for periodontal disease. Oral health interventions should also be packaged and presented in a simple form to allow easy comprehension by the target population.
## What is known about this topic
There is a high prevalence of periodontal disease in patients with diabetes mellitus;The factors associated with periodontal disease in patients without diabetes mellitus increase the risk for development of this condition in patients with diabetes mellitus.
## What this study adds
Unlike the general consensus that diabetes mellitus mainly affects individuals of the medium to high socioeconomic status, most of the participants from this study were of low socioeconomic status; this is evidenced by the lower level of education and low-income status;Apart from the common factors associated with periodontal disease in both patients with and without diabetes mellitus, type of diabetic medication may play an important role in predisposing patients with diabetes mellitus to increased risk of development of periodontal disease; further studies need to be conducted to confirm this association.
## Competing interests
The authors declare no competing interests.
## Authors' contributions
Conception, study design and data collection: Haruna Muhmood Kiryowa; data analysis and interpretation: Haruna Muhmood Kiryowa, Erisa Mwaka Sabakaki, William Buwembo, Mark Kaddumukasa and Ian Guyton Munabi; manuscript drafting: Haruna Muhmood Kiryowa; manuscript revision: Haruna Muhmood Kiryowa, Ian Guyton Munabi, William Buwembo, Charles Mugisha Rwenyonyi, Mark Kaddumukasa and Erisa Mwaka Sabakaki; guarantor of the study: Haruna Muhmood Kiryowa. All the authors read and approved the final version of this manuscript.
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|
---
title: 'Effect of the Delivery Mode on Pelvic Floor Function and Coping With Birth-Related
Pain and Fear: A Prospective Survey Six Months Postpartum'
journal: Cureus
year: 2023
pmcid: PMC10024571
doi: 10.7759/cureus.35065
license: CC BY 3.0
---
# Effect of the Delivery Mode on Pelvic Floor Function and Coping With Birth-Related Pain and Fear: A Prospective Survey Six Months Postpartum
## Abstract
Background and objective *Delivering a* baby is one of the most decisive events in a woman`s life and brings along psychological and physical challenges. Therefore, the question arises: which mode of delivery is the best for the woman’s health and her future life? The aim of this study was to evaluate the influence of the delivery mode on pelvic floor function and coping with birth-related pain and fear six months postpartum.
Materials and methods A total of 200 primiparous women, who delivered during 2018-2019, were included in this prospective case-control study and were asked to fill out the “Pelvic floor questionnaire for pregnant women and women after childbirth” six months after delivery. The women were separated into the following groups: spontaneous vaginal delivery ($$n = 113$$), operative vaginal delivery ($$n = 44$$), and cesarean section ($$n = 41$$). The pelvic floor function as well as coping with birth-related pain and fear six months after delivery was compared.
Results A significantly higher body mass index was found in the cesarean section group. A significantly worse bladder score was shown in the group with an operative vaginal delivery ($$p \leq 0.006$$). The total score of the questionnaire as well as the anal, prolapse, and sexual subscores showed no difference between the delivery modes. Concerning coping with birth-related pain and fear postpartum, significant differences could be seen between the modes of delivery (pain: $p \leq 0.001$; fear: $p \leq 0.001$). Women with spontaneous vaginal delivery showed better coping with pain and fear postpartum.
Conclusion It must be highlighted that women who have had a surgical delivery, including the operative vaginal delivery and cesarean section, stated a lower coping with birth-related pain and fear. This study showed that an operative vaginal delivery has a negative influence on bladder function and the use should be well-indicated. Obstetricians should always be aware of this, as they can contribute to better coping. It is essential to give women the opportunity to talk about the delivery and individual experiences both in pre- and postnatal situations.
## Introduction
Pelvic floor dysfunction is considered by many women as a minor health problem and a natural consequence of childbirth. Historically the well-being of the newborn was the main priority. Nowadays, the role of the mother and the associated potential risk of postpartum physical and psychological problems come into focus. Many postpartum problems originate from pelvic floor dysfunction. Up to 33 % of women suffer from urinary incontinence and $10\%$ report stool incontinence after delivery [1]. Pain after delivery and the coping strategies for birth-related pain and fear have been examined in a few studies but with a low number of participants [2,3]. Khamehchian et al. conducted semi-structured interviews during the first day after spontaneous vaginal delivery (SVD) among 17 primiparous women, demonstrating the role of stress and pain during childbirth [3].
It is known that birth-related pain correlates with postpartum depression, post-traumatic stress [4,5], and worse pelvic floor function years after delivery [6]. All these factors have a huge impact on a woman’s life. Therefore, the question arises of whether the delivery mode has an influence not only on pelvic floor function but also on birth-related pain and fear. The current literature shows conflicting results. Regarding the physical side (in this case, the pelvic floor function), MacLennan et al. stated that cesarean section (CS) is not associated with a significant reduction in long-term pelvic floor morbidity compared to SVD [7]. In contrast, Blomquist et al. showed that CS, compared with SVD, was associated with a significantly lower risk for stress, urinary incontinence, overactive bladder, and pelvic organ prolapse 5-10 years postpartum [8]. On the psychological side, Guittier et al. demonstrated that women with CS reported emotions related to anxiety, fear, disappointment, or feelings of failure [2].
The goal of this study was to evaluate the influence of the delivery mode on the physical and psychological well-being of women after childbirth. This included the influence on the pelvic floor function and on coping with birth-related pain and fear six months postpartum of primiparous women.
## Materials and methods
This study was approved by the Ethics Committee of the University of Ulm, Germany ($\frac{377}{16}$ - FSt/Sta) and registered in the German Clinical Trial Register (DRKS00024725). Informed consent was obtained from all participants. The study was carried out in the University Hospital Ulm, Germany Study design, participants Primiparae, who delivered in our hospital during 2018-2019, were asked to take part in this prospective survey study. They were informed about the study by a gynecologist using written and verbal information. Inclusion criteria were the ability to speak German fluently and primiparous women with a singleton pregnancy > 36+6 pregnancy weeks. Exclusion criteria were multiparous women, a premature delivery, multiple pregnancies, and language barriers. For all patients, age, body mass index (BMI), birth weight of the child, and information about epidural anesthesia in case of SVD or operative vaginal delivery (OVD) were collected. The participants received the validated “Pelvic floor questionnaire for pregnant women and women after childbirth” (PFQ) [9] six months postpartum and were asked to answer all questions. For the analysis, the women were divided according to their mode of delivery. There were 11 primary CSs and 30 secondary CSs, both were grouped together to avoid small groups (see Limitations section).
Pelvic floor questionnaire for pregnant women and women after childbirth (PFQ) The PFQ is a validated questionnaire developed for pregnant women and women after childbirth [9]. This questionnaire contains questions regarding the delivery, anamnestic data, including age, BMI before pregnancy, fetal weight, and 42 questions, which count in the total PFQ score. Besides the total score, the PFQ can be divided into subscores to evaluate the bladder, anal, prolapse, and sexual functions. The subscores can reach values between 0 (no dysfunction) and 10 (dysfunction) and can be calculated separately. These subscores can be added together to get the PFQ total score reaching from 0-40. The higher the value, the worse the pelvic floor function. Even if one answer was missing, the associated subscore and the total score were included in the analysis. Many questions regarding the delivery mode were intended for multiparous women and were therefore not relevant to this study. The following questions were of interest for this study: Did you have pain in your vagina, perineum, or bowel after giving birth? Do you have the feeling that you have dealt with the pain during the delivery and/or afterward? Do you have the feeling that you have dealt with possible fears during childbirth?
Statistical analysis *Data analysis* was performed with IBM SPSS Statistics for Windows, Version 26.0 (Released 2019; IBM Corp., Armonk, New York, United States) and Microsoft Excel (V16.3; Microsoft Cooperation, Redmond, Washington, United States). Demographic characteristics were described as medium and range, absolute and relative frequencies for metric, ordinal, and nominal variables respectively. ANOVA and Kruskal-Wallis tests were performed to find mean differences between groups; chi-square tests were used to compare distributions of discrete variables.
## Results
Study population Out of the 300 women who wanted to take part, 200 completed the questionnaire six months postpartum. Two cases were excluded because they had an OVD attempt and subsequently a CS. Table 1 demonstrates the distribution regarding the mode of delivery and the associated demographic data with a group comparison (Table 1).
**Table 1**
| Unnamed: 0 | Spontaneous vaginal delivery | Operative vaginal delivery | Cesarean section | Group comparison |
| --- | --- | --- | --- | --- |
| Number of participants in the group | 113 | 44 | 41 (11 primary; 30 secondary) | |
| Percentage of the total participants | 57.1% | 20.7% | 22.2% | |
| Age | 31.49 (SD 4.31) | 32 (SD 5.15) | 32.83 (SD 4.77) | p = 0.275 |
| Body mass index before delivery | 23.69 (SD 4.60) | 25.35 (SD 5.07) | 25.88 (SD 6.42) | p = 0.032 |
| Fetal birth weight (g) | 3298.94 (SD 444.60) | 3380.45 (SD 438.03) | 3249.88 (SD 508.31) | p = 0.406 |
Of the participants, $57.1\%$ had an SVD, $20.7\%$ an OVD, and $22.2\%$ a CS. In the group comparison, age and fetal birth weight showed no difference between the groups. The BMI showed a significant difference between the groups (ANOVA $$p \leq 0.032$$ with higher BMI in the CS group) Pelvic floor function Analyzing the influence of the mode of delivery on the pelvic floor function, no difference was found for the total PFQ score between the groups ($$p \leq 0.647$$). There was a significant difference in the bladder subscore ($$p \leq 0.008$$) while all the other subscores showed no significant difference regarding the mode of delivery. A significantly higher bladder score was shown in the OVD group compared to the SVD group ($$p \leq 0.006$$; Figure 1).
**Figure 1:** *Influence of the delivery mode on the bladder subscore of the PFQ* Significant difference between spontaneous vaginal delivery and the operative vaginal delivery (p = 0.006)PFQ: pelvic floor questionnaire for pregnant women and women after childbirth*
Coping with pain and fear Pain after delivery showed a significant difference between the delivery modes ($p \leq 0.001$). In the CS group, only $12.5\%$ of women stated to have pain after delivery compared to $62.5\%$ in the SVD group and $77.3\%$ in the OVD group. In the case of an SVD or an OVD, no difference was found for the stated pain after delivery whether the women had epidural anesthesia or not.
Concerning coping with birth-related pain postpartum, $74.1\%$ of participants with SVD stated to have processed the pain completely (comparison: CS: $52.6\%$; OVD: $43.2\%$) and in $2.7\%$ to have not processed the pain (comparison: CS $7.9\%$; OVD $11.4\%$) (Figure 2).
**Figure 2:** *Coping with postpartum birth-related pain between the different modes of delivery (both p < 0.001), with regard to the question: Do you have the feeling that you have dealt with the pain during the delivery and/or afterwards? SVD: spontaneous vaginal delivery; CS: cesarean section; OVD: operative vaginal delivery*
A significant difference could be shown between the modes of delivery and coping with birth-related pain ($p \leq 0.001$). In detail, a worse coping with pain was found for the participants who had an OVD compared to the SVD group ($$p \leq 0.001$$). No significant results were observed between CS/OVD and CS/SVD.
Concerning coping with birth-related fear postpartum, $77.7\%$ of the participants with SVD stated that they processed the fear completely (comparison: CS: $48.7\%$; OVD: $38.6\%$), and $3.6\%$ stated that they had not processed the fear (comparison: CS $12.8\%$; OVD $15.9\%$) (Figure 3).
**Figure 3:** *Coping with birth-related fear postpartum in the different modes of delivery (both p < 0.001), with regard to the question: Do you have the feeling that you have dealt with possible fears during childbirth?SVD: spontaneous vaginal delivery; CS: cesarean section; OVD: operative vaginal delivery*
A significant difference could be seen between the modes of delivery and coping with birth-related fear ($p \leq 0.001$). Specifically, significant differences were found between OVD and SVD ($p \leq 0.001$) as well as between CS and SVD ($$p \leq 0.004$$).
## Discussion
As one of the most decisive events in a woman’s life, childbirth brings along psychological and physical challenges and therefore there always arises the question of which mode of delivery is the best for the woman’s health and her future life. The aim of this study was to evaluate the influence of the delivery mode on pelvic floor function and on coping with birth-related pain and fear six months postpartum in primiparous women.
In this study, $57.1\%$ of participants had an SVD, $20.7\%$ an OVD, and $22.2\%$ a CS. Compared with the data of Voigt et al., a lower number of CS can be shown in this study with an increased number of OVD. Voigt et al. demonstrated a rise in OVD and CS with increasing maternal age. Women older than 32 years experienced $53.1\%$ VSD, 32.3 % CS, and $11\%$ OVD [10]. Despite age, several studies showed that BMI influences the delivery mode. Dietz et al. examined primiparous women analyzing the number of CS in relation to the maternal BMI, showing an increased rate of CS with rising BMI [11]. The rising number of secondary CS in obese women is due to a known prolonged birth with higher obstetrics risks leading to a more generous indication of CS [12]. This could explain the significantly higher BMI in the CS group in this study.
There is already evidence that the delivery mode influences the physical and psychological outcome after birth. For example, in line with our results, multiple studies showed that OVD is an additional risk factor for pelvic floor function [8], [13-15]. Blomquist et al. compared the influence of the delivery mode on the pelvic floor function 5-10 years postpartum. They showed a significantly higher hazard of anal incontinence and pelvic organ prolapse for women undergoing OVD [8]. However, Crane et al. compared OVD and CS and found no significant impact on pelvic floor function one year after delivery, except for bulge symptoms in the OVD group [16]. In our study, only the bladder function was negatively affected by an OVD. Maybe the rising hazard, as described by Blomquist et al. [ 8], for anal incontinence and pelvic organ prolapse develops over time and the follow-up period in this study is too short. However, based on the found results it must be emphasized to conduct an OVD only in indicated cases, if the fetal or maternal health needs an intervention.
Regarding pain after delivery, CS is associated with lesser pain than SVD or OVD. This could be due to two factors. On the one hand, the pain after delivery might be not as painful and on the other hand, the exact question was whether there was pain in the genital region and not in the surgical area. The last-mentioned aspect could have falsified the result. In the case of an SVD or an OVD it can be assumed that epidural anesthesia does not protect the development of pain and cannot prevent long-term pain and fear processing.
The best coping with birth-related pain was reported in the SVD group, and the worst in the OVD group. Comparing this with the coping of birth-related fear, similar results were evaluated. These results are surprising, due to the fact that SVD goes along with a high level of pain. It can be assumed, that women prepare themselves cognitively and in prenatal classes for the delivery with the goal of spontaneous delivery. Both OVD and secondary CS are surgical interventions that are not planned and occur in situations where decisions must be made quickly. Therefore, the made plan and wish deviate from reality, and fear appears. Women in these situations reported feelings of helplessness and loss of perceived control in the study of Guittier et al. [ 2]. Probably many women feel a sense of failure as society paints the picture of an easy spontaneous delivery. The obstetricians should always be aware of this, as they can contribute to better coping with good communication. As recommended in the literature, this study underlines the importance to prepare women during prenatal classes for the eventuality of a CS or an OVD and to offer all women and, if possible, their partners, the opportunity to talk about the experience of childbirth during the postpartum period [2,6].
One limitation of this study is the CS group includes both primary and secondary CS. More women should have been included to be able to compare both groups separately. Besides, six months after delivery is a short time and further studies should be conducted to compare a longer follow-up.
## Conclusions
In summary, OVD has a negative influence on bladder function and its use should be well-indicated. It must be highlighted that the surgical delivery modes, including OVD and CS, have a worse coping with birth-related pain and fear. It is essential to give women the opportunity to talk about the delivery and individual experiences both in pre- and postnatal situations. Besides, obstetricians should always be aware of this, as they can contribute to better coping.
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|
---
title: The Risk Factors of Self-Management Behavior among Chinese Stroke Patients
authors:
- Huang Shuqi
- Li Siqin
- Wu Xiaoyan
- Yang Rong
- Zhao Lihong
journal: International Journal of Clinical Practice
year: 2023
pmcid: PMC10024618
doi: 10.1155/2023/4308517
license: CC BY 4.0
---
# The Risk Factors of Self-Management Behavior among Chinese Stroke Patients
## Abstract
### Background
Stroke is associated with a high number of disability-adjusted life years globally, so long-term care is necessary and important for those survivors, so self-management is becoming a more significant concept in stroke rehabilitation.
### Methods
Ischemic stroke patients ($$n = 354$$) were enrolled from the outpatient department of Neurology in West China Hospital from September 2018 to December 2019. *The* general demographic and disease-related data of stroke patients were collected. The stroke self-efficacy questionnaire (SSEQ), the brief cognition questionnaire (BIPQ), and the stroke self-management scale (SSMS) were used to collect data on self-efficacy, disease cognition, and self-management behavior separately. The chi-square test, Fisher exact test, independent sample t-test, and Mann–Whitney U test were used for comparison among groups. The logistic regression analysis was used to explore the independent risk factors of the different levels of self-management behavior in stroke patients.
### Results
The score of self-management among Chinese stroke patients was 151.07 ± 18.53. Multivariate analysis showed that the way of paying medical expenses (OR = 3.215, $95\%$ CI (1.130, 7.769)), self-management efficacy (OR = 2.467, $95\%$ CI (1.534, 3.968)), health education before discharge (OR = 2.354, $95\%$ CI (1.457, 3.802)), age (elder) (OR = 2.060, $95\%$ CI (1.265, 3.355)), educational level (OR = 1.869, $95\%$ CI (1.169, 2.988)), and mRS score (OR = 1.850, $95\%$ CI (1.129, 3.031)) were statistically significant ($P \leq 0.05$).
### Conclusions
The self-management behavior of Chinese stroke patients was at the middle level. Patients with medical insurance, high self-efficiency of management, and better limb function may have better self-management behavior. Besides, patients with a high educational level who accept health education before discharge may also have better self-management behavior. For patients, it is important to know this disease in the right way and set up the faith to take care of themselves independently gradually. For medical staff, it is necessary and important to give all patients health education about self-management before discharge. It is urgent to call for attention to this disease, and the government and all of society should give more support to stroke patients.
## 1. Introduction
As one of the most important cerebrovascular diseases, stroke has been the second-most fatal and third-most disabling disease in the world, but it has been the leading cause of death in China in recent years, where almost one-fifth of the world's population resides [1–3]. A newly performed comprehensive assessment of the trends in China from 2013 to 2019 found the prevalence of stroke in China and most provinces has continued to increase in the past 7 years (2013–2019) and warrants a broad-based nationwide strategy for improved prevention as well as greater efforts in screening and more effective and affordable interventions [4]. Although in-hospital outcomes have improved because of a greater availability of evidence-based therapies and supportive care, adherence to secondary prevention strategies and long-term care are inadequate [5]. According to the scientific statement for health care professionals from the American Heart Association, there is strong evidence that self‐management is effective in achieving the goals of the treatment plan and cannot be ignored [6]. Furthermore, behavior, especially self-management or self-care behavior, is thought to be the predominant factor affecting illness and disease. Understanding self-management, knowing the risk factors of stroke, having a healthy diet consisting of physical activity and exercise, adhering to medication, and correcting unhealthy lifestyles are part of the self-management behavior of stroke patients. However, in China, medication adherence is poor among community-dwelling patients [7], and the necessary monitoring of stroke patients after discharge is scarcely in practice; besides, about 30 to $60\%$ of patients do not have access to rehabilitation in hospitals [5]. Despite all this, it is hard to know the whole level of self-management behavior from single aspects of self-management behavior among Chinese stroke patients. Therefore, the purpose of this study is to describe the actual situation of Chinese stroke patients as a whole and explore the risk factors of self-management behavior.
## 2.1. Study Design and Participants
Stroke patients were continuously enrolled in West China Hospital, Sichuan University, from September 2018 to December 2019 and evaluated face-to-face at a clinic of the neurology department in this prospective cross-sectional study. The study was approved by the Ethics Committee of West China Hospital [2018496] and registered before conducting the research (ChiCTR1900022959). We obtained written informed consent from all patients before the assessment. All stroke patients were diagnosed according to Chinese guidelines for the diagnosis and treatment of acute ischemic stroke in 2018. The inclusion criteria for ischemic stroke patients are detailed as follows: [1] age more than 18 years old; [2] no serious aphasia, speech expression, or communication barriers, can communicate with the researcher through words or body language; [3] time of stroke onset is known more than 2 weeks; [4] the modified Ranking score less than 3 and Barthel index score more than 20; [5] informed consent and volunteer to participate in the study. Patients were excluded if they met any of the following conditions: [1] with moderate or severe encephalatrophy; [2] with cognitive impairment or mental illness previously; [3] with malignant tumor, hematonosis, serious heart, liver, lung, and kidney disease; [4] dwelling in rehabilitation institutions; and [5] quitting during the investigation process.
## 2.2.1. Demographic Data
*The* general demographic data of patients includes gender, age, educational level, marital status, working status, body mass index (BMI), payment way for medical expenses, average monthly income of the family, and so on.
## 2.2.2. Disease Related Data
Disease-related data, including smoking, drinking, chronic disease, family history of stroke, recurrence frequency, and health education about self-management before discharge.
## 2.2.3. Self-Efficacy Data
The stroke self-efficacy questionnaire (SSEQ) was compiled by Jones and translated into Chinese by Li Hongyan in 2015 with good reliability and validity [8]. The revised Chinese version of the SSEQ includes 2 dimensions and 11 items, including items on activity function efficacy (6 items) and self-management efficacy (5 items). The total Cronbach's α coefficient of the scale is 0.969 [9]. Each item is scored by 0–10 grades. The higher score in each dimension indicates better self-efficacy, respectively. In order to classify the efficacy, we used the standard score index to make it comparable: the standard score index = (the actual score of this dimension/the highest score of the dimension) ∗ $100\%$; the score index less than $60\%$ indicates poor efficacy, 60 to $80\%$ indicates medium efficacy, and more than $80\%$ indicates good efficacy.
## 2.2.4. Disease Cognition Data
With the in-depth study of disease cognition, Broadbent evolved from the revised cognition questionnaire compiled by Weinman et al. [ 10] and Rona et al. [ 11], and the brief cognition questionnaire (BIPQ) was completed in 2006. The questionnaire includes 8 dimensions (items 1 to 8): consequences, timeline, personal control, treatment control, identity, concerns, understanding, and emotional response. Item 9 of the BIPQ is an open question that explores causal representation and assesses the top three causes of stroke. The higher score means the more negative disease cognition, which can quickly and conveniently evaluate the patients' status of disease cognition [12, 13].
## 2.3.1. Modified Rankin Scale (mRS)
Warlow designed mRS based on Rankin scale in 1988 to evaluate the disability level after stroke comprehensively. In this study, mRS was used to measure the ability to live independently and was divided into 7 levels: 0 for asymptomatic (no need for assistance), 5 for severe disability (complete dependence), and 6 for death. The scale has been widely used and has good reliability and validity [14].
## 2.3.2. The Barthel Index (BI)
The Barthel index (BI) was published in 1965 by American scholars Florence Mahoney and Dorothea Barthel. It includes 10 items with a full score of 100. The lower the score is, the more serious the disability is, and the more help is needed. 0–20 points reflect extremely serious functional defects; 25–45 points reflect serious dysfunction; 50–70 points reflect moderate dysfunction; 75–95 points reflect mild dysfunction; and 100 points reflect complete self-care [15].
## 2.3.3. Self-Management Data
The stroke self-management scale (SSMS) was designed by Wang Yanqiao, a Chinese scholar, on the basis of the self-management connotation and other chronic disease self-management behavior scales. It has seven dimensions, including disease management, medication management, diet management, daily life management, emotion management, social function and interpersonal management, rehabilitation, and exercise management. Out of the total of 50 items, 49 items were scored using a 5-level scoring method (1–5 points). In the dimension of food management, 1 item scored 1–10 points. The higher total score shows better self-management behavior. The total Cronbach's α coefficient of the scale is 0.835, the content validity is 0.95, and the structural validity is 0.594–0.771 [16]. In order to compare the scores of different dimensions, the standard score index is used. The standard score index = (the actual score of this dimension/the highest score of the dimension) ∗ $100\%$, the standard score index less than $60\%$ indicates poor self-management, 60 to $80\%$ indicates the medium self-management, and more than $80\%$ indicates the good self-management.
## 2.4.1. Study Sample Size
PASS 11.0 software (NCSS., Rijswijk, The Netherlands) was used to calculate the sample size. 318 cases that need to be investigated. Assuming $10\%$ nonresponse rate, the final sample size became 354.
## 2.4.2. Data Analysis
Data input was performed using EpiData 3.1 (The Epidata Association, Odense, Denmark), and statistical analysis was performed using SPSS 19.0 (IBM Corp., Armonk, NY).
The classification data were described by frequency and proportion. The continuous data were described by means and standard deviation if they conform to normal distribution. If they do not conformed to a normal distribution, they are described by means of median and interquartile range.
Classification data were conducted by chi-square test or Fisher exact test. Continuous data were conducted by independent sample t-test or Mann–Whitney U-test. The variables of $P \leq 0.15$ in single factor analysis and those closely related to self-management in clinical settings served as the independent variables for binary logistic regression analysis. Box Tidwell was used to test whether the logit conversion value between a continuous independent variable and a dependent variable is linear.
## 3.1. Data Collection
In this study, 400 questionnaires were sent out, 367 questionnaires were taken back, the response rate was $91.75\%$, 13 invalid questionnaires were eliminated, the remaining 354 valid questionnaires were left, and the effective rate was $96.45\%$. Please see Figure 1 research flow chart for details.
## 3.2. Demographic Characteristics
As shown in Table 1, this study included 354 effective stroke patients, of whom 108 ($30.5\%$) were women and 246 ($69.5\%$) were men, with an average age of 61.16 ± 13.01 years.
## 3.3. Diseases-Related Characteristics
Of the 354 patients who were included, 192 ($54.2\%$) patients never smoke, 107 ($30.2\%$) patients have quit smoking, and 206 ($58.2\%$) patients never drink alcohol. The most common chronic disease was hypertension ($58.8\%$). In this study, $50.8\%$ of patients had no primary caregiver. The most common sequelae were motor dysfunction ($27.1\%$). $46.9\%$ of patients did not receive self-management health education before discharge. See Table 2 for details.
The disability level and activities of daily living are shown in Table 3. Of the 354 patients who were included, $75.7\%$ patients in our study could completely self-care.
## 3.4. Descriptive Analysis of Self-Management Behavior of Stroke Patients
According to the Shapiro–Wilk normal test, the data conformed to the normal distribution. The score of the SSMS was 151.07 ± 18.53; 210 patients ($59.3\%$) had poor self-management behavior, 141 patients ($39.8\%$) were at medium level, and 3 patients ($0.8\%$) had good self-management behavior. See Table 4 for details.
## 3.5. Single Factor Analysis
Since only three patients in the good level group, so we combined the good and middle group. Comparing the difference of self-management behavior between middle-high-level group and low-level group.
Tables 5 and 6 showed that the educational level, average monthly income of the family, payment method of medical expenses, health education of self-management before discharge, SSEQ self-management efficacy dimension, and the score of SSEQ and BIPQ between the two groups were statistically significant ($P \leq 0.05$).
## 3.6. Multivariable Analysis
21 items were included in the linear test model, and the significance level after Bonferroni correction was 0.0024. The linear test results showed that there is a linear relationship between all the continuous independent variables and the logit conversion value of the dependent variable.
Finally, the logistic model was statistically significant (χ2 = 53.876, $P \leq 0.001$) (Table 7). The model can correctly classify $66.9\%$ of the research objects. Among the variables included in the model were statistical significance ($P \leq 0.05$) in terms of payment method for medical expenses, SSEQ self-management efficacy, self-management health education before discharge, age (old age), educational level, and mRS score. See Table 7 for details.
## 4. Discussion
This study reported the actual whole status of self-management behavior and studied the risk factors of self-management in Chinese stroke patients. The results indicated that the payment way of medical expense, self-management efficiency, health education about self-management before discharge, age, educational level, and mRS score were independently influencing factors of self-management behavior.
Although Healthy China 2030 emphasized the importance of structuring a better health security system, especially for chronic diseases [17], patients still need to pay a part of their medical bills, even if they have medical insurance. Our study shows socioeconomic factors such as inadequate health insurance may have a negative impact on self-management behavior. Our study is consistent with the national registry study in China, which demonstrated that health insurance status was significantly associated with 1-year outcomes for patients with stroke. Patients who are covered by the New Rural Cooperative Medical Scheme, which has lower reimbursement rates than the Urban Basic Medical Insurance Scheme [18]. However, stroke is a leading cause of long-term disability globally and a resource-intensive disease, both directly and indirectly [19]. A Sweden study based on regional administrative systems found that the monetary burden of stroke is very high, and patients who are affiliated with stroke may also have a socioeconomic gradient in the utilization of Swedish health insurance [20]. Prior studies have demonstrated that insurance status is an independent predictor of patient safety events after stroke. The absence of private insurance is associated with higher mortality, longer lengths of stay, and worse clinical outcomes [21]. Thus, it is important to call for more attention and more resources for stroke. It is an urgent task around the world to increase economic investment, expand the coverage of medical insurance, and increase the proportion of reimbursement for major diseases such as stroke.
Patients with high efficiency in self-management are more likely to have better self-management behaviors. More and more studies have pointed out the importance of self-efficacy in the long-term care of people with enduring illnesses; patients with high efficacy function better in daily activities, perceive a better quality of life, and have worse emotional disorders than patients with low self-efficacy [22–24]. Among many theories on behavior change, Social Cognitive Theory emphasizes it is a multifaceted causal structure in which self-efficacy plays an important role in this process[25]. Actually suffering from stroke may be a traumatic experience for patients, it is a huge challenge for them. A study shows that perceived coping self-efficacy emerges as a focal mediator of posttraumatic recovery, which lends support to the centrality of the enabling and protective function of belief in one's capability to exercise some measure of control over traumatic adversity [26]. Nowadays, some interventions based on self-efficacy aiming at improving outcomes in stroke patients are showing up gradually [27–29]. Thus, we should explore more effective interventions to improve self-management efficiency, especially for those needing long-term care diseases, eliminate the negative emotions and pessimistic attitude, so as to increase the possibility to change unhealthy lifestyle and improve self-management ability.
Patients who accepted the self-management health education before discharge may have better self-management behaviors. Health education is a continuous, dynamic, complex, and planned teaching process throughout the whole process of life [30]. Health education could help patients and their caregivers know the right knowledge about this disease and enhance their faith in changing bad behaviors. A study found that nurses can help improve patients' knowledge and cognition of the risks of stroke by playing the health education CD-ROM and providing printed information during the patients' wait time before appointments [31]. In our study, this question was reported by themselves, nearly half of the patients answered they didn't receive the content of stroke in hospital. On the one hand, patients may have a misperception due to low sensitivity and acceptance of health knowledge. On the other hand, the information we provided to patients they may don't need. However, providing health education about how to manage themselves before discharge is the theoretical basis for long-termin-home management. With the development of the times, more and more health education models emerged. The health belief model is generally used to reduce the risk of developing a disease or manage an already existing disease. According to the health belief model, patients may change their behavior if they perceive a health threat [32]. Besides, social cognitive theory also posits that health behaviour is determined by the interaction of personal cognitive factors, socioenvironmental factors, and behavioral factors [33]. Therefore, we should formulate the content and form of health education under the guidance of these health education theories, taking into account the patients' cognitive, social, and economic factors, individual factors, and other specific factors. The form of health education is also changing with time; social media has now become a popular and broad way to do so as the world continues to advance technologically [34]. It is a global trend to transform traditional health education into digital education. So, future research can also continue to explore the form and effect of health education with the help of artificial intelligence and other new technologies.
Age is an unalterable factor for self-management behavior; our study found that the elderly (age more than 60 years) may have better self-management behavior compared to the young (less than 44 years). The elderly patients, especially the retired, have more time and energy to manage their own diseases. While the young who have a long life expectancy after stroke may consider return to the society more. It is hard for the young population to devote themselves to long-term rehabilitation as peers all working and taking family responsibilities. With an increasing incidence of stroke in young adults, the optimal and specific management of this population needs more research studies.
In our study, patients with better limb function had better self-management behaviors than those who had a severer motor disability. Physical well-being seems to be the most affected component of quality of life. A review showed that the level of disability, presence of comorbidities, and motor function were the key predictors for quality of life among stroke survivors in Africa [35]. The lives of patients with better limb function are less affected, which in turn will promote these patients to better implement self-management behavior. Naturally, patients with better limb function have fewer difficulties with self-management, so they can change their poor lifestyle and improve their self-management abilities faster. Nowadays, more and more studies are focusing on different interventions to improve the motor function of stroke patients, including repetitive transcranial magnetic stimulation [36], virtual reality [37], mirror therapy [38], and so on. Within these fields, upper limb function is the most concerning.
*In* general, the self-management behavior among Chinese stroke patients is at the middle level. The independent factors that affect the self-management behavior of stroke patients are: the payment way for medical expenses, SSEQ self-management efficacy, self-management health education before discharge, age (elderly), educational level, and mRS. It is not only necessary to popularize the knowledge of basic prevention and control of the disease, but it is also necessary to continuously encourage the patients to establish the determination and confidence to effectively control the disease and gradually improve self-efficacy, so as to change the unhealthy behaviors affecting health and finally improve self-management ability.
## 5. Limitations
This study has some limitations. First, because the survey was finished in one hospital, the representativeness of the samples was limited. Second, this study only investigated the score of mRS less than 3. Some patients with severe disabilities still have part of self-management ability, which needs further research to explore the self-management ability of patients with severe disabilities. Third, the regression models we used may have several limitations in determining the risk factor of the given variable. Further studies may use a muticenter, large survey to verify our conclusion. If possible, use the SEMs to discover the severity and direction of the association between self-management and other factors.
## 6. Conclusions
The self-management behavior of Chinese stroke patients is at the middle level. Patients with medical insurance, high self-management efficiency, and better limb function may have better self-management behavior. Besides, patients with a high educational level who accept health education before discharge may also have better self-management behavior. It is important for patients to get knowledge about stroke in a right way and set up the faith to take care of themselves independently gradually. Furthermore, it is necessary and important for medical staff to give all patients health education about self-management before discharge and to increase the effectiveness of interventions for rehabilitation.
## Data Availability
The data that support the findings of this study are available from the corresponding author.
## Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
## Authors' Contributions
Huang Shuqi and Li Siqin contributed equally to this work.
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|
---
title: Hyperoside Ameliorates Renal Tubular Oxidative Damage and Calcium Oxalate Deposition
in Rats through AMPK/Nrf2 Signaling Axis
authors:
- Hongyang Tian
- Qi Liang
- Zhen Shi
- Hang Zhao
journal: 'Journal of the Renin-Angiotensin-Aldosterone System: JRAAS'
year: 2023
pmcid: PMC10024623
doi: 10.1155/2023/5445548
license: CC BY 4.0
---
# Hyperoside Ameliorates Renal Tubular Oxidative Damage and Calcium Oxalate Deposition in Rats through AMPK/Nrf2 Signaling Axis
## Abstract
### Background
Nephrolithiasis is a common disease that seriously affects the health and life quality of patients. Despite the reported effect of hyperoside (Hyp) against nephrolithiasis, the specific mechanism has not been clarified. Therefore, this study is aimed at investigating the effect and potential mechanism of Hyp on renal injury and calcium oxalate (CaOx) crystal deposition.
### Methods
Rat and cell models of renal calculi were constructed by ethylene glycol (EG) and CaOx induction, respectively. The renal histopathological damage, CaOx crystal deposition, and renal function damage of rats were assessed by HE staining, Pizzolato staining, and biochemical detection of blood and urine parameters. MTT and crystal-cell adhesion assays were utilized to determine the activity of HK-2 cells and crystal adhesion ability, biochemical detection and enzyme-linked immunosorbent assay (ELISA) to measure the levels of oxidative stress-related substances and inflammatory factors, and western blot to test the expression levels of proteins related to the AMPK/Nrf2 signaling pathway.
### Results
Briefly speaking, Hyp could improve the renal histopathological injury and impaired renal function, reduce the deposition of CaOx crystals in the renal tissue of rats with renal calculi, and decrease the adhesion of crystals to CaOx-treated HK-2 cells. Besides, Hyp also significantly inhibited oxidative stress response. Furthermore, Hyp was associated with the downregulation of malondialdehyde, lactate dehydrogenase, and reactive oxygen species and upregulation of superoxide dismutase activity. Additionally, Hyp treatment also suppressed inflammatory response and had a correlation with declined levels of interleukin (IL)-1β, IL-6, IL-8, and tumor necrosis factor. Further exploration of mechanism manifested that Hyp might play a protective role through promoting AMPK phosphorylation and nuclear translation of Nrf2 to activate the AMPK/Nrf2 signaling pathway.
### Conclusion
Hyp can improve renal pathological and functional damage, decrease CaOx crystal deposition, and inhibit oxidative stress and inflammatory response. Such effects may be achieved by activating the AMPK/Nrf2 signaling pathway.
## 1. Introduction
Nephrolithiasis, also known as kidney stones or renal calculi, is the third most common urinary tract problem in urology [1, 2]. Although renal calculi are benign lesion, they bring urinary tract obstruction, renal colic, and hydronephrosis to patients, and some severe cases even develop into uremia, septic shock, and tumors [3]. In fact, renal calculi seriously affect the health and quality of life of patients. It is reported that $60\%$-$80\%$ of renal calculi belong to calcium calculi, such as calcium oxalate (CaOx) and calcium phosphate; CaOx calculi account for $80\%$ of all cases [4]. The 1-year recurrence rate of CaOx calculi without drug treatment is $10\%$, the 5-year recurrence rate is $35\%$, and the 10-year recurrence rate is $50\%$ [5]. What is worse, patients with recurrent calculi are more prone to recurrence again [4]. Nephrolithiasis is influenced by various factors, such as lifestyle, diet, ethnicity, and geographic location [6], but its specific pathogenesis remains undefined. Additionally, recent studies have reported that inflammation and oxidative stress response are closely related to nephrolithiasis, and these two factors interact with each other to create a vicious cycle [7–10]. Currently, even though surgery is effective in solving most urinary calculi, it cannot prevent the formation of new calculi. Domestic and foreign scholars are committed to revealing the mechanism of renal calculi characterized with high recurrence and complex etiology, aiming to find new, effective, inexpensive, and nontoxic drugs that can not only be administered safely for a long duration but also can prevent and treat the recurrence of renal calculi.
Hyperoside (Hyp), also known as quercetin 3-O-β-d-galactoside, is a flavonol glycoside extracted from plants of the genera Crataegus or Hypericum [11]. Hyp has a wide range of pharmacological activities, such as anti-inflammatory, antioxidant, antidepressant, antihyperglycemic, antibacterial, antiviral, anticoagulant, and anticancer effects [11, 12]. Through the role in antioxidative stress and against inflammation [13], Hyp can alleviate cardiovascular injury and liver injury [14] caused by a variety of factors. Besides, Hyp can inhibit oxalic acid-induced oxidative damage and cytotoxicity in human renal tubular epithelial cells, but its mechanism remains to be expounded [15]. Zhu et al. proposed that intragastric administration of Hyp and quercetin could relieve renal crystal deposition and reduce urinary citrate excretion [16]. In this study, ethylene glycol (EG) and CaOx were adopted to construct a rat model of renal calculi and to induce HK-2 cells, respectively. After that, the effects of Hyp on kidney injury, CaOx crystal deposition, oxidative stress, inflammatory factors, and cell viability in the rat and cell models were explored. Additionally, the possible mechanisms of the antioxidant, anti-CaOx deposition, and anti-inflammatory functions of Hyp were further investigated. Based on the above experiments, this study provided a theoretical basis for Hyp treating nephrolithiasis.
## 2.1. Construction of Rat Model and Grouping
This study was approved by the Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University (No. 2022027) and performed in accordance with the approved guidelines. EG (analytical reagent) and Hyp standards were purchased from Kunshan Horizon Chemical Co., Ltd. and Shanghai Yuanye Biotechnology Co., Ltd., respectively. A total of 40 male SD rats (age: 12 weeks; weight: about 250 g) were purchased from Shanghai Model Organisms Center and randomly divided into 4 groups (control group, Hyp group, EG group, and EG+Hyp group), with 10 rats in each group. Referring to the research of Saleem et al. [ 17], rat models of nephrolithiasis were established in this study. After modelling, the rats were given corresponding drugs by gavage. The EG group and the EG+Hyp group rats received drinking water with $0.75\%$ EG, while the control group and the Hyp group rats were given an equal volume of distilled water. In the meanwhile, the Hyp group and the EG+Hyp group rats were also intragastrically administered with 50 mg/kg/d of Hyp once daily [18]. After 5 weeks, kidney tissue, blood, and urine were collected from rats in each group.
## 2.2. Histopathological Observation
After the rats were sacrificed, the left kidney tissues of three rats in each group were fixed in formalin solution, followed by paraffin embedding and sectioning (4 μm thick). Hematoxylin and eosin (HE) staining was carried out according to the instructions of HE staining kit (Beijing Solarbio Science & Technology Co., Ltd., China). Upon staining, the sections were mounted with neutral resin. Subsequently, the pathological changes of kidney tissues were observed and photographed under a light microscope (CKX31, Olympus, Japan) at a magnification of ×100 and ×200.
## 2.3. Deposition of CaOx Crystals
Pizzolato staining was performed on some paraffin-embedded sections [19]. Firstly, the sections were dried in an oven at 37°C then placed in xylene for 10 min for deparaffinization. Next, they were washed with gradient ethanol then treated with $5\%$ silver nitrate solution and $30\%$ hydrogen peroxide for 15-30 min. Subsequently, after removal of the above working solution and rinsing samples using distilled water, $0.1\%$ nuclear fast red staining solution was added dropwise to the sections for 1-2 min counterstaining. After that, the sections were rinsed in distilled water for 10 s, followed by dehydration and clearing. Finally, the treated sections were mounted with neutral resin. The outcomes of staining were observed under an inverted microscope, and the images were captured. The amount of CaOx crystals was analyzed using ImageJ software (version 1.8.0; National Institutes of Health, USA).
## 2.4. Detection of Biochemical Indicators
Rat kidney tissue, serum, and urine were collected. The right kidney tissues of rats were supplemented with appropriate amount of normal saline and then grind into tissue homogenates with a tissue grinder. Then, the homogenates were centrifuged (4°C, 3000 r/min for 10 min), and the supernatant was collected. Also, the blood was centrifuged (3500 rpm for 15 min) to collect serum. In the light of the instructions of biochemical assay kits (Nanjing Jiancheng Bioengineering Institute, China), serum, urine, and supernatant of the tissue homogenates were diluted into gradient solutions with different concentration, respectively. Subsequently, a biochemical automatic analyzer was utilized to determine the levels of blood urea nitrogen (BUN), creatinine (Cr), and 24 h urinary protein (24-up) in serum, oxalate (Abcam, USA) in urine, and malondialdehyde (MDA), superoxide dismutase (SOD), lactate dehydrogenase (LDH), and reactive oxygen species (ROS) in tissue supernatant [20].
## 2.5. Enzyme-Linked Immunosorbent Assay (ELISA)
The levels of TNF-α, interleukin (IL)-1β, IL-6, and IL-8 in the supernatant of tissue homogenates obtained in Section 2.4 were determined according to the instructions of the ELISA kit (Wuhan Cusabio Co., Ltd., China).
## 2.6. Cell Culture and Grouping
Human tubular epithelial cells HK-2 were purchased from the National Collection of Authenticated Cell Cultures. The samples were cultured in a DMEM/F12 (Thermo Fisher Scientific, USA) containing $10\%$ fetal bovine serum (FBS, Gibco, USA) and $1\%$ penicillin-streptomycin. By the way, the medium was placed in a 37°C incubator with $5\%$ CO2 and changed every two days.
CaOx (analytical reagent) was purchased from Kunshan Horizon Chemical Co., Ltd. Briefly, CaOx was prepared into a solution of 0.75 mmol/L with PBS and treated with ultrasound for 15 minutes to obtain uniform crystal conditions [21]. HK-2 cells were grouped and treated as follows: the control group samples did not receive any treatment, the Hyp group samples were treated with 100 μM Hyp for 4 h [22], the OX group samples were cultured with 0.75 mM CaOx medium for 48 h, and the OX+Hyp group samples were treated with 0.75 mmol/L CaOx medium for 48 h+100 μM Hyp for 4 h.
## 2.7. Cell Viability Assay
The treated cells in each group were collected after trypsin digestion. The collected cells were seeded in a 96-well plate at 1 × 104 cells/well and cultured in an incubator for 24 h before cell viability assay. Subsequently, 20 μL MTT solution (5 mg/mL, Beyotime, China) was added to each well, and after 4 h culture, the supernatant was removed. Next, 150 μL DMSO was added and mixed well with each well cells at ambient temperature for 5 min. Finally, the absorbance at 490 nm was measured by a microplate reader, with 6 duplicate wells set for each group [23].
## 2.8. Crystal-Cell Adhesion Assay
Cells and crystals were incubated together for 6 h at 4°C. Then, the supernatant aspirated, and the slides were taken out and washed by PBS three times to wash off the nonadhered crystals on the cell surface. The slides with adhered crystals and cells were digested in the mixture solution of 10 mL concentrated HNO3 and 1.0 mL HClO4 until clear. The solution was evaporated by heating, followed by cooling and addition of 3 mL ultrapure water. The concentration of Ca2+ ions was determined by an inductively coupled plasma emission spectrometer and then converts it into the adhered quantity of CaOx crystal. In the control group, a mixed solution of 10 mL concentrated HNO3 and 1.0 mL HClO4 was utilized to digest cells without adherent crystals (i.e., the Ca2+ contained in the cells themselves was subtracted) [24].
## 2.9. Detection of Protein Levels
Total proteins were extracted from rat kidney tissue and HK-2 cells, respectively, using RIPA lysate (Beijing Solarbio Science & Technology Co., Ltd., China). After quantification using BCA kit, 30 g of proteins were separated through SDS-PAGE. After that, the proteins were transferred to polyvinylidene fluoride (PVDF) membranes, followed by 1 h of blocking using $5\%$ skimmed milk powder. Subsequently, incubation of the membranes and primary antibodies (AMPK (ab32047), p-AMPK (ab92701), HO-1 (ab305290), Nrf2 (ab76026), H3 (ab1791), and GAPDH (ab8245); Abcam; USA) was carried out at 4°C overnight. On the next day, the membranes were rinsed with TBST buffer for 10 min × 3 times, incubated with diluted secondary antibodies (Abcam; USA) for 1 h at ambient temperature, and then rinsed again. Finally, enhanced chemiluminescence agent (Beyotime, China) was used to develop proteins, with FliorChem HD2 imaging system for scanning and analyzing.
## 2.10. Statistical Analysis
All results were expressed as mean ± standard deviation (SD). SPSS 21.0 statistical software was employed for statistical analysis, T-test for comparison between two groups, and one-way analysis of variance for comparison among multiple groups. $P \leq 0.05$ acted as the criterion for significant difference.
## 3.1. Hyperoside Improves Renal Pathological Injury and CaOx Crystal Deposition in Rats with Renal Calculi
Firstly, we confirmed the effectiveness of a rat model of renal calculi and the effects of Hyp on renal injury and CaOx crystal deposition. The specific outcomes of each group were revealed by HE staining and Pizzolato staining. In the control group, the morphology and structure of renal tissue were normal, the basement membrane of glomerular capillaries and tubular epithelium was intact, the tubular structure was clear, and no CaOx crystal deposition was observed. In the EG group, the renal tissue presented with evident pathological damage such as glomerular edema and dilatation, inflammatory cell infiltration, and epithelial cell necrosis and shedding. Additionally, more CaOx crystal deposition was observed at the junction of renal cortex and medulla in the renal tissue of EG-treated rats. In contrast, intragastrical administration of Hyp significantly alleviated the pathological damage of rats with renal calculi and reduced CaOx crystal deposition in the renal tissue ($P \leq 0.01$) (Figures 1(a) and 1(b)). All above results not only suggested the successful establishment of a rat model of renal calculi by EG induction but also indicated the efficacy of Hyp in improving renal pathological injury and CaOx crystal deposition.
## 3.2. Hyperoside Alleviates Renal Function Damage in Rats with Renal Calculi
BUN, Cr, and 24-up in the blood and oxalate in urine are common indicators for assessing the degree of renal function damage [25]. Based on corresponding biochemical tests, the levels of these indicators were significantly increased in the EG group compared with those in the control group ($P \leq 0.01$), but after treatment of Hyp, an obvious decrease could be observed in these indicators ($P \leq 0.01$). Besides, there was no statistical difference in the above test indicators in the control group and the Hyp group ($P \leq 0.05$) (Figures 2(a)–2(d)). Collectively, Hyp treatment was able to significantly alleviate renal function impairment in rats with renal calculi.
## 3.3. Hyperoside Inhibits Oxidative Stress Injury and Inflammatory Response in the Kidney of Rats with Renal Calculi
For clarifying the effects of Hyp on oxidative stress injury and inflammatory response in rats with renal calculi, we measured the levels of oxidative stress-related substances (MDA, LDH, ROS, and SOD) and inflammatory factors (IL-1β, IL-6, IL-8, and TNF-α) in the kidney tissue of rats. To be specific, there was a distinct rise in the levels of MDA, LDH, ROS, IL-1β, IL-6, IL-8, and TNF-α and an obvious decrease in the activity of SOD in the kidney tissue of rats in the EG group compared with those in the control group ($P \leq 0.01$). Except for significantly increased SOD activity ($P \leq 0.01$), the expression levels of other indicators were remarkably declined after Hyp treatment ($P \leq 0.01$) (Figures 3(a)–3(h)). The above indicated that Hyp treatment could make a significant reduction in oxidative stress injury and inflammatory response in the kidneys of rats with EG-induced nephrolithiasis.
## 3.4. Hyperoside Increases the Viability of HK-2 Cells Treated with Calcium Oxalate and Inhibits Crystal Adhesion
Subsequently, the effects of Hyp on viability and crystal adhesion ability of CaOx-treated HK-2 cells were further explored. Results of in vitro experiments (Figures 4(a)–4(c)) showed that compared with the control group, the viability of HK-2 cells in the OX group decreased markedly ($P \leq 0.01$), and the number of crystals adhered to the cells increased significantly ($P \leq 0.01$). In comparison with the OX group, the viability of HK-2 cells in the OX+Hyp group signaled an increment ($P \leq 0.01$), while the number of crystals adhered to the cells was observably decreased ($P \leq 0.05$). No significant difference was observed in the corresponding phenotype between the control group and the Hyp group ($P \leq 0.05$). Taken together, Hyp could increase the viability and inhibit the crystal adhesion ability of CaOx-treated HK-2 cells.
## 3.5. Hyperoside Alleviates Oxidative Stress and Inflammatory Response in HK-2 Cells Treated with Calcium Oxalate
In vivo experiments proved that Hyp could inhibit ROS and inflammatory response in the kidney of rats with renal calculi. In this study, we further clarified if Hyp had the same antioxidant and anti-inflammatory effects on CaOx-treated HK-2 cells. The results indicated that the levels of MDA, LDH, ROS, IL-1β, IL-6, IL-8, and TNF-α in the cells of the OX group went up markedly, whereas the activity of SOD went down obviously, relative to the control group ($P \leq 0.01$). The intervention of Hyp could observably lower the levels of MDA, LDH, ROS, IL-1β, IL-6, IL-8, and TNF-α in HK-2 cells treated with CaOx ($P \leq 0.01$), and increase the activity of SOD ($P \leq 0.01$). The difference between the control group and the Hyp group was not statistically significant ($P \leq 0.05$) (Figures 5(a)–5(h)). In a nutshell, Hyp could alleviate oxidative stress and inflammatory response in HK-2 cells treated with CaOx.
## 3.6. Hyperoside Activates the AMPK/Nrf2 Signaling Pathway in the Kidney Tissue of Renal Calculi Rats and the Calcium Oxalate-Treated HK-2 Cells
Studies have revealed that the AMPK/Nrf2 signaling pathway is closely linked to renal inflammation and oxidative stress in diabetic rats [26]. However, whether Hyp plays a protective role in renal injury caused by renal calculi by regulating this pathway remained to be explored. To clarify this speculation, we examined the expression levels of the AMPK/Nrf2 signaling pathway-related proteins (p-AMPK, AMPK, Nrf2, and HO-1) in kidney tissues of nephrolithiasis rats and HK-2 cells treated with CaOx (Figures 6(a)–6(d)). Both in vitro and in vivo experiments demonstrated that, in comparison with the control group, p-AMPK was evidently inhibited in the kidney tissue of the EG group and the cells of the OX group; besides, the protein expression levels of total Nrf2, nuclear Nrf2, and HO-1 were all remarkably declined ($P \leq 0.01$) in these two groups. Hyp treatment increased the phosphorylation level of AMPK in the kidney of rats with renal calculi and HK-2 cells treated with CaOx; also, it augmented the nuclear translocation of Nrf2 and HO-1 expression ($P \leq 0.01$). The difference between the control and Hyp groups was not statistically significant ($P \leq 0.05$). The above experiment results suggested that Hyp could reverse AMPK/Nrf2 activity inhibition in the CaOx-treated HK-2 cells and kidney tissue from nephrolithiasis rats.
## 4. Discussion
Nephrolithiasis is a common urological disease. Drugs such as sodium oxalate, ammonium oxalate, hydroxy-l-proline, EG, and glycolic acid are usually employed to induce acute or chronic hyperoxaluria to establish the model of rats with CaOx nephrolithiasis [27]. The occurrence of renal stone depends not so much on the formation of crystals but on their retention in the kidney. It is reported that crystal retention is predominantly caused by the adherence of crystals to the epithelial cells lining the renal tubules [28]. The severity of nephrolithiasis can be assessed by the adhesion of CaOx crystals to the epithelial cells. In this study, drinking water with $0.75\%$ EG was applied to establish a rat model of CaOx nephrolithiasis. HE and Pizzolato staining results of pathological tissue sections (glomerular edema and dilatation, inflammatory cell infiltration, and CaOx crystal deposition) and biochemical tests of blood and urine indicators (BUN, Cr, 24-up, and oxalate) proved that we successfully constructed a rat model of CaOx nephrolithiasis.
Rats with EG-induced renal calculi present with a systemic increase in ROS; increased ROS will gather in the kidney through blood circulation and disrupt the balance of redox in the body, thereby causing renal injury [29]. SOD, whose level can reflect the body's capability against oxidative stress, plays a role in scavenging excessive ROS [30]. MDA, as a product of fatty acid peroxidation, can reflect the degree of cellular peroxidation in the body [31]. LDH is able to convert glyoxylate to oxalic acid in the body and induce recurrent urinary calculi and loss of renal function, thereby resulting in renal failure [32]. In this study, Hyp treatment could effectively relieve the renal function damage and histopathological damage caused by renal calculi and CaOx crystal deposition. Additionally, Hyp exerted an antioxidant function in the models of nephrolithiasis, indicated by increased SOD activity and decreased expression of MDA, LDA, and ROS after Hyp treatment. Similarly, Chen et al. also discovered that Hyp was capable of notably reducing the levels of oxidative stress-related substances such as ROS and LDH in oxalic acid-treated HK-2 cells [15].
A study has manifested that, due to the accumulation of a large number of macrophages in a rat model of renal calculi, inflammatory factors released by macrophages may be related to the formation of CaOx crystals [33]. Liu et al. reported that with the increase of the Ox/Ca ratio, the amount of CaOx crystals induced by cells increased, the degree of crystal aggregation increased, and the toxicity to the cells increased [34]. HK-2 cells in a supersaturated CaOx solution can induce CaOx crystal formation and then cause damage to the cells. We explored the effects of Hyp on viability and crystal adhesion ability of CaOx-treated HK-2 cells and found that Hyp could increase the viability and inhibit the crystal adhesion ability of CaOx-treated HK-2 cells. In this study, Hyp intervention exhibited a notable decrease in the levels of IL-1β, IL-6, IL-8, and TNF-α in cell and rat models, suggesting the anti-inflammatory role of Hyp in nephrolithiasis. Our findings are consistent with the existing reports regarding the anti-inflammatory effect of Hyp. For example, Huang et al. revealed that Hyp inhibited lipopolysaccharide- (LPS-) induced inflammatory response in HT22 cells by suppressing the levels of IL-1β, IL-6, IL-8, and TNF-α [35]; in the meanwhile, Zhou et al. also discovered that Hyp inhibited IL-1β, IL-6, and TNFα to play an anti-inflammatory role in LPS-induced endothelial cells [36].
AMPK is a serine/threonine protein kinase that is mainly involved in the regulation of glucose, lipid, and energy metabolism in vivo. Studies have reported that the AMPK activation can inhibit the inflammatory response and oxidative stress response of mouse macrophages [37]. Nrf2 is a key molecule that regulates the transcription of antioxidant factors in the body, which has been proved to be activated by AMPK [38]. AMPK/Nrf2 plays a critical role in kidney injury. For instance, the prescription of dispersing blood stasis and dredging collateral (HTR) can protect the kidney by inhibiting renal oxidative stress and inflammation by activating the AMPK/Nrf2 signaling pathway [25]. Hyp can also improve the endogenous antioxidant and detoxification functions of kidney cells through the Nrf2/HO-1/quinone oxidoreductase 1 (NQO1) pathway [15]. Moreover, Gao et al. revealed that Hyp was able to increase the phosphorylation level of AMPK in particulate matter-induced lung injury [39]. Therefore, we speculated that Hyp may exert antioxidant and anti-inflammatory functions in nephrolithiasis through AMPK/Nrf2. Further experiments indeed demonstrated that the AMPK/Nrf2 pathway was remarkably inhibited in the rat and cell models of nephrolithiasis. Specifically, after Hyp intervention, the AMPK phosphorylation, nuclear translocation level of Nrf, and HO-1 expression were evidently increased. These findings suggest that Hyp can promote AMPK phosphorylation and nuclear translocation of Nrf2 to activate the AMPK/Nrf2 signaling pathway. Unfortunately, the antioxidant and anti-inflammatory effects of Hyp on the kidney injury were not explored by pathway reagents in this study, so further experimental validation is needed.
There are some limitations in the study. First, long-term application of high-dose Hyp should be avoided in clinical practice due to its renal toxicity [40]. However, the concentration gradient of Hyp was not set when exploring the effect of Hyp in the models of nephrolithiasis, so the optimal dose and whether its effect was concentration-dependent were unclear. Notably, an in vitro study by Chen et al. presented that the protective effect of Hyp on oxalic acid-induced OS injury in HK-2 cells was concentration-dependent [15]. Second, concerning the possible molecular mechanism of Hyp, we only examined the AMPK/Nrf2 pathway activity, and more clear mechanisms need further experimental exploration.
## 5. Conclusion
To summarize, Hyp can not only improve renal pathological injury, functional injury, and CaOx crystal deposition in rats with renal calculi but also can inhibit their oxidative stress and inflammatory response, according to the results of in vivo experiments. Additionally, Hyp can inhibit the cell activity, crystal adhesion ability, and oxidative stress and inflammatory response in CaOx-related HK-2 cells. Hyp may promote the AMPK phosphorylation and nuclear translation of Nrf2 to activate the AMPK/Nrf2 signaling pathway, thereby exerting protective functions in renal injury.
## Data Availability
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
## Ethical Approval
This study was approved by the Ethics Committee of The First Affiliated Hospital of Jinzhou Medical University (No. 2022027) and performed in accordance with the approved guidelines.
## Conflicts of Interest
The authors declare that they have no conflict of interest.
## Authors' Contributions
Hongyang Tian and Hang Zhao designed the study. Qi Liang was involved in the data collection. Zhen Shi and Hang Zhao performed the statistical analysis and preparation of figures. Hongyang Tian and Qi Liang drafted the paper. All authors read and approved the final manuscript.
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|
---
title: 'Liver Assessment in Patients with Ataxia-Telangiectasia: Transient Elastography
Detects Early Stages of Steatosis and Fibrosis'
authors:
- H. Donath
- S. Wölke
- V. Knop
- U. Heß
- R. P. Duecker
- J. Trischler
- T. Poynard
- R. Schubert
- S. Zielen
journal: Canadian Journal of Gastroenterology & Hepatology
year: 2023
pmcid: PMC10024628
doi: 10.1155/2023/2877350
license: CC BY 4.0
---
# Liver Assessment in Patients with Ataxia-Telangiectasia: Transient Elastography Detects Early Stages of Steatosis and Fibrosis
## Abstract
### Background
Ataxia-telangiectasia (A-T) is a rare autosomal-recessive multisystem disorder characterized by pronounced cerebellar ataxia, telangiectasia, cancer predisposition, and altered body composition. Liver diseases with steatosis, fibrosis, and hepatocellular carcinoma are frequent findings in older patients but sensitive noninvasive diagnostic tools are lacking.
### Objectives
To determine the sensitivity of transient elastography (TE) as a screening tool for early hepatic tissue changes and serum biomarkers for liver disease.
### Methods
Thirty-one A-T patients aged 2 to 25 years were examined prospectively from 2016–2018 by TE. In addition, we evaluated the diagnostic performance of liver biomarkers for steatosis and necroinflammatory activity (SteatoTest and ActiTest, Biopredictive, Paris) compared to TE. For calculation and comparison, patients were divided into two groups (<12, >12 years of age).
### Results
TE revealed steatosis in $\frac{2}{21}$ ($10\%$) younger patients compared to $\frac{9}{10}$ ($90\%$) older patients. Fibrosis was present in $\frac{3}{10}$ ($30\%$) older patients as assessed by TE. We found a significant correlation of steatosis with SteatoTest, alpha-fetoprotein (AFP), HbA1c, and triglycerides. Liver stiffness correlated significantly with SteatoTest, ActiTest, HbA1c, and triglycerides.
### Conclusion
Liver disease is a common finding in older A-T patients. TE is an objective measure to detect early stages of steatosis and fibrosis. SteatoTest and ActiTest are a good diagnostic assessment for steatosis and necroinflammatory activity in patients with A-T and confirmed the TE results.
## 1. Introduction
Ataxia-telangiectasia (A-T) is a rare, autosomal-recessive multisystem disorder characterized by progressive cerebellar ataxia, telangiectasia, immunodeficiency, and cancer predisposition [1–5]. Apart from the name-giving brain involvement, the disease also affects the lungs, endocrine system, and liver [6–13]. A-T-associated liver disease is an upcoming health issue that emerges in the second decade of life [7, 14]. Autopsy reports in A-T patients showed liver-specific pathological findings like nonalcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma (HCC) [7, 15–18].
Hepatopathy in A-T is usually mild and does not lead to a limitation of the synthesis or detoxification function of the liver [7]; however, up to $92.9\%$ of older A-T patients are affected [14]. Liver disease certainly belongs to the complex phenotype of premature aging [19], which also includes insulin resistance (IR), diabetes mellitus type 2 [6, 20], and dyslipidemia and thus leads to an incomplete metabolic syndrome. All these factors naturally favor fatty remodeling of the liver with a consecutive increase in liver enzymes. In addition, a few severe cases of liver failure and HCC have been published as case reports on A-T patients [15, 17].
One of the central aims of modern hepatology is the search for noninvasive diagnostic procedures for presymptomatic liver diseases in at-risk patient groups. In order to prevent severe courses and identify critical patients at an early stage, sensitive, fast, and minimally invasive screening tools are needed to monitor the course of the disease [21, 22]. Currently, a liver biopsy is the gold standard for assessing the severity of nonalcoholic fatty liver disease (NAFLD), NASH, and the stage of liver fibrosis. Nevertheless, limitations and complications include invasiveness, severe bleeding, sampling error, and pneumothorax [23].
Transient elastography (TE) is a precise, noninvasive method to determine the extent of fibrosis and the degree of fatty degeneration of the liver [24]. In contrast to biopsy, it is painless and noninvasive. In addition, it records a multiple of the parenchyma. The classification into fibrosis and steatosis stages is objective in comparison to normal sonography, as it is carried out using fixed cutoff values [22, 24, 25]. Measurements are reproducible and independent of the user [25, 26].
In this cross-sectional study, we prospectively evaluated liver assessment by TE and liver scores (FibroMax) for A-T-associated liver disease to predict the extent of liver disease and identify at-risk patients for severe disease courses.
## 2. Patients and Methods
The current study is a prospective, cross-sectional, clinical, single-center trial.
## 2.1. Patients
From November 2016 to May 2018, 31 patients with a clinically and/or genetically confirmed diagnosis of A-T aged between two and 25 years were included in the study (Table 1). Malignancy and clinical and laboratory-associated infections were defined as the exclusion criteria.
The Ethics Committee of University Hospital Frankfurt approved the trial (Reference No. $\frac{504}{15}$). The study was registered at clinicaltrials.gov (NCT03357978). One study visit was conducted. Written consent was obtained from all patients and/or caregivers. The study was conducted according to the ethical principles of the Declaration of Helsinki and regulatory requirements and the code of Good Clinical Practice.
We compared patients <12 years of age (group 1) with those ≥12 years (group 2).
## 2.2. Transient Elastography
The examination was performed with FibroScan® (Echosens, Paris, France). The examination probe is formed by a vibration generator and an ultrasonic probe (3.5 MHz) aligned on the same axis. The vibration generator oscillates at a frequency of 50 Hz, which leads to shear waves in the liver tissue. The speed of propagation of this shear wave correlates directly with liver stiffness and therefore with the extent of fibrosis. The result of this liver stiffness measurement (LSM) is given in kilopascals (kPa) [25].
The interpretation of the measurement results was based on the limit values of a study on mixed hepatopathy by Fraquelli et al. [ 26]. A distinction was made between three fibrosis stages: F ≥ 2 = pronounced fibrosis, F ≥ 3 = severe fibrosis, and F4 = cirrhosis.
At the same time, the controlled attenuation parameter (CAP) was measured using the same signals. The attenuation of the ultrasound signal (3.5 MHz) in the liver is measured in dB/m. The attenuation correlates with the degree of liver steatosis [27]. The different stages of steatosis were defined as follows: S ≥ 1 = steatosis in 11–$33\%$ of hepatocytes, S ≥ 2 = steatosis in 34–$66\%$ of hepatocytes, and $S = 3$ steatosis in 67–$100\%$ of hepatocytes. The cutoff values proposed by Karlas et al. in 2017 were used [28].
The examination was performed by an experienced physician in the supine position and maximum abduction of the right arm through a right intercostal space. In order to perform a standardized measurement, patients were asked to fast for at least 4 hours before the examination. A success rate of at least $60\%$ or the interquartile range below $30\%$ of the median measurement result was considered necessary.
The accuracy of the measurement may be reduced in obesity or ascites.
## 2.3. Liver Biomarkers and Liver Scores
FibroMax® (BioPredictive, Paris, France) is a noninvasive blood test for NAFLD screening that has been validated against liver biopsies [29] and is recommended by European guidelines [30]. FibroMax is composed of three different tests (SteatoTest, ActiTest, and FibroTest) for the assessment of steatosis, necroinflammatory activity, and fibrosis, respectively. The serum parameters such as α2-macroglobulin, haptoglobin, apolipoprotein A1, bilirubin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), fasting glucose, cholesterol, and triglycerides, as well as gender, age, weight, and height, were recorded for the calculation of the liver score FibroMax.
The FibroMax data were calculated by BioPredictive (Paris, France) using a patented algorithm.
The FibroTest results were interpreted using the METAVIR score from F0 to F4: F0 = no fibrosis, F1 = portal fibrosis without septa, F2 = few septa, F3 = many septa without cirrhosis, and F4 = cirrhosis.
The steatosis test results were interpreted using the steatosis score from S0 to S4: S0 = no steatosis, S1 = mild steatosis, S2 = moderate steatosis, S3 = pronounced steatosis, and S4 = severe steatosis.
The ActiTest results were interpreted using the METAVIR score from A0 to A3: A0 = no necroinflammatory activity, A1 = mild activity, A2 = moderate activity, and A3 = severe activity. Data were interpreted according to Poynard et al. [ 31, 32].
In addition to liver biomarkers, alpha-fetoprotein (AFP), hemoglobin A1c (HbA1c), complete lipid profile (including cholesterol, high-density lipoprotein (HDL) and low-density lipoprotein (LDL) cholesterol, and triglycerides) and C-reactive protein (CRP) as inflammatory markers were determined.
## 2.4. Statistical Analysis
For statistical analysis, GraphPad Prism 5.01 (GraphPad Software, Inc.) was used. Values are presented as arithmetic means with standard deviation (SD). For comparisons between the two study groups, the two-tailed Mann–Whitney U test was applied. Correlations were analyzed by Spearman's correlation coefficient. p values ≤ 0.05 were considered significant.
## 3. Results
In a study period of 19 months (November 2016 to May 2018), 31 patients with A-T were examined. Patients' characteristics are shown in Table 1. The age distribution ranged from 2 to 25 years (mean age: 10.7 years). The patients were divided into the two groups for evaluation. Twenty-one patients were <12 years (group 1), and ten patients were ≥12 years (group 2). No patient had a history of infectious hepatitis or was taking hepatotoxic drugs regularly.
## 3.1. Transient Elastography
On average, 16.5 measurements were performed with a mean success rate of $74\%$. TE revealed steatosis in $\frac{2}{21}$ ($10\%$) of younger patients. In both cases, grade 2 steatosis was present. Fibrosis was not evident in any of younger patients. In comparison, steatosis was detectable in $\frac{9}{10}$ ($90\%$) (2 ($20\%$) patients with grade 2 steatosis and 7 ($70\%$) patients with grade 3 steatosis), and fibrosis was observed in $\frac{3}{10}$ ($30\%$, median age: 21 years) of older patients. Of these, one patient ($10\%$, aged 20 years) had pronounced fibrosis and the other two patients ($20\%$, aged between 21 and 25 years) had liver cirrhosis (fibrosis stage 4). These results are summarized in Supplementary Table 1. The corresponding LSM (group 1: 4.5 ± 0.93 kPa vs. group 2: 8.9 ± 6.9 kPa; $p \leq 0.001$) and CAP (group 1: 174.7 ± 45.08 bD/m vs. group 2: 302.2 dB/m ± 57.68 dB/m; $p \leq 0.001$) of the older group were significantly increased compared to those of the younger group (Figures 1(a) and 1(b)). Furthermore, a positive correlation of the values with age was shown (LSM: $r = 0.59$, $p \leq 0.001$; CAP: $r = 0.82$, $p \leq 0.0001$) (Figures 2(a) and 2(b)). The average age for steatosis in group 2 was 20.3 ± 2.7 years.
In addition, CAP and LSM correlated with ALT, AST, GGT, AFP, HbA1c, and triglycerides. There was a significant correlation of steatosis (CAP values) with ALT ($r = 0.77$, $p \leq 0.0001$), AST ($r = 0.39$, $p \leq 0.05$), GGT ($r = 0.83$, $p \leq 0.0001$), AFP ($r = 0.42$, $p \leq 0.05$), HbA1c ($r = 0.59$, $p \leq 0.01$), and triglycerides ($r = 0.74$, $p \leq 0.00001$). LSM correlated significantly with ALT ($r = 0.53$, $p \leq 0.01$), AST ($r = 0.42$, $p \leq 0.05$), GGT ($r = 0.67$, $p \leq 0.0001$), HbA1c ($r = 0.63$, $p \leq 0.001$), and triglycerides ($r = 0.62$, $p \leq 0.001$).
## 3.2. Serum Biomarkers and Liver Scores
A complete data set for biomarkers was available for 30 patients. The results of FibroMax are shown in Table 2. Significantly lower values for group 1 concerning SteatoTest and ActiTest (SteatoTest: $p \leq 0.0001$; ActiTest: $p \leq 0.001$) were calculated, showing a normal function in group 1 and mild to moderate dysfunction in group 2. FibroTest did not show significant differences between the two groups, with normal to slightly elevated levels in all patients.
The results of SteatoTest indicated steatosis in $\frac{8}{10}$ ($80\%$) patients of group 2, whereas no patient in group 1 was affected. Three of ten ($30\%$) older patients had mild steatosis, $\frac{2}{10}$ ($20\%$) had mild to moderate steatosis, $\frac{2}{10}$ ($20\%$) had moderate steatosis, and one patient ($10\%$) had pronounced steatosis. SteatoTest had a significant correlation with age, LDL-HDL ratios, CRP, and necroinflammatory activity (ActiTest) (age: $r = 0.74$, $p \leq 0.0001$; LDL-HDL ratio: $r = 0.79$, $p \leq 0.0001$; CRP: $r = 0.51$, $p \leq 0.01$; and ActiTest: $r = 0.89$, $p \leq 0.0001$), as shown in Table 3.
The ActiTest for the assessment of necroinflammatory activity showed a minimal activity level of stage A0-1 in $\frac{3}{20}$ ($15\%$) patients of group 1 and a pathological result in $\frac{9}{10}$ ($90\%$) of older patients. One older patient had stage A0-1, $\frac{6}{10}$ ($60\%$) patients had stage A1-2, and $\frac{2}{10}$ ($20\%$) patients had stage A2, i.e., moderate necroinflammatory activity. ActiTest correlated significantly with triglycerides, CRP, CAP, and LSM, as shown in Figures 3(a)–3(d) and Table 3 (triglycerides: $r = 0.61$, $p \leq 0.001$; CRP: $r = 0.41$, $p \leq 0.05$; CAP: $r = 0.77$, $p \leq 0.0001$; and LSM: $r = 0.53$, $p \leq 0.01$). In addition, there was a significant correlation with age and the LDL-HDL ratio (age: $r = 0.8$, $p \leq 0.0001$; LDL-HDL ratio: $r = 0.8$, $p \leq 0.0001$).
FibroTest did not show a significant difference between the two patient groups. Six of twenty ($30\%$) younger patients had fibrosis according to FibroTest. $\frac{4}{20}$ ($20\%$) patients had stage 0-1, one patient had stage 1-2, and one patient had stage 2. In the older group, $\frac{3}{10}$ ($30\%$) patients had stage 0-1 and $\frac{2}{10}$ ($20\%$) had stage 1-2 fibrosis. This means that a total of $\frac{5}{10}$ ($50\%$) patients were affected. The correlations of SteatoTest and ActiTest are shown in Table 3.
The number of patients in each category of SteatoTest, ActiTest, and FibroTest is shown in Supplementary Table 2.
## 3.3. Examination of Metabolic Biomarkers, Inflammation, and AFP
Table 4 shows lipid parameters and HbA1c. Four of ten older patients had type 2 diabetes. No difference was found between the two groups in total cholesterol. However, when broken down into HDL and LDL cholesterol, the older group showed significantly lower HDL cholesterol and significantly higher LDL cholesterol values (HDL cholesterol: p ≤ 0.001; LDL cholesterol: p ≤ 0.05).
The LDL cholesterol values of the two groups were within the normal range. HDL cholesterol was lower in $\frac{8}{20}$ ($40\%$) younger patients and in all older patients ($100\%$). There was a significant difference in the LDL-HDL ratio between the two patient groups (p ≤ 0.0001). Triglycerides were significantly increased in group 2 ($p \leq 0.0001$). Five of ten ($50\%$) older patients had values above the normal range. In group 1, the triglyceride values were all within the normal range. In addition, there was a significant correlation of triglycerides with age ($r = 0.66$, p ≤ 0.0001).
As expected, AFP was elevated in all patients. However, there was a significant correlation of AFP values with age ($r = 0.54$, $p \leq 0.01$).
For CRP, no significant difference between the two groups was found.
## 4. Discussion
A-T is a life-limiting systemic disease clinically characterized by neurodegeneration, radiosensitivity, increased risk of malignancy, immunodeficiency, failure to thrive, and hepatopathy [1, 3, 9–11, 14, 33]. To date, the clinical significance of liver disease is unclear, but up to over $90\%$ of patients develop an elevation of liver enzymes with advancing age, which is associated with a high degree of fatty degeneration and sometimes fibrosis of the liver tissue [14].
To the best of our knowledge, the present study is the first prospective trial addressing noninvasive procedures to characterize liver disease in A-T and relate outcomes of TE to liver biomarkers and liver scores. Our results demonstrate for the first time that $10\%$ of younger patients as opposed to $90\%$ of older patients have liver steatosis. In line with this finding, higher degrees of steatosis were present in the older group. Pronounced fibrosis was found in $\frac{3}{10}$ ($30\%$) older patients. In two patients, cirrhosis was already present according to TE. We found a significantly higher necroinflammatory activity (ActiTest) in the older patient group. Necroinflammation can be defined as the immune response to necrosis [34]. Therefore, ActiTest is a good marker for the progression of liver disease to steatosis and apoptosis with increased necroinflammatory activity. The presence of necroinflammatory activity, which also correlates with the CRP value, indicates NASH, which according to our data mainly affects older patients. In summary, SteatoTest and ActiTest are a suitable diagnostic assessment for steatosis and necroinflammatory activity in patients with A-T and have confirmed the TE results.
FibroTest did not show a significant difference between the two patient groups or a significant correlation with age or the results of TE, most likely since FibroMax is not licensed below the age of 14 years due to the physiological increase of some of the serum markers used for calculation.
We were also able to show a significant correlation of AFP with steatosis (CAP). However, the mechanism has not been elucidated so far. AFP is mainly known as a tumor marker for HCC. Serum AFP may also be elevated by germ cell tumors, viral hepatitis, liver fibrosis, and neurodegenerative diseases such as A-T [35–37]. Among other mechanisms, tumor suppressor p53 acts as a repressor on the AFP gene during development and regeneration of the liver [38–40]. Via the reduced activation of p53 due to the absence of the ataxia-telangiectasia-mutated (ATM) kinase, an increased expression of AFP could thus occur in A-T [41]. A mutation of p53 is also frequently found in HCC [42], which could be a possible explanation for an increase in AFP. In contrast to CAP, however, no significant correlation of LSM with AFP was found. The missing correlation of AFP with the LSM could thus be explained by a loss of functional liver tissue in cirrhosis.
Dyslipidemia is common in older A-T patients [6, 8]. While triglycerides and LDL cholesterol were significantly higher, HDL cholesterol was significantly lower in the older patient group. In addition, there was a significant correlation of triglycerides with age. The association between elevated liver enzymes and dyslipidemia has been described before [6]. We could also show a significant correlation of HbA1c and triglycerides with CAP and LSM, emphasizing the effects of metabolic risk factors for liver tissue remodeling. Diabetic metabolism leads to fatty liver remodeling due to hyperglycemia and hypertriglyceridemia and thus increases the risk of NASH. In addition, the described alterations in cholesterol and triglyceride levels in patients indicate an increased atherosclerotic risk profile [43]. They could also play an important role in the development of steatosis, through the accumulation of fat in the liver [44]. The increased influx of free fatty acids to the liver leads to an increased synthesis of triglycerides and very low-density lipoprotein (VLDL), as well as a reduced synthesis of HDL cholesterol [45].
ATM is induced by the accumulation of fat and thus elevates oxidative stress in the liver [46] and acts as an activator of p53, which in turn activates the p53 upregulated modulator of apoptosis (PUMA) [47]. PUMA is a crucial player in steatosis and apoptosis in hepatocytes [48]. Since hepatocyte apoptosis correlates with the severity of NASH and the stage of fibrosis [49], it can be assumed that steatosis related apoptosis is partly responsible for the progression of liver disease [46]. In the absence of ATM, alternative signaling pathways must be activated, which ultimately cause fibrotic remodeling and death of functional liver tissue.
Fibrosis results from connective tissue remodeling due to chronic inflammation of the liver tissue [50, 51], e.g., in response to steatosis and apoptosis [46]. The progression of fibrosis to cirrhosis of the liver then appears to develop through repetitive phases of inflammation and the subsequent reparative immune response [52]. This leads to a loss of functional liver tissue, which is replaced by scar tissue [50, 53].
ATM can be activated directly by oxidative stress, even independently by double-strand breaks in DNA [54]. The absence of ATM leads to low antioxidant capacity [55]. As a result, macromolecules, lipids, and DNA are exposed to permanent oxidative stress and damage it causes. Similar results have been shown for NAFLD and in particular NASH, where oxidative stress and lipid peroxidation are also elevated [56]. For example, hepatocytes with excess fat are particularly susceptible to oxidative stress and DNA damage [57]. This may be related to the fact that saturated free fatty acids promote the formation of ROS, thereby inducing apoptosis and inflammation [58]. It is also known that oxidative stress increases with the severity of liver disease [57]. Oxidative stress and resulting inflammation appear to play a major role in the development and progression of liver disease. A link between hepatopathy in A-T and the already known increased oxidative stress levels in patients therefore seems very likely.
The type of liver involvement in A-T is not yet defined, as the published cases with severe portal hypertension causing ascites and varices had no evidence of cirrhosis at a liver biopsy [15, 16, 18]. It seems that whether these complications are to be attributed to steatosis/NASH leading to portal hypertension in the absence of cirrhosis [59] or to vascular liver disease/porto-sinusoidal vascular disorder (PSVD) is still open for discussion [60]. In this view, clinicians have to consider the value of a liver biopsy, especially in a research context where little is known about liver disease in A-T (i.e., the diagnosis of PSVD can be histological). The rate of adverse events in the modern era is negligible, and the information one can obtain is precious.
The study has some limitations. Due to the size of the study population and the monocentric study design, no general statements can be made. There are no corresponding ultrasound reports that can be compared with the TE results. This is a major limit, not only because it could have shown how liver disease (i.e., fibrosis staging) would have been underestimated by ultrasound only, but also because signs of portal hypertension (i.e., splenomegaly, dilated portal vein, and collaterals) could have been detected and correlated with the absence/presence of liver cirrhosis at TE. In addition, the application of spleen stiffness would have been useful to address more specifically the severity of portal hypertension, as is complementary to LSM [61], and the spleen stiffness measurement (SSM)/LSM ratio could be informative in these cases [62].
Additionally, the examination methods used in this study are not yet established practice in pediatrics, so there are no universally accepted cutoff values for children, but several studies have highlighted the value of TE with CAP [63–66]. The proposed cutoff values are largely consistent with those for adults, which were used in this study. Also FibroTest could show its diagnostic value in some pediatric studies and could distinguish between severe fibrosis and absence of fibrosis [67]. To the best of our knowledge, there are no pediatric studies on FibroMax to date.
Due to the mildness of liver disease, validation of TE by biopsy (gold standard) was not performed. Although CAP is not recommended as a standard tool for stratification of hepatic steatosis [68], there are data showing a good correlation with other steatosis markers, and our data also support this. Another limitation is the lack of a healthy control group to compare the results. As only one TE measurement was performed in each patient, no interobserver comparison is possible. For detection of steatosis by TE, larger cohorts and multicenter studies are needed in order to validate the clinical application.
## 5. Conclusion
Premature aging, low-grade inflammation, oxidative stress, dyslipidemia, and IR as common features of A-T contribute to the development of NAFLD. Liver disease in the context of A-T should be monitored regularly in order to prevent long-term consequences, such as NASH, cirrhosis, and HCC. TE and liver scores are a well reproducible, noninvasive method detecting early stages of liver disease. SteatoTest and ActiTest are useful to assess steatosis and necroinflammatory activity in patients with A-T and have confirmed the TE results.
## Data Availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
## Additional Points
At a Glance Commentary. Scientific knowledge on the subject: The majority of older A-T patients suffer from liver disease. This is the first prospective measurement of transient elastography in a larger A-T cohort. What This Study Adds to the Field. Transient elastography is a new, noninvasive, and reproducible technique that measures liver stiffness and detects early stages of NASH and cirrhosis in A-T patients.
## Ethical Approval
The Ethics Committee of University Hospital Frankfurt approved the trial (Reference No. $\frac{504}{15}$). The study was registered at clinicaltrials.gov (NCT03357978). The study was conducted according to the ethical principles of the Declaration of Helsinki and regulatory requirements and the code of Good Clinical Practice.
## Consent
Written consent was obtained from all patients and/or caregivers.
## Disclosure
H. Donath and S. Wölke are the co-first authors.
## Conflicts of Interest
The authors declare no conflicts of interest.
## Authors' Contributions
HD, SW, UH, SZ, JT, RS, and RPD designed the study, collected the data, and interpreted and carried out statistical analysis. HD, SW, UH, and SZ made patient visits. VK performed TE. TP performed the FibroMax calculation. HD and SZ wrote the manuscript. All authors have read, revised, and approved the final manuscript. H. Donath and S. Wölke equally contributed to this work.
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|
---
title: N-Acetylcysteine Decreases Myocardial Content of Inflammatory Mediators Preventing
the Development of Inflammation State and Oxidative Stress in Rats Subjected to
a High-Fat Diet
authors:
- Klaudia Sztolsztener
- Wiktor Bzdęga
- Katarzyna Hodun
- Adrian Chabowski
journal: International Journal of Inflammation
year: 2023
pmcid: PMC10024630
doi: 10.1155/2023/5480199
license: CC BY 4.0
---
# N-Acetylcysteine Decreases Myocardial Content of Inflammatory Mediators Preventing the Development of Inflammation State and Oxidative Stress in Rats Subjected to a High-Fat Diet
## Abstract
Arachidonic acid (AA) is a key precursor for proinflammatory and anti-inflammatory derivatives that regulate the inflammatory response. The modulation of AA metabolism is a target for searching a therapeutic agent with potent anti-inflammatory action in cardiovascular disorders. Therefore, our study aims to determine the potential preventive impact of N-acetylcysteine (NAC) supplementation on myocardial inflammation and the occurrence of oxidative stress in obesity induced by high-fat feeding. The experiment was conducted for eight weeks on male Wistar rats fed a standard chow or a high-fat diet (HFD) with intragastric NAC supplementation. The Gas-Liquid Chromatography (GLC) method was used to quantify the plasma and myocardial AA levels in the selected lipid fraction. The expression of proteins included in the inflammation pathway was measured by the Western blot technique. The concentrations of arachidonic acid derivatives, cytokines and chemokines, and oxidative stress parameters were determined by the ELISA, colorimetric, and multiplex immunoassay kits. We established that in the left ventricle tissue NAC reduced AA concentration, especially in the phospholipid fraction. NAC administration ameliorated the COX-2 and 5-LOX expression, leading to a decrease in the PGE2 and LTC4 contents, respectively, and augmented the $\frac{12}{15}$-LOX expression, increasing the LXA4 content. In obese rats, NAC ameliorated NF-κB expression, inhibiting the secretion of proinflammatory cytokines. NAC also affected the antioxidant levels in HFD rats through an increase in GSH and CAT contents with a simultaneous decrease in the levels of 4-HNE and MDA. We concluded that NAC treatment weakens the NF-κB signaling pathway, limiting the development of myocardial low-grade inflammation, and increasing the antioxidant content that may protect against the development of oxidative stress in rats with obesity induced by an HFD.
## 1. Introduction
N-acetyl-cysteine or N-acetylcysteine (NAC) is a wide-known drug acknowledged by the World Health Organization (WHO) as an essential medication. It is commonly used to treat paracetamol overdose, likewise as a mucolytic agent in certain respiratory diseases [1]. In many countries, it is available as an over-the-counter drug or nutraceutical, known for its antioxidative properties. Firstly, this acetylated precursor of L-cysteine has a direct antioxidant impact due to the free thiol group property—counteracting reactive oxygen species (ROS) and reactive nitrogen species (RNS) as well. Indirectly, by upregulating the intracellular cysteine level NAC increases the rate of reduced glutathione (GSH) synthesis, the most common cellular antioxidant. Additionally, it possesses significant anti-inflammatory properties as a result of nuclear factor κ B (NF-κB) suppression followed by diminished production of proinflammatory cytokines, i.e., tumor necrosis factor α (TNF-α), interleukin 1 (IL-1), and interleukin 6 (IL-6) [1]. Some studies have shown the effect of NAC on the modulation of arachidonic acid (AA) metabolism as the main inflammatory lipid precursor, which belongs to n − 6 polyunsaturated fatty acids (PUFA). The effect of NAC on AA metabolism and prostaglandin formation has been demonstrated in activated monocytes and neurons after nerve tissue injury [1, 2]. The administration of NAC together with specific and nonspecific cyclooxygenase (COX) inhibitors—rofecoxib and diclofenac— significantly reduced the formation of prostaglandin E2 (PGE2) induced by lipopolysaccharides, enhancing the action of the above-mentioned COX inhibitors [2].
Given its properties, NAC is intensively studied in various clinical studies covering chronic metabolic disorders, including cardiovascular diseases (CVD), metabolic syndrome, liver complications, and psychiatric illnesses, in which oxidative stress and inflammation are considered as risk factors for the development of mentioned conditions [3, 4]. It is not debatable that the pandemic of obesity and other metabolic syndrome components has a huge impact on chronic cardiovascular diseases, which possess a relevant, growing problem in the global healthcare system [5]. The chronic enhanced availability and influx of fatty acids (FA) favors ectopic lipid accumulation in peripheral tissues such as the liver and kidneys [6–8]. Consequently, an increase in lipid storage intensifies the low-grade inflammation, the impairment in mitochondrial functioning, and oxidative stress development in the cardiac tissue favoring myocardial cell death and heart failure [9, 10]. So, it is important to search for a therapeutic agent that will reveal new potentially anti-inflammatory and antioxidative properties to improve cardiac functioning. Several clinical trials and animal studies have shown that NAC exerts noteworthy actions in cardiovascular disorders, particularly in acute myocardial ischemia and acute myocardial infarction, although its role in chronic cardiovascular diseases is still not fully understood [11, 12]. Thus, our study aims to determine the potential protective impact of N-acetylcysteine supplementation on the occurrence of myocardial inflammation in rats with obesity induced by a high-fat diet (HFD). In this sense, we will examine how NAC can alter the inflammatory response by suppressing the activation of the arachidonic acid pathway. We will also explore the influence of NAC on enzymatic and nonenzymatic antioxidant protection and the products of lipoperoxidation in cardiac injury induced by high-fat feeding.
## 2.1. Animals and Experimental Protocol
The experiment was conducted for eight weeks on male, four-week-old Wistar rats with an initial body weight of approximately 50–70 g. All animals were housed throughout the entire duration of the study in standard laboratory animal living conditions: plastic autoclavable cages, 22 ± 2°C air temperature, a 12 h reverse light/dark cycle, and unlimited access to water and standard rodent chow. After the first week of adaptation to the new environment, the rats were randomly divided into four groups in equal numbers: 10 rats per experimental group; 40 rats for all groups in the experiment. The characteristics of the groups were as follows: [1] control group—rats fed a standard rodent chow ($65.5\%$ calories from carbohydrate, $24.2\%$ calories from protein, and $10.3\%$ calories from fat; nutritional composition of the standard diet is presented in the Table 1; and Agropol, Motycz, and Poland); [2] the HFD group—rats fed a high-fat diet ($59.8\%$ calories from fat, $20.1\%$ calories from protein, and $20.1\%$ calories from carbohydrate; nutritional composition of the high-fat diet is shown in the Table 2; D12492, Research Diet, New Brunswick, NJ, USA); [3] NAC group—rats fed the above-described standard diet plus N-acetylcysteine (Sigma-Aldrich, St. Louis, MO, USA); and [4] HFD + NAC group—rats fed a high-fat diet as well as N-acetylcysteine. The experimental model of high-fat feeding was selected based on an accessible protocol to contribute to hyperlipidemic occurrence as a crucial factor for the development of obesity-related heart diseases [13–15]. The NAC solution was administered to the appropriate groups once daily, between 8-9 am. The substance was dissolved in a saline solution and immediately administered intragastrically by gastric gavage at a dose of 500 mg/kg of body weight. The individuals from the control and HFD groups received only saline solution. The amount of administered NAC was adjusted according to the current body weight of rats, and it was recalculated every two days. The intragastric administration of NAC ensured that rats obtained the full dose appropriate for body weight. The NAC dose was established based on available data to provide a satisfactory effect and eliminate the risk of toxicity in Wistar rats [16]. The NAC solution was supplemented concomitantly with standard or high-fat diets to determine the potential preventive effect of NAC on cardiac lipid metabolism with particular reference to inflammatory and oxidative alterations. At the end of the eight weeks, after a 12-hour overnight fast, the animals were anesthetized by intraperitoneal phenobarbital injection (80 mg/kg of body weight). The left ventricle was excised and immediately frozen in liquid nitrogen using precooled aluminum thongs. Also, blood was collected in the tubes containing EDTA and then centrifuged to obtain plasma fractions. All samples (left ventricle tissue and plasma) were stored at −80°C until further measurements. The study was approved by the Ethical Committee for Animal Testing in Bialystok (No. $\frac{21}{2017}$).
## 2.2. Determination of the Myocardial and Plasma Arachidonic Acid Concentration
Lipids obtained from the left ventricle and plasma samples were extracted using a solution of chloroform/methanol at a 2: 1 volume ratio according to the procedure previously described by Folch et al. [ 17]. Then, an internal standard composed of heptadecanoic acid, diheptadecanoic acid, and triheptadecanoic acid was added to the obtained extracts. In the next step, the extracts were distributed on silica gel-coated glass chromatographic plates (Silica Plate 60, 0.25 mm; Merck, Darmstadt, Germany) and separated into four individual lipid fractions—phospholipid (PL), triacylglycerol (TAG), diacylglycerol (DAG), and free fatty acid (FFA)—by the Thin-Layer Chromatography (TLC) method in the separation buffer composed of heptane/isopropyl ether/acetic acid at 60: 40: 3 volume ratio. The obtained eluents comprising the individual lipid fractions were trans-methylated in a $14\%$ boron trifluoride-methanol solution and dissolved in hexane. Following that, the Gas-Liquid Chromatography (GLC; Hewlett-Packard 5890 Series II gas chromatograph equipped with a flame ionization detector and Hewlett-Packard-INNOWax capillary column; Agilent Technologies, Santa Clara, CA, USA) method was used to quantify the particular fatty acid methyl esters (FAME) level in each lipid fraction, depending on the retention times of the standard, as previously described by Chabowski et al. [ 18]. On the basis of the fatty acid composition in the selected lipid fractions, the concentration of arachidonic acid was estimated and expressed in nanomoles per milliliter of plasma or per gram of tissue.
## 2.3. Immunoblotting
The Western blot technique was used to measure the expression of proteins included in eicosanoid synthesis pathways and the inflammatory response.
In brief, before the immunoblotting procedure, samples of the left ventricle tissue were homogenized in a radioimmunoprecipitation assay (RIPA) buffer containing a cocktail of phosphatase and protease inhibitors (Roche Diagnostic, Mannheim, Germany). In prepared tissue homogenates, the total protein concentration was assessed with the bicinchoninic acid (BCA) method using bovine serum albumin (BSA) as a standard. The homogenates were reconstituted in Laemmli buffer (Bio-Rad, Hercules, CA, USA) to obtain the same amount of protein (30 μg) and separated on $10\%$ Criterion TGX Stain-Free Precast Gels (Bio-Rad, Hercules, CA, USA) during electrophoresis. After that, proteins were transferred onto membranes—polyvinylidene fluoride (PVDF) or nitrocellulose membranes, which are suitable for semi-dry or wet conditions, respectively. After blocking in tris-buffered saline complemented with Tween-20 (TBST) with $5\%$ nonfat dry milk or $5\%$ BSA, the membranes were incubated overnight with proper primary antibodies as follows: cyclooxygenase-1 (COX-1, sc-19998, 1: 500; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), cyclooxygenase-2 (COX-2, sc-166475, 1: 500; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), 5-lipoxygenase (5-LOX, ab169755, 1: 1500; Abcam, Cambridge, UK), $\frac{12}{15}$-lipoxygenase ($\frac{12}{15}$-LOX, sc-133085, 1: 500; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), nuclear factor erythroid 2-related factor 2 (Nrf-2, sc-365949, 1: 500; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), transforming growth factor β (TGF-β, 3711, 1: 500; Cell Signaling Technology, Danvers, MA, USA), nuclear factor κ B (NF-κB, 4764, 1: 500; Cell Signaling Technology, Danvers, MA, USA), and B cell lymphoma 2 (Bcl-2, 2870, 1: 1000; Cell Signaling Technology, Danvers, MA, USA). In the next stage, the membranes were incubated with appropriate secondary antibodies conjugated with horseradish peroxidase (HRP), i.e., anti-rabbit (7074, 1: 3000; Cell Signaling Technology, Danvers, MA, USA) and anti-mouse (sc-546102, 1: 3000; Santa Cruz Biotechnology, Inc., Dallas, TX, USA). Then, the protein bands were visualized using a chemiluminescent substrate (Clarity Western ECL Substrate; Bio-Rad, Hercules, CA, USA), and the obtained signals were densitometrically measured using the ChemiDoc (Image Laboratory Software; Bio-Rad, Warsaw, Poland). The expression of each analyzed protein was normalized to the total protein expression and expressed as the percentage of the control group ($100\%$).
## 2.4. Determination of the Myocardial Arachidonic Acid Derivatives and Oxidative Stress Parameters
We applied the commercial enzyme-linked immunosorbent assay (ELISA) and the colorimetric assay kits to determine the concentration of arachidonic acid derivatives—prostaglandin E2 (Cusabio, Houston, TX, USA), prostaglandin I2 (PGI2; Cusabio, Houston, TX, USA), leukotriene B4 (LTB4; Cusabio, Houston, TX, USA), leukotriene C4 (LTC4; MyBioSource, San Diego, CA, USA), lipoxin A4 (LXA4; MyBioSource, San Diego, CA, USA) and oxidative stress parameters—superoxide dismutase 2 (SOD2; Cloud-Clone Corp., Houston, TX, USA), catalase (CAT; Cloud-Clone Corp., Houston, TX, USA), reduced glutathione (MyBioSource, San Diego, CA, USA), 4-hydroxynonenal (4-HNE; Biorbyt, Cambridge, UK), malondialdehyde (MDA; Cayman Chemical, Ann Arbor, MI, USA), and advanced glycation end products (AGE; Biorbyt, Cambridge, UK). The assays were performed following the manufacturer's protocols.
Before the determinations, the left ventricle tissue (25 mg) was homogenized in 1 ml of ice-cold phosphate buffer saline (PBS) for measurements of PGE2, PGI2, LTB4, LTC4, LXA4, SOD2, CAT, GSH, 4-HNE, and AGE, or in 250 μl of ice-cold RIPA buffer only for MDA testing. The prepared homogenates suspended in PBS or RIPA buffer were centrifuged as reported by the manufacturer's protocols. After that, the supernatants were transferred into new tubes and frozen immediately at −80°C for analysis.
For the quantitative determinations, the absorbance of all parameters (except for the MDA determination) was detected spectrophotometrically at 450 nm on a microplate reader (Synergy H1 Hybrid Reader; BioTek Instruments, Winooski, VT, USA). The calorimetric measurement at 530 nm was used to assess the content of MDA. The concentration of the analyzed parameters was elaborated depending on the individual standard curves obtained for each measurement. The results are expressed in millimoles (GSH), micromoles (MDA), nanograms (PGI2, LTC4, LXA4, CAT, AGE), or picograms (PGE2, LTB4, SOD2, 4-HNE) per milligram of tissue.
## 2.5. Determination of the Myocardial Anti-Inflammatory and Proinflammatory Cytokines and Chemokines
The left ventricle lysates were prepared in cell lysis buffer (Bio-Rad, Hercules, CA, USA) with the addition of protease inhibitors—factor I and factor II (Bio-Rad, Hercules, CA, USA) and phenylmethylsulfonyl fluoride (PMSF; Sigma Aldrich, Saint Louis, MO, USA). The lysates were centrifuged at 15,000 × g for 10 min at 4°C. Subsequently, the obtained supernatants were transferred to new tubes and used to determine the total protein concentration. The range of protein concentrations was 200–900 μg/ml.
The multiplex assay procedure was performed according to the manufacturer's protocol. The concentration of selected cytokines, i.e., granulocyte colony-stimulating factor (G-CSF), granulocyte-macrophage colony-stimulating factor (GM-CSF), growth-regulated oncogenes/keratinocyte chemoattractant (GRO/KC), interleukin 1α (IL-1α), interleukin 1β (IL-1β), interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 5 (IL-5), interleukin 6 (IL-6), interleukin 7 (IL-7), interleukin 10 (IL-10), interleukin 12 p70 (IL-12 p70), interleukin 13 (IL-13), interleukin 17A (IL-17A), interleukin 18 (IL-18), interferon γ (IFN-γ), macrophage inflammatory protein 1α (MIP-1α), macrophage inflammatory protein 3α (MIP-3α), regulated upon activation, normal T-cell expressed and secreted (RANTES), tumor necrosis factor α, and vascular endothelial growth factor (VEGF) was measured by the Bio-Plex Immunoassay Kit (Bio-Plex ProRat Cytokine 23-Plex Assay, Bio-Rad; Warsaw, Poland). This procedure is based on multiple assays with covalently coupled magnetic beads. Firstly, a diluted couple of beads were added to each well of the assay plate. After washing the plate twice with Bio-Plex Wash Buffer, blanks, standards, and samples were applied to appropriate wells and incubated for 1 h. Subsequently, the plate was washed, and next, the detection antibodies were added to each well and incubated for 1 h. After adding streptavidin-phycoerythrin (SA-PE) solution, the next incubation, and more washes, the beads were resuspended and added to each well. At last, the 96-well plate was immediately shaken and read on the Bio-Plex 200 System (Bio-Rad Laboratories, Inc.; Hercules, CA, USA) fitted with Bio-Plex Manager Software. The concentration of cytokines was estimated according to the individual standard curves.
## 2.6. Statistical Analysis
All data are expressed as the mean ± standard deviation (SD) based on ten independent determinations in each experimental group, except for the Western blot method, in which results are based on six independent determinations. The statistical assessment was performed using a GraphPad Prism 8.2.1. ( GraphPad Software; San Diego, CA, USA). The normality of data distribution and homogeneity of the variance were assessed using the Shapiro–Wilk test and Bartlett's test. The statistical comparisons were performed by a two-way ANOVA followed by a respective post hoc test (Tukey's test and t-test). A statistical significance was set at $p \leq 0.05.$
## 3.1. Effect of Eight-Week NAC Treatment on the Concentration of Arachidonic Acid in the Plasma of Rats Subjected to a Standard and a High-Fat Diets
Our study revealed that an HFD caused a significant increase in plasma AA concentration in the PL fraction (HFD: +$14.9\%$, $$p \leq 0.0393$$, vs. control group; Figure 1(a)). No obvious changes in the phospholipid's AA content were observed in rats treated with NAC (NAC: $$p \leq 0.5320$$, vs. control group, HFD + NAC: $$p \leq 0.6206$$ and $$p \leq 0.2595$$, vs. the control and HFD groups; Figure 1(a)). In the TAG fraction, NAC supplementation to rats fed an HFD resulted in a crucial reduction in the AA level (HFD + NAC: −$34.6\%$, $$p \leq 0.0040$$; Figure 1(b)) compared to the HFD group, which is the objective manifestation of impaired inflammation development. In relation to the standard condition, there were no significant changes in all examined groups (HFD: $$p \leq 0.4206$$, NAC: $$p \leq 0.8413$$, HFD + NAC: $$p \leq 0.0599$$; Figure 1(b)). Furthermore, in the DAG fraction, the AA content was noticeably increased in the HFD and HFD + NAC groups (HFD: +$64.7\%$, $$p \leq 0.0035$$, HFD + NAC: +$47.3\%$, $$p \leq 0.0129$$; Figure 1(c)) and noticeably decreased in the NAC alone group (NAC: −$33.7\%$, $$p \leq 0.0002$$; Figure 1(c)) in relation to the control group. Plasma AA levels in the DAG pool did not substantially differ in the HFD with NAC treatment group (HFD + NAC: $$p \leq 0.2014$$; Figure 1(c)) compared to the HFD group. As excepted, the considerable increase in the content of AA in the FFA fraction caused by high-fat feeding (HFD: +$21.0\%$, $$p \leq 0.0186$$, vs. control group; Figure 1(d)) was abolished by the NAC treatment (HFD + NAC: −$23.4\%$, $$p \leq 0.0172$$, vs. HFD group; Figure 1(d)), indicating a preventive action of NAC on the inflammation development. No effect in the FFA's AA level was observed in rats receiving a NAC alone and in combination with HFD (NAC: $$p \leq 0.1986$$, HFD + NAC: $$p \leq 0.4685$$; Figure 1(d)) than in the control group.
## 3.2. Effect of Eight-Week NAC Treatment on the Concentration of Arachidonic Acid in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
In the lipid overload condition, the arachidonic acid content was considerably elevated in the PL fraction (HFD: +$77.6\%$, $p \leq 0.0001$, vs. control group; Figure 2(a)). Concomitantly, there were appreciable changes in the myocardial AA content in the PL fraction in rats fed a high-fat diet with NAC supplementation (HFD + NAC: +$60.4\%$, $p \leq 0.0001$ and −$9.7\%$, $$p \leq 0.0159$$, vs. control and HFD groups, respectively; Figure 2(a)), which we can suppose that NAC supplementation has a preventive action by the reduction of cardiac AA content in the phospholipid pool. In comparison with the standard condition, we observed a pronounced rise in the arachidonic acid level in the TAG fraction in the HFD and HFD + NAC groups (HFD: +$212.6\%$, $p \leq 0.0001$, HFD + NAC: +$141.2\%$, $p \leq 0.0001$; Figure 2(b)). Moreover, NAC administration to rats receiving an HFD caused a significantly diminished the amount of AA in the TAG fraction (HFD + NAC: −$22.8\%$, $$p \leq 0.0079$$, vs. HFD group; Figure 2(a)) for precautionary the onset of low-grade inflammation. In the NAC alone group we observed no relevant alteration in the AA content in the PL and TAG fractions (PL − NAC: $$p \leq 0.7285$$; Figure 2(a); TAG − NAC: $$p \leq 0.8853$$; Figure 2(b)) in relation to the appropriate control group. Our study also revealed that the myocardial AA content in the DAG fraction was remarkably increased in the HFD group (HFD: +$41.3\%$, $$p \leq 0.0007$$, vs. control group; Figure 2(c)). Additionally, in both NAC-treated groups we noticed a substantial decrease in the concentration of AA in the DAG fraction (NAC: −$15.8\%$, $$p \leq 0.0079$$, vs. control group, HFD + NAC: −$23.2\%$, $$p \leq 0.0001$$, vs. HFD group; Figure 2(c)), indicating a prophylactic NAC effect on an early indicator of inflammation. In the HFD + NAC group we noticed no markedly elevation in the diacylglycerol's AA content (HFD + NAC: $$p \leq 0.2222$$; Figure 2(c)) compared to the standard condition. The content of arachidonic acid in the FFA fraction was substantially enhanced in all examined groups (HFD: +$40.2\%$, $p \leq 0.0001$, NAC: +$6.7\%$, $$p \leq 0.0218$$, HFD + NAC: +$14.3\%$, $$p \leq 0.0411$$; Figure 2(d)) in relation to the control group. We also observed a pronounced decrease in the AA level in the FFA fraction (HFD + NAC: −$18.5\%$, $$p \leq 0.0009$$; Figure 2(d)) than the HFD group, so we can suppose that NAC has an anti-inflammatory properties.
## 3.3. Effect of Eight-Week NAC Treatment on the Expression of Proteins from Eicosanoid Synthesis Pathway in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
Under lipid overload condition, we noticed a significant increment in the total expression of COX-1 (HFD: +$20.0\%$, $$p \leq 0.0022$$, vs. control group; Figure 3(a)), which was abolished by the chronic NAC administration (HFD + NAC: −$16.5\%$, $$p \leq 0.0046$$, vs. HFD group; Figure 3(a)), thus revealing its preventive effect on inflammation occurrence. Similar protective influence was disclosed in the COX-2 expression (HFD: +$24.4\%$, $$p \leq 0.0263$$, vs. Control group, HFD + NAC: −$23.9\%$, $$p \leq 0.0020$$, vs. HFD group; Figure 3(b)). We also noticed a substantial increase in the expression of 5-LOX in rats fed a high-fat diet (HFD: +$17.7\%$, $$p \leq 0.0252$$, vs. control group; Figure 3(c)), which was abolished by preventive eight-week N-acetylcysteine treatment (HFD + NAC: −$15.1\%$, $$p \leq 0.0078$$, vs. HFD group; Figure 3(c)). In comparison with proper control group no significant changes induced by NAC alone and HFD with NAC treatment were found in the expression of COX-1, COX-2 and 5-LOX (COX-1 − NAC: $$p \leq 0.4820$$, HFD + NAC: $$p \leq 0.8204$$; Figure 3(a); COX-2 − NAC: $$p \leq 0.8109$$, HFD + NAC: $$p \leq 0.3036$$; Figure 3(b); 5-LOX − NAC: $$p \leq 0.1702$$, HFD + NAC: $$p \leq 0.8458$$; Figure 3(c)). Moreover, the $\frac{12}{15}$-LOX expression was remarkably increased in the HFD and HFD with NAC treatment groups (HFD: +$39.3\%$, $$p \leq 0.0070$$, HFD + NAC: +$25.6\%$, $$p \leq 0.0499$$; Figure 3(d)) than in the control group. The changes observed in the NAC and HFD + NAC groups in the $\frac{12}{15}$-LOX expression did not reach the significant level (NAC: $$p \leq 0.2601$$, HFD + NAC: $$p \leq 0.2292$$; Figure 3(d)).
## 3.4. Effect of Eight-Week NAC Treatment on the Concentration of Arachidonic Acid Derivatives in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
In all examined groups the concentration of PGE2 was significantly elevated (HFD: +$57.8\%$, $p \leq 0.0001$, NAC: +$28.5\%$, $$p \leq 0.0306$$, HFD + NAC: +$22.7\%$, $$p \leq 0.0424$$; Figure 4(a)) in relation to the standard condition. Moreover, NAC supplementation to rats fed an HFD provoked a significant reduction in the PGE2 amount (HFD + NAC: −$22.3\%$, $$p \leq 0.0151$$; Figure 4(a)) compared to rats receiving a high-fat diet alone. We found that the concentration of PGI2 was no relevant in rats treated by HFD or/with NAC (HFD: $$p \leq 0.2213$$, NAC: $$p \leq 0.0703$$, HFD + NAC: $$p \leq 0.2978$$; Figure 4(b)) in comparison with the control group. In the HFD-induced obesity group, NAC treatment revealed a crucial increment in the PGI2 content (HFD + NAC: +$26.0\%$, $$p \leq 0.0300$$, vs. HFD group; Figure 4(b)). As excepted, in the HFD group the concentration of LTB4 was greater (HFD: +$15.9\%$, $$p \leq 0.0254$$; Figure 4(c)) than in the control group. After eight-week NAC supplementation to rats fed an HFD, the concentration of LTB4 was remarkably decreased (HFD + NAC: −$11.7\%$, $$p \leq 0.0070$$, vs. HFD group; Figure 4(c)). In both NAC-treated group the LTB4 concentration was statistically unchanged (NAC: $$p \leq 0.3017$$, HFD + NAC: $$p \leq 0.6816$$, vs. control group; Figure 4(c)). In rats receiving a high-fat diet we noticed a significant increase in the LTC4 content (HFD: +$8.2\%$, $$p \leq 0.0191$$, vs. control group; Figure 4(d)). Furthermore, in both NAC-treated group LTC4 concentration was relevant impairment (NAC: −$20.4\%$, $p \leq 0.0001$, vs. Control group, HFD + NAC: −$9.4\%$, $$p \leq 0.0001$$ and −$16.3\%$, $p \leq 0.0001$, vs. control and HFD groups, respectively; Figure 4(d)). There was significantly diminished concentration of LXA4 in the HFD group (HFD: −$30.7\%$, $$p \leq 0.0210$$, vs. control group; Figure 4(e)). In rats treated by NAC application we found a markedly elevation in the level of LXA4 (NAC: +$65.0\%$, $$p \leq 0.0037$$, vs. control group, HFD + NAC: +$59.5\%$, $$p \leq 0.0007$$ and +$130.0\%$, $p \leq 0.0001$, vs. control and HFD groups, respectively; Figure 4(e)). Treatment of NAC implies that obesity-induced inflammation was decreased by the reduction of PGE2, LTB4 and LTC4 levels with simultaneously the elevation of PGI2 and LXA4 levels.
## 3.5. Effect of Eight-Week NAC Treatment on the Expression of Proteins Involved in the Inflammatory Processes in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
In the left ventricle homogenates, a high-fat diet induced a relevant reduction in the total expression of Nrf-2 (HFD: −$24.1\%$, $$p \leq 0.0035$$; Figure 5(a)) in relation to the control group. In both NAC-treated groups we observed no significant increase in the Nrf-2 expression (NAC: $$p \leq 0.0617$$, vs. control group, HFD + NAC: $$p \leq 0.5949$$ and $$p \leq 0.1121$$, vs. control and HFD groups, respectively; Figure 5(a)). What is more, in rats fed a high-fat diet alone and a high-fat diet with NAC application the total expression of TGF-β was notably increased (HFD: +$39.8\%$, $$p \leq 0.0135$$, HFD + NAC: +$27.2\%$, $$p \leq 0.0314$$; Figure 5(b)) compared to the standard condition. In the HFD alone and NAC-treated HFD groups a significant increase in the total expression of NF-κB was noticed (HFD: +$73.3\%$, $p \leq 0.0001$, HFD + NAC: +$30.5\%$, $p \leq 0.0001$, vs. Control group; Figure 5(b)). In addition, eight-week NAC treatment disclosed a vital decrease in the expression of NF-κB (NAC: −$28.2\%$, $$p \leq 0.0003$$, vs. control group, HFD + NAC: −$24.7\%$, $p \leq 0.0001$, vs. HFD group; Figure 5(c)). The total expression of Bcl-2 was considerably reduced in the rats fed a high-fat diet alone and a high-fat diet with NAC supplementation (HFD: −$36.6\%$, $$p \leq 0.0249$$, HFD + NAC: −$24.0\%$, $$p \leq 0.0131$$; Figure 5(d)) in comparison with the control group. The expression of TGF-β and Bcl-2 had no significant values in both NAC-treated groups (TGF-β–NAC: $$p \leq 0.4736$$, HFD + NAC: $$p \leq 0.3765$$; vs. control and HFD groups, respectively; Figure 5(b); Bcl-2 − NAC: $$p \leq 0.4430$$, HFD + NAC: $$p \leq 0.1722$$, vs. control and HFD groups, respectively; Figure 5(d)).
## 3.6. Effect of Eight-Week NAC Treatment on the Concentration of Cytokines and Chemokines in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
A high-fat diet application to rats resulted in a significantly elevated concentration of the following cytokines and chemokines, i.e., G-CSF, IL-1α, IL-1β, IL-12 p70, MIP-3α, RANTES, TNF-α, VEGF (HFD: +$39.4\%$, $$p \leq 0.0129$$; +$86.5\%$, $$p \leq 0.0009$$; +$19.9\%$, $$p \leq 0.0022$$; +$38.3\%$, $$p \leq 0.0067$$; +$22.0\%$, $$p \leq 0.0492$$; +$30.2\%$, $$p \leq 0.0219$$; +$17.0\%$, $$p \leq 0.0007$$; +$31.9\%$, $$p \leq 0.0031$$; respectively; Table 3) in comparison to the standard condition. Concomitantly, in the untreated HFD group, the concentration of IL-13 was considerably decreased (HFD: −$14.4\%$, $$p \leq 0.0484$$; Table 3) than in the control group. In addition, eight-week NAC supplementation to rats fed a standard diet caused a substantial decrease in the content of IL-1α, IL-1β, MIP-1α, MIP-3α, RANTES, and also TNF-α (NAC: −$13.3\%$, $$p \leq 0.0494$$; −$15.2\%$, $$p \leq 0.0046$$; −$26.1\%$, $$p \leq 0.0094$$; −$16.1\%$, $$p \leq 0.0086$$; −$28.6\%$, $$p \leq 0.0120$$; −$12.8\%$, $p \leq 0.0001$; respectively; Table 3) in relation to the control group. Furthermore, we revealed that NAC application to rats fed a high-fat diet caused a significant increase in the concentration of IL-1α (HFD + NAC: +$18.8\%$, $$p \leq 0.0167$$; Table 3) than in the control group. Moreover, in the NAC-treated HFD group, we observed a vital diminishment in the content of selected cytokines and chemokines, i.e., G-CSF, IL-1α, IL-12 p70, MIP-3α, RANTES, TNF-α and VEGF (−$26.7\%$, $$p \leq 0.0188$$; −$36.3\%$, $$p \leq 0.0022$$; −$27.8\%$, $$p \leq 0.0019$$; −$31.2\%$, $$p \leq 0.0115$$; −$22.1\%$, $$p \leq 0.0045$$; −$12.3\%$, $$p \leq 0.0067$$; −$21.8\%$, $$p \leq 0.0024$$; respectively; Table 3) compared to the HFD alone group, indicating a protective influence of NAC administration on secretion proinflammatory cytokines. In other no listed parameters no statistically significant changes were observed ($p \leq 0.05$) in all examined group.
## 3.7. Effect of Eight-Week NAC Treatment on the Concentration of Oxidative Stress Parameters in the Left Ventricle Tissue of Rats Subjected to a Standard and a High-Fat Diets
In the HFD alone group the concentration of SOD2 was substantially downgraded (HFD: −$12.1\%$, $p \leq 0.0001$; Figure 6(a)) than in the control group. Whereas, in the HFD + NAC group the SOD2 level was no statistically different (HFD + NAC: $$p \leq 0.9361$$ and $$p \leq 0.0627$$, vs. Control and HFD groups, respectively; Figure 6(a)). Interestingly, in the HFD alone group we noticed a significant decrease in the level of CAT and GSH (CAT–HFD: −$20.4\%$, $$p \leq 0.0293$$, vs. control group; Figure 6(b); GSH–HFD: −$17.1\%$, $$p \leq 0.0035$$, vs. Control group; Figure 6(b)), which was restored by the chronic NAC administration (CAT − HFD + NAC: +$31.1\%$, $$p \leq 0.0022$$, vs. Control group; Figure 6(b); GSH–HFD + NAC: +$15.1\%$, $$p \leq 0.0031$$, vs. Control group; Figure 6(b)). The preventive effect of NAC was disclosed as the increment in antioxidant capacity, i.e., increases in the content of CAT and GSH levels. The lipid overload conditions disclosed a notable elevation in the concentration of 4-HNE (HFD: +$61.9\%$, $$p \leq 0.0020$$, vs. control group; Figure 6(d)) whereby this change was abolished by NAC treatment (HFD + NAC: −$25.3\%$, $$p \leq 0.0127$$, vs. HFD group; Figure 6(d)). Moreover, we found a pronounced increase in the amount of MDA in the HFD and HFD with NAC administration groups (HFD: +$63.0\%$, $$p \leq 0.0001$$, HFD + NAC: +$40.4\%$, $$p \leq 0.0022$$; Figure 6(e)) in comparison with the control subjects. In relation to the HFD group, the MDA concentration was remarkedly lower in rats fed a high-fat chow with NAC supplementation (HFD + NAC: −$13.9\%$, $$p \leq 0.0078$$; Figure 6(e)). The reduction of 4-HNE and MDA levels after application of NAC with HFD may be reflect the protective effect on the deterioration of heart function caused by obesity. In all examined groups the concentration of AGE was appreciable changed (HFD: +$41.3\%$, $$p \leq 0.0022$$, NAC: −$22.0\%$, $$p \leq 0.0293$$, HFD + NAC: +$37.2\%$, $p \leq 0.0001$; Figure 6(e)) than in the control group. In the NAC group the content of SOD2, CAT, GSH, 4-HNE and MDA were statistically unchanged (SOD2 − NAC: $p \leq 0.9999$; Figure 6(a); CAT − NAC: $p \leq 0.9999$; Figure 6(b); GSH − NAC: $$p \leq 0.8290$$; Figure 6(c); 4-HNE − NAC: $$p \leq 0.6753$$; Figure 6(d); MDA − NAC: $$p \leq 0.1620$$; Figure 6(e)) than in the proper control group. Moreover, in rats receiving an HFD with NAC solution no obvious changes in the 4-HNE and AGE levels were observed (4-HNE − HFD + NAC: $$p \leq 0.1128$$, vs. control group; Figure 6(d); AGE − HFD + NAC: $p \leq 0.9999$, vs. HFD group; Figure 6(f)).
## 4. Discussion
Inflammation is a concomitant factor in obesity-related cardiovascular diseases, contributing to the progression of myocardial fibrosis and loss of heart function [19]. The regulation of the inflammatory response is based on the control of the level and biological activity of mediators, commonly known as eicosanoids. We investigated the effect of N-acetylcysteine on AA derivatives as key parameters of inflammation state and related parameters of oxidative balance in the left ventricle tissue of HFD-fed obese rats (the precise characterization of the obesity model in rats was previously described in our studies [20–22]). In our research, chronic oversupply of FA caused an increase in the myocardial AA content in each examined fraction, i.e., PL, TAG, DAG, and FFA. Similarly, an elevated concentration of AA in plasma PL, DAG, and FFA fractions was observed. The increment of AA content in the various lipid pools suggests the development of inflammation in the heart, which may deteriorate heart functioning [23]. In line with our observations, the results obtained by Pakiet et al. showed an increase in the content of n-6 PUFA, including AA, in the FFA and PL fractions in the plasma as well as in the heart tissue of HFD-treated mice [24]. Interestingly, we exhibited that NAC supplementation ameliorated an increase in myocardial arachidonic acid concentration induced by HFD-feeding in all examined fractions. We can suppose that the present reduction in AA concentration is the result of NAC supplementation revealing its anti-inflammatory properties. The AA pathway remains under the control of two crucial types of enzymes, i.e., cyclooxygenases catalyzed the generation of 2-series prostaglandins and thromboxanes (TX), and also lipoxygenases (LOX), which participate in the formation of 4-series leukotrienes (LT) and lipoxins (LX) [25]. In our research, a high-fat diet resulted in elevated myocardial expression of both COX isoforms (COX-1 and COX-2), which mediates the production of prostanoids. Studies conducted on hypertensive rats revealed that animals receiving an HFD for 10-week had an increased COX-1 expression with no changes in COX-2 expression in the aorta [26]. On the other hand, in research conducted on mice fed an HFD for 8-week a rise in COX-2 expression in the myocardial tissue was reported [27]. This small discrepancy in COX-2 expression may be a result of different experimental materials—aorta and myocardium. COX-1 is constitutively secreted by most mammalian tissues and is called a “housekeeping” enzyme that regulates metabolism processes under physiological conditions [28]. COX-1 is mainly responsible for the production of thromboxane A2 (TXA2), but PGI2 is also responsible for maintaining cell integrity. In addition, PGI2 is considered a potent vasodilator and inhibits platelet aggregation [29, 30]. Many studies have noticed that PGI2 attenuates cardiac hypertrophy and fibrosis through inhibited collagen synthesis [30, 31]. On the other hand, COX-2 is an enzyme, which expression is revealed in response to the development of inflammation state and might be enhanced by the release of proinflammatory cytokines [29]. Moreover, COX-2 regulates the first step of AA conversion to proinflammatory PGE2 and also anti-inflammatory PGI2 which contribute to the inflammatory response [23]. Several findings showed that PGE2 levels increase in cardiovascular diseases, e.g., cardiac hypertrophy or myocardial fibrosis [32, 33]. In our study, we demonstrated that an enhanced expression of COX-2 resulted in a rise in PGE2 level, which was abolished by N-acetylcysteine supplementation. Our observations are consistent with Guo et al.' data in which 8-week NAC administration decreased the expression of COX-2 in the heart of diabetic rats [34]. NAC intensified the influence of common nonsteroidal anti-inflammatory drugs (rofecoxib and diclofenac) by inhibiting COX-2 activity, resulting in a significant reduction in PGE2 level, which was observed by Hoffer et al. on human monocytes with lipopolysaccharide-induced PGE2 formation [2]. The above data show a decline in the content of PGE2 after N-acetylcysteine treatment, thus pointing to a potential role of NAC in inflammation-associated cardiac obesity. As we mentioned, LOX is a second major enzyme family involved in the AA metabolism pathway, leading to the formation of anti-inflammatory LX and proinflammatory LT [23]. In our study, we demonstrated an elevation in the total expression of 5-LOX after 8 weeks of high-fat feeding. As a consequence of the mentioned alteration in 5-LOX expression, the concentration of LTB4 and LTC4 increased. LTB4 is one of the crucial chemoattractant in the early phase of inflammation that causes the infiltration of neutrophils [35]. LTB4 also participates in the development of atherosclerosis and myocardial impairment [29]. In contrast, LTC4 is associated with the occurrence of oxidative stress and apoptosis in the myocardial tissue [36]. Becher et al. revealed that the incubation of cardiomyocytes with LTC4 led to an elevated generation of reactive oxygen species, resulting in the activation of the apoptosis process, which was reflected in fragmented and/or pyknotic cell nuclei. Importantly, pharmacological inhibition of the of LTC4 receptor prevented ROS production and simultaneously attenuated cardiomyocyte apoptosis [36]. The above effects prove that LTB4 and LTC4 weaken myocardial function. In our study, the administration of NAC abolished the increased 5-LOX expression, which resulted in a decline in the content of LTB4 and LTC4. Following our results, the study conducted by Karuppagounder et al. disclosed that NAC prevented against hemin-induced ferroptosis through scavenger proinflammatory lipid derivatives such as LTB4 and LTC4 generated by 5-LOX activity in nerve cells [37]. We can presume that N-acetylcysteine has a protective influence on the inflammation state in HFD-induced cardiac injury by ameliorating the expression of 5-LOX and the generation of 4-series leukotrienes. Herein, we also demonstrated a significant increase in the total expression of $\frac{12}{15}$-LOX in cardiac tissue of HFD-treated rats. A previous study showed that 3-month of high-fat feeding prompted a rise in the expression of $\frac{12}{15}$-LOX in the arteries of wild-type mice, causing the breakage of tight junctions and macrophage adhesion that underlie the mechanism of atherosclerosis [38]. The data also suggested that an enhanced expression of $\frac{12}{15}$-LOX in a late inflammation state could result from an increased PGE2 level [23]. It is important to note that $\frac{12}{15}$-LOX catalyzes the synthesis of 4-series lipoxins such as LXA4, which concentration in the HFD group was decreased. LXA4 is considered an atheroprotective factor due to the inhibition of proinflammatory cytokines generation and the prevention of neutrophil chemotaxis [23, 29]. In the present study, chronic NAC treatment of rats fed an HFD enhanced the LXA4 level in the left ventricle, which may limit obesity-induced inflammation by decreasing proinflammatory cytokines content [39]. We also demonstrated a decrease in the expression of Nrf-2 and Bcl-2 with simultaneous increases in the expression of NF-κB in the left ventricle of obese rats, which regulates the synthesis of inflammatory cytokines and chemokines [40, 41]. In light of these reports, we observed that in obese rats NAC ameliorated myocardial NF-κB expression, inhibiting the production of proinflammatory cytokines, such as TNF-α. Another study conducted on a rabbit model with doxorubicin-induced heart failure showed that NAC, by increasing the total antioxidant capacity and reducing NF-κB activation, diminished cardiomyocyte apoptosis and the expression of proinflammatory 8-iso-prostaglandin F2α. This finding suggests that NAC improves the structure and functioning of the myocardium under an inflammation state [42]. So, it may be presumed that in the left ventricle of rats from the HFD + NAC group we observed a decrease in the total expression of NF-κB, which led to a weakness in the inflammation processes by reducing the level of cytokines, i.e., IL-1α and TNF-α. What is more interesting, we also revealed a significant influence of NAC on inflammatory parameters in the cardiac tissue of rats fed a standard diet. In our research, the chronic administration of NAC to rats from the control group caused the diminishment of AA concentration in the DAG and FFA pools along with a decrease in the myocardial content of its proinflammatory eicosanoid derivatives, i.e., PGE2 and LTC4. According to existing literature, the impact of NAC on inflammatory mediators in an animal model fed a standard diet is unclear. Following our results, studies conducted on astrocytes cultured under normoxic conditions showed that NAC reduced the release of arachidonic acid into the media, which can potentially be responsible for the development of inflammation, and thus protected against the toxic effects of AA [1]. Concomitantly, we also observed the reduction in NF-κB expression in the left ventricle from the control rats treated with NAC. In line with this alteration, a decrease in the content of the following proinflammatory cytokines: IL-1α, IL-1β, MIP-1α, MIP-3α, RANTES, TNF-α was observed, limiting the occurrence of inflammation. The reduction of the above-mentioned parameters in the group of rats fed a standard chow indicates the beneficial role of NAC as an anti-inflammatory compound preventing the development of heart dysfunction.
It is known that oxidative stress is one of the important factors for cardiac damage incidence. Oxidative stress is defined as an imbalance between the antioxidant capacity and the generation of reactive oxygen species and free radicals [43]. There is some evidence showing a decrease in the content of enzymatic antioxidants—SOD2, CAT, and nonenzymatic antioxidants—GSH in CVD induced by high-fat feeding [44, 45]. In line with the above reports, in our study, the content of antioxidant markers, i.e., SOD2, CAT, and GSH, was decreased during feeding with HFD. It should be noted that reduced glutathione is a common antioxidant in the cardiovascular system that plays a major role in the inactivation of ROS or as a cofactor for glutathione peroxidase, causing the degradation of hydrogen peroxide [46]. Thus, GSH restores intracellular redox balance and also inhibits the inactivation of nitric oxide generated by the endothelium, altering vasomotor reactivity [43]. In agreement with our study, Andrich et al. revealed a decreased GSH level in the skeletal muscle of rats fed an HFD for 2 weeks [47]. In our study, NAC supplementation increased the concentration of GSH in rats fed an HFD. The beneficial effects of NAC administration stem from the counteraction of ROS and the supplementation of a greater content of cysteine, which is a precursor for the most common antioxidant—GSH [46, 48]. De Mattia et al. showed that NAC treatment caused an increase in GSH concentration and ameliorated the adhesion of molecules to the endothelium, ensuring its proper functioning [49]. In turn, catalase also exhibits action against cardiac oxidative injury by catalyzing the decomposition of hydrogen peroxide and inactivating this reactive form of oxygen into nontoxic products [50]. Mabrouki et al. demonstrated a reduction in CAT, and SOD levels in cardiac tissue within 12 weeks of HFD-treated rats [51]. In our study, an 8-week NAC application also restored the lowered level of CAT in rats receiving an HFD. Qin et al. demonstrated that catalase overexpression in a cardiomyopathy-transgenic mice model prevented against cardiac remodeling and its progression to heart failure [52]. In the herein study, we also noticed that feeding an HFD induced an increase in the concentration of lipid glycation and peroxidation products, i.e., 4-HNE, MDA, and AGE in the left ventricle tissue. The research conducted by Hartog et al. revealed that AGE causes the formation of additional bonds called cross-links between extracellular matrix proteins (collagen, elastin, and laminin) decreasing their elasticity and promoting cardiac dysfunction [53]. Previous studies showed that increased oxidative stress results in the accumulation of AGE, which is associated with the development and progression of myocardial failure [54]. In the case of 4-HNE, it is a toxic lipid peroxidation product, which is formed in the reaction of ROS with cellular biomolecules, such as lipids, especially PUFA, leading to oxidative modifications which further result in impairment in cellular activities [55]. Another product of lipid peroxidation processes is MDA. In patients with coronary heart disease, the serum MDA concentration was increased with a concomitant increase in oxygen-free radicals, indicating the proatherogenic property of this oxidative damage marker [56]. In our study, we observed that alterations in the level of lipid peroxidation products, such as 4-HNE and MDA, induced by HFD were abolished by NAC treatment. Interestingly, Arstall et al. 's study established that NAC administration in combination with standard treatment (streptokinase and/or nitroglycerine) in patients with acute myocardial infarction significantly reduced oxidative stress via diminished MDA content and increased GSH concentration in plasma, resulting in improved left ventricle function [11]. These findings imply that N-acetylcysteine protects cardiomyocytes from damage by decreasing lipid peroxidation products and enlarging the level of antioxidants thereby improving the function of cardiac muscle.
In our study, to obtain a homogeneous group, we used only male rats. There is a lot of evidence that gonadal hormones, especially estradiol in females alter lipid metabolism, leading to higher accumulation or conversion of AA from precursors compared to males [57]. Studies conducted by Lyman et al. showed that female rats maintained an increase content of AA in the plasma PL fraction than did males, pointing to the direct influence of estradiol, as the main cause of this difference [58]. There is one limitation to this study, which does not compare both males and females causing impossible an accurate assessment of NAC effects of on HFD-induced obesity depending on gender. What is more, hyperphagia and weight gain are strongly correlated with sex. Studies demonstrated that male rats presented higher caloric intake and mass body gain caused by HFD compared to females. Further, a delay in HFD-induced obesity and the occurrence of metabolic disturbances due to a lower level of hyperphagia and higher energy expenditure in female rats were observed. Interestingly, female rats are more active than males, resulting in lesser weight gain, which could be one of the possible factors for the obesity resistance in females [59].
## 5. Conclusion
Obesity is closely related to higher circulating FFA concentrations, which promote ectopic lipid accumulation, thereby adversely affecting cellular structure and functions. These changes lead to the development of inflammation state and oxidative stress, which are important factors for cardiovascular disease occurrence. In this study, we established that NAC supplementation might be a potential agent for preventing the occurrence of inflammation in obesity-related cardiac diseases by a diminishment in the AA concentration, especially in the phospholipid fraction. N-acetylcysteine administration reduced the expression of COX-2, leading to a decrease in the content of proinflammatory PGE2, and also reduced the expression of 5-LOX, resulting in a decrease in the concentration of leukotrienes, namely LTB4 and LTC4. Noteworthy, the observed reduction in the NF-κB expression after NAC supplementation weakened inflammatory signaling via a decline in the content of proinflammatory cytokines. NAC also ameliorated myocardial oxidative stress in rats fed an HFD through an increase in antioxidant content, especially GSH and CAT, with simultaneously a decrease in the level of lipid peroxidation products—4-HNE and MDA. Based on the presented results (Scheme 1) it can be concluded that N-acetylcysteine has a great potential to protect against the development of myocardial inflammation and oxidative stress in rats with obesity induced by a high-fat diet.
## Data Availability
The data deposited in a manuscript.
## Ethical Approval
The experiment with rats was performed in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethics Committee on animal care at the Medical University of Bialystok (approval number: $\frac{21}{2017}$).
## Conflicts of Interest
The authors declared that there are no conflicts of interest.
## Authors' Contributions
Klaudia Sztolsztener was responsible for conceptualization, methodology, validation, formal analysis, software, writing–original draft, and writing—review & editing. Wiktor Bzdęga was responsible for methodology, writing—original draft. Katarzyna Hodun was responsible for methodology, validation, software, and writing—original draft. Adrian Chabowski was responsible for writing—review and editing, project administration, supervision, and funding acquisition.
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|
---
title: Ameliorative Effects of some Natural Antioxidants against Blood and Cardiovascular
Toxicity of Oral Subchronic Exposure to Silicon Dioxide, Aluminum Oxide, or Zinc
Oxide Nanoparticles in Wistar Rats
authors:
- Riyadh Musaed Naji
- Mohamed A. Bashandy
- Abdallah H. Fathy
journal: International Journal of Food Science
year: 2023
pmcid: PMC10024631
doi: 10.1155/2023/8373406
license: CC BY 4.0
---
# Ameliorative Effects of some Natural Antioxidants against Blood and Cardiovascular Toxicity of Oral Subchronic Exposure to Silicon Dioxide, Aluminum Oxide, or Zinc Oxide Nanoparticles in Wistar Rats
## Abstract
The present study determines the possible protective role of fig fruit extract with olive oil and date palm fruit extract (FOD) in decreasing the oral subchronic blood and cardiovascular toxicity of SiO2NPs, Al2O3NPs, or ZnONPs. The present study used 80 male Wistar rats (8 groups, $$n = 10$$) distributed according to the treatment. The FOD treatments were used at their recommended antioxidant doses. All nanoparticles (NPs) were given orally and daily at doses of 100 mg/kg for 75 days. The oral administration of different NPs alone led to dramatic, oxidative stress, inflammatory markers, blood coagulation, endothelial dysfunction markers, myocardial enzymes, hematological parameters, lipid profile, and histopathological features compared with the control group. The FOD-NP-treated groups recorded significantly ameliorated blood and cardiovascular toxicity hazards compared to the groups administered with the NPs alone. In conclusion, the administration of FOD provides considerable chemopreventive and ameliorative effects against NP toxicity.
## 1. Introduction
Human exposure to nanomaterials is increased with the increased application of nanotechnology [1]. During the nanoparticle's transport in the blood, it can alter the blood and the blood vessels [2]. However, nanoparticles can be translocated between all organs causing toxicity at the whole-body level [3, 4].
Silica nanoparticles, also known as silicon dioxide (SiO2NPs), have good property including large surface-area-to-volume ratios, biocompatibility, and ease of modification, so it is considered a good tool in biomedicine and biotechnology [5, 6]. Studies on silica nanoparticles indicated that exposure to these nanoparticles and their accumulation in various organs could induce ROS generation, cause oxidative stress, proinflammatory stimulation, many side effects, and toxicity [7, 8]. Aluminum oxide nanoparticles (Al2O3NPs), a class of porous nanomaterials, belong to the family of metal oxide nanomaterials and contribute to $20\%$ of all nanosized chemicals. In addition, the bioinertness and easy surface functionalization allow their use in the biological environment [9]. Following the EU and US EPA criteria, the toxicity of Al2O3NPs was rated high [10]. Al2O3NPs have been listed as one of the metal oxide nanoparticles that are harmful to organisms [11]. Zn is an essential element with fundamental biological functions. The superior properties render ZnONPs used in different applications and biomedical imaging [12, 13]. Exposure to zinc oxide NPs might lead to toxicity to various body organs [14].
The body has a built-in defense mechanism to protect itself from free radical damage but eventually aging and diseases deplete the body of antioxidants [15]. It was thought that dietary antioxidants act as radical scavengers and decrease free radical attacks on cellular molecules [16].
The olive tree was considered one of the oldest trees. The olive oil phenolic compounds were found to possess antimicrobial, antioxidant, inhibitors of LDL-C oxidation and anti-inflammatory activities as well as affect the early phases of atherosclerosis [17]. Boskou [18] reported that olive oil is abundant in linoleic acid ($3\%$ to $21\%$) and oleic acid ($56\%$ to $84\%$) of the oil.
The dried fig fruit was found to contain alkaloids, flavonoids, coumarins, saponins, sterols, and terpenes that show therapeutic and anti-inflammatory activities and promote apoptosis [19, 20].
The date palm is one of the oldest known fruit crops. In addition, date palm fruit contains thirteen flavonoid glycosides that possess antineoplastic effects such as quercetin and glucans [21–23]. Studies on the extract of date palm fruit confirmed that it possesses antioxidative, antimutagenic, antimicrobial, and antimutagenic activities. In addition, date palm fruit is also traditionally used to treat many diseases like hypertension [24].
The present work aims at investigating the possible chemoprevention of FOD against different nanoparticle-induced subchronic blood and cardiovascular toxicity in Wistar rats including oxidative stress, inflammatory markers, blood coagulation, endothelial dysfunction markers, and myocardial enzymes, hematological parameters, lipid profile, and histopathological features.
## 2.1. Chemicals
We used high-grade chemicals.
## 2.2. Nanoparticles
The SiO2NPs, Al2O3NPs, or ZnONPs were prepared at an average size of less than 50 nm and characterized in the Nanogate Laboratory, Cairo, Egypt. The structure of the nanoparticle was confirmed using a TEM (JEM-2100, Jeol, Akishima, Japan) at a voltage of 200 kV and X-ray diffraction (XRD) analysis using a powder diffractometer system (X'pertPro-Panalytical, Malvern, United Kingdom) as shown in Figure 1.
## 2.3. Preparation of Nanoparticle (NP) Treatments
The different nanoparticles were homogeneously suspended in water and vibrated by vortex before administration to rats. All nanoparticles were given orally (100 mg/kg.b.wt) for 75 days. The used doses were confirmed by a pilot study conducted in our lab (data not shown). The doses of SiO2NPs were following Gmoshinski et al. [ 25], while Al2O3NP doses were following Park et al. [ 26], and the doses of ZnONPs were following Yousef et al. [ 27].
## 2.4. Plant Materials and Authorities
We purchased the extravirgin olive oil from the Grup Pons Company (Spain), the fig fruit from Kafoods Ltd. (Turkey), and the date palm fruit from the Al-MADINA AL-MUBARAK market (Saudi Arabia). The plant materials were identified and authenticated by Dr. Al-Baraa El-Saied, Al-Azhar University, Egypt. The voucher specimens were placed in the unit of medicinal plants.
## 2.5. Preparation of Crude Extracts
The fig fruit hydroalcoholic extract was prepared according to the method of Gilani et al. [ 28], while the hydroalcoholic extract of the date fruit was prepared according to the method of Al-Qarawi et al. [ 29].
## 2.6. Preparation of the Antioxidant Treatments
The extravirgin olive oil (7 g/kg.b wt.) and the fig and date palm fruit extracts (1 g/kg.b wt) were supplemented on rats orally and daily by gavage [30]. The different human-recommended antioxidant doses of the used materials were calculated and converted to rat doses [31].
## 2.7. The Experimental Animals
We purchased the male Wistar rats from VACSERA, Giza, Egypt. The study was conducted at the animal house, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt, under standard conditions of a temperature-controlled environment, food, and water ad libitum.
## 2.8. Animal Welfare
The animal experiment was conducted under the National Research Centre guidelines [32], and the study was approved by an independent ethics committee of the Faculty of Pharmacy, Ain Shams University.
## 2.9. Experimental Design
The present study used 80 male Wistar rats of average weights from 150 g to 170 g and divided into 8 groups ($$n = 10$$) as the following: Group I (control): this group did not receive any treatments for 75 days.
Group II (FOD): this group was treated orally and daily with the antioxidants (extravirgin olive oil (7 g/kg), fig extract (1 g/kg), and date palm fruit extract (1 g/kg)) for 2 weeks before and 75 days during the experiment.
Group III (SiO2NPs): this group was administered orally and daily with silicon oxide nanoparticles (SiO2NPs, 100 mg/kg.bwt).
Group IV (FOD-SiO2NPs): this group was treated with FOD and SiO2NPs on the same schedule mentioned above in groups II and III.
Group V (Al2O3NPs): this group was administered orally and daily with aluminum oxide nanoparticles (Al2O3NPs, 100 mg/kg.bwt).
Group VI (FOD-Al2O3NPs): this group was treated with FOD and Al2O3NPs on the same schedule mentioned above in groups II and V.
Group VII (ZnONPs): this group was administered orally and daily with aluminum oxide nanoparticles (ZnONPs, 100 mg/kg.bwt).
Group VIII (FOD-ZnONPs): this group was treated with FOD and ZnONPs on the same schedule mentioned above in groups II and VII.
## 2.10. Collection and Preparation of Samples
The drained blood samples from each animal under anesthesia were divided into two portions, one portion was put in plain tubes for serum preparation (centrifuged at 1006 g for 10 min), and the other portion was collected in a vial containing 0.5 M EDTA for hematological measurements. The separated sera samples were frozen at -80°C until future use. After that, the hearts were immediately dissected and fixed in $10\%$ neutral buffered formalin for histopathological preparations.
## 2.11.1. Oxidative Stress Markers
We used ready-made kits from the Bio-diagnostic Co., Egypt in the measurements of the serum systemic oxidative stress markers (reduced glutathione (GSH), superoxide dismutase (SOD), total antioxidant capacity (TAC), and thiobarbituric acid reactive substances (TBARS)).
The reactive oxygen species modulator-1 (ROS) was estimated in the serum of rats by ELISA technique according to the instructions provided with the kit. The used kit was purchased from MyBioSource, Inc. San Diego, CA 92195-3308, USA (Catalog No: MBS775259).
## 2.11.2. Inflammatory Markers
The interleukin-1beta (IL-1β) (Elabscience Biotechnology, Inc., Texas, USA, Catalog No: E-EL-R0012), the interleukin-6 (IL-6) (Elabscience Biotechnology, Inc., Texas, USA, Catalog No: E-EL-R0015), and the tumor necrosis (TNF-α) (MyBioSource, Inc. San Diego, CA 92195-3308, USA, Catalog No: MBS2507393) were estimated in the serum of the rats by ELISA technique using methods outlined in the kits.
## 2.11.3. Blood Coagulation and Endothelial Dysfunction Markers
The D-Dimer (Catalog No: MBS700162, MyBioSource, Inc. San Diego, CA 92195-3308: USA), the endothelin-1 (ET-1) (Catalog No: MBS704215, MyBioSource, Inc. San Diego, CA 92195-3308, USA), the P-selectin (Catalog No: MBS727217, MyBioSource, Inc. San Diego, CA 92195-3308, USA), intercellular adhesion molecule-1 (ICAM-1/CD54) (Catalog No: E-EL-R285096T, Elabscience Biotechnology, Inc., Texas, USA), and the vascular cell adhesion molecule-1 (VCAM-1) (Catalog No: E-EL-R1061, Elabscience Biotechnology, Inc., Texas, USA) were estimated in the blood serum of the rats by ELISA technique using methods outlined in the diagnostic kits.
## 2.11.4. The Measurements of Serum Myocardial Enzymes
The lactate dehydrogenase (LDH) was measured in the serum following the method of Mittal et al. [ 33] using commercial kits purchased from Abnova Company USA. The creatine phosphokinase (CPK) was measured in the serum following the method of Steen et al. [ 34] using commercial kits purchased from Abnova Company, USA. The creatine kinase MB isoenzyme (CK-MB) (Catalog No: E-EL-R1327, Elabscience Biotechnology, Inc., Texas, USA) was estimated in the blood serum of the rats by ELISA technique using methods outlined in the diagnostic kits.
The CK index was calculated to confirm the heart damage as the following: [1]The CK index=CK−MBng/mL×100/total CK activityIU/L.
## 2.11.5. Measurements of Hematological Parameters and Blood Indices
The hematocrit (Hct), red blood corpuscle (RBC), hemoglobin (Hb), Platelet count, white blood cell (WBC) count, differential leukocyte count, and blood indices) were estimated in the blood samples containing 0.5 M EDTA by the autoanalyzer (CBC counter, Sino thinker, sk9000, U.S.A).
## 2.11.6. The Measurements of Lipid Parameters and their Risks
The triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and nonhigh-density lipoprotein cholesterol (non-HDL-C), and risk ratios were estimated in the blood serum using readymade kits from Bio-diagnostic Co., Egypt, for research kits.
## 2.11.7. The Histopathological Study
The heart tissue samples were dissected from each animal, fixed, processed in alcohols, sectioned (5 μm), and stained with Hx & E according to certain methods [35].
## 2.12. Statistical Analysis
We analyze the data by using SPSS/PC program. The results were expressed as mean ± SE. The data were analyzed using one-way ANOVA followed by LSD post hoc for comparisons ($p \leq 0.05$).
## 3.1. The Effects of FOD on the Serum Oxidative Stress Markers of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Table 1
The FOD-treated group shows insignificant changes in TAC, GSH, ROS, SOD, and TBARS in the serum when compared with the control group.
The SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant reduction in the TAC ($36.84\%$, $52.51\%$, and $58.45\%$, respectively), GSH ($9.89\%$, $13.51\%$, and $16.57\%$, respectively), and SOD ($13.98\%$, $24.17\%$, and $30.37\%$, respectively) in the serum, in contrast to a significant elevation in the ROS ($107.13\%$, $257.93\%$, and $325.06\%$, respectively), and TBARS ($22.87\%$, $46.39\%$, and $64.86\%$, respectively) in the serum as compared with the control group.
In the same concern, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant reduction in the TAC ($21.50\%$, $37.33\%$, and $40.43\%$, respectively), GSH ($4.38\%$, $5.17\%$, and $9.28\%$, respectively), and SOD ($7.58\%$, $17.36\%$, and $19.08\%$, respectively), in the serum in contrast to a significant elevation in the ROS ($82.07\%$, $219.08\%$, and $277.47\%$, respectively), and TBARS ($11.46\%$, $32.31\%$, and $42.35\%$, respectively) in the serum as compared with the control group.
The FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant elevation in the TAC, GSH, and SOD in the serum, in contrast to a significant reduction in the ROS, and TBARS in the serum as compared with the SiO2NPs, Al2O3NPs, and ZnONP-treated groups, respectively.
## The Effects of FOD on the Serum Inflammatory Markers of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Figures 2–4
The FOD-treated group showed insignificant changes in the serum TNF-α, serum IL-Iβ, and serum IL-6 when compared with their corresponding control values.
The SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant elevation in the TNF-α ($34.23\%$, $42.70\%$, and $54.95\%$, respectively), IL-Iβ ($273.12\%$, $432.48\%$, and $748.32\%$, respectively), and IL-6 ($35.95\%$, $46.58\%$, and $74.24\%$, respectively) in the serum as compared with the control group.
In the same concern, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant elevation in the TNF-α ($23.42\%$, $23.06\%$, and $29.37\%$, respectively), IL-Iβ ($169.60\%$, $345.28\%$, and $595.52\%$, respectively), and IL-6 ($22.71\%$, $30.42\%$, and $34.79\%$, respectively) in the serum when compared with the control.
Moreover, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant reduction in the serum TNF-α, serum IL-Iβ, and serum IL-6 when compared with their corresponding values in the SiO2NPs, Al2O3NPs, and ZnONP-treated groups, respectively.
## 3.3. The Effects of FOD on the Blood Coagulation and Endothelial Dysfunction Markers Serum Inflammatory Markers of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Table 2
The FOD-treated group shows insignificant changes in the serum D-dimer, serum ET-1, serum P-selectin, serum ICAM-1, and serum VCAM-1 when compared with their corresponding control values.
The SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant elevation in the D-dimer ($143.23\%$, $182.35\%$, and $290.27\%$, respectively), ET-1 ($69.65\%$, $157.17\%$, and $268.40\%$, respectively), P-selectin ($96.56\%$, $91.31\%$, and $124.73\%$, respectively), ICAM-1 ($91.63\%$, $122.59\%$, and $155.61\%$, respectively), and VCAM-1 ($118.83\%$, $142.97\%$, and $172.89\%$, respectively) in the serum as compared with the control.
In the same concern, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant elevation in the D-dimer ($93.34\%$, $134.78\%$, and $234.78\%$, respectively), ET-1 ($34.51\%$, $123.70\%$, and $217.46\%$, respectively), P-selectin ($46.58\%$, $52.44\%$, and $86.84\%$, respectively), ICAM-1 ($57.81\%$, $98.04\%$, and $130.54\%$, respectively), and VCAM-1 ($92.10\%$, $121.95\%$, and $142.65\%$, respectively) in the serum as compared with the control.
In addition, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant reduction in the serum D-dimer, serum ET-1, and serum P-selectin, serum ICAM-1, and serum VCAM-1 as compared with their corresponding values in the SiO2NPs, Al2O3NPs, and ZnONP-treated groups, respectively.
## 3.4. The Effects of FOD on the Serum Myocardial Enzymes of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Table 3
The FOD-treated group showed insignificant changes in the LDH, CPK, CK-MB, and CK index in the serum as compared with the control.
In addition, the SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant elevation in the LDH ($53.41\%$, $86.01\%$, and $87.33\%$, respectively), CPK ($22.43\%$, $36.90\%$, and $41.62\%$, respectively), CK-MB ($1793\%$, $3736\%$, and $3194\%$, respectively), and CK index ($1452\%$, $2685\%$, and $2811\%$, respectively) in the serum as compared with the control.
Similarly, the FOD-Al2O3NPs and FOD-ZnONP-treated groups recorded a significant elevation in the LDH ($55.01\%$, and $60.51\%$, respectively), CPK ($25.50\%$, and $28.40\%$, respectively), CK-MB ($2830\%$, and $3194\%$, respectively), CK index ($2230\%$, and $2455\%$, respectively) in the serum as compared with the control.
In the same concern, the FOD-SiO2NP-administered group showed a significant elevation in the LDH ($29.82\%$), CPK ($10.03\%$), CK-MB ($1265\%$), and CK index ($1141\%$) in the serum when compared with the control.
Moreover, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant reduction in the LDH, CPK, CK-MB, and CK index in the serum as compared with the SiO2NPs, Al2O3NPs, and ZnONP-treated groups, respectively.
## 3.5. The Effects of FOD on the Hematological Parameters and Blood Indices of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Table 4
The FOD-treated group shows insignificant changes in the Hct, RBC count, Hb, MCV, MCH, MCHC, platelet count, WBC count, neutrophils percentage, lymphocytes percentage, and monocytes percentage in the blood when compared to the control group.
The SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant reduction in the Hct ($19.89\%$, $32.18\%$, and $38.01\%$, respectively), RBC count ($11.16\%$, $22.68\%$, and $27.57\%$, respectively), Hb ($36.59\%$, $44.38\%$, and $49.07\%$, respectively), MCV ($9.59\%$, $12.03\%$, and $14.34\%$, respectively), MCH ($28.33\%$, $27.79\%$, and $29.78\%$, respectively), MCHC ($20.63\%$, $16.20\%$, and $17.68\%$, respectively), platelet count ($25.43\%$, $33.22\%$, and $31.75\%$, respectively) in contrast to a significant elevation in the WBC count ($33.60\%$, $43.70\%$, and $37.64\%$, respectively), neutrophils percentage ($55.35\%$, $61.89\%$, and $74.53\%$, respectively), lymphocytes percentage ($23.18\%$, $32.96\%$, and $44.93\%$, respectively), and monocytes percentage ($51.05\%$, $61.28\%$, and $63.32\%$, respectively) in the blood when compared with the control group.
Similarly, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant reduction in the Hct ($13.85\%$, $24.36\%$, and $32.64\%$, respectively), RBC count ($3.99\%$, $10.90\%$, and $18.78\%$, respectively), Hb ($24.80\%$, $36.24\%$, and $40.21\%$, respectively), MCV ($10.07\%$, $14.84\%$, and $17.28\%$, respectively), MCH ($21.33\%$, $28.07\%$, and $26.69\%$, respectively), MCHC ($12.68\%$, $15.32\%$, and $10.85\%$, respectively), platelet count ($13.66\%$, $15.84\%$, and $19.39\%$, respectively) in contrast to a significant elevation in the WBC count ($19.39\%$, $22.43\%$, and $20.92\%$, respectively), neutrophils percentage ($26.35\%$, $32.57\%$, and $31.01\%$, respectively), lymphocytes percentage ($12.65\%$, $12.27\%$, and $14.15\%$, respectively), and monocytes percentage ($26.63\%$, $30.12\%$, and $32.66\%$, respectively) in the blood as compared with the control.
In addition, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-treated groups recorded a significant elevation in the Hct percentage, RBC count, Hb conc., and platelet count, in contrast to a significant reduction in the WBC count, neutrophils percentage, lymphocytes percentage, and monocytes percentage in the blood as compared with the SiO2NPs, Al2O3NPs, and ZnONP-treated groups, respectively.
In addition, the FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONP-administered groups did not record any changes in the MCV, MCH, and MCHC in the blood as compared with the SiO2NPs, Al2O3NPs, and ZnONP-administered groups, respectively.
## 3.6. The Effects of FOD on the Serum Lipid Parameters and Their Risks of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Table 5
The FOD and the FOD-SiO2NP-treated groups did not show any changes in the serum lipid parameters and their risks when compared with the control.
The SiO2NPs, Al2O3NPs, and ZnONP-treated groups recorded a significant elevation in the TG ($19.37\%$, $48.95\%$, and $54.97\%$, respectively), TC ($16.41\%$, $38.22\%$, and $43.05\%$, respectively), LDL-C ($20.78\%$, $35.93\%$, and $51.52\%$, respectively), non-HDL-C ($36.41\%$, $82.19\%$, and $93.50\%$, respectively), TG/HDL-C ($19.37\%$, $48.95\%$, and $54.97\%$, respectively), TC/HDL-C ($16.41\%$, $38.22\%$, and $43.05\%$, respectively), and LDL-C/HDL-C ($20.78\%$, $35.93\%$, and $51.52\%$, respectively), in contrast to a significant reduction in the HDL-C ($17.84\%$, $37.04\%$, and $43.32\%$, respectively) in the serum as compared with the control.
In the same concern, the FOD-Al2O3NPs and FOD-ZnONP-treated groups recorded a significant elevation in the TG ($27.23\%$, and $35.08\%$, respectively), TC ($21.62\%$, and $26.45\%$, respectively), LDL-C ($19.48\%$, and $20.78\%$, respectively), non-HDL-C ($48.01\%$, and $59.33\%$, respectively), TG/HDL-C ($27.23\%$, and $35.08\%$, respectively), TC/HDL-C ($21.62\%$, and $26.45\%$, respectively), and LDL-C/HDL-C ($19.48\%$, and $20.78\%$, respectively) in contrast to a significant reduction in the HDL-C ($23.56\%$, and $29.84\%$, respectively) in the serum as compared with the control.
However, the FOD-Al2O3NPs and the FOD-ZnONP-treated groups recorded a significant reduction in all serum lipid parameters and their risks, except for HDL-C which recorded a significant elevation as compared with the Al2O3NPs, and ZnONP-administered groups, respectively.
In addition, the FOD-SiO2NP-treated group did not record any changes in all serum lipid parameters and their risks as compared with the SiO2NP-administered group.
## The Effects of FOD on the Heart Histopathological Characters of Rats Administered with SiO2NPs, Al2O3NPs, or ZnONPs for 75 Days Are Represented in Figure 5
The control heart tissue showed a normal morphological appearance. However, the antioxidant-treated NP-administered groups (FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONPs) recorded significantly ameliorated histopathological characters in the heart tissues as compared with the nonantioxidant-treated NP-administered groups (SiO2NPs, Al2O3NPs, and ZnONPs, respectively).
## 4. Discussion
Human and animal exposure to various nanoparticles increased with the progress in the field of nanotechnology increasing the possibility of detrimental health impacts due to exposure to these NPs [23, 36, 37]. As nanoparticles enter the body, the blood and lymph carry them to various human organs and tissues where they harm cells and cause significant cytotoxicities [38]. In agreement, the current study confirmed the blood and cardiovascular toxicity of subchronic exposure to SiO2NPs, Al2O3NPs, or ZnONPs due to induction of lipid peroxidation, oxidative stress, systemic inflammation, blood coagulation, endothelial dysfunction, myocardial enzymes, hematological parameter dysfunction, and many histopathological features [39, 40].
The nanoparticles can reach many organs via the circulatory system, so the NP's toxicity could be evaluated at the cardiovascular level [41]. The SiO2NPs, Al2O3NPs, and the ZnONP-administered groups for consecutive 75 days as subchronic oral administration recorded a significant reduction in the serum TAC, GSH, and SOD, in contrast to significant elevations in the serum ROS, and TBARS compared with the control as agreed by many previous studies [27, 40, 42] who recorded enhanced oxidative stress after exposure to different NPs through the generation of various ROS and RNS that interact with and cause damage to cells and finally causing cell death [27, 43]. The oxidative damage due to exposure to different NPs could alter the cell membrane permeability through the production of ROS that interacts with the phospholipids portion of the cell membrane and initiate the lipid peroxidation chain reaction and increases the production of TBARS [40, 44]. In agreement with the current data, different NPs were found to interact with different cell proteins, antioxidant defense mechanisms, ROS generation, and inflammatory response and eventually lead to apoptosis and necrosis [27, 40, 45].
Exposure to nanoparticles results in the activation of proinflammatory factors and proteins [46]. In agreement, the inflammatory status due to SiO2NPs, Al2O3NPs, or ZnONP administrations was confirmed by higher concentrations of the measured circulatory cytokines (TNF-α, IL-Iβ, and IL-6) which hint the inflammatory reaction [47, 48]. Different NP administrations could increase the inflammatory mediators and cause changes in the inflammatory cytokines [27, 49]. In line with the current study, amorphous silica (SiO2NPs) nanoparticles were found to significantly increase the inflammatory response and release of many cytokines [50]. Faddah et al. [ 51] found that ZnONP administration increases many inflammatory markers. Furthermore, Hou et al. [ 52] reported that entering Al2O3NPs into the systemic circulation stimulates the immune system and alters cytokine levels.
The diet of plants is important in the protection against free radicals attack and several diseases in the blood and cardiovascular due to the presence of many varied and potent natural antioxidants [53]. The current work confirmed that the used natural antioxidant (FOD) treatments for two weeks before and during the administration of the nanoparticles (75 days) effectively reduced oxidative stress and inflammatory markers induced by the NP administration. That could be due to the wide variety of antioxidants present in olive oil such as vitamin E, oleuropein, and carotenoids and to the cardiovascular, hepatoprotective, and anti-inflammatory effects of the oil. In addition, olive oil contains potent antioxidants that can scavenge free radicals and inhibit LDL oxidation [54]. It was recorded that the use of potent antioxidants to protect the lipids from the free radicals attack decreased lipid peroxidation significantly. Accordingly, olive oil phenolics were found to protect against LDL-C oxidation [55]. In agreement, the human diet containing olive oil for 3 weeks has led to decreased oxidized LDL-C together with increased TAC, GSH, and SOD activities [56]. In agreement with the current data, Saafi et al. [ 57] showed that the date fruit extract ameliorated the stress in the rats exposed to toxins as revealed by the inhibition of TBARS and enhancement of antioxidant parameters of TAC, SOD, CAT, and GSH. In agreement, the human supplementation with 40 g/day of dried figs was found to potently reduce the oxidative stress and the inflammatory markers [31].
The hematopoietic system is highly sensitive to the oxidative stress resulting from NP administration [42]. In agreement with the present study, the NP administration groups recorded significant alterations in various hematological parameters and blood indices that might be due to erythropoiesis failure, hemolysis of circulating cells, leakage of cells through the capillary wall, reduced cell production, or increased plasma volume [27, 50]. In addition, the hematological alterations in the present study caused by different NP administrations might be due to bone marrow syndrome [58]. The altered hematological values in the present study might be due to the damaged cell membranes from the increased lipid peroxidation products [59]. The administration of SiO2NPs, Al2O3NPs, or ZnONPs was associated with many hematological abnormalities as severe microcytic hypochromic anemia as agreed with previous studies [27, 50]. In agreement, the significantly elevated levels of WBC count in contrast to significantly reduced levels of RBC, Hb, HCT, and PLT in the NP-administered groups hinted the systemic inflammation [27, 50]. Following these results, Ben [60] and Yousef et al. [ 27] found that the administration of different NPs to rats caused anemia and many changes in the hematological parameters and a significant alteration in the cell indices (HCT and MCHC) which reinforces our findings.
The antioxidant-treated NP-administered groups showed significantly ameliorated hematological parameters as compared with the nonantioxidant-treated NP-administered groups. In agreement, the improvements in different hematological parameters in response to FOD supplementation for two weeks before and during the administration of the nanoparticles (75 days) might be attributed to the amelioration in the antioxidant enzymes. In addition, the improved Hct percentage might be attributed to improved liver functions [61, 62].
In agreement, Viola P. and Viola M. [63] attributed the improved hematological parameters against toxicity to the oleuropein and other active ingredients of the olive oil. In addition, Joseph and Raj [64] and Fathy [61] reported that figs are the ideal diet in anemic conditions. Moreover, Al-Jowari et al. [ 65] concluded that the aqueous fig extract improved the blood parameters and affected the hematopoiesis in the female rabbits. The figs are rich in fatty acids and vitamins which are necessary for the process of blood cell formation (hematopoiesis) in the red bone marrow and the production of the formed elements [66]. Interestingly, an active principle of ficin from this plant was shown to possess a hemostatic effect through the activation of factor X [67]. The present results also agree with Saafi et al. [ 57] who revealed the potent antioxidant phenolic acids in the date palm fruit extract. In the same concern, Wahab et al. [ 68] and Orabi and Shawky [69] confirmed the hemopoietic activity of the date palm fruits and the significant amelioration of the hematocrit, RBCs, WBCs, hemoglobin concentration, lymphocyte, monocyte, and neutrophil count after treatment of toxicity in rats with the date fruit extract.
As recorded in the present study, the cardiovascular toxicities of the SiO2NPs, Al2O3NPs, or ZnONPs show the effect of these NPs on blood circulation and the heart. In agreement, these toxicities could be attributed to the size of the NPs and their dosage schedule [27, 50]. In addition, different NPs can reach the heart via the cardiovascular system and can induce ROS production leading to cardiovascular dysfunction [41], so the NP toxicity could be proved at the level of the cardiovascular. In addition, increased amounts of ROS production and inflammatory response were involved in myocardial stunning, vascular dysfunction, and cell death [42, 70]. In line with the current data, the administration of silica, alumina, or zinc NPs to rats was found to enhance ROS production and cause endothelial inflammation and serious cardiovascular injury [40, 59].
The levels of sICAM-1 and sVCAM-l expression were elevated significantly in all NP-administered groups (SiO2NPs, Al2O3NPs, or ZnONPs) in the present study which reflect the endothelial cell injury and the tendency to thrombosis as agreeing with Du et al. [ 50]. In addition, these molecules can secret some agents which could affect the endothelial cells and aggravate WBCs adhesion to the vascular endothelium [71].
As a type-1 transmembrane protein, the cell adhesion molecule (CAM) P-selectin expression level increased in all NP-treated groups in the present study which reflect the coagulation system disorders through recruitment of leukocytes and platelets to the site of injury during inflammation [72]. Zhou et al. [ 7] and Du et al. [ 50] recorded that exposure to different NPs such as silica, alumina, or ZnO NPs could induce overexpression of P-selectin causing both DNA damage and cytokinesis block in blood vessels endothelial cells.
The potent vasoconstrictor endothelin-1 (ET-l) secreted from vascular endothelial cells significantly elevated in the SiO2NPs, Al2O3NPs, or ZnONPs administered to rats which may be attributed to atherosclerosis, endothelial injury, and heart failure [73, 74].
The small fibrin degradation product D-dimer is used as a marker for blood and coagulation disorders [75]. The increased D-dimer levels in the present study in all NPs administered groups could be attributed to the disseminated intravascular coagulation due to thrombosis and secondary fibrinolysis which is strongly related to cardiac events [50, 76]. The present results also suggest that the SiO2NPs, Al2O3NPs, or ZnONP administrations could activate the coagulation system due to thrombosis formation and could alter the stability of the coagulation/fibrinolytic system as agreed by many studies [50, 75].
The myocardial enzymes (CK, CK-MB, and LDH) are normally present inside the myocardial cells. The alteration in the plasma membrane integrity and/or permeability is reflected by the amount of these cellular enzymes in the blood [27, 77]. In agreement, the elevated serum myocardial enzymes in the present study in all NP-administered groups could be attributed to cell damage or ischemic necrosis and leakage of CK, CK-MB, and LDH into the circulation which are considered diagnostic markers of myocardial injury [42, 50].
The present study demonstrated that the antioxidant-treated NP-administered groups (FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONPs) recorded significantly ameliorated blood coagulation, endothelial dysfunction, and myocardial enzymes parameters in the blood serum comparing with the nonantioxidant-treated NP-administered groups (SiO2NPs, Al2O3NPs, and ZnONPs, respectively). Defense against free radicals is fundamental to protect cellular molecules against several diseases including atherosclerosis and cardiovascular diseases [78, 79]. Therefore, there is a need to boost the antioxidant capacity and reduce releasing of free radicals [80]. Functional foods, such as olive oil, fig, and date palm, were good sources of exogenous antioxidants and are used in traditional medicine to manage various diseases [30, 81, 82]. Our results showed that FOD effectively ameliorated blood coagulation, endothelial dysfunction, and myocardial enzyme parameters induced by the NP administration indicating that FOD treatments have beneficial effects as antioxidants and protect against cardiovascular diseases [81, 82].
In agreement, the traditional Mediterranean diet including olive oil is beneficial against cardiovascular and atherosclerosis diseases due to its antioxidant activity, vasodilatory, antiplatelet aggregation, and anti-inflammatory effects [83, 84]. In the same concern, oxidation of the LDL-C was a hallmark of atherosclerosis and coronary heart disease. In addition, olive oil in the diet has been shown to reduce LDL-C oxidation [85].
The oxidative stress involved errors in lipid and protein metabolism that can induce liver injury and alterations in lipid metabolism [86]. All NP-administered groups in the current work recorded significant alterations in the lipids. In agreement, the SiO2NPs, Al2O3NPs, or ZnONPs administered to rats alter lipid metabolism and lipoproteins due to increased ROS production and hormonal imbalance [87]. This imbalance could induce hyperlipidemia from the adipose tissue's lipid mobilization or due to the decreased lipoprotein lipase activity that reduced the uptake of lipids by the adipose cells [88, 89]. Moreover, higher levels of cholesterol could result from the increased synthesis to restore the damaged membranes or from the increased activation of the HMG-CoA reductase enzyme. In agreement, the elevated serum triglyceride level may be attributed to the inhibition of enzyme activity leading to the reduction in the uptake of triacylglycerols and increasing the damage of cells due to lipid peroxidation [5, 70].
The alteration of lipid profile parameters after different NP administrations in the present study might be due to their interference with the lipid metabolism and decrease the cytochrome P450 which suppresses the cholesterol 7-hydroxylase that decreases the bile acids biosynthesis from cholesterol [42, 90]. NP administrations were also found to suppress the fatty acid β-oxidation and alter the lipids that might result in cardiomyopathy [91]. In agreement, the alteration of lipids in NP-treated groups might be due to the increased activity of cholesteryl esters synthetase and the inhibition of the carnitine palmitoyl-transferase system [92].
The present study demonstrated that the antioxidant-treated NP-administered groups (FOD-SiO2NPs, FOD-Al2O3NPs, and FOD-ZnONPs) recorded significantly ameliorated lipid profile compared with the nonantioxidant-treated NP-administered groups (SiO2NPs, Al2O3NPs, and ZnONPs, respectively). Our results showed that FOD effectively ameliorates lipid profile parameters induced by the NP administration. The increased ROS, LDL-C, triglycerides, and cholesterol are key factors for heart disease and may cause damage to the blood vessels. The ameliorative FOD results in the present study might be due to the olive oil's potent antioxidants that prevent lipid oxidation due to the prevention of cholesterol absorption or its production or increased cholesterol secretion and excretion [93, 94]. In agreement, unsaturated lipids like that of olive oil decreased the plasma cholesterol and ameliorated the blood lipids and the risk ratios [63]. In agreement with the present results, Lee et al. [ 95] recorded ameliorated lipid profile after fig extract supplementation to irradiated rats due to the protection of the plasma lipoproteins from oxidation and significantly elevated the plasma antioxidant capacity. In addition, the human supplementation with 40 g/day of dried figs potently reduced the oxidation of LDL-C [31, 96] and controls hyperlipidemia [97]. In agreement, the date palm fruit could cause activation of the hormone-sensitive lipase or lipogenic enzymes that caused decreased lipogenesis and increased lipolysis [98, 99]. Similarly, the date palm fruit inhibited LDL oxidation and stimulated the total cholesterol removal from the macrophages [100, 101].
In agreement, the heart tissue damage caused by SiO2NPs, Al2O3NPs, or ZnONPs is further confirmed by histopathological examination, which showed many pathological features including necrosis, scattered apoptotic (anucleated) cardiac muscle fibers, and others, showing small pyknotic nuclei with intracytoplasmic inclusions and markedly dilated thrombosed subpericardial and myocardial blood vessels with mild interstitial edema [27, 102].
The antioxidant-treated NP groups recorded significantly ameliorated histopathological characteristics compared with the nonantioxidant-treated NP groups. The protection of the heart tissues in response to FOD administration may be attributed to the decreased or prevented lipid peroxidation and protein oxidation due to the scavenging of the produced free radicals [27, 44].
## 5. Conclusion
The supplementation of FOD in NP-administered rats provides considerable protective effects against different NP-induced subchronic blood and cardiovascular toxicity in rats. The present findings also suggested the potential efficacy of FOD as a chemopreventive agent in the treatment of NP toxicity by modulating the oxidative stress, inflammatory markers, blood coagulation and endothelial dysfunction markers, myocardial enzymes, hematological parameters, lipid profile, and histopathological features.
## Data Availability
Data analyzed during this study are all included in the main manuscript.
## Ethical Approval
The study protocol was approved by the Research Ethical Committee, Faculty of Pharmacy, Ain Shams University, and conducted according to the regulations and recommendations of the ethical guidelines and complied with the guide for the care and use of laboratory animals.
## Conflicts of Interest
No competing interests were declared by the authors.
## Authors' Contributions
This work was carried out in collaboration with all authors. All authors read and approved the final manuscript.
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|
---
title: Integrin αVβ3 Signaling in the Progression of Osteoarthritis Induced by Excessive
Mechanical Stress
authors:
- Fanglong Song
- Xiaoyu Mao
- Jun Dai
- Bingchen Shan
- Zhentao Zhou
- Yifan Kang
journal: Inflammation
year: 2022
pmcid: PMC10024670
doi: 10.1007/s10753-022-01770-6
license: CC BY 4.0
---
# Integrin αVβ3 Signaling in the Progression of Osteoarthritis Induced by Excessive Mechanical Stress
## Abstract
Osteoarthritis (OA) is believed to be linked with cartilage degeneration, subchondral bone sclerosis, and synovial inflammation that lead to joint failure, and yet treatment that can effectively reverse the pathological process of the disease still not exists. Recent evidence suggests excessive mechanical stress (eMS) as an essential role in the pathogenesis of OA. Increased levels of integrin αVβ3 have been detected in osteoarthritic cartilage and were previously implicated in OA pathogenesis. However, the role of integrin αVβ3 in the process of eMS-induced OA remains unclear. Here, histologic and proteomic analyses of osteoarthritic cartilage in a rat destabilization of the medial meniscus model demonstrated elevated expression of integrin αVβ3 as well as more serious cartilage degeneration in the medial weight-bearing area. Furthermore, results of in vitro study demonstrated that eMS led to a significant increase of integrin αVβ3 expression and phosphorylation of downstream signaling molecules such as FAK and ERK, as well as upregulated expressions of inflammatory and degradative mediators. In addition, we found that inhibition of integrin αVβ3 could alleviate chondrocyte inflammation triggered by eMS both in vivo and in vitro. Our findings suggest a central role for upregulation of integrin αVβ3 signaling in OA pathogenesis and demonstrate that activation of integrin αVβ3 signaling in cartilage contributes to inflammation and joint destruction in eMS-induced OA. Taken together, our data presented here provide a possibility for targeting integrin αVβ3 signaling pathway as a disease-modifying therapy.
## INTRODUCTION
Currently, the treatment of osteoarthritis (OA) is mainly to relieve symptoms with pharmacological therapy at early stage, and joint replacement is often required in the late stage after suffering from pain and limited mobility for decades [1–3]. Due to the lack of effective disease-modifying therapy, enormous studies have focused on OA pathogenesis in recent years. It has been generally accepted various factors to be associated with OA pathogenesis, including aging, obesity, joint instability, trauma, mechanical stress (MS), and joint inflammation, among which MS has received increasing attention [4–9].
Recent evidence from both in vitro and in vivo studies suggests that chondrocytes embedded in the cartilage are sensitive and respond to a variety of MS such as fluid shear stress, cyclic stretch, continuous compressive force, and MS generated by liquid perfusion or compressed air [6, 10–13]. There is increasing evidence that MS affects chondrocyte proliferation, inflammation, metabolism, apoptosis, and even immune response [6, 14]. Physiological MS facilitates extracellular matrix (ECM) synthesis and anti-inflammatory effects of chondrocytes, while excessive MS (eMS) is thought to play an essential role in the pathogenesis of OA [5, 11, 15]. As a classic signaling pathway that mediates inflammation, the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling has been reported to be regulated by mechanical loading [15]. However, the precise molecular mechanisms underlying chondrocyte inflammation by eMS have not been fully elucidated.
Integrins are heterodimeric transmembrane proteins composed of α and β subunits that mediate cell adhesion, ECM organization, mechanosensing, signaling, survival, proliferation, and differentiation [16–18]. These heterodimers can be classified into several groups based on their ligand-binding specificities and signaling properties [19]. Evidence from numerous studies suggests a potential role for integrins in the pathogenesis of OA [17, 18]. Elevated levels of α1β1, α3β1, α2β1, α4β1, αVβ3, and possibly α6β1 have been detected in osteoarthritic cartilage [20]. Among them, integrin αVβ3 plays an extremely important role in signal transduction, differentiation, and proliferation in chondrocytes [18, 20, 21]. Moreover, integrin αVβ3 has been reported to promote multiple processes in OA, such as inflammation, apoptosis, angiogenesis, and bone sclerosis, and its level varies with the degree of cartilage degeneration in OA [17, 22, 23].
Integrins have been reported to percept extraneous stimuli both physically and chemically and transduce them into intracellular signaling [16]. Wang et al. found that inflammatory factors and cartilage metabolites in the OA joint cavity can activate and upregulate the expression of integrin αvβ3, and inhibition or knockout of integrin αVβ3 can reduce the degree of OA [17]. This indicates that the upregulation of integrin αVβ3 could be regarded as the reason as well as the result of OA development. Since a general hallmark of integrin is its mechanosensitivity [24], we propose that eMS activates and upregulates the expression of integrin αVβ3, which leads to the onset and progression of OA.
Destabilization of the medial meniscus (DMM) has been widely used to establish animal OA models [25–29]. The meniscus is often regarded to possess features of shock absorption and load distribution. It has been believed that medial meniscectomy induces changes in joint stability and local stress concentration between the cartilage surface of the femoral condyle and the tibial plateau, leading to cartilage degeneration and wear [30, 31]. Recent studies have shown that MS-induced cartilage degeneration in OA goes far beyond wear and tear. MS could activate several intracellular signaling pathways such as integrin/focal adhesion kinase (FAK), mitogen-activated protein kinases (MAPKs), and NF-κB, thus regulating proliferation, metabolism, and inflammation in chondrocytes [12, 32, 33].
Numerous studies have shown that cartilage damage in the medial part of the knee joint is more severe in a DMM model [17, 26, 27], yet the different activation of signaling pathways between medial and lateral knee chondrocytes has not been fully investigated. The extent of articular inflammation and damage is believed to associate with the severity of OA and local MS conditions [5, 15, 34]. Zhen et al. reported that distribution of TGFβ activity in osteoarthritic articular cartilage is altered because of aberrant mechanical loading in different areas of the joint which is increased in high-stress areas and decreased in low-stress areas [9]. Therefore, the phenomenon that different levels of cartilage degeneration in the two parts result from the different MS that chondrocytes suffered after DMM [30]. It is reasonable to speculate that MS may activate different signaling pathways or a certain signaling pathway in different degrees in the medial and lateral parts of the knee joint.
The hypothesis of the present study was that eMS led to the upregulation of integrin αVβ3 which resulted in the onset and progress of OA. To test this hypothesis, a rat OA model with DMM was used to investigate the effect of different MS conditions on cartilage degeneration by examining integrin αVβ3 as well as OA-related inflammatory and degradative mediators such as collagen II, aggrecan, MMP-9, 13, runt-related transcription factor 2 (Runx2), and a disintegrin and metalloproteinase with thrombospondin type-5 motif (Adamts-5) in the chondrocytes of medial and lateral weight-bearing areas of the knee, respectively. Furthermore, the effect of eMS on chondrocytes in vitro was also examined by detecting the mediators mentioned above and the activation of integrin αVβ3-dependent signaling molecules. Moreover, we investigated whether inhibition of integrin αVβ3 could alleviate inflammation triggered by eMS in vivo and in vitro and verify αVβ3 potentially a cartilage degeneration preventer.
## Chemicals and Reagents
Cyclo (RGDyK) (CYC, catalog S7844) was purchased from Selleck chem, USA. Anti-p-ERK (catalog 4377), ERK$\frac{1}{2}$ (catalog 4695), p-FAK (catalog 3283), FAK (catalog 3285), β-actin (catalog 4970), and HRP-conjugated secondary (catalog 7074) antibodies were purchased from Cell Signaling Tech, USA. DMSO (catalog C6164) and collagenase II (catalog C2-BIOC) were purchased from Sigma, USA.
## Animal Study
All the animal experiments were approved by the Animal Research Ethics Committee of Soochow University (201708A105).
A total of 80 male SD rats weighing 300–350 g were purchased from the Soochow University Animal Administration Center.
Firstly, 16 rats underwent DMM surgery on the right knee joints, and all specimens were harvested at 12 weeks postoperatively and evaluated using histological observation ($$n = 8$$) and qRT-PCR assay ($$n = 8$$) to determine the degree of cartilage degradation and differences of gene expressions in the medial and lateral region of the degenerated knees. Then, the last 64 animals that underwent DMM surgery on the right knee joints were randomly divided into two groups: the control group ($$n = 32$$) and the CYC-treated group ($$n = 32$$). Animals of the CYC-treated group received an intra-articular injection of CYC (4 mg/kg) per week into the right knee, while 0.1 ml PBS was injected into the right knees of the control group 3 days after DMM for 6 or 12 weeks. Sixteen specimens from each group were harvested at 6 and 12 weeks postoperatively and evaluated using histological observation ($$n = 8$$) and qRT-PCR assay ($$n = 8$$), respectively.
For the DMM surgery, all animals were operated under general anesthesia with $3\%$ intraperitoneal sodium phenobarbital (1.0 ml/kg). A middle anterior incision was made on their right knees and followed by medial meniscectomy via an incision on the medial aspect of the joint capsule. Following surgery, all the rats were allowed free activity without immobilization.
## Histological Analysis
Rats were sacrificed by cervical dislocation at 6- or 12-week post-surgery. Knee joints were removed and fixed in $10\%$ formalin solution at 4 °C for 24 h and then decalcified with EDTA for 4 weeks. The decalcified specimens were subsequently dehydrated in alcohol and embedded in paraffin blocks and serial 4-mm thick coronal sections were made. The serial sections including the severely degenerated area were stained with toluidine blue (TB). The stained sections were analyzed to determine the degree of cartilage degradation at the femoral condyle and tibial plateau which was comparable in locations in each group. The histological evaluations were performed independently using light microscopy by two observers in a blinded fashion according to the modified Mankin score system. There were 8 specimens in each group at each time point for histological analysis. Representative histological images matching the conclusions were presented.
## Collection, Isolation, and Culture of Rat Articular Chondrocytes
The cartilage of 1-week-old Sprague–Dawley rats was harvested and minced from knee joints before being digested with $0.25\%$ trypsin (Gibco, USA). The trypsin was then removed, and the cartilage was washed with phosphate-buffered saline (PBS) three times, after which $0.2\%$ collagenase II was added for digestion at 37 ℃ for 4 h. A 200-lm mesh strainer was used to filter the above solution, and the cells were collected by centrifugation and then cultured in Dulbecco’s modified Eagle’s medium (DMEM)-F12 medium (Hyclone, USA) supplemented with $10\%$ fetal bovine serum (FBS, Gibco, USA) and $1\%$ penicillin/streptomycin (Gibco, USA) and incubated with $5\%$ CO2 at 37 ℃. The culture medium was changed every other day. The cells were digested and reseed when reached 70–$80\%$ confluence as passage 1 (P1). The P1 generation of chondrocytes was used for the experiments in this report. Following starvation in serum-free medium for 12 h, cells were exposed to eMS for 5, 15, or 30 min to explore the effect of MS on the phosphorylation of ERK and FAK in chondrocytes. 10 μM CYC was added to the culture medium [35, 36] or DMSO alone (as vehicle control) for a further 24 h and subsequently exposed to MS for 30 min (cell lysates were measured by Western blot for the kinase assays) or for another 24 h in normal culture medium to investigate expression levels of MMP-9, MMP-13, ADAMTS-5, Runx2, collagen II, and aggrecan with qRT-PCR analyses.
## Cyclic MS Application
When the cells reached 90–$95\%$ confluence (about 5 × 105 cells/chamber), the medium was replaced with DMEM/F12 supplemented with $0.5\%$ FBS and antibiotics for 12 h. Following synchronization, cells were exposed to cyclic MS.
As in Fig. 1, briefly, a small round iron plate coated with thin cover glass is placed over the confluent cell layers in the 6-well plate. The cyclic MS was generated as magnetic force loaded on the iron plate by an electromagnet below the 6-well plate, which was modified from the device we had reported [37, 38]. The magnitude and frequency of the stress were controlled by an arbitrary waveform generator and a power amplifier which linked to the electromagnet. Cells of the control group were covered with a thin glass without the electromagnet energized below. The experiment of MS application was performed in a humidified atmosphere incubator containing $95\%$ air and $5\%$ CO2 at 37 °C. Although we did not measure the exact magnitude of the pressure on the chondrocytes below cover glass when MS loaded, we choose a sinusoidal waveform power of which the peak voltage and frequency were 15 V and 2 Hz, respectively, as eMS in this experiment, based on the results of our previous research (unpublished).Fig. 1Schematic of the electronically controlled MS-generating device.
## Proteins Extraction and Western Blot
After treatment of MS, cells were washed with cold PBS and lysed in a lysis buffer (Beyotime, China). Lysates were mixed and incubated on ice for 15 min, and then, cell debris was spun down at a speed of 14,000 rpm for 15 min at 4 °C. Concentrations of protein samples in the supernatant were determined using the bicinchoninic acid (BCA) method with the protein assay kit (Beyotime, Shanghai, China). Equal quantities (20 μg) of proteins samples (dissolved in 5 × loading buffer) were separated using SDS-PAGE gels ($5\%$ stacking gel and a $10\%$ running gel) and then electro-transferred to polyvinylidene fluoride (PVDF) membranes at 250 mA for 30 min. After blocking in $5\%$ bovine serum albumin (BSA) in TBST at room temperature for 1 h, membranes were then incubated with primary antibodies (1:1,000 except for ERK (1:2,000) and β-actin (1:2,000)) overnight at 4 °C. After washing with TBST, membranes were then incubated with HRP-conjugated secondary antibodies (1:2,000) at room temperature for 1 h. Protein bands were visualized using ECL reagents. The intensity values of each phosphorylated kinase were quantified using densitometric analysis with ImageJ 1.36 and normalized to the intensity of corresponding total protein bands. Unless otherwise stated, β-actin was used as an internal control.
## RNA Extraction and qRT-PCR
To isolate total RNA from the articular cartilage, cartilage from the femoral condyles was shaved with a scalpel and subsequently frozen immediately in liquid nitrogen. Using the Bio-Gen PRO200 Homogenizer (PRO Scientific Inc., USA), the cartilage was homogenized in TRIzol (Invitrogen, USA) according to the manufacturer’s instructions. Then, the RNA was dissolved in $0.1\%$ diethylpyrocarbonate water and quantified by spectrophotometry, and samples with values between 1.7 and 2.0 were used. A total of 1 µg RNA was used to synthesize complementary DNA (cDNA) by reverse transcription using a PrimeScript™ RT reagent kit (Takara, Japan) according to the manufacturer’s instructions. Subsequently, the samples were subjected to qPCR using SYBR Premix Ex Taq (TaKaRa, Japan) with specific primers (Table 1). *The* gene for β-actin acted as an endogenous reference for normalization of fluorescence thresholds (Ct) values of target genes. Table 1Primer sequence for qRT-PCRPrimer nameSequenceCollagen II XM_006242308.4F: 5′-CTGTCTGCAGAATGGGCAGAGG-3′R: 5′-GCCAGGAGGTCCTTTAGGTCCT-3′AggrecanXM_039101034.1F: 5′-AGGACAGGTTCGAGTGAACAGC-3′R: 5′-GGTCAAAGTCCAGTGTGTAGCG-3′MMP-13NM_133530.1F: 5′-CTGGTCTGATGTGACACCTCTG-3′R: 5′-CTTTGGAGCTGCTTGTCCAGGT-3′MMP-9NM_031055.2F: 5′-TTGTCCTGCACCACGGATGGCC-3′R: 5′-ACCAGCGATAACCATCCGAGCG-3′Adamts-5 NM_198761.2F: 5′-AGCGCAGCTGCGCTGTGATTGA-3′R: 5′-TCTGTGATCGTGGCTGAAGTGC-3′Runx2 XM_039083955.1F: 5′-GGCCTTCAAGGTTGTAGCCCTC-3′R: 5′-TAGCTCTGTGGTAAGTGGCCAC-3′Integrin αV XM_039106448.1F: 5′-CGTCCTCCAGGATGTTCCTCCT-3′R: 5′-GGCTCCAAACCACTGGTGGGAT-3′Integrin β3 XM_039085714.1F: 5′-CGTCGGAGAGTCCAACATCTGT-3′R: 5′-TGTCTCCTGAGCCCTTGCTGCT-3′β-actinNM_031144.3F: 5′-TCATGCCATCCTGCGTCTGGAC-3′R: 5′-TGCCGATAGTGATGACCTGACC-3′
## Statistical Analysis
SPSS version 13 for Windows was used for all statistical analyses. All data were presented as mean ± standard deviation (SD). Differences among groups were compared using one-way ANOVA with Bonferroni post-hoc test. Differences between two groups were tested using paired-samples t test when appropriate. All experiments were repeated at least three times. $P \leq 0.05$ was considered statistically significant.
## Upregulated Integrin αVβ3 In the Weight-Bearing Area of the OA Knee Joint
Firstly, we tested the contribution of MS to OA progression in a rat DMM-induced OA model by comparing the differences between medial and lateral areas of the OA knee joint, where there were different MS conditions. Twelve weeks after DMM surgery, histological observation demonstrated normal cartilage of the left knees (Fig. 2A) and the medial area of the femoral condylar and tibial plateau exhibited severe cartilage loss and abrasion with loss of ECM, exposed subchondral bone, and irregular arrays and shape of chondrocytes compared with that of the lateral area in the right knees (Fig. 2B). The modified Mankin score was significantly higher in the medial area than that of the lateral area, which implied more severe OA in the weight-bearing area of the knee (Fig. 2C).Fig. 2Different severity of OA in medial and lateral regions of rat knee joints and upregulated integrin αVβ3 in the weight-bearing area (A normal joint; B DMM joint). Histological observations of representative sections of the medial and lateral regions of rat joints stained with toluidine blue. Arrowheads indicate areas of cartilage degeneration. Scale bars in the low-magnification (left) images: 1 mm; scale bars in the high magnification (middle and right) images: 250 μm. C Quantification of cartilage degeneration in toluidine blue-stained sections of the medial and lateral regions of joints from rats 12 weeks after DMM surgery ($$n = 8$$). D qRT-PCR analysis of mRNA levels for MMP-9, MMP-13, collagen II, aggrecan, Runx2, Adamts 5, integrin αV, and integrin β3 in cartilage from the medial and lateral tibial plateau of rat knee joints 12 weeks after DMM surgery. Data are presented as mean ± SD of triplicates and are representative of 8 individual samples. * $P \leq 0.05$ compared with cartilage from the medial tibial plateau. M, medial; L, lateral.
We also investigated the role of integrin αVβ3 in the pathogenesis of OA by examining OA-related inflammatory and degradative mediators in the rat OA model. Cartilage of the medial area of the OA knees exhibited elevated mRNA levels of integrin αV and β3, as revealed by qPCR analyses. Results of qPCR also demonstrated significantly increased expressions of MMP-9, MMP-13, ADAMTS-5, and Runx2 with decreased collagen II and aggrecan expressions at the medial area, compared with that of the lateral area (Fig. 2D). These results reflected that elevated integrin αV and β3 expressions in the weight-bearing area of the OA knee joint may play an important role in the MS-induced OA.
## Excessive MS-Induced Phosphorylation of FAK and ERK and Promoted the Production of Osteoarthritis-Related Inflammatory and Degradative Mediators in Rat Chondrocytes
To investigate the effect of eMS on rat chondrocytes, the downstream molecules of integrin αVβ3 signaling were evaluated by Western blot analysis, and the expression of osteoarthritis-related inflammatory, degradative, hypertrophic mediators as well as integrin αVβ3 was tested by qPCR analysis. The results of Western blot indicated that eMS led to rapid and transient phosphorylation of FAK and ERK over time. As the peak level of phosphorylated ERK occurred at 15 min and 30 min, while FAK phosphorylation increased approximately tenfold at 30 min compared with the control, we chose 30 min for the experiments in this report (Fig. 3A, B). qPCR analysis demonstrated that 24 h after MS stimulation, the expression of integrin αVβ3 was significantly increased. Meanwhile, the mRNA levels of MMP-13, MMP-9, ADAMTS-5, and Runx2 were increased, while the levels of collagen II and aggrecan were decreased (Fig. 3C). These outcomes implied that integrin αVβ3, FAK, and ERK were activated in chondrocytes in response to eMS stimulation, thereby inducing osteoarthritis-related inflammatory and degradative mediators production. Fig. 3Crucial role of integrin αVβ3 in the development of rat osteoarthritis. A Western blot analysis of the phosphorylation of molecules downstream of integrin αVβ3 signaling in rat chondrocytes stimulated by eMS changes over time. B and C Integrin αVβ3-dependent phosphorylation of FAK and ERK and expression of inflammatory and degradative mediators induced by eMS. * $P \leq 0.05$ compared with control group. # $P \leq 0.05$ compared with MS group.
## Integrin αVβ3-Dependent Expression of Inflammatory and Degradative Mediators Induced by eMS
To investigate the role of integrin αVβ3 in eMS-induced chondrocytes inflammation, we pretreated the cells with the integrin αVβ3 inhibitor, CYC for 24 h to clarify whether the inhibitor had any effect on ERK and FAK phosphorylation induced by MS. The results showed that pretreatment with CYC significantly inhibited the expression of integrin αVβ3 in chondrocytes induced by eMS and attenuated eMS-induced increasing of ERK and FAK phosphorylation (Fig. 3B). The results of qPCR revealed that inhibition of integrin αVβ3 resulted in upregulation of collagen II, aggrecan expressions, and decreased expressions of MMP-13, MMP-9, ADAMTS-5, and Runx2 compared with cells treated with MS alone (Fig. 3C). These results indicated that elevated integrin αVβ3 expression in chondrocytes under eMS was a key factor in OA progression.
## Attenuation of Rat Osteoarthritis by Pharmacological Inhibition of Integrin αVβ3
To explore the inhibition of integrin αVβ3 in the treatment of OA induced by eMS, we treated the DMM rat model with knee joint injection of CYC. At 6 weeks after DMM surgery, the joints of the control group exhibited partial cartilage loss and abrasion without subchondral bone exposed in the medial area of the femoral condylar and tibial plateau, and the matrix surrounding the chondrocytes was partially lost. In the joints injected with CYC, the articular cartilage was smooth, without any evidence of injury or abrasion. The matrix surrounding the chondrocytes was arranged in columns that were smooth and evenly stained, just like the normal knee joint. Histologic analysis revealed that CYC-treated group exhibited a lower Mankin score than that of control (Fig. 4A).Fig. 4Attenuation of rat osteoarthritis by pharmacological inhibition of integrin αVβ3. A Histological observations of representative sections stained with Toluidine blue of the medial regions from rat joints 6 and 12 weeks after destabilization of the medial meniscus (DMM) and quantification of cartilage degeneration ($$n = 8$$). Arrowheads indicate areas of cartilage degeneration. Scale bars in the low magnification (left) images: 1 mm; scale bars in the high-magnification (middle and right) images: 250 μm. * $P \leq 0.05$ compared with DMM group. B qRT-PCR analysis of mRNA levels for MMP-9, MMP-13, collagen II, aggrecan, Runx2, Adamts 5, integrin αV, and integrin β3 in cartilage from the medial tibial plateau of rat knee joints 6 and 12 weeks after DMM surgery. Data are presented as mean ± SD of triplicates and are representative of 8 individual samples. # $P \leq 0.05$ compared with DMM group. CYC, Cyclo (RGDyK).
The joints of the control group displayed severe cartilage abrasion, loss of extracellular matrix, irregular array and shape of chondrocytes, and even subchondral bone exposing 12 weeks after DMM surgery. The CYC-treated group exhibited reasonable cartilage regeneration compared with that of the control group, but still exhibited surface discontinuity, including shallow vertical fissures through the cartilage superficial zone at numerous points across the surface and delamination of the superficial zone. The Mankin score of the CYC-treated group was significantly lower than that of the control (Fig. 4A).
We also detected MMP-13, MMP-9, ADAMTS-5, Runx2, collagen II, aggrecan, and integrin αVβ3 expressions using qPCR analysis. At 6-week post-injection, higher levels of collagen II and aggrecan expressions were found in the CYC-treated group compared with that of the PBS-treated group. Furthermore, CYC injection led to significantly lower expressions of MMP-13, MMP-9, ADAMTS-5, and Runx2, compared with those of the PBS-treated group. At 12 weeks after CYC injection, qPCR analysis demonstrated downregulated MMP-13, MMP-9, ADAMTS-5, and Runx2 expressions and upregulated collagen II and aggrecan expressions in cartilage compared with the PBS group (Fig. 4B).
## DISCUSSION
Enormous studies have verified an important role of the meniscus in shock absorption and load distribution of knee joints [30, 39–41]. It has been extensively reported that medial meniscectomy could lead to stress concentration in the medial part of the knee joint which results in early degeneration of the joint [17, 30, 42, 43]. However, the molecular mechanisms underlying the inconsistent degree of medial and lateral cartilage OA in the knee after medial meniscus resection have not been fully researched. As a key receptor in the classical mechanotransduction pathway, integrins have been widely studied. In the present study, by examining the medial weight-bearing area of the knee after DMM surgery, we found a significant increase in the expression of integrin αVβ3 and OA-related inflammatory and degradative mediators in the cartilage of the medial weight-bearing area, which is consistent with the findings of Wang et al. [ 17]. They believed that the upregulation of integrin αVβ3 expression in chondrocytes was attributed to the inflammatory environment of the joint, and the upregulation of integrin αVβ3 further caused the aggravation of OA, forming a vicious circle. However, they did not investigate the different degrees of cartilage degeneration in the medial and lateral weight-bearing areas of the knee, nor did they take mechanical stress into consideration. Therefore, it seems difficult to make a perfect explanation to the phenomenon.
The effects of mechanical stress on chondrocyte inflammation and related signaling pathways have been extensively studied. It has been reported that moderate stress suppressed IL-1β-induced chondrocyte apoptosis by inhibiting the PI3K/Akt pathway [12]. However, Lohberger et al. found that in response to MS, human normal and OA chondrocytes showed different trends in Akt phosphorylation [33]. Our previous studies revealed that Akt phosphorylation could be regulated by different strengths of MS [37, 38]. The different results between the above reports may be attributed to the types and parameters of MS, cell source and culture methods, and even inflammation conditions [10]. The results of our present study demonstrated that eMS could induce the OA phenotype of chondrocytes. At the same time, the expression of integrin αVβ3 was significantly increased after eMS stimulation. The results of Naoto et al. demonstrated the upregulation of integrin (αVβ3 and αVβ5) expressions under MS in chondrocytes, which is consistent with our results [44].
FAK is a crucial signaling molecule and is initially stimulated by integrin activation, which can result in tyrosine (Tyr397) phosphorylation of FAK (depending on integrin-ECM interaction) and potentially activation of MAPKs under MS [45]. Then, the activated integrin-FAK-MAPKs axis will regulate OA-related factors such as MMPs, ADAMTs, IL- 6, and TNF-α [44]. In the present study, we also examined the signaling pathways above. Consistent with in vivo experiments, a significant increase of integrin αVβ3 expression as well as upregulated phosphorylation of FAK and ERK was found in chondrocytes after eMS stimulation, which is consistent with the report by Naoto et al. [ 44].
As a member of Runxs family, Runx2 plays an important role in bone mineralization by stimulating osteoblast differentiation [46]. Runx2 has also been reported to contribute to the pathogenesis of OA through upregulation of inflammatory factors and chondrocyte hypertrophy after the induction of joint instability [31]. These findings prompted us to investigate the effect of MS on Runx2 expression. Our findings showed that chondrocytes stimulated with eMS exhibited upregulated expressions of Runx2 and other OA-related inflammatory factors such as MMP-9, 13, and Adamts-5, indicating that eMS can exert a pro-inflammatory effect through integrin-FAK-ERK-Runx2 signaling.
Our data suggested that cartilage degeneration was closely associated with integrin αVβ3 expression and MS. However, different levels of cartilage degradation suggest that chondrocytes of medial and lateral parts with different ECM volume and quality can protect cells from the damage of inflammatory mediators [47]. Therefore, based on the results of animal studies, we cannot rule out the possibility of the influence of OA inflammation on integrin expression, just as reported by Wang et al. [ 17]. In order to figure out the role of integrin αVβ3 in the pathogenesis of MS-induced OA, we used CYC, a specific inhibitor of integrin αVβ3 [35, 48], to block transduction of MS in chondrocytes. The present study showed that inhibition of activated integrin αVβ3 led to a significant decrease in phosphorylation of FAK and ERK as well as cellular inflammation under eMS. Therefore, it can be preliminarily considered that eMS mediated chondrocyte inflammation through the upregulation of integrin αVβ3. Naoto et al. showed that inhibition of integrin αVβ3 and αVβ5 signaling with cilengitide significantly downregulated several inflammatory factors such as IL-1β, TNF-α, and MMPs and downregulated phosphorylation of MAPKs (ERK, JNK, and p38) in chondrocytes under eMS [44]. Cilengitide is a nonselective inhibitor of integrins that blocks the activation of both αVβ3 and αVβ5. In the present study, however, we modulated the integrin signaling with CYC, a specific inhibitor of αVβ3, to focus on the effects of this unique heterodimer on OA. Furthermore, in vivo studies showed that injection of CYC led to less cartilage damage and decreased expressions of OA-related inflammatory and degradative mediators in the medial weight-bearing area of the knee joint, indicating that inhibition of integrin αVβ3 delayed the development of OA, which we attributed this in part to the blocking of the integrin αVβ3-mediated MS transduction. Nevertheless, the role of αVβ3 in chondrocyte inflammation remains controversial, and some studies have suggested that αVβ3 plays a protective role in chondrocyte inflammation [22, 49, 50]. Lu et al. reported that IL-1β could induce chondrocyte inflammation, accompanied by a decrease in integrin αVβ3. Their study demonstrated that an increased expression of αVβ3 could inhibit inflammation in chondrocyte [50]. However, Attur et al. suggested that an inflammatory environment would lead to a high expression of integrin αVβ3 on the surface of chondrocyte, which could inhibit IL-1β transcription and attenuate inflammatory response [49]. Based on our findings, we believe that regulation of integrin αVβ3 on chondrocyte inflammation is an extraordinary complicated process, which needs further investigation.
Histological observation and qPCR analysis demonstrated that knee OA still progressed in the CYC-treated group at 12 weeks compared with 6 weeks postoperatively, suggesting that inhibition of integrin αVβ3 could not completely block the pathological process of OA, implying that other types of integrins in addition to αVβ3 and/or other signaling pathways may also participate in the pathogenesis of MS-induced OA. OA has always been considered a multifactorial disease with complex pathogenesis, and simply inhibiting one certain signaling pathway often fails to completely prevent the development of OA [34]. Our study provided preliminary evidence that integrin αVβ3, or at least partially, mediated eMS-induced OA via FAK-ERK-Runx2 signaling pathway.
One of the innovations of our study was to compare cartilage degradation in the medial and lateral weight-bearing areas of the same joint to illustrate the effect of different stress conditions on chondrocyte inflammation, and the results showed that integrin αVβ3 expression was positively correlated with the severity of OA. After excluding the effect of inflammatory mediators in the joint cavity on cartilage chondrocytes, we supposed that it is reasonable to attribute OA pathogenesis to MS. Additionally, we performed in vitro experiments using an electronically controlled MS-generating device of our own design, which has never been reported before. Due to the advantages of normal culture conditions and infinitely variable MS parameters, this device demonstrates the potential for future studies of cell biology in response to mechanical stimuli in vitro.
This study has multiple limitations. Firstly, we did not measure the exact value of the stress under the cover plate applied to the cells. The aim of this study was to elucidate the molecular mechanisms of chondrocyte inflammation under eMS instead of details about the exact values of stress. The effects of different types and strengths of MS on cells and the intracellular properties of mechanical transduction to MS still need further investigation. Secondly, we used CYC to inhibit integrin αVβ3 in animal studies, which ultimately delayed the development of OA. In vitro experiments confirmed that chondrocyte inflammation could be reduced after integrin αVβ3 inhibition under eMS. However, the animal study still has some shortcomings, as integrins have been reported to mediate the exacerbating effect of OA-related inflammatory mediators on chondrocyte inflammation [9, 16, 17, 51], attenuation of rat OA by pharmacological inhibition of integrin αVβ3 cannot be fully attributed to the blocking of high stress. Taken together, although a more complex situation exists in animal experiments, our data demonstrate that integrin αVβ3 plays an important role in eMS-induced OA. Future studies will be needed to clarify that the exact role of integrin αVβ3 signaling and to elucidate more specific molecular mechanisms in MS-induced OA will be needed to treat OA for the safe and effective treatment of OA.
In conclusion, we describe a signaling pathway linking eMS to cartilage degeneration. In the present study, we focused on the role of integrin αVβ3 in OA induced by eMS. We examined its expression in the medial and lateral parts of knee articular cartilage, its role in OA development in vitro and in vivo, and further downstream pathways linking eMS to cartilage inflammation. Upregulation of integrin αVβ3 induced by eMS in chondrocytes further activates downstream FAK-ERK signaling and alters chondrocyte metabolism to promote inflammation and degradation of ECM. Our findings suggest modulating the integrin αVβ3 signaling pathway as a potential disease-modifying therapy for OA.
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---
title: Antioxidant and hypoglycemic potential of phytogenic cerium oxide nanoparticles
authors:
- Maarij Khan
- Naveed Iqbal Raja
- Muhammad Javaid Asad
- Zia-ur-Rehman Mashwani
journal: Scientific Reports
year: 2023
pmcid: PMC10024689
doi: 10.1038/s41598-023-31498-8
license: CC BY 4.0
---
# Antioxidant and hypoglycemic potential of phytogenic cerium oxide nanoparticles
## Abstract
Plants provide humans with more than just food and shelter; they are also a major source of medications. The purpose of this research was to investigate the antioxidant and hypoglycemic potential of green synthesized CeONPs using *Mentha royleana* leaves extract. The morphological and physicochemical features of CeONPs were evaluated by UV–Visible spectrophotometry, Scanning Electron Microscopy, Energy Dispersive X-rays and Fourier-transform infrared spectrometry, Dynamic light scattering, Atomic Force Microscopy, Zeta Potential. The average size range of synthesized CeONPs diameter between 46 and 56 nm, crystalline in shape, with Polydispersity index value of 0.2 and subatomic particles mean diameter was 4.5–9.1 nm. The antioxidant capability of CeONPs was assessed using DPPH, ABTS+, hydrogen peroxide, hydroxyl radical scavenging, and reducing power tests. The hypoglycemic potential of CeONPs was investigated using alpha-amylase, alpha-glucosidase, glucose absorption by yeast cells, and antisucrase. The effective concentrations were 500 and 1000 µg/ml found good in suppressing radical species. To explore the hypoglycemic potential of CeONPs, alpha-amylase, alpha-glucosidase, glucose absorption by yeast cell, and antisucrase assays were performed. Glucose absorb by yeast cells assay was tested for three distinct glucose concentrations: 5 mmol/L, 10 mmol/L, and 25 mmol/L. Green synthesize CeONPs showed a dose-dependent response, higher concentrations of CeONPs imposed a stronger inhibitory impact on the catalytic site of enzymes. This study suggest that CeONPs could possibly binds to the charge carrying species and act as competitive inhibitor which slow down the enzyme substrate reaction and prevents enzymatic degradation. The study’s findings were outstanding, which bodes well for future medicinal applications of CeONPs.
## Introduction
The plants themselves are like little factories producing phytochemicals. In nature, there is a plethora of phytometabolites that are toxic-reducing1. It is possible to produce nanoparticles by reducing bulk salts into nanostructures utilizing the reducing power of plant extract. In order to reduce the toxicity of the chemical, the NPs are coated with a significant number of phytochemicals on their surfaces2. Many methods, including physical, chemical, and biological ones, are now in use for the synthesis of NPs. The physical and chemical methods are completely chemical based techniques, costly, and special equipment’s are required to control conditions for NPs preparation3. NPs can be synthesized using algae, fungus, bacteria, and plants or their derivatives. In the given study we prefer the plants to synthesize NPs because plants are more cost-effective, largely and easily available organisms, and less control conditions are required for storage, fewer chances of contamination4. The CeONPs have wide applications in engineering and biological sectors, such as high-temperature oxidation protection materials, potential pharmacological compound, solid-oxide fuel cells, solar cells, and catalytic materials, heterogeneous catalytic reactions. CeONPs are popular due to their unique surface charge chemistry and 3D position to interact with others molecules, compounds and living tissues. During industrial processes CeONPs are utilized as active chemical component for oxidative coupling of methane, automobile exhaust-gas treatments, and water–gas shift reaction. Recent researches reported that CeONPs display multi-enzymes properties including peroxidase, catalase, superoxide dismutase mimetic properties and emerged as a lucrative and fascinating material in biological fields like biomedicines, drug delivery, bioanalysis, and bioscaffolding5. ROS generation is a significant mechanism in immune system and become active in case of nay infection. The ROS generation play an important role in cell signaling but the excessive and misdirected generation of ROS responsible for inflammatory disorders such as cancer, diabetes, cardiovascular disorder, and neurodegenerative diseases. The antioxidant scavenging potential of CeONPs in natural environment mimic multi-enzymatic catalytic activities and rapidly bind with the free radicals in the natural environment. The electronic configuration of cerium is interconverting between Ce+3 and Ce+4. The surface grooves at CeONPs surface exhibit electropositive charge and efficiently interact with free charge carrying species and reduce oxidative stress inside the body6.
In the present study, CeONPs were synthesized by using *Mentha royleana* leaves extract. The *Mentha royleana* belongs to the family Lamiaceae and is a wild species of Mint. The *Mentha royleana* is commonly called wild mint and found in cold and hilly areas of Pakistan7,8. All Lamiaceae family members possess scented leaves because they store unique phytochemicals combinations. The phytochemicals possess magical medicinal capabilities9. In previous times mint leaves were used as carminative, antiulcer, antidiabetic, antiseptic, and anti-inflammatory agents10,11. The *Mentha royleana* phytochemicals profile consists of volatile components, essentials oils, hydrocarbons, and phenolic compounds, and flavonoids, metabolites. The *Mentha royleana* linalyl acetate and linalool volatile components, essentials oils profile including menthol, carvone, and menthone, linalool, metabolites composition of *Mentha royleana* menthol, menthone, 1,8-cineole, 3-octyl acetate, beta-caryophyllene, carvone,1,2-epoxyneomenthyl acetate, 3-octanol, 3-octanone, geraniol, decyl acetate, caryophyllene oxide, elemol, limonene, isomenthone, phenolic components of *Mentha royleana* included rosmarinic acid, chlorogenic, and caffeic acid. Flavonoids 5-hydroxy-6,7,3′,4′-tetramethoxyflavone, methylated luteolin-glucuronide, Eriodictyolglucopyranosyl-rhamnopyranoside, and luteolin-glucuronide.
Recent advances in the synthesis of NPs have increased their applications in a variety of fields, making nanotechnology an increasingly popular scientific topic. The NPs are small miniatures, are aggregates of atoms and molecules, and possess a high surface-to-volume ratio and high absorption power. The physicochemical properties differentiate NPs from bulk salt12. Cerium is a ductile metal that belongs to the lanthanide category in the periodic table. Cerium captured researchers’ attention due to its unique oxidation state13. The cerium salt exhibits an eccentric electronic configuration [Xe]4f15d16s2 which is found in few metals in the periodic table12. The distinguishing features make cerium metal enchanting and charming in various industries including pharmacy, agriculture, medical, electrical, and chemicals. The peculiar and iconic oxidation state makes CeONPs matchless with other metallic NPs. The CeONPs contain positively charge holes on their surface due to vacancy in d” and f” orbitals. Sometimes electrons in s” orbital jump from lower oxidation state to higher then CeONPs show CeO3 reduced form13,14. The electropositive charge surface magnetizes CeONPs antioxidant nature. The CeONPs bind free radicle species hydroxyl radical, hydrogen peroxide, superoxide anion radical, and singlet oxygen effectively and reduced oxidative stress. The antioxidant nature makes CeONPs an effective agent against oxidative stress induced diseases15–18.
Glucose is the primary source of metabolic energy approximately for all living organisms. Glycolysis is an essential cellular process for ATP (Adenosine triphosphate) generation (Ajila et al., 2008). Diabetes is caused by a chronically elevated blood sugar level and a decreased generation of insulin. Due to damage to pancreatic cells, insulin production decrease, and glucose is never absorbed inside the cells and continuously accumulates in the blood. The high blood glucose level deteriorates the other function of the body. in the given study CeONPs were checked to reduce oxidative stress and to reduce the activity of enzymes involved in the digestion of polysaccharides19. Diabetes is a world-leading metabolic disorder. Around $10.5\%$ of the global adult population suffered from diabetes in 2021—by the year 2045, this number is expected to rise to over $12\%$20. Diabetes is included among top ten leading causes of deaths15. Type-2-diabetes arise due to constant intake of high glucose or sometimes due to degradation of insulin producing pancreatic cells. Diabetes increase the chances of cardiovascular diseases, joint swelling, chronic kidney disorders, uncontrolled urination, and heart attack15. More than $95\%$ of diabetic patients are affected with type-2-diabetes and the remaining $5\%$ with type-1-diabetes (characterized by genetic abnormalities in insulin-producing cells)21. According to WHO (World Health Organization) previous data in 2014, $8.5\%$ of adults aged 18 and more had diabetes. In 2019, the death toll from diabetes rose to 1.5 million, accounting for $48\%$ of all diabetes-related deaths, with a median age of about 70 years20.
The goal of this study was the green synthesis of CeONPs by using *Mentha royleana* leaves extract which makes CeONPs highly biocompatible and biodegradable for biological systems. The mechanism of CeONPs synthesis involve three basic steps [1] the ionization of cerium nitrate hexahydrate salt in deionized water [2] the reduction of the cerium nitrate hexahydrate with plant extract and this is the stage at which cerium (Ce) separate from nitrate (NO3) 3) and finally CeONPs encapsulated and coated with various phytochemicals of the plant extract. The second goal was the exploration of antioxidant potential of green synthesis CeONPs in presence of different radical’s species. And the third most crucial target was the determination of the hypoglycemic potential of green synthesis CeONPs by inhibiting the catalytic activity of enzymes involved in the digestion of disaccharides and polysaccharides to lower the glucose release in the body. CeONPs act as a competitive inhibitor in the enzymatic reaction and perform the antienzyme role. This declines the enzyme–substrate reaction and prevents the enzymatic breakdown22. CeONPs have the possibility of functioning as substrate analogs and competitive inhibitors and CeONPs are unable to metabolize in enzyme-catalyzed reactions. The electropositive charge on the CeONPs surface promotes its activity to form a bond between the active site and CeONPs23. The results of all in-vitro activities flaunt that green synthesized CeONPs are good antioxidants and contain marvelous antienzymes potential. Green synthesis CeONPs can be used for in-vivo studies and pharmacological trials based on the results of this investigation.
## Preparation of leaves extract and CeONPs
The *Mentha royleana* was collected in Upper Dir (Latitude: 35° 12′ 20.99″ N Longitude: 71° 52′ 32.02″ E) District of Khyber Pakhtunkhwa, Pakistan. To make extract, 10 g of dried *Mentha royleana* leaves powder was mixed with 120 ml of deionized water and heated to 100 °C on a hot plate covered with aluminum foil for 2 h (Fig. 1A). The extract was filtered by Whatman no.1 filter paper. For the green synthesized CeONPs, 0.04 g salt of Ce (NO3)3.6H2O was dissolved with 400 ml of distilling water and left in the solution for 40 min on a hotplate at 150 °C24. The next step was to add the 75 ml of plant extract to the salt solution drop by drop. The solution was kept on a hotplate for 4 h. The synthesis of CeONPs was confirmed by the brown color precipitate in the solution. Remove the solution from the hotplate. Left the solution overnight, after overnight treatment transfer solution on the hotplate again for 2hand finally centrifuge CeONPs solution and washed 3 to 4 times with concentrated methanol. Transfer CeONPs in the oven at 64 °C for 12 h (Fig. 1C). The purified green synthesized CeONPs were calcined in a muffle furnace at 400 °C for 2 h (Fig. 1B)25.Figure 1(A) Aqueous extract of Mentha royleana, (B) Calcined CeONPs at 400 C0 for 2 h, (C) Schematic representation of the general process of green synthesized CeONPs synthesis.
## Morphological and optical characterization
To evaluate the size, shape and chemical nature of green synthesized CeONPs, different characterization technique has been used in this study26. The scanning electron microscopy (SEM) was carried out to collect micrographic images1. Energy depressive X-Rays were used to reveal the chemical composition of green synthesized CeONPs while chemical groups attached to the surface of CeONPs were analyze by using Fourier transform infrared spectroscopy (FTIR)12. The principle of Dynamic Light Scattering (DLS) is based on the Brownian movement of suspended particles in water. When particles in a solution are uniformly dispersed, they travel in all directions, and solute particles constantly collide with solvent molecules.
## Antioxidant DPPH (2,2-diphenyl-1-picrylhydrazyl) Assay
To determine the antioxidant capacity of phytofabricated CeONPs, the protocol developed by7 was used. Different concentration of CeONPs (62.5, 125, 250, 500, and 1000 µg/ml) were investigated for the antioxidant potential. The reaction mixture contained 1 ml of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 1 ml of different concentration of CeONPs. The mixture was shaken vigorously and incubated at room temperature for 30 min, and the absorbance was measured at 517 nm. Except for phytofabricated CeONPs, all compounds were present in the control sample, while ascorbic acid was used as a standard reagent. The final percentage of inhibition was calculated as follows:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Antioxidant Percentage }}\% \, = \, \left[{\left({{\text{A}}_{0} {-}{\text{ A}}_{{1}} } \right)/{\text{A}}_{0} } \right] \, \times { 1}00$$\end{document}Antioxidant Percentage%=A0-A1/A0×100where: A0 = Absorbance of control, A1 = Absorbance of treatments.
## ABTS (2,2'-azino-bis (3-ethylbenzothiazoline-6-sulphonic acid) antioxidant assay
The ABTS+ radical scavenging assay of green synthesized CeONPs was performed as per the stated protocol25. The reaction mixture was prepared by mixing 1 ml CeONPs of different concentrations (62.5, 125, 250, 500, 1000 µg/ml), and 1 ml of ABTS+ in K2S2O8. The methanol (1 ml) used as a negative control and ascorbic acid as a positive control. The absorbance was measured at 734 nm after 6 min the percentage of inhibition.
## Hydrogen peroxide antioxidant assay
To assess the hydrogen peroxide assay Yakoob et et al.7 method was used. The reaction mixture contained 100 µl of CeONPs, 300 µl phosphate buffer (50 mM, pH 7.4) and 600 µl hydrogen peroxide (2 mM H2O2 in phosphate buffer, 50 mM, pH, 7.4). The mixture was shaken vigorously for 10 min and the absorbance was measured at 230 nm by using spectrophotometer (Model U-2900). Ascorbic acid was used as standard and phosphate buffer as blank solution.
## Hydroxyl radical antioxidant assay
The hydroxyl radical assay was carried out in accordance with the published protocol of21. The reaction mixture contained of 750 µl phytofabricated CeONPs, 45 µl Sodium phosphate buffer (200 mM, pH 7.0), 15 µl Deoxyribose (10 mM), 150 µl FeSO4 and EDTA (10 mM), 15 µl H2O2 (10 mM) and 525 µl deionized water. The solution was kept on incubator for 4 h. The reaction was stopped by the addition of ($2.8\%$) 75 µl trichloroacetic acid and 75 µl TBA ($1\%$ in 50 mM NaOH). The mixture was then kept on boiling water bath for 10 min. The absorbance was recorded at 520 nm by using a UV–visible spectrophotometer (Model U-2900). Methanol was employed as a blank and ascorbic acid as a standard sample.
## Reducing power assay
Reducing power assay of green synthesized CeONPs were determined by method of Pavithra et al.27. About 100 µl of different concentration of green synthesized CeONPs (62.5, 125, 250, 500, 1000 µg/mL) were mixed with 2.5 mL of ($1\%$) potassium ferricyanide and 250 µl of 0.2 mol/L sodium phosphate buffer. The mixture was then incubated at 50 °C for 30 min. The reaction was terminated by adding 2.5 mL of $10\%$ trichloroacetic acid following centrifugation at 3000 rpm for 10 min. The upper pellet of the centrifuged sample was mixed with 0.5 mL of $0.1\%$ ferric chloride and 2.5 ml of de-ionized water. The absorbance of the sample was noted as 700 nm using spectrophotometer (Model U-2900).
## CeONPs effect on glucose uptake by yeast cells
To check glucose uptake, 5 µg of yeast was dissolved in 1 ml of deionized water, vortexed for 10–15 min, following centrifugation at 21,000 rpm for 5 min7. In deionized water, $10\%$ (v/v) concentration of yeast suspension was prepared. About 100 µl of different concentration of green synthesized CeONPs (62.5, 125, 250, 500, 1000 µg/mL) were mixed with 1 ml of glucose solution (5, 10, 25 mmol/L), followed by incubation at 37 °C for 10 min oC. To begin the reaction, 100 μl of yeast suspension was combined and vortexed before incubating at 37 °C for 60 min. The reaction mixture was centrifuged at 3800 rpm for 5 min, and the glucose concentration was measured at 540 nm. The following formula was used to calculate the percentage of glucose uptake by yeast cells:\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Hypoglycemic }}\% \, = \, \left({{\text{Abs control }}{-}{\text{ Abs sample }}/{\text{ Abs control}}} \right) \, \times { 1}00$$\end{document}Hypoglycemic%=Abs control-Abs sample/Abs control×100
## Alpha-amylase inhibition assay
The methodology developed by27 was used to calculate the Alpha-amylase inhibition assays. In DMSO, different CeONPs concentrations of 62.5, 125, 250, 500, and 1000 g/ml were prepared. The reaction mixture was prepared by dissolving 3 mg of Alpha-amylase in 20 mM phosphate solution (pH 6.7) containing 6.5 mM sodium chloride. About 250 µl from the reaction mixture were added to various concentrations of green synthesized CeONPs followed by incubation at 37 0C for 30 min. After pre incubation, 250 µl of $0.5\%$ starch solution in 20 mM phosphate buffer pH 6.9 was added. The reaction mixture was then incubated 37 °C for 15 min. The reaction was stopped with 2 ml of 96 mM 3, 5 dinitrosalicylic acid colour reagent. The micro plate was then incubated in a boiling water bath for 5 min and cooled to room temperature. The absorbance of the sample was measured at 540 nm using a UV–visible spectrophotometer (Model U-2900) with glucobay as the standard drug.
All animals, including humans, have alpha-amylase in their saliva. This enzyme is a vital component of the digestive system that is secreted by the salivary glands, with a working pH of 6.7–7.032,39. Alpha-amylase is an intestinal enzyme that breaks down polysaccharide -1,4 glycosidic bonds into monosaccharides for easy absorption into the blood39. Salivary amylase is the first step in the chemical digestion of food material. In the given assay suppression of the catalytic activity of alpha-amylase through CeONPs was checked to reduce the hydrolysis of polysaccharides, resulting low amount of glucose liberated into the blood system40. The metallic CeONPs bind the catalytic site of the enzyme and prevent the binding of substrate for the initiation of catalytic activity and lessen the broken-down of sugar compounds. Through the in-vitro procedure, green synthesized CeONPs was investigated against alpha-amylase at various concentrations ranging from 62.5 to 1000 µg/ml. In a dose-dependent manner, CeONPs have shown effectual and potent inhibition of $77.77\%$ against α-amylase and the incubation of solution in a boiling water bath turn reduced sugar into brown–red color. The glucobay is known as modish and a well-known medicine against hyperglycemia was used as a standard drug in a given exploratory study. The proportion of sugar decrease for glucobay was $71\%$ percent, which is much less than CeONPs (Fig. 6b). To strengthen our findings with a previously published paper, *Cassia angustifolia* leaves extract mediated CeONPs were used as an obstruct and trammel weapon against alpha- amylase, and the inhibitory value IC50 was found to be 89.96 µg/ml.44.Figure 6Image (a) show the inhibition percentage of sucrase enzyme and standard glucobay (b) reduction percentage of alpha-amylase and glucobay comparison (c) inhibitory activity of CeONPs and Glucobay against alpha-glucosidase. The statistical analysis value for level of significance was $p \leq 0.05$ for all anti-enzyme assays. A p-value lesser than 0.05 was consider significant along with the post hoc test.
## Alpha-glycosidase inhibition assay
Alpha-glucosidase assay was assisted by using the methodology of28. Approximately 5 mg of enzyme powder was dissolved in 1 ml of maleate buffer for preparation of serial dilutions. The enzymatic reaction was started by adding 200 μL of the substrate (p-nitrophenyl-a-D-glucopyranoside 2 mmol) following incubation at 37 °C for 30 min. The reaction was then stopped by standing the test tube in boiling water for 5 min. The reaction mixture was equipped with 100 µl of disodium hydrogen phosphate (0.1 M), and the absorbance of the liberated p-nitrophenol was recorded at 400 nm using a spectrophotometer (Model U-2900) while glucobay was utilized as a standard drug.
## Sucrase inhibition assay
In order to assess the sucrase inhibition assay, 10 µl of crude enzyme solution, 10 µl of different concentration of CeONPs (62.5, 125, 250, 500, 1000 µg/ml), were mixed with 100 µL of maleate buffer following incubation at 37 °C for 10 min. The reaction was started by the addition of substrate 100 µl (60 mmol of sucrose) and then incubation at 37 °C for 30 min in water bath. The reaction was stopped by immersing glass tubes in boiling water bath for 10 min. The released glucose was measured with a glucometer. The control was taken with containing CeONPs while glucobay was utilized as the standard drug21.
Every day, hundreds of enzymes perform numerous roles in the human body. Each enzyme plays a unique role in managing cellular metabolism. Sucrase is a key enzyme in the hydrolysis of sucrose (table sugar), a significant component of our daily diet45. The sucrase break alpha 1–4 glycosidic bond and release simple glucose and fructose molecules in the body which are easily absorbed through microspores of microvilli32. The sucrase is secreted from the endpoints or head of the microvilli of the epithelium in the small intestine during food digestion. The breakdown of disaccharides to monosaccharides and absorption into blood rise blood glucose levels. This causes metabolic difficulties in the form of hyperglycemia7. The inhibition of sucrase by green synthesized CeONPs results in a reduction of sucrose catalysis, which indirectly lowers blood glucose levels43. At higher doses of 1000 µg/ml CeONPs, the inhibitory potential of green synthesized CeONPs was $55\%$ and that of standard Glucobay was $67\%$ respectively (Fig. 6a). CeONPs' inhibitory power was seen to grip the enzyme active site and cause it to lose its catalytic function. Several studies have been reported for NPs exhibiting sucrase activity, but unfortunately, no such reports were published on the CeONPs sucrase activity. Different species i.e. Azadirachta indica, Calotropis procera, Cephalandra indica, *Syzygium jambolanum* were combined to use for the synthesis of gold and silver and gold NPs and these NPs were checked to reduce the catalytic power of sucrase enzyme to decline the hydrolysis of sucrose. The results obtained were noticeably exhibiting a record inhibition of sucrase activity48.
## Statistical analysis
All experiments were done with three biological replicates. The data were statistically processed using SPSS 20 for ANOVA, and the mean significant differences were separated using Duncan's Multiple Range Test (DMRT).
## Methods
We confirmed that all methods were performed in accordance with the relevant guidelines and regulations.
## Research involving plants
The fresh leaf material used for the greens synthesis of NPs in the study were purchased from the National Agriculture Research Center Islamabad with prior permission to use for research purposes.
## Characterization of CeONPs
In order to confirm the synthesis and the catalytic activity of CeONPs, some important parameters are necessary to check like average particles size, radius, chemical nature, and surface properties of CeONPs. The SEM image revealed that CeONPs average particle size 46 nm to 56 nm (Fig. 2A). A considerable size stub was chosen for the SEM, and double-sided tape conductive carbon tape was adhered to the stub after dispersing 1 µg of CeONPs in methanol and sonication for 30 min. Then attached a substrate on a double-sided conductive carbon tape and placed a drop of CeONPs on the substrate surface and let them dry. The store stub was used to explore the size of CeONPs at various magnifications12. UV–Visible results confirmed the presence of CeONPs in the sample showing a wide peak at 337 nm, a surface plasma resonance (SPR) band characteristic of CeONPs was found (Fig. 2B). The EDX results authenticate the presence of cerium in the sample. The peaks of (Ce) attest to the presence of cerium salt that was utilized for CeONPs green synthesis (Fig. 2C)12. For the DLS CeONPs were suspended in Milli-Q ultrapure water and fabricated using a probe sonicater (Fisher Scientific Model FB120). The Polydispersity index value measured was 0.2 and the Polydispersity index width was 146 d. nm indicating the homogeneity of sampled solution. The DLS image is generated mostly by the Brownian movement of the solute in the solution. The uniformity of the suspended particles is required for pharmaceutical drug material applications. ( Fig. 2D)7. The FTIR peaks and stretching bands, which are common between three CeONPs, –CH (Alkene) at 2962 cm−1 and –CH (Alkyl) at 3404.47 cm−1. The wavelength 2850.88 and 2929.97 cm−1 indicated the presence of alkene (C = C) group. 1558.54 cm− 1 indicated the presence of Alpha–beta unsaturated ketone group C = C, O–H at 1384.94 cm−1 phenol and C–O at 1261.48 cm−1 indicate Alkyl-aryl-ether and C–O at 1103.32 cm−1 indicate the presence of aliphatic group C–O. 1028.09 cm−1 depicted the presence of Anhydride CO–O–CO. 860.28, 802.41 cm−1 depicted the occupation of C = C Alkene group. 470.65 cm−1 indicated the habitation of C-I bending belong to Halo compound. The FTIR spectral wavelength for CeONPs was ranged between 400 and 4000 nm (Fig. 2E)12,12. Zeta potential is a test to evaluate the electric charge on the NPs surface. The net charge on NPs surface is screened through the abundance of ions carrying the opposite charge near the NPs body. The loop of these opposite electric charges float along with NPs. Actually the zeta potential is a test to identify the difference between the charge in bulk fluid in which NPs are suspended and the charge on the loop of ions that encirculate the opposite charge NPs surface. The particles containing positive charge always bind with negative charge ions. Higher the electrostatic repulsion increase the stability of NPs in medium. Zeta potential was measured by using two gold electrodes that were added in the deionized water contained the dispersed NPs. When the voltage was applied the NPs always move towards the electrode possess the opposite charge. A Doppler method is used to evaluate the NPs velocity (speed) as function of voltage. A laser beam passes through the cell (contain solution of dispersed NPs), and laser pass through the NPs solution, the intensity of scattered light fluctuates at a frequency proportional to the NPs velocity (speed). The NPs velocity (speed) can measure at different voltages, and this data is used to calculate the zeta potential. The zeta potential of the NPs measures around or less than − 28 mV, confirm that the particles are agglomerated Fig. 2F. Various techniques are currently used to characterize NPs size. The calculation of exact NPs size is a necessary step to determine NPs physical interactions with biological material. AFM is consider an attractive tool for the characterization of NPs [2]. The AFM resolution power is very high reaches about 0.1 nm, related with sample thickness, taping or non-contacting mode and applied voltage. AFM is a 3D digital way of measuring the NPs magnitude. CeO2NPsM.R. magnitude was measured through AFM and average particles size was 4.5 to 9.1 nm, which was an efficient size to penetrate inside the organelles Fig. 2G.Figure 2(A) SEM image of green synthesized CeONPs with 46 to 56 nm size (B) UV–Visible spectrometry image of green synthesized CeONPs peak at 260 nm (C) EDX image of green synthesized CeONPs confirm the presence of Cerium (D) DLS image of green synthesized CeONPs which show size less the 100 d.nm (E) The peaks at FTIR image of green synthesized CeONPs express the presence of compounds on NPs surface. ( F) Atomic Force Microscopy observe nanoparticles magnitude (size) of CeONPs M.R 4.5–9.1. ( G) Zeta potential measurement of the effective electric charge on the NPs surface and the electro mobility was -3.293 mV depicts the positive charge at CeONPs M.R.
## DPPH Assay
The DPPH antioxidant assay was used to assess the antioxidant capacity of green synthesized CeONPs against free radicals29. The imbalance between ROS formation and quenching by the endogenous antioxidant system disrupts physiological processes and is responsible for mutations in macromolecules such as DNA, RNA, proteins, and lipids, as well as accelerating cellular stress in cell signaling pathways30–32. ROS production is accelerated by the presence of high glucose concentration through various mechanisms including glucose auto-oxidation, oxidative phosphorylation, protein kinase C activation, glycation (non-enzymatic bonding of free reduced sugar with free amino acids like DNA, RNA) hexosamine metabolism, methylglyoxal formation (Fig. 3). Pancreas is crucial for the production of enzymes involved in the digestion of food and maneuver blood glucose level. Oxidation of pancreatic cells proceeds destabilization of the hormonal content of the pancreas and the result is high blood glucose level (hyperglycemia). In current experimental work, *Mentha royleana* mediated CeONPs were found effective in decrement of ROS levels. The green synthesized CeONPs show the maximum antioxidant activity of $31\%$ and ascorbic acid of $46\%$. The inhibition percentage of green synthesized CeONPs is quite close to ascorbic acid (Fig. 4a). The medicinal plants deposit various essential metabolites which plant used against various biotic and abiotic stresses32. According to one study, the antioxidant potential of green synthesized CeONPs in DPPH solution rose as the concentration of CeONPs in the solution increased33. Another study discovered that the antioxidant potential of green synthesized CeONPs is greater than that of butylated hydroxytoluene. ( BHT)24.Figure 3The antioxidant mechanisms of green synthesized CeONPs on numerous organelles within the cell are represented schematically. The CeONPs easily cross plasma membrane due to nano-size and binding with organelles directly or indirectly. The CeONPs possess efficient anti-oxidative strength. The direct and indirect reaction of CeONPs due to their electropositive nature binds free charge carrying species in the environment53. In this way, it directly integrates with free radical species. In the indirect response when one organelle produces ROS and its products are transferred to another organelle and reach another organelle nanosize CeONPs bind these ROS before arising disturbance. In this way, CeONPs are helpful to reduce direct and indirect oxidative stress54.Figure 4(a) Figure explain the DPPH radicals inhibition percentage of CeONPs was little low compared with standard ascorbic acid (b) Figure demonstrate the ABTS+ radicals scavenging potential of CeONPs show high antioxidant activity compared with ascorbic acid (c) Reducing power of CeONPs was slightly low form ascorbic acid (d) Hydroxyl radicles cleaving potential of CeONPs was higher than ascorbic acid (e) Figure exhibit the CeONPs quenching power was against hydrogen peroxide radicles in comparison with ascorbic acid The value of level of significance was $p \leq 0.05$ for all antioxidants activities. A p-value lesser than 0.05 was consider significant along with the post hoc test.
## ABTS antioxidant assay
The radical scavenging capacity of green synthesized CeONPs underpins their ABTS+ scavenging activity. The ABTS+ assay assesses antioxidants' ability to scavenge oxidative species produced by ABTS +. The ABTS+ radicals are formed by a strong reaction between the ABTS+ salt and the highly oxidizing agent potassium persulfate. A decrease in absorbance is observed as antioxidant scavenging potential is increased. The blue/green ABTS+ solution turned pale yellow and then colorless. The IC50 value for ascorbic acid was 5.39 g/ml and the percentage was $49.7\%$, while the IC50 value for *Mentha royleana* was 5.57 g/ml, and the percentage scavenging potential of *Mentha royleana* mediated CeONPs was $46.7\%$, which is excellent for commercial applications (Fig. 4b). In a dose-dependent manner, the higher percentage of green synthesized CeONPs reduced the ABTS+ radical's species25. Other data suggest that CeONPs scavenge ROS preferentially from normal cells and protect normal cells against reactive oxygen species34. The findings of the given activity revealed that Mentha royleana, a wild mint species, has unique and useful phytochemicals with higher antioxidant potential than others.
## Hydrogen peroxide scavenging assay
Hydrogen peroxide generates hydroxyl radicals that cause lipid peroxidation in exposed cells, resulting in DNA damage and cell death. Acute exposure to hydrogen peroxide is hazardous to one's health. The hydrogen peroxide irritates the skin at the point of contact. The mitochondria are organelles that are produced by a specific enzyme that governs cell development and death. However, the enzyme that decomposes hydrogen peroxide before it converts into hydroxyl radicals is already present in the cell35. Literally, hydrogen peroxide synthesis in the body cells is a pursuit to protect the body from even more dangerous substances like superoxide radicles but when a living body facing disease conditions, the function of these enzymes declines then the hydrogen peroxide level increase from its limits, and scavenging potential is reduced and cause lipid peroxidation. In the current study, the Mentha royleana-mediated CeONPs have been discovered to be particularly effective at lowering the level of hydrogen peroxide in cells. The green synthesized CeONPs scavenging potential was $8\%$ and (IC50 28.89), indicating weak activity when compared to the ascorbic acid antioxidant potential of $30\%$ and (IC50 6.98), which is greater than CeONPs (Fig. 4e). Another study compared benzoate derivatives to cinnamate analogs (organic nature) and discovered that benzoate derivatives were more powerful at quenching hydrogen peroxide reactive species36.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{H}}_{{2}} {\text{O}}_{{2}} {\text{scavenging activity percentage }} = \, \left[{\left({{\text{A}}_{0} {-}{\text{ A}}_{{1}} } \right)/{\text{A}}_{0} } \right] \, \times { 1}00$$\end{document}H2O2scavenging activity percentage=A0-A1/A0×100where: A0 = Absorbance of control, A1 = Absorbance of sample.
## Hydroxyl radical (OH.) scavenging activity
The ability of oxyhemoglobin and methemoglobin to produce hydroxyl radicals (OH.) from hydrogen peroxides H2O2 has been studied using deoxyribose and phenylalanine as OH "detector molecules"37. The high level of H2O2 breakdown methaemoglobin, and liberate iron ions that react with H2O2 to form a species that appears to be OH38. Radicles without oxygen possess one or more unpaired electrons, which makes them unstable. Hydroxyl radicals and peroxynitrite radicals are the most damaging ROS in any biological system. The breakdown of H2O2 produces primarily hydroxyl radicals37. Another troubling element of the hydroxyl radical is that whereas ROS species such as superoxide can be quenched by superoxide dismutase, no enzyme exists in living systems to quench the hydroxyl radical37. In living organisms there are two major ROS species, superoxide radicals and hydroxyl radicals continuously produce through a process of reduction of oxygen to water35. Moreover, hydroxyl radicals are especially harmful because of their ability to reduce disulfide bonds in proteins, and destroy fibrinogen, as a result, unfolding and unnatural refolding into the abnormal configuration of proteins39. Hydroxyl radicles generate in an in-vivo environment due to hypoxic conditions40. Most notably the hydroxyl radicals are produced from the decomposition of hydrogen peroxides (ROOH) is one of the most reactive forms of oxygen radicle. Its half-life in the biological system is about 1 ns and shows reaction with organic molecules at rates that approach diffusion-limited, with rate constants of 109–1010 M−1 s−1 16. The green synthesized *Mentha royleana* mediated CeONPs as shown $50\%$ degradation potential towards hydroxyl radicle and for ascorbic acid is $53\%$ at the highest concentration 1000 µg/ml (Fig. 4d). As the concentration of CeONPs increases gradually degradation potential of nanomaterial was increased. Similarly, in another study researcher tested a model plant *Aradopsis thaliana* L. Heynh. that lack enzymatic pathways for scavenging hydroxyl radicals in case of salinity stress, and applied only CeONPs to check their scavenging potential against hydroxyl radical. According to the study's findings, CeONPs have increased K+ retention in leaf mesophyll, which improves plant photosynthetic performance and biomass resistance to environmental stress41.
## Reducing power
The CeONPs contain transition oxidation state between Ce+4 and Ce+342. The CeONPs immediately accommodate and adapt electronic configuration according to its in available medium42. CeONPs exhibits oxygen vacancies on their surface, these arise through the loss of oxygen or its electrons on their surface. It shows alternation between CeO2 and CeO2x amid redox reactions34). Green synthesized CeONPs mimic the capabilities of various antioxidant enzymes like as catalase, superoxide oxidase, and peroxide, which initiate a variety of biological consequences such as toxic towards intracellular ROS. An Interesting feature of CeONPs play both roles as oxidation and reduction catalyst, these structural properties grant CeONPs regenerative properties but it is related with the medium in which the reaction occur. Due to efficient reducing properties, the presence of any radicle species in the environment CeONPs quenches them quickly. In the particular experimental studies, green synthesized CeONPs were used as a reducing agent the results obtained for CeONPs were $21.19\%$ scavenging potential and $37.71\%$ for Ascorbic acid respectively (Fig. 4c). The findings obtained from CeONPs were least near ascorbic acid but were effective in quenching radicles as CeONPs concentrations increased. Similar results were observed for another study in which CeONPs were used as reducing material within the FRAP level following the administration of CeONPs and reduction potential increased as the treatments of CeONPs were increased43. Another study explains the antioxidant power of CeONPs to reduce the toxicity of Malathion (which is a common pesticide in agriculture) which is tested on rays and drug is responsible to cause testicular toxicity and lower sperm count. CeONPs were found to have a lower toxic and antioxidant effect, as well as improved protection against male rat infertility14.
## Glucose uptake by yeast cells
Insulin's role is to absorb glucose from the blood and store it as glycogen in muscle and liver cells. During glycolysis, glucose is broken down to generate ATP (cellular work currency), and the residual glucose is transformed into fat to act as energy storage. In a present assay The oxidation of pancreatic cells and higher glucose uptake through diet raise the sugar level in the blood and lower insulin content fail to absorb glucose into liver cells and result in hyperglycemia The oxidation of pancreatic cells and increased glucose uptake via meal elevate blood sugar levels, but reduced insulin content fails to absorb glucose into liver cells, resulting in hyperglycemia2,43. The various types of medications are currently available on the market for hyperglycemia but the continuous increase in diabetic patients encourages researchers to explore new ways to combat diabetes. The green synthesized CeONPs are used to portrayal the insulin mechanism by binding the additional glucose from the available medium and fetching or moving the glucose into the yeast cells. The metformin was standard medicine, which is emphatically well–known for hyperglycemia. Three different concentrations of glucose were used (5, 10, 25 mmol/L) and at 1000 µg/ml for 25 mmol/L of glucose solution, *Mentha royleana* mediated CeONPs have shown $76\%$ absorption of glucose. For 5 and 10 mmol/L of glucose concentration CeONPs has shown $72\%$ glucose absorbance in yeast cells (Fig. 5b). Metformin was used as a standard drug famous in the market against hyperglycemia (Fig. 5a). The glucose absorption percentage for the standard was $74\%$, $80\%$, and $85\%$ for 5, 10, 25 mmol/L gradually. As the concentrations of glucose in the solution were increased, the affinity of CeONPs for glucose was also increased. Overall, M. royleana-mediated CeONPs have a high affinity for glucose molecules, which is similar to metformin. Similarly, CeONPs derived from *Stachys japonica* Miq. leaf extract was discovered to be useful in regulating glucose metabolism as well as its underlying molecular mechanism for beneficial therapy against hyperglycemia12,12.Figure 5(a) explains the standard metformin effects on glucose absorption. The graph (b) explain the graphical percentage of CeONPs with three different concentrations 5, 10, and 25 mmol/L and The level of significance was $p \leq 0.05$ for the alpha-amylase assay. p-value lesser than 0.05 was considered significant along with the post hoc test.
## Alpha-glucosidase Inhibition Assay
The alpha-glucosidases use the hydrolysis of dietary cellulose, carbohydrates, and starch to convert polysaccharides into monosaccharides, which are then converted into usable glucose45. Glucose absorbs through the small intestine into blood vessels46. Alpha-glucosidases bring about the hydrolysis of terminal, alpha-1,4-linkages of various carbohydrate residues (Fig. 7). The alpha-glucosidases also called maltases. Alpha-glucosidase produce in the small intestine and positioned of the microvilli of small intestine and catalyzes the alpha (1 → 4) bonds of polysaccharides and liberates monosaccharides I environment46. In the current work, green synthesized CeONPs were utilized as an alpha-glucosidase inhibitor to inhibit enzyme activity and minimize polysaccharide hydrolysis, which indirectly regulated blood sugar levels. According to the results of the present research work, green synthesized CeONPs have shown $68.6\%$ inhibition of alpha-glucosidase, and the standard glucobay a market famous drug for hypoglycemia has shown $72.6\%$ inhibition at 1000 µg/ml concentration. For the 500 µg/ml concentration, enzyme inhibition was $82\%$ percent for green synthesized CeONPs and $42\%$ for glucobay, which is effective in comparison to the standard (Fig. 6c). The inhibitory percentage results of green synthesized CeO2NPs is very close to the output of standard medicine. Few similar examples are proposed to support our research objectives, such as *Stachys japonica* leaves extract manufactured CeONPs were investigated for their antioxidant and antidiabetic activities. Even at low concentrations, CeONPs showed significant inhibition of alpha-glucosidase12. Similarly *Cassia angustifolia* (Senna) mediated green synthesized CeONPs parade visible reduction in alpha-glucosidase catalyzing activity. The findings are plausible and well-supported for commercial uses of green synthesized CeONPs in the pharmaceutical industry44. In another study *Crinum viviparum* flower extract based δ-Bi2O3 were tested against New Delhi metallo-β-lactamase 1 (NDM-1) enzyme that is important because compel and enhance bacterial resistant against broad range of beta-lactam antibiotics δ-Bi2O3 NPs were tested as (NDM-1) enzyme inhibitor, and molecular docking study revealed that δ-Bi2O3 NPs showed good interaction with various amino acids residues and exhibit good hydrogen bonding47.Figure 7Schematic representation of inhibition potential of CeONPs against alpha-glucosidases. The alpha-glucosidase is naturally present in the microvilli in the small intestine and accelerates the breakdown of polysaccharides. CeONPs reduce the formation of the enzyme–substrate complex by covering the active site of the enzyme. The reduction of metabolic activity declines the release of glucose in the blood12,12.
## Conclusion
This study investigates the biocompatibility, bioavailability, and biodegradability of non-toxic CeONPs in the presence of oxidative stress and hypoglycemia. Various characterization data show that CeONPs are 35 nm in size, circular in shape, and have a PDI of 0.2, making the CeONPs more suitable for biological applications. This research touches on both the fields of nanotechnology and phytochemistry. The applications of *Mentha royleana* fabricated green synthesized CeONPs showed maximum antioxidant and hypoglycemic potential. The response of CeONPs is dose-dependent, with 500 µg/ml and 1000 µg/ml being proven to be effective doses. The findings of the hydrogen peroxide scavenging assay and the hydroxyl radical scavenging assay were higher than those of ascorbic acid and DPPH, whereas the results of ABTS+ and reducing power were closer to the standard. CeONPs inhibited alpha-amylase more effectively than Glucobay in antienzyme assay. The inhibitory potential of alpha-glucosidase, antisucrase, and glucose absorption by yeast cells was comparable to that of glucobay and metformin. Mentha royleana phyto-constituents may play a synergistic role in improving antioxidant and hypoglycemic characteristics CeONPs. In-vitro assay results explain the anti-diabetic and antioxidant effect of green synthesized CeONPs for future in-vivo and clinical investigations, as well as drug manufacturing49–52.
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|
---
title: Urinary exosomal miRNA-663a shows variable expression in diabetic kidney disease
patients with or without proteinuria
authors:
- Nisha Sinha
- Veena Puri
- Vivek Kumar
- Ritambhra Nada
- Ashu Rastogi
- Vivekanand Jha
- Sanjeev Puri
journal: Scientific Reports
year: 2023
pmcid: PMC10024703
doi: 10.1038/s41598-022-26558-4
license: CC BY 4.0
---
# Urinary exosomal miRNA-663a shows variable expression in diabetic kidney disease patients with or without proteinuria
## Abstract
Heterogeneity in the Diabetic Kidney Disease (DKD) diagnosis makes its rational therapeutics challenging. Although albuminuria characterizes DKD, reports also indicate its prevalence among non-proteinuric. Recent understanding of disease progression has thus inclined the focus on proximal tubular cell damage besides the glomeruli. A non-invasive approach exploiting exosomal miRNA derived from human kidney proximal tubular cell line was, hence, targeted. Upon miRNA profiling, three miRNAs, namely, hsa-miR-155-5p, hsa-miR-28-3p, and hsa-miR-425-5p were found to be significantly upregulated, while hsa-miR-663a was downregulated under diabetic conditions. Among these, hsa-miR-663a downregulation was more pronounced in non-proteinuric than proteinuric DKD subjects and was thus selected for the bioinformatics study. Ingenuity Pathway Analysis (IPA) narrowed on to IL-8 signaling and inflammatory response as the most enriched ‘canonical pathway’ and ‘disease pathway’ respectively, during DKD. Further, the putative gene network generated from these enriched pathways revealed experimentally induced diabetes, renal tubular injury, and decreased levels of albumin as part of mapping under ‘disease and function’. Genes target predictions and annotations by IPA reiterated miR-663a’s role in the pathogenesis of DKD following tubular injury. Overall, the observations might offer an indirect reflection of the underlying mechanism between patients who develop proteinuria and non-proteinuria.
## Introduction
The frequency of type 2 diabetes mellitus (T2DM) is rising at an alarming rate. It is predicted by the Institute for Alternative Futures, that the number of diabetics will increase by $54\%$ between 2015 and 20301. Almost one-third of type 2 diabetics end up suffering from diabetic kidney disease (DKD) which is characterized by progressively increasing proteinuria followed by a gradual decline in glomerular filtration rate (GFR) and is often detectable in the advanced stages of DKD. A proportion of patients also show GFR decline without proteinuria, the so-called non-proteinuric DKD (NPDKD)2. In an Italian multicentric Renal Insufficiency And Cardiovascular Events study, $56.6\%$ of all the Type 2 diabetics with renal impairment were normoalbuminuric, $30.8\%$ were moderately albuminuric and $12.6\%$ were severely increased albuminuric3. Moreover, possible effects of other factors including cholesterol emboli, interstitial fibrosis, etc. in renal function loss cannot be ruled out. Further, at times, kidney function deterioration precedes proteinuria4. Hence, a need exists for the identification of biomarkers reflecting the early effects of disease during the development and progression.
MicroRNAs (miRNAs) are non-coding RNAs that translationally repress or degrade RNA by binding to the 3’ untranslated region of the mRNA. Their presence in the urine makes them an ideal candidate for diagnosing DKD early5. There are several kinds of extracellular vesicles (EVs) in urine, with exosomes being the best characterized6. Exosomes are 20–100 nm-sized vesicles formed by the fusion of multivesicular bodies with the plasma membrane and carry protein and/or nucleic acid cargo of renal dysfunction or structural injury7. Since exosomal miRNAs are protected from endogenous RNase, their stability is superior to free urinary miRNAs6. Moreover, according to Kamal et al. exosomal miRNAs are most commonly used for biomarker studies over non-exosomal miRNAs8. Growing evidence suggested the importance of urinary exosomes (UEs) in DKD pathogenesis9–11. Though these have been explored as a biomarker for DKD, a puzzle still exists to differentiate non-proteinuric from proteinuric DKD. Nowak et al. study showed that the progression of DKD in the absence of proteinuria was associated with renal tubular injury, as seen in the cases of non-proteinuric patients who have more significant tubulointerstitial fibrosis and atrophy than patients with proteinuria. His study also concluded that the proximal tubular injury developed early in DKD and contributed to the progression of the disease12.
In this study, therefore, we first identified the dysregulated miRNAs from proximal tubular cells (PTC)-derived exosomes involved in DKD by miRNA profiling. The targeted miRNAs were then validated under in vitro conditions followed by their expression analysis in the UEs of DKD subjects with varying degrees of albuminuria. The miR-663a was the only miRNA to be differentially expressed between non-proteinuric and proteinuric DKD. The target genes retrieved by various bioinformatics tools for enriched canonical pathways were analyzed by Ingenuity Pathway Analysis (IPA) software. *The* genes from these pathways were constructed into a topological network, which when overlaid with the ‘diseases and functions’ module revealed endocrine system disorders, renal tubular injury, and decreased levels of albumin as relevant functions during DKD. Overall, these findings identified miR-663a as a novel molecule involved in segregating proteinuric from non-proteinuric DKD.
## Study definitions, setting, and subjects
Patients attending the outpatient clinics in the Department of Nephrology and Endocrinology at the Postgraduate Institute of Medical Education and Research (PGIMER), Chandigarh, India were screened for enrollment after due approval from PGIMER (PGIMER; PGI/IEC/$\frac{2014}{517}$). All the methods and the experiments were carried out in accordance with relevant guidelines and regulations. T2DM was defined as the diagnosis of Type 2 diabetes mellitus as per the prevailing American Diabetes Association (ADA) definition. Chronic kidney disease (CKD) was defined as either CKD Epidemiology Collaboration (CKD-EPI) estimated GFR (eGFR) < 60 ml/min/1.73m2 or 24-h urine protein excretion ≥ 150 mg for at least 3 months. In patients with T2DM and CKD, diagnosis of DKD was based on the attending clinician’s judgment and exclusion of non-diabetic causes of CKD. Proteinuric DKD in patients with diabetes mellitus and CKD was defined as CKD-EPI eGFR < 60 ml/min/1.73m2 and 24-h urine protein excretion of ≥ 500 mg. Non-proteinuric DKD in patients with diabetes mellitus and CKD was defined as CKD-EPI eGFR < 60 ml/min/1.73m2 and 24-h urine protein excretion of < 500 mg. Prospective living kidney donors were defined as healthy if they had eGFR ≥ 60 ml/min/1.73m2, 24-h urine protein excretion < 150 mg, and no co-morbidities based on pre-transplant screening protocol. This protocol included clinical details, blood, and urine investigations, and imaging of the kidneys and urinary tract. Written informed consent was obtained from the recruited patients and healthy volunteers after explaining the details about the aim of the study, the extent of their involvement, the benefits and risks involved, and freedom of choice of participation in the study.
The inclusion criteria included age between 18 and 65 years, non-smoking status, and belonging to one of the following four categories: 1) patients with type 2 diabetes mellitus but without chronic kidney disease (T2DM), 2) patients with type 2 diabetes mellitus and proteinuric DKD (PDKD), 3) patients with type 2 diabetes mellitus and non-proteinuric DKD (NPDKD), and 4) healthy prospective living kidney donors (HC).
Exclusion criteria were the presence of any micro- or macrovascular complication in patients with the diagnosis of diabetes mellitus but without CKD, history or diagnosis of nephrolithiasis or urinary tract stones or any other kidney disease, present or past urinary tract infections, present or past diagnosis of malignancy, or life expectancy < 12 months or unstable clinical course over last 3 months as judged by treating physician.
Each participant's demographic and clinical details were recorded. Blood and urine biochemistry measurements were done on the AU5800 Clinical Chemistry Analyzer (Beckman Coulter, USA). Serum creatinine was measured by Modified Jaffe’s assay traceable to IDMS standards (total precision < $6\%$ coefficient of variation). The urine protein was determined by pyrogallol red dye-based colorimetric assay (total precision < $5\%$ coefficient of variation).
## Exosome isolation
Exosome isolation was divided into two phases- in vitro (from human kidney PTC, HK-2 cells) and in vivo (from human urine). The most common way to isolate exosomes from samples is to centrifuge them at high speed for at least 70 min at 100,000 to 200,000 × g13. Therefore, based on the studies performed by most of the researchers the following low and high-speed centrifugation was carried out for isolating exosomes.
HK-2 cells were procured from the American Type Culture Collection (ATCC, CRL 2190). HK-2 cells were cultivated in Keratinocyte Serum-Free Media (Gibco, USA) supplemented with epidermal growth factor (5 ng/ml) (Gibco, USA) and bovine pituitary extract (0.05 mg/ml) (Gibco, USA) as growth factors followed by $1\%$ penicillin–streptomycin (MP Biomedicals, USA). They were treated with low glucose (LG; 5 mM) and high glucose (HG; 30 mM) for 96 h. Exosomes were isolated from these cells as described by Valadi et al.14 with minor modification. Briefly, the conditioned media (90 ml) was passed through two series of centrifugation-500 × g for 10 min followed by 16,500 × g for 30 min at 4 °C. The supernatant was centrifuged at high speed at 1,20,000 × g for 70 min at 4 °C. The pellet dissolved in PBS was filtered through a 0.1 µm syringe filter (EMD Millipore, USA) and washed with PBS at 1,20, 000 × g for 70 min at 4 °C. The pellet was resuspended in PBS with a protease inhibitor cocktail (PI) (1.67 ml of 100 mM sodium azide, 2.5 ml of 10 mM Phenylmethylsulphonyl fluoride (PMSF), 50 µl of 1 mM leupeptin for 50 ml of fluid) and stored at − 80 °C until use.
For the in vivo study, the second-morning mid-stream urine sample (100 ml) was collected from all the subjects in a sterile urine container with a PI cocktail and stored at − 80 °C until further use. To reduce variability between subjects based on diurnal patterns, second-morning urine collected from large bladder residues was used instead of first-morning urine15. Exosomes were isolated from urine as described by Patricia et al.16. Briefly, on the removal of urine samples from − 80 °C, they were vortexed extensively followed by centrifugation at 17,000 × g for 15 min at 4 °C. The supernatant was collected, and the pellet was resuspended in an isolation solution (250 mM sucrose, 10 mM Tri-ethanolamine, pH-7.6) following treatment with dithiothreitol (200 mg/ml, Sigma-Aldrich, USA). The resuspended pellet was again centrifuged in the same condition as mentioned above. All the supernatants were pooled and ultracentrifuged at 2,00,000 × g for 70 min at 4 °C. The pellet dissolved in PBS was filtered via a 0.1 µm syringe filter and washed with PBS at 2,00,000 × g for 70 min at 4 °C. The pellet was resuspended in PBS with PI and stored at − 80 °C until use.
## Flow cytometry analysis
Exosomes were incubated overnight at 4 °C with aldehyde sulfate latex beads (Invitrogen, USA) coated with purified anti-human CD63 (2 × 105 CD63 coated beads/sample) (# 312002; BioLegend, USA). This complex was probed with PE anti-human CD81 (# 349505; BioLegend, USA)17, and data were acquired using FACSAria II (BD, Biosciences) followed by the data analysis in the FlowJo v10.8.1 trial version (Ashland, OR). A more detailed description of the coating of CD63 to the beads is provided in Supplementary Methods.
## Transmission electron microscopy (TEM)
Exosomes purified by differential centrifugation were loaded on Nickle carbon grids (TAAB Laboratories Equipment Ltd, England), negatively stained with $1\%$ phosphotungstic acid, and viewed on Hitachi H7500-120 kV Electron Microscope18.
## Western blot
The exosomal and cellular samples were resuspended in RIPA Buffer (Sigma-Aldrich, USA) with PI and vortexed every 2 min for 30 min (on ice) followed by high spin at 4 °C at 14,000 × rpm for 15 min. The supernatant containing the protein was quantified by the Bicinchoninic Acid (Sigma-Aldrich, USA) assay method. 30 µg of protein (from HK-2 cells derived exosomes) and 2–3 mg of urinary protein (from urinary exosomes; normalized by urine creatinine) were resolved on $10\%$ polyacrylamide gel electrophoresis, electrotransferred to polyvinylidene fluoride membrane (EMD Millipore, USA). The membrane was blocked with $5\%$ skimmed milk for 2 h and probed sequentially for Tumor Susceptibility Gene (TSG) 101 (sc-7964, Santa Cruz Biotechnology, USA) and Calnexin (sc-11397, Santa Cruz Biotechnology, USA) overnight at 4 °C. Subsequently, the membranes were incubated for 90 min with the respective secondary antibodies. The membrane was covered by ECL solution (Clarity Max Western ECL Substrate, BioRad, USA) and visualized for the antibody binding in ChemiDoc XRS + (BioRad, USA). HK-2 cells were taken as a positive control.
## miRNA profiling
Total RNA was isolated from HK-2 cells derived exosomes by mirVana miRNA Isolation Kit (Invitrogen, United States) as recommended by the manufacturer. These samples were submitted to Exiqon (Vedbaek, Denmark) for miRNA real-time-based expression profiling. 19 μl RNA was reverse transcribed in 95 μl reactions using the miRCURY LNA Universal RT microRNA PCR, Polyadenylation, and cDNA synthesis kit (Exiqon). cDNA was diluted 50 × and assayed in 10 µl PCR reactions according to the protocol for miRCURY LNA Universal RT microRNA PCR; each microRNA was assayed once by qPCR on the microRNA Ready-to-Use PCR, Human panel I + II using ExiLENT SYBR Green master mix. Negative controls excluding template from the reverse transcription reaction were performed and profiled like the samples. The amplification was performed in a LightCycler 480 Real-Time PCR System (Roche) in 384 well plates. The amplification curves were analyzed using the Roche LC software, both for the determination of Ct (Cycle threshold) (by the 2nd derivative method) and for melting curve analysis. Using NormFinder, the best normalizer was found to be the average of assays detected in all samples. All data were normalized to the average of assays detected in all samples (average –assay Ct).
## miRNA validation
Total RNA was isolated from exosomes by mirVana miRNA Isolation Kit (Invitrogen, USA) with slight modification. 1 µl synthetic spike-in mix (UniSp2, UniSp4, UniSp5; RNA Spike-In Kit, Exiqon) per RNA preparation was added to 60 µl of lysis buffer to reduce the technical variance between the samples. This lysis buffer was added to the sample and vortexed vigorously for 10 min followed by the instructions mentioned in the mirVana miRNA Isolation Kit. RNA was eluted in 20 µl of RNase- free water.
Furthermore, determining RNA yield by spectrophotometric reading is frequently not achievable; instead, we employed RNA quantities based on starting volume in the PCR reaction as a measure, followed by spike-in quantification19. Cellular RNA was quantified with the Infinite 200 Pro NanoQuant microplate reader (Tecan, Männedorf, Switzerland). The reverse transcription reaction employed 4 µl of each RNA solution or 5 ng/µl of cellular RNA by using the Universal cDNA Synthesis Kit II (Exiqon) and UniSp6 RNA Spike-in template (to control for the potential inhibitors) according to the manufacturer's instructions. cDNA samples (diluted to 1:40) were probed by Exiqon primers for hsa-miR-155-5p (E24308), hsa-miR-28-3p (E24119), hsa-miR-425-5p (E24337), and hsa-miR-663a (E24284) via ExiLENT SYBR Green master mix (Exiqon) in a 7500 Real-Time PCR machine (Applied Biosystems, United States) with 40 amplification cycles. The cycling parameters were followed as recommended by Exiqon. Raw data were processed in 7500 software v2.0.1 to assign the baseline and threshold. We utilized spike-in UniSp2 and UniSp4 for normalization because there is no consistent endogenous control for exosomal miRNA20,21 while cellular RNA was normalized to U6 (E203950). The expression levels of differentially expressed miRNA were calculated as relative fold change22.
## miRNA target identification
*The* genes of the targeted miRNAs were scrutinized by the bioinformatic tool mirDIP v4.1 (http://ophid.utoronto.ca/mirDIP/) (confidence class as medium and prediction by at least three different algorithms) which integrates across 30 resources for miRNA-mRNA target prediction. *These* genes from mirDIP v4.1 were verified for their presence in the other four updated prediction tools—miRTarBase 7.0 (http://miRTarBase.mbc.nctu.edu.tw/), TarBase v.8 (http://www.microrna.gr/tarbase), miRWalk 3.0 (http://mirwalk.umm.uniheidelberg.de), and TargetScan 7.2 (http://www.targetscan.org/vert_72/). Genes predicted by at least three of the databases were considered speculative targets for the miRNA.
## IPA analysis
The plausible target genes were run for Core Analysis in IPA software (Ingenuity Systems, www.qiagen.com/ingenuity) to identify enriched ‘canonical pathways’ as well as ‘disease and functions’. Using right-tailed Fisher's exact test, a p-value was calculated for each biological function or disease assigned to that data set based strictly on chance. General settings, networks, and data sources were the same as the default software configuration. Supplementary File 1, Sheet 1 summarizes changes made in other filters used for this analysis. *The* genes from these pathways were connected using the information contained in Ingenuity Knowledge Database findings by the ‘Connect tool’. This filter was not changed except for the disease category that was selected as ‘endocrine system illnesses’ and ‘renal and urological diseases’. ‘ Path Explorer’ tool was used to connect miR-663 and the genes of the network formed above. This tool had the same filters as the default set up by the program. The Molecule Activity Predictor (MAP) tool was used to find the association between miR-663 and genes in the network. This complete network was then overlaid with the ‘Disease and Function’ tool to determine the significant diseases and functions relevant to our study. The flowchart of the different tools utilized in IPA is illustrated in Supplementary Fig. S1.
## Statistical analysis
SPSS v22.0 for Windows (IBM SPSS, USA) was used for descriptive analysis of the variables from each patient group. RT-PCR graphs for both in vitro and in vivo were assessed by GraphPad Prism version 7.0 for Windows (GraphPad Software, San Diego, CA, USA, www.graphpad.com/). Differential expressions of miRNAs were analyzed by a two-tailed Student t-test or Mann–Whitney as appropriate. Relative expression of the PCR data was expressed in terms of fold change (2^- (delta delta Ct)).
In each group, we enrolled at least five patients with adequate urine samples using convenience sampling.
## Characterization of exosomes
TEM examination of HK-2 cells and human urine-derived exosomes revealed spherical vesicles of sizes 25–60 nm (Fig. 1a and b; red arrows) thereby confirming the presence of exosomes in the samples isolated. The protein bands of TSG 101 were expressed in HK-2 cells, human urine and HK-2 cells derived exosomes while calnexin, an endoplasmic reticulum marker was absent in all the samples except in HK-2 cells (Fig. 1c, Supplementary Fig. S2) Thus, this showed that the pellet collected after ultracentrifugation was indeed exosomes. The side/forward scatter showed the population of CD63-coated beads with cells-derived exosomes (Fig. 1d) and human urine-derived exosomes (Fig. 1g). The presence of known exosome marker CD8123 in the samples isolated as assessed by flow cytometry confirmed the successful isolation of exosomes from HK-2 cells (Fig. 1f) as well as human urine (Fig. 1i) in comparison to unstained controls (Fig. 1e and h) respectively. Figure 1Characterization of exosomes. Electron microscopy of (a) HK-2 cells-derived exosomes. ( b) Human urinary exosomes. The exosomes are shown by the red arrows. ( c) Western blot of classical exosomal marker TSG 101 (exposure time: 120 s) and non-exosomal marker Calnexin (exposure time: 30 s); 1: HK-2 cells; 2,3: 2 and 3 mg of protein of healthy volunteer normalized to urine creatinine; 4: 30 μg of protein from HK-2 cells-derived exosomes. The original blots are presented in Supplemental Fig. S2. Scatter plot analysis of CD63 coated beads with (d) HK-2 cells-derived exosomes and (g) Human urine-derived exosomes. Gating of antibody bead conjugated exosomes derived from (e) HK-2 cells (h) human urine. The antibody bead exosome complex stained with anti-CD81 PE exhibited (f) $98\%$ of positive CD81 vesicles derived from HK-2 cells. ( i) Exosomes derived from human urine showed $80.3\%$ of positive exosomes. TSG, Tumour Susceptibility Gene; SSC, Side Scatter; FSC, Forward Scatter; PE, Phycoerythrin.
## miRNA expression profiling of HK-2 derived exosomes under diabetic conditions
To mimic diabetes under in vitro system, the HK-2 cells were exposed to HG in comparison to the control i.e., LG. cDNA synthesis control UniSp6 was constant in LG (LE1, LE2, LE3) and HG (HE1, HE2, HE3) treated exosomal samples, showing no evidence of PCR inhibitors. ( Supplementary Fig. S3A). A quality control assessment using miR-23a, miR-30c, miR-103, miR-142-3p, and miR-451 showed the quality and purity of the samples (Supplementary Table ST1, Supplementary Fig. S3B). For the differential miRNA profiling, the heat map (Fig. 2a) demonstrated a two-way hierarchical cluster analysis on LG (LE1, LE2, LE3) and HG (HG1, HG2, HG3) treated exosomal groups. Differential clustering by principal component analysis confirmed the distinction of the group (Fig. 2b). In the volcano plot, log2 (fold change) on X-axis and negative log p-value on Y-axis represented the differences in miRNA expression between HG and LG exosomes (Fig. 2c). Based on Wilcoxon or t-test as appropriate, eight miRNAs ($p \leq 0.05$) viz. hsa-miR-425-5p (1.7 fold), hsa-miR-155-5p (1.2 fold), hsa-miR-103a-3p (1.2 fold), hsa-miR-28-3p (1.4 fold) were upregulated while four miRNAs, hsa-miR-151a-5p (− 1.3 fold), hsa-miR-664a-3p (− 3.2 fold), hsa-miR-663b (− 3.1 fold), hsa-miR-663a (− 2.5 fold) were downregulated in HG as compared to LG treated HK-2 cells derived exosomes (Table 1).Figure 2miRNA profiling by Real-Time PCR identified differentially expressed miRNAs in HK-2 derived exosomes under hyperglycaemia. ( a) Heat map and unsupervised hierarchical clustering. Clustering was performed in all the samples for the top 50 miRNAs with the highest standard deviation. The color scale shown at the bottom illustrates the relative expression level of a microRNA across all samples: red color represents an expression level above the mean, and green color represents expression lower than the mean. ( b) Principal Component Analysis was performed on all the samples and the top 50 miRNAs with the highest standard deviation. The normalized (dCt) values have been used for the analysis. ( c) The volcano plot shows the relation between the P- values and the fold change. Highlighted spots are microRNAs with $p \leq 0.05$ after Benjamini–Hochberg correction for multiple testing. The significantly upregulated miRNAs are shown in red while downregulated miRNAs are shown in green color. miRNAs, Micro Ribonucleic Acid; PCA, Principal Component Analysis; LE1, LE2, LE3, Low glucose treated HK-2 cells-derived exosomes from three different samples (control); HE1, HE2, HE3, High glucose treated HK-2 derived exosomes from three different samples (experimental).Table 1Differentially expressed miRNAs in HK-2 cells-derived exosomes under high glucose versus low glucose conditions.miRNA nameAverage dCt expAverage dCt controlFold changep-valuehsa-miR-425-5p− 0.89− 1.61.70.0053hsa-miR-151a-5p0.390.81− 1.30.0066hsa-miR-664a-3p− 5.8− 4.1− 3.20.010hsa-miR-663b− 0.421.2− 3.10.017hsa-miR-155-5p2.72.41.20.032hsa-miR-103a-3p3.02.81.20.034hsa-miR-663a1.52.8− 2.50.040hsa-miR-28-3p− 0.15− 0.661.40.042p-value was determined by two tailed Student’s t-test or Wilcoxon test as appropriate. miRNA, Micro Ribonucleic Acid; HK-2, Human Kidney Proximal Tubular Cells; hsa, Human; dCt, Normalized Threshold Values; exp, Experimental Sample (High glucose treated exosome sample); control, Low glucose treated exosome sample.
## Real-time PCR
For microarray data validation, RT-PCR analysis of four miRNAs namely, hsa-miR-155-5p, hsa-miR-425-5p, hsa-miR-28-3p, and hsa-miR-663a from both in vitro and in vivo diabetic models were carried out. Significant overexpression was observed for hsa-miR-155-5p ($$p \leq 0.017$$, Fig. 3a, Panel I), hsa-miR-425-5p ($$p \leq 0.046$$, Fig. 3a, Panel II), hsa-miR-28-3p ($$p \leq 0.028$$, Fig. 3a, Panel III), while a significant downregulation was observed for hsa-miR-663a ($$P \leq 0.0099$$, Fig. 3a, Panel IV) in fresh batch of HG than to LG treated HK-2 cells-derived exosomes. The threshold values of the RNA spike-in controls (UniSp2 and UniSp4) and cDNA synthesis control (UniSp6) utilized during this experimental set up is represented in Fig. 3b. The miRNA profiling data also validated these observations (Table 1).Figure 3Validation of enriched miRNAs from HK-2 cells and human urine-derived exosomes by Real-Time PCR and their internal controls. hsa-miR-155-5p (Panel I), hsa-miR-425-5p (Panel II), hsa-miR-28-3p (Panel III) and hsa-miR-663a (Panel IV) were analyzed for their expression in (a) HK-2 cells-derived exosomes ($$n = 3$$). These cells were treated with 5 mM and 30 mM glucose for 96 h before exosome isolation. ( c) Human urine-derived exosomes from HC, PDKD, NPDKD and T2DM. Relative fold change = (2^- (delta delta Ct)). UniSp2 and UniSp4 were used to estimate the RNA isolation quality; UniSp6 was used as a cDNA synthesis control for (b) HK-2 cells derived exosomes (d) human urine derived exosomes. In all scatter plots, the centre line represents the mean; the smallest number in the data set was shown at the end of the lower whisker; the largest number in the data set was shown at the end of the upper whisker as determined by the GraphPad Prism version.7.0 software. Normally distributed samples were analyzed by two-tailed Student’s tests otherwise by Mann -Whitney Test. Data is represented as Mean ± SD. hsa, Homo sapiens; Ct, Threshold; HC, Healthy Control; PDKD, Proteinuric Diabetic Kidney Disease; NPDKD, Non-Proteinuric Diabetic Kidney Disease; T2DM, Type 2 Diabetes Mellitus.
## Study population
For 6 months between January and June 2018, 60 patients were screened (Supplementary Fig. S4). 14 patients were excluded, and 11 patients refused to participate. Out of 35 participants who were enrolled in giving urine specimens, 10 participants either did not turn up for the scheduled visit or withdrew consent. Therefore, we had clinical details and adequate urine specimen collection for 25 participants (T2DM, $$n = 7$$; PDKD, $$n = 8$$; NPDKD, $$n = 5$$; HC, $$n = 5$$). By using spike-in controls to check for technical variability during RNA isolation, eight samples were excluded. Finally, the in vivo study consisted of 17 samples (T2DM, $$n = 5$$; PDKD, $$n = 5$$; NPDKD, $$n = 4$$; HC, $$n = 3$$) which were assessed for the targeted miRNA expression. The descriptive and clinical characteristics of the study groups are depicted in Table 2.Table 2Demographic and clinical parameters of the study groups. ParametersProteinuric Diabetic Kidney Disease (PDKD; $$n = 5$$)Non-Proteinuric Diabetic Kidney Disease (NPDKD; $$n = 4$$)Type 2 Diabetes Mellitus (T2DM; $$n = 5$$)Healthy Volunteers (Control; $$n = 3$$)Age (years)55.40 ± 8.0158.0 ± 4.0854.2 ± 8.7844.33 ± 5.85Sex (Male/Female) (%)$\frac{60}{4075}$/$\frac{2520}{8033.3}$/66.7Duration of diabetes (months)156.40 ± 87.57111.00 ± 73.56108.00 ± 20.78NADuration of kidney problem (months)22.80 ± 16.1016.25 ± 13.28NANAFasting blood glucose (mg/dl)131.80 ± 49.44137.65 ± 38.64134.50 ± 39.1089.66 ± 1.52Serum creatinine (mg/dl)1.69 ± 0.631.89 ± 0.530.81 ± 0.140.5 ± 0.06eGFR44.84 ± 14.9239.17 ± 15.3686.06 ± 14.39122.07 ± 4.2324 h urine protein (mg/dl)3059.44 ± 2003.7186.80 ± 41.7294.35 ± 32.33110.60 ± 34.73Data are shown as Mean ± Standard Deviation. NA, not applicable; mg/dl, milligrams/deciliter.
The same four miRNAs, as validated in HK-2 derived exosomes, were also verified for their expression levels in these groups (Fig. 3c). The hsa-miR-663a was significantly downregulated in NPDKD than to PDKD ($$p \leq 0.018$$) and elevated in PDKD than to the HC ($$p \leq 0.035$$). The hsa-miR-663a was found to be expressed at a higher level in T2DM as compared to NPDKD, though data was statistically non-significant (Fig. 3c, Panel IV). The hsa-miR-28-3p was significantly elevated in PDKD ($$p \leq 0.043$$) and T2DM ($$p \leq 0.007$$) than to HC (Fig. 3c, Panel III). The hsa-miR-425-5p (Fig. 3c, Panel II) was upregulated while has-miR-155-5p was decreased (Fig. 3c, Panel I) in all the groups i.e., PDKD, NPDKD, and T2DM than to the HC (non-significant) The expression level of different miRNAs varied with variable levels of albuminuria in the affected individuals. The internal controls (UniSp2, UniSp4 and UniSp6) threshold values are represented in Fig. 3d.
## Target genes identification for miR-663
We performed an in-silico analysis of the genes for miR-663a, as it was the only miRNA that showed differential expression between NPDKD and PDKD. *These* genes were analyzed by IPA for pathways and functional analysis as well as network construction. IPA retrieved 104 canonical pathways (Supplementary File 1, Sheet 2) and 51 diseases and bio functions (Supplementary File 1, Sheet 3) for these plausible genes ($p \leq 0.05$). Stringent conditions ($p \leq 0.01$; the number of molecules > = 5) yielded four significant pathways namely IL-8 Signaling, Cardiac Hypertrophic Signaling, Hepatic Fibrosis Signaling, and Senescence Pathway (Fig. 4a) and 22 annotations under the diseases and bio functions tool (Fig. 4b).Figure 4Core Analysis of miR-663a target genes by Ingenuity Pathway Analysis Software. The plausible targeted genes of miR-663a were analyzed for Core Analysis by Ingenuity Pathway Analysis software which yielded (a) 4 enriched canonical pathways. ( b) Significant 22 diseases and functions. Blue bars represent the p-value for each pathway as well as disease and functions and are expressed as − log (p-value). The threshold line corresponds to 0.05 (red line). The ratio (orange points) on each pathway represents the ratio of the number of genes from the dataset that meet the cut-off criteria divided by the total number of genes that map to that pathway from the Ingenuity Knowledge Database. $p \leq 0.01$ and number of molecules ≥ 5 were the criteria for selecting canonical pathways as well as disease and functions.
The top five significant cellular and molecular function identified were cellular development, cellular growth and proliferation, cell to cell signaling and interaction, cell death and survival, and cellular function and maintenance. The ‘inflammatory response’ and ‘organismal injury and abnormalities’ were the most significantly enriched disease in the present study. To detect the association of kidney injury with ‘organismal injury and abnormalities’, the ‘diseases and functions’ annotation displayed apoptosis of podocytes and permeability of microvascular endothelial cells. Both these abnormalities were associated with DKD24. Similarly, ‘inflammatory response’ annotated antibody-dependent cell-mediated cytotoxicity (ADCC) of natural killer (NK) cells as its only function. IL-8 signaling, the top-ranked canonical pathway coincided well with the top-ranked disease i.e., inflammatory response.
We built a network of relationships between the genes of these enriched pathways and the other genes of the Ingenuity Knowledge Database (IKDB) which was further linked to miR-663 by the shortest route analysis (Supplementary File 1, Sheet 4). To build a network of associations, the top five routes were mapped (Supplementary Fig. S5, Supplementary File 1, Sheet 5). Interestingly, this network focused on the three major DKD-associated ‘diseases and functions’ relevant to our study—endocrine system disorders (Fig. 5a), renal tubule injury (Fig. 5b), and decreased levels of albumin (Fig. 5c). These data suggested that the genes in the proposed network would facilitate the pathogenesis of the DKD (Fig. 5d).Figure 5Disease and function analysis of the network by Ingenuity Pathway Analysis. The topological network created by Ingenuity Pathway Analysis Software when overlaid by the ‘Disease and Function’ tool generated (a) Endocrine System Disorders ($$p \leq 1.53$$E−07–3.89E−5). Genes involved in experimentally induced diabetes were—CXCR4, HRAS, and TGF-β1 (purple line). ( b) Renal Tubule Injury ($$p \leq 5.76$$E−04–9.09E−03). This included sub-categories which were—Damage to tubule cells which consisted of gene YBX1 (sky blue line); Damage to renal tubules which involved genes YBX1 and TGF-β1 (green line); Damage of tubulointerstitium indicated TGF-β1 gene (pink line) and proximal tubular toxicity pointed CXCR4 and YBX1 gene (orange line). ( c) Decreased levels of albumin ($$p \leq 2.28$$E−03–3.04E−3) included decreased synthesis (cyan line) and decreased secretion of albumin (light green line) with TGF-β1 as the central gene for both. ‘ Molecule Activity Predictor’ predicted the relationship between miR-663 and CXCR4 (Reference no. 47) as well as miR-663 and HSPG2 (Reference no. 48) to be inhibited when the miR-663 expression was elevated based on the research findings of Ingenuity Knowledge Database. ( d) Complete network showing Endocrine System Disorders, Renal Tubule Injury, and Decreased levels of albumin altogether. All molecules are represented in different shapes which are the default icon in Ingenuity Pathway Analysis software and all details of color-coded lines are mentioned in the legend. miR, Micro Ribonucleic Acid; HRAS, Harvey Rat Sarcoma Viral Oncogene Homolog; CEBPB, CCAAT/Enhancer-Binding Protein (C/EBP), beta; ARAF, A-RAF Proto-Oncogene, Serine/Threonine Kinase; PIK3CD, Phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit delta; CXCR4, C-X-C Chemokine Receptor 4; TGF-β1, Transforming Growth Factor-Beta 1; MEF2D, Myocyte Enhancer Factor 2D; VASP, Vasodilator-Stimulated Phosphoprotein; MYL9, Myosin, Light Chain 9, regulatory; HSPG2, Heparan Sulfate Proteoglycan 2; YBX1, Y-Box Binding Protein 1.
## Discussion
To the best of our knowledge, this is the first study wherein, differential miRNA expression profiling was identified in HK-2 cells derived exosomes under hyperglycemic conditions. Among the four shortlisted human miRNAs i.e., miR-155-5p, miR-425-5p, miR-28-3p, and miR-663a, only miR-663a expression was different between NPDKD and PDKD subjects. miR-155-5p, miR-425-5p, and miR-28-3p were also associated with T2DM and regulated insulin signaling25,26 which were validated in our experimental setup. However, variability in the expression of miR-155-5p, and miR-425-5p (non-significant) in the human urines of DKD excluded their further analysis. Though the source of isolation varied, increased expression of miR-425-5p was observed in the plasma and peripheral blood mononuclear cells of diabetic subjects than in controls27. While for miR-28-3p, which was up-regulated in our study and was increased in urine EVs of type 1 diabetics28. Since it has already been pointed out as a predictor of T2DM29, the focus was thus inclined towards miR-663a.
This indeed is the most intriguing finding of our study discovering miR-663a as an indirect reflection of mechanism between PDKD and NPDKD patients. A solitary study related to this miRNA reiterates miR-663a down-regulation in the kidneys of hypertensive patients as compared to normotensive males30.
Proteinuria is generally considered for diagnosing DKD, however, a proportion of patients develop renal impairment before albuminuria3. Under pathological conditions pattern of miRNA differs from the control and being secretory, they possess a strong potential with diagnostic value31.
Interestingly, miR-663a was significantly upregulated ($p \leq 0.0001$) in HK-2 cells while downregulated ($$p \leq 0.0099$$) in HK-2 cells derived exosomes (Supplementary Fig. S6). Further, the significant differential expression of miR-663a observed between the NPDKD and PDKD led to the investigation of pathways and networks regulated by this miRNA. The network analysis, through IPA, showed that miR-663a and its plausible target genes were involved in the pathogenesis of DKD with PTC being the primary contributors (Fig. 5d). Therefore, it provides the first line of evidence for miR-663a’s involvement in DKD. Bioinformatics analysis revealed 60 targets (Supplementary File 1, Sheet 6) by the intersection of five databases. Analyzing these genes by IPA uncovered the ‘canonical pathways’ and ‘disease and functions’ of miR-663 which were extrapolated in a topological network.
IL-8, a pro-inflammatory cytokine signaling turned out to be the most ‘enriched pathway’ and ADCC of NK cells was the only function of the top-ranked disease i.e., inflammatory response. Under HG, increased secretion of IL-8 with tubular injury was observed in human renal PTC32. Its urinary levels were also found to be elevated in the early stage of type 2 DKD33. NK Group 2D receptor known to protect the host against infections was aberrant in type 1 diabetics which were involved in its pathogenesis34, highlighted the connection of miR-663a target genes with the dysfunctional pathways and other complications related to DKD. For example, diabetes caused more pronounced fibrosis in the heart and liver35. Exposure of PTC to HG resulted in accelerated senescence. A similar expression was observed in the PTC at the early stages of type 2 DKD patients36. These studies validated our approach for the identification of genes and pathways connected with DKD paving the way for developing potential therapeutic interventions owing to PTC injury. *The* genes of these pathways were mapped to form an association among themselves by several direct and indirect connections and linked to miR-663. The observations pointed to endocrine system disorders, renal tubule injury, and decreased levels of albumin as the enriched ‘diseases and functions’.
Under endocrine disorder ‘experimentally induced diabetes’ was linked to Harvey Rat Sarcoma Viral Oncogene Homolog (H-RAS), C-X-C motif chemokine receptor 4 (CXCR4) and transforming growth factor-beta 1 (TGF-β1). All these genes were elevated in DKD37,38.
The renal tubular injury had four sub-divisions. The first category included ‘damage of renal tubule’ which was related to Y-box binding protein 1 (YBX1) and TGF-β1. The second category ‘damage to tubular cells’ was associated with YBX1. ‘ Proximal tubular toxicity’, the third sub-division pointed towards YBX1 and CXCR4. TGF-β1 impaired renal tubular cell integrity leading to tubular atrophy, a hallmark of tubule-interstitial fibrosis, thereby damaging tubule-interstitium, represented the fourth category39. In various CKD mouse models and human PTC, CXCR4 was increased40 which in turn upregulated TGF-β141 thereby promoting tubular injury and renal fibrosis in DKD42. In A459 cells, TGF-β1 increased the expression of YBX1 which regulated epithelial-mesenchymal transition (EMT)43, a common insult to PTC in diabetes44. Upregulation of rat Myb1a (YBX-1) and LCR1 (CXCR4) was also associated with the proximal toxicity in the male rat45.
For ‘decreased levels of albumin’, TGF-β1 was the central gene. HG elevated TGF-β1 which reduced the lysosomal activity, thereby reducing the processing of albumin by the kidney. Therefore, TGF-β1 regulated the uptake of albumin46. Overall, the network showed an inhibitory relationship between CXCR4 and heparan sulfate proteoglycan 2 (HSPG2) with miR-663 based on the findings from IKDB. Upregulated miR-663 inhibited CXCR4 in glioblastoma, thereby, attenuating the oncogenic properties47. This was complemented through an observation showing elevated miR-663 regulated chemoresistance by decreasing HSPG2 expression in breast cancer cells48.
Taking these cues, we hypothesize that decreased level of miR-663a might elevate the expression of CXCR4 and HSPG2 (Fig. 6) in non-proteinuric DKD. CXCR4 was upregulated in the biopsy of DKD patients and the experimental model which upon inhibition caused increased urinary albumin excretion and enhanced the proximal tubular cell death49. It would be noteworthy to verify this relationship between PDKD and NPDKD. Additionally, there might be a possibility that miR-663a increased expression observed in PDKD would reduce the CXCR4 expression resulting in albuminuria in PDKD, which warrants additional study. Decreased expression of heparan sulfate observed in the biopsy of type 2 overt DKD patients correlated inversely with the degree of proteinuria50. There could be a possibility of such phenomena occurring in PDKD. Contrarily, no proteinuria was detected in mice lacking heparan sulfate binding sites but the onset of the progression of DKD was observed51. Therefore, segregation of the patients into PDKD and NPDKD would aid in achieving better mechanistic approach for heterogenous Type 2 DKD.Figure 6Schematic representation of the proposed hypothesis of the study. According to IPA, increased miR-663a expression reduced CXCR4 and HSPG2 expression (Fig. 6d). As a result, it is possible that elevated miR-663a expression in PDKD reduced CXCR4 expression, leading to albuminuria. Reduced HSPG2 expression was inversely associated with the severity of proteinuria. In the lack of heparan sulfate binding sites, however, there is no proteinuria, but the development of DKD is probable51. Reduced expression of miR-663a in NPDKD would increase CXCR4 and HSPG2 expression. As a result, albuminuria in these groups of subjects would be reduced. miR, Micro Ribonucleic Acid; CXCR4, C-X-C Motif Chemokine Receptor 4; HSPG2, Heparan Sulfate Proteoglycan 2.
Exosomes may have different miRNA profiles than their parents, which could explain the differential expression of miR-663a in cells and exosomes observed under HG. Therefore, cells may have an active exosome and cargo selection process52. One of the RNA binding proteins implicated in miRNA sorting in exosomes was YBX-153. YBX-1 was shown to be related to miR-663a in this study as well, according to IPA analysis (Fig. 5d), implying that YBX1 might be involved in the differential expression of miR-663a observed in cells and exosomes in this experimental setup. The major limitation of this study is the small sample size. The study was exploratory in nature and no preliminary data was available in this regard. The results of this pilot exploratory study need to be validated in a larger cohort of patients with DKD.
We did not include patients with proteinuric or non-proteinuric CKD other than diabetes mellitus and hence, are not able to infer whether the same observations would be present outside a diagnosis of diabetes mellitus or not. Therefore, we cannot conclusively say that differential expression of miR-663a in proteinuric versus non-proteinuric CKD is exclusive to DKD.
Because bio-fluids contain less amount of total RNA, it is challenging to determine the quality and concentration of total RNA isolated for extracellular miRNA research. Furthermore, in a disease condition, a greater number of miRNAs can be released extracellularly than in a healthy state. To obtain accurate findings for biomarker detection studies, it is recommended to use an equivalent volume of starting material (serum, plasma, or any other biological fluid) rather than the same amount of total RNA54,55.
As mentioned by Nowak et al. PTC are dominant cells to be injured in non-proteinuric subjects and the fact that miR-663a expression was down-regulated significantly under HG both in vitro and in vivo in this study, might also suggest the involvement of PTC in the non-proteinuric subjects. But it cannot be denied that urine is composed of all the cells of the nephron, hence which cells from urine are predominant in NPDKD needs to be further investigated. Nevertheless, the results need to be explored further to identify the underlying mechanism between these two subsets of patients existing within DKD.
In closing, the expression of miR-663a in urine exosomes may be reflective of underlying mechanistic differences in kidney involvement in PDKD versus NPDKD. Analyses of the likely genes associated with miR-663a highlighted the significance of tubular cells in the pathophysiology of DKD. These observations need validation and further exploration in larger and diverse populations.
## Supplementary Information
Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Information. The online version contains supplementary material available at 10.1038/s41598-022-26558-4.
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|
---
title: Microbial diversity and metabolic function in duodenum, jejunum and ileum of
emu (Dromaius novaehollandiae)
authors:
- Ji Eun Kim
- Hein M. Tun
- Darin C. Bennett
- Frederick C. Leung
- Kimberly M. Cheng
journal: Scientific Reports
year: 2023
pmcid: PMC10024708
doi: 10.1038/s41598-023-31684-8
license: CC BY 4.0
---
# Microbial diversity and metabolic function in duodenum, jejunum and ileum of emu (Dromaius novaehollandiae)
## Abstract
Emus (Dromaius novaehollandiae), a large flightless omnivorous ratite, are farmed for their fat and meat. Emu fat can be rendered into oil for therapeutic and cosmetic use. They are capable of gaining a significant portion of its daily energy requirement from the digestion of plant fibre. Despite of its large body size and low metabolic rate, emus have a relatively simple gastroinstetinal (GI) tract with a short mean digesta retention time. However, little is known about the GI microbial diversity of emus. The objective of this study was to characterize the intraluminal intestinal bacterial community in the different segments of small intestine (duodenum, jejunum, and ileum) using pyrotag sequencing and compare that with the ceca. Gut content samples were collected from each of four adult emus (2 males, 2 females; 5–6 years old) that were free ranged but supplemented with a barley-alfalfa-canola based diet. We amplified the V3-V5 region of 16S rRNA gene to identify the bacterial community using Roche 454 Junior system. After quality trimming, a total of 165,585 sequence reads were obtained from different segments of the small intestine (SI). A total of 701 operational taxonomic units (OTUs) were identified in the different segments of small intestine. Firmicutes (14–$99\%$) and Proteobacteria (0.5–$76\%$) were the most predominant bacterial phyla in the small intestine. Based on species richness estimation (Chao1 index), the average number of estimated OTUs in the small intestinal compartments were 148 in Duodenum, 167 in Jejunum, and 85 in Ileum, respectively. Low number of core OTUs identified in each compartment of small intestine across individual birds (Duodenum: 13 OTUs, Jejunum: 2 OTUs, Ileum: 14 OTUs) indicated unique bacterial community in each bird. Moreover, only 2 OTUs (*Escherichia and* Sinobacteraceae) were identified as core bacteria along the whole small intestine. PICRUSt analysis has indicated that the detoxification of plant material and environmental chemicals seem to be performed by SI microbiota, especially those in the jejunum. The emu cecal microbiome has more genes than SI segments involving in protective or immune response to enteric pathogens. Microbial digestion and fermentation is mostly in the jejunum and ceca. This is the first study to characterize the microbiota of different compartments of the emu intestines via gut samples and not fecal samples. Results from this study allow us to further investigate the influence of the seasonal and physiological changes of intestinal microbiota on the nutrition of emus and indirectly influence the fatty acid composition of emu fat.
## Introduction
The gastrointestinal (GI) microbiota has been recognized as an essential component of the intestinal ecosystem, which contributes to the wellbeing, energy metabolism and disease resistance in animals1–3.They play a critical role in the health of animals through nutrient utilization, immunological development and other physiological systems4. Animals maintain complex and intimate associations with a diverse community of GI microbes5. In order to characterize the GI microbiota diversity, earlier studies applied selective and cultivation-based techniques to identify potential pathogenic microbes6,7. However, these studies revealed limited number of bacteria communities. Subsequently, the approach of the pyrotag sequencing of 16S rRNA genes and metagenomics has made it possible to better characterize the GI microbiota communities and examine their interaction with host and diet.
Avian represents interesting study models in which to investigate the roles of intestinal microbes in the nutrition, immune function, and development because they have unique diets, physiological traits, and developmental strategies8. Moreover, avian has a shorter GI tract and faster digesta transit time less than 3.5 h9. This anatomic feature selects a very different intestinal microbiome in avian than mammals10. The GI microbiota community of both domestic and wild bird species including chicken, turkey, duck and ratites has been studied by pyrosequencing2,11–13.
Most studies of avian species have focused on characterizing the microbiota in the ceca due to its large bacterial diversity13–16. Examination of omnivorous avian species shows that *Bacteroidetes is* the dominate phylum in the ceca15,17–21. In contrast, Firmicutes dominate in the ceca of the ostrich (Struthio camelus), Japanese quail (Cotuenix japonica), and capercaille (Tetrao urogallus), which are predominantly herbivores14,22. Research foci have subsequently included the variation in microbial community along the GI tract23. Several studies in chickens (*Gallus gallus* domestica) showed that Lactobacilli to be the major bacterial population in three segments (duodenum, jejunum, and ileum) of the small intestine, whereas Clostridium spp. and Bacteroides spp. are dominant in the ceca17,24.
Unfortunately, until recently, the lack of standardized protocols in avian microbiota studies and the mainly use of fecal or cloacal samples [25. 26] prevents meaningful comparisons of microbiome across different intestinal segments23,27. Moreover, analysis of the small intestine microbiota precludes non-lethal sampling because of its location in the GI tract. As a result, microbiota variation along different segments of the GI tract has only been studied in very few species: chicken28,29, turkey (Meleagris gallopavo)30, hoatzin (Opisthocomus hoazin)31, kakapo (Strigops habroptilus)32, and Japanese quail33,34.
Emu (Dromaius novaehollandiae) is a flightless omnivorous bird native to Australia. Oil extracted from the fat tissue of emu has been traditionally used by aborigines for wound healing. Presently, emu oil is commonly used in cosmetics preparations35. Veterinary, alternative and traditional medicine has also included the use emu oil for the treatment of wounds and inflammatory skin conditions.
Despite emu’s behavior of seasonal dietary intake and fat deposition, there is little known about the host interaction with GI microbiota ecosystem. Our previous study has shown that the predominant bacterial phyla is Bacteroidetes in emu ceca13. The objective of this study is to characterize the bacteria community and predict microbial metabolic function in the three segments (duodenum, jejunum and ileum) of the small intestine (SI) in comparison with that in the ceca13 using pyrotag sequencing with 16S rRNA gene.
## Methods
The study was conducted in accordance with the relevant guidelines and regulations. Methods are reported in accordance with ARRIVE guidelines (https://arriveguidelines.org).
## Experimental animals
Along with the emu ceca in our previous study13, SI segments (duodenum, jejunum and ileum) were collected from the same four adult emus (2 males, 2 females) at TryHarder Farm (Saskatchewan, Canada) for use in this study. The emus were free-ranged (natural forage) but supplemented with a barley-alfalfa-canola based diet. Detailed rearing and processing procedure can be found in Bennett et al.13. The ceca and SI segment samples were collected in early November, just prior to the onset of their breeding season. From past studies13,36 seasonal decline in their feed intake should have begun. However, we did not measure individual feed intake. The Ceca and SI samples were frozen immediately after collection and kept at − 80 ℃ until use. The study was approved by the Animal Care and Use Committee at University of British Columbia (Certificate # A10-0106).
## DNA extraction and 16S rRNA gene amplicons
Together with the cecal samples, the SI samples were thawed on ice and the contents were gently scraped from the intestinal wall of each sample. Using the same protocol described by Bennett et al.13, genomic DNA was extracted from each of the four duodenal, jejunal and ileal samples using the PowerMax Soil DNA Isolation Kit (Mo Bio Laboratories Inc., Carlsbad, CA) according to the instructions of the manufacturer with 200 mg/sample as starting material. Extracted DNA was amplified by PCR using FastStart high fidelity PCR system (Roche Molecular Diagnostics, Branchburg, NJ, USA). A universal primer set of 341F (5’- ACTCCTACGG GAGGCAGCAG-3’) and 926R (5’- CCGTCAATTCMTTTGAGTTT-3’) was adopted for amplifying the variable region 3 to 5 (V3-V5) of the bacterial 16S rRNA gene. The forward primer bore a multiplex identifier (MID) sequences for sample identification, and the primer set was modified by adding adaptor A and B sequence respectively for pyrotag sequencing. The amplification program consisted of an initial denaturation step at 94 °C for 2 min; 32 cycles of denaturation at 94 °C for 30 s, annealing at 60 °C for 30 s, and elongation at 72 °C for 30 s; and a final extension step at 72 °C for 7 min. The size of the PCR products was confirmed by gel electrophoresis, and then, the PCR products was purified using Invitrogen Purelink Quick Gel Extraction Kit (Invitrogen, Oregon, USA) and were quantified using the Nanodrop (ND-2000) spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA). The sequencing of the 16S rRNA genes was performed by 454 GS Junior (454 Life Sciences—Roche, Branford, CT, USA) according to the manufacturer’s instructions. Tag-encoded pyrosequence data were deposited into NCBI Sequence Read Archive under accession number SRA071216.
## Sequence analysis
Sequencing reads obtained from pyrosequencing were subjected to processing with QIIME (quantitative insights into microbial ecology) 1.8.0 software package37 for downstream analysis. For quality trimming, reads are removed as per the following criteria: a mean quality score less than 25, length of < 150 or > 900 bp, without primer sequence, containing ambiguous characters, homopolymer run exceeding 8 nt, or uncorrectable. Based on the barcode sequences, the remaining sequences were de-multiplexed, followed by denoising using DENOISER v. 0.9.138 and removal of chimeric sequences using ChimeraSlayer (http://microbiomeutil.sourceforge.net/). UCLUST (https://drive5.com/usearch/manual/uclust_algo.html) was used to cluster the remaining sequence into Operational Taxonomic Units (OTUs) at $97\%$ sequence similarity. Next, the taxonomy was assigned to each representative sequence of each OTU using Ribosomal Database Project (RDP) classifier 2.0.139. Alignment of the OTU representative sequences was performed using PyNAST with a minimum alignment length of 150 bp and a minimum identity at $75\%$40. The hypervariable regions were filtered by using PH LANE mask (http://greengenes.lbl.gov/). FastTree 2.1 (http://www.microbesonline.org/fasttree/) was adopted for building the phylogenetic tree with Kimura’s 2-parameter model (https://www.megasoftware.net › mega4 › distance_models). The estimation of diversity indices and generation of rarefaction plots were completed in QIIME. A Venn diagram for each intestinal segment was generated based on the OTUs distributed among four emu samples. Comparison of SI microbiota diversity was done using SYSTAT 9 for Windows (SPSS Science, Chicago, Illinois) and pair-wise comparison was done with Wilcoxon Test.
## Microbial metabolic function prediction
The PICRUSt (phylogenetic investigation of communities by reconstruction of 185 unobserved states)41 was employed to predict functional genes of the classified members of the microbiome (including Cecal OTU data (SRE accession number SRA071216) obtained from Bennett et al.13 through closed-reference based OTU mapping against the Greengenes database41. Mapped closed-reference OTUs are normalized based on the copies of 16S rRNA gene within the known bacterial genomes in Integrated Microbial Genomes (IMG). *Predicted* genes were clustered hierarchically and categorized on the basis of KEGG42 orthologues (KO’s) and pathways (level -3). Significantly different pathways were identified by using STAMP software43. To compare differences in predicted metagenomic functions among ceca and different intestinal segments, Welch’s t-test was applied on the predicted microbiome functions determined by KEGG functional modules (level-3) under various microbiome metabolism44.
## Ethics approval
All experiments were performed in accordance with protocols reviewed and approved by the University of British Columbia Animal Care and Use Committee (Certificate # A10-0106).
## Richness of SI microbiota
After stringent quality filtering and trimming, a total of 165,585 sequencing reads (average 41,396 ± 3,266 seqs/bird) were generated from the 3 SI segments (duodenum, jejunum and ileum) in the 4 emus (2 males and 2 females). Average sequencing reads were 12,982 ± 1062 seqs/bird (See also Supplemental Table S1 and Supplemental Fig. S7).
The sequences were classified into 701 (average 262.8 ± 55.3/sample) species-level OTUs in the different SI segments. Only 2 OTUs (*Escherichia and* Sinobacteraceae) were identified as core bacteria along the small intestine segments and only *Escherichia was* core OTU of both small intestine and ceca13. The two OTUs (OTU_73 and OTU_773) were shared by four individuals among 8 samples (see Supplemental Table S3). Refraction curves (Fig. 1) showed that the curves for all 3 SI segments were much flatter than the curve for cecum13. Since the cecum curve indicated that we were capturing about $50\%$ of possible cecal OTUs in the population13, the much flatter and approaching plateau curves for the 3 SI segments would indicate that we have captured most of the possible OTUs in the sampled population. Figure 1Rarefaction analysis, calculated at $97\%$ dissimilarity, for the assessment of operational taxonomic unit (OTU) coverage within the16S rRNA gene–based bacterial communities in the gastrointestinal tract of the four emus (Dromaius novaehollandiae) sampled in this study. ( A) The number of OTUs as a function of the number of sequence reads. ( B) The number of OTUs as a function of the number of individual emu sampled.
## Duodenum
The duodenum segment yielded a total of 52,880 sequences (13,220 ± 290 seqs/bird) (Table 1). The duodenum sequences were classified into 343 OTUs (125.0 ± 32.6 OTUs/bird) mainly belonging to 4 microbial phyla; Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria (Fig. 2). In total, 13 OTUs, accounting for $74.1\%$ of the sequence reads, were common to all 4 duodenal samples and 243 OTUs were unique to individual emus (Fig. 3A). Notably, Turicibacter (Firmicutes) accounted for $31.7\%$ of total sequence reads (Table 2).Table 1The number of sequence reads and OTUs detected in 4 emus. Cecum data are obtained from Bennett et al.13Sequence readsNumber of OTUTotalMean ± SETotalMean ± SEDuodenum165,58513,220 ± 290343125.0 ± 32.6Jejunum15,285 ± 2,36621975.3 ± 21.3Ileum12,982 ± 1,062438145.3 ± 48.7Cecum169,19417,299 ± 2,113821335.0 ± 70.31Darin et al.13.Figure 2Phylum level microbial profile of the 4 intestinal compartments (D: duodenum, J: jejunum, I: ileum, C: ceca).Figure 3Venn diagram showing (A) the distribution of all 343 duodenum OTUs, calculated at $97\%$ dissimilarity, identified in the 52,880 16S rDNA sequences. 13 OTUs were common to all 4 individuals. ( B) The distribution of all 219 Jejunum OTUs identified in 61,139 sequences. 2 OTUs were common to the 4 individuals. ( C) The distribution of all 438 ileum OTUs identified in 51,567 sequences. 14 OTUs were common to the 4 individuals. ( D) The distribution of 129 OTUs common in all 4 individual in the different intestinal segments. Only 1 OUT was found in all 4 segments. The cecum data were obtained from Bennett et al.13 (D1–D4: duodenum of the 4 emus examined, J1–J4: jejunum, I1–I4: ileum, C1–C4: ceca).Table 2Dominant OTUs found in duodenum and ileum. DuodenumIleumGenus% Sequence reads% Sequence readsTuricibacter (OTU748)31.719.7Unknown Proteobacteria (OTU512)12.94.3Escherichia (OTU73)11.218.9Unknown Clostridiaceae (OTU132)8.56.3Lactobacillales (OTU305)5.76.1Streptococcus (OTU430)1.6Not foundBacteroids (OTU459)1.1Not foundSinobacteraceae (OTU773)0.51.0Total73.250.2
## Jejunum
The jejunum segment yielded 61,139 sequences (15,285 ± 2,366 seqs/bird) (Table 1). The sequences were classified into the same 4 main phyla as in the duodenum (Fig. 2). In total, 2 OTUs, accounting for $11.2\%$ of the sequence reads, were common in all 4 jejunal samples and 10 OTUs, accounting for $50.9\%$, were common in 3 emus. One hundred and fifty-one OTUs were unique to individual emus (Fig. 3B). In all 4 emus, Escherichia (Proteobacteria) and Sinobacteraceae (Proteobacteria) accounted for $6.6\%$ and $4.6\%$ of the sequence reads, respectively. Lactobacillales ($19.8\%$), unknown Clostridiaceae (Firmicutes) ($17.6\%$) and Streptococcus (Firmicutes) ($10.1\%$) were found in at least 3 emus. Turicibacter was found in 2 emus ($5.1\%$).
## Ileum
The ileum segment yielded 51,567 sequences (12,892 ± 1,062 seqs/sample) (Table 1). The sequences were classified into the same 4 main phyla as in the previous 2 segments (Fig. 2). In total, 14 OTUs, accounting for $59.7\%$ of the total sequence reads, were common to all 4 ileum samples and 342 OTUs were unique to individual emus (Fig. 3C). Turicibacter and *Escherichia accounted* for $19.7\%$ and $18.9\%$ of the total sequence reads, respectively (Table 2).
## Ceca
Combining with data obtained from Bennett et al.13, the distribution of 129 OTUs common in all 4 individuals in the different intestinal segments was plotted in a Venn diagram (Fig. 3D). Only 1 OTU, Escherichia (Proteobacteria), was found in all 4 segments.
## A comparison of SI microbiota diversity
The estimated microbial richness by Chao1 index of the duodenum, jejunum, and ileum was 150 ± 77 OTUs, 164 ± 106 OTUs, and 91 ± 50 OTUs, respectively. The estimated microbial diversity by Shannon index was 1.97 ± 0.71, 2.58 ± 1.1, and 1.99 ± 1.18, respectively; by Simpson index was 0.7 ± 0.14, 0.78 ± 0.18, and 0.7 ± 0.25, respectively (Table 3). The Ceca Chao1 index (624 ± 170) was significantly ($$P \leq 0.0011$$) higher than those of the SI segments, whether Ceca was compared with the whole SI or the three SI segments respectively (Ceca vs duodenum $$P \leq 0.01429$$; ceca vs ileum $$P \leq 0.0286$$; ceca vs jejunum $P \leq 0.01429$; by Wilcoxon Test) (Fig. 4). There was no significant difference in the Shannon and the Simpson indices among the 4 intestinal segments. Table 3Richness and diversity estimation for bacterial community, as indicated by Chao1, Shannon and Simpson indices, in 3 SI segments (duodenum, ileum and jejunum) of the 4 emus sampled. IntestineSpecies richnessSpecies diversityCompartmentSampleIndicesIndicesChao1ShannonSimpsonDuodenumD1145.362.920.88D2259.921.950.62D3931.810.74D4101.551.20.57Mean ± SD150 ± 771.97 ± 0.710.7 ± 0.14IleumI1322.893.880.93I2118.753.040.92I3112.671.780.64I4102.911.630.6Mean ± SD164 ± 1062.58 ± 1.10.78 ± 0.18JejunumJ1762.990.9J215230.89J332.50.740.39J4101.671.240.6Mean ± SD91 ± 501.99 ± 1.180.7 ± 0.25Cecum1Mean ± SD624 ± 1703.40 ± 0.200.79 ± 0.021Darin et al.13.Figure 4Pair-wise comparison of ceca microbiota species richness and diversity with SI microbiota species richness and diversity (D1–D4: duodenum of the 4 emus examined, J1–J4: jejunum, I1–I4: ileum, C1–C4: ceca).
## A comparison of SI and cecal microbiota between female and male emus
There were 7 OTUs that were found only in the cecal contents of all female emus (Table 4) and 8 OTUs that were found only in all male ceca (Table 5). The SI contents were more variable. There were 18 OTUs found only in the SI contents of all female emus (Table 6) and 59 OTUs in males (Table 7). There was not enough replicated samples for statistical comparison of male and female microbiota. Table 4OTUs found only in the cecal contents of all female emus. PhylumClassOrderFamilyGenusNumber of OTUsUnclassified––––1ProteobacteriaGammaproteobacteriaEnterobacterialesEnterobacteriaceaeUnclassified2FirmicutesClostridiaClostridialesLachnospiraceaeUnclassified1BacteroidetesBacteroidiaBacteroidalesBacteroidaceaeBacteroides2FusobacteriaFusobacteriaFusobacterialesFusobacteriaceaeUnclassified1Total7Table 5OTUs found only in the cecal contents of all male emus. PhylumClassOrderFamilyGenusNumber of OTUsUnclassified––––1FirmicutesClostridiaClostridialesLachnospiraceaeEubacterium1Unclassified2RuminococcaceaeUnclassified3ActinobacteriaActinobacteriaCoriobacterialesCoriobacteriaceaeEggerthella1Total8Table 6OTUs found only in the small intestinal contents of all female emus. PhylumClassOrderFamilyGenusNumber of OTUsUnclassified––––1ProteobacteriaGammaproteobacteriaEnterobacterialesEnterobacteriaceaeEscherichia1Unclassified5FirmicutesBacilliLactobacillalesStreptococcaceaeLactococcus1Streptococcus1Unclassified1Unclassified–4ClostridiaClostridialesClostridiaceaeUnclassified1Unclassified–––1ActinobacteriaActinobacteriaActinomycetalesMicrococcaceaeArthrobacter1BacteroidetesBacteroidiaBacteroidalesPrevotellaceaeUnclassified1Total18Table 7OTUs found only in the small intestinal contents of all male emus. PhylumClassOrderFamilyGenusNumber of OTUsProteobacteriaGammaproteobacteriaXanthomonadalesXanthomonadaceaeLysobacter1Unclassified1PseudomonadalesMoraxellaceaeAcinetobacter*1Unclassified1AlteromonadalesAlteromonadaceaeAlishewanella1AlphaproteobacteriaRhodobacteralesRhodobacteraceaeParacoccus3Rhodobacter1RhizobialesPhyllobacteriaceaeMesorhizobium1Devosia1Bosea1SphingomonadalesSphingomonadaceaeSphingopyxis1Unclassified–1BetaproteobacteriaHydrogenophilalesHydrogenophilaceaeHydrogenophilus1BurkholderialesOxalobacteraceaeUnclassified1Unclassified–1FirmicutesBacilliBacillalesStaphylococcaceaeMacrococcus1Jeotgalicoccus1BacillaceaeBacillus1LactobacillalesStreptococcaceaeStreptococcus1CarnobacteriaceaeTrichococcus1AerococcaceaeAerococcus1Unclassified––1ClostridiaClostridialesPeptostreptococcaceaeTepidibacter1FamilyXI.IncertaeSedisUnclassified1CatabacteriaceaeUnclassified1ClostridiaceaeClostridium1Unclassified––1ActinobacteriaActinobacteriaActinomycetalesMicrococcaceaeArthrobacter1CorynebacteriaceaeCorynebacterium2NocardioidaceaeMarmoricola1Nocardioides1Unclassified1MicrobacteriaceaeMicrobacterium1GordoniaceaeGordonia1DietziaceaeDietzia1NocardiaceaeRhodococcus1PropionibacteriaceaeTessaracoccus1DermabacteraceaeBrachybacterium1BrevibacteriaceaeBrevibacterium1Unclassified–2AcidimicrobialesIamiaceaeUnclassified1BacteroidetesSphingobacteriaSphingobacterialesFlexibacteraceaeCytophaga1SphingobacteriaceaeSphingobacterium2Unclassified1Unclassified–2BacteroidiaBacteroidalesPorphyromonadaceaeUnclassified1FlavobacteriaFlavobacterialesFlavobacteriaceaeGelidibacter1Flavobacterium1Capnocytophaga1Myroides1ChloroflexiThermomicrobiaHN1-15Unclassified–2Cyanobacteria4C0d-2mle1-12Unclassified–1Total59*Also found in the cecal content of 1 male emu.
## A comparison of predicted microbial metabolic function
PICRUSt nMDS plot (Fig. 4) showed that the predicted metagenomics functions of the cecum are very different from that of the small intestine. The size of the cecum cluster in the nMDS plot was much smaller than the clusters formed by samples from other intestinal segments and did not overlap with the other segment clusters. In the small intestine, Duodenum and Ileum have more similar microbiome metabolic functions compared to that of the jejunum. The 4 emus sampled were more uniform in the distribution of Ceca OTUs and more diverse in small intestine OTUs (Supplemental Fig. S1). Similarly, individual differences of microbiome functions in ceca are less than the small intestine segments (Fig. 5). ( D1–D4: duodenum of the 4 emus examined, J1–J4: jejunum, I1–I4: ileum, C1–C4: ceca).Figure 5Two-dimensional non-parametric multidimensional scaling (NMDS) ordination plots of predicted bacterial KEGG pathways in cecal and small intestinal segments (Duodenum, jejunum and Ileum) samples of emus ($$n = 4$$ per gut site). Each dot represents an individual samples; the circles indicate the SD. The label box is the mean of each group.
The PICRUSt comparison of predicted microbiome metabolic functions among the cecum and the small intestinal compartment is shown in Supplemental Figs. S2 (Cecum vs Duodenum), S3 (Cecum vs Ileum), S4 (Cecum vs Jejunum), S5 (Duodenum vs Jejunum) and S6 (Ileum vs Jejunum). There was no significant difference between Duodenum and Ileum.
To summarize, microbiotic metabolic functions mostly in the duodenum are shown in Table 8. Microbiotic metabolic functions mostly in the Jejunum are shown in Table 9. Microbiotic metabolic functions more in the Ileum than the cecum are shown in Table 10. Microbiotic metabolic functions mostly or exclusively in the cecum are shown in Table 11.Table 8Significant differences in microbiota metabolic functions between the Duodenum and the other three intestine segments. Duodenum Microbiota metabolic functions > Jejunum > Ileum > CecaMetabolism of co-factors and vitaminsP < 0.004D-alanine metabolismP < 0.02Riboflavin metabolismP < 0.032P < 0.025Atrazine degradationP < 0.03Xylene degradationP < 0.035See also Figs. S2 and S5.Table 9Significant differences in microbiota metabolic functions between the Jejunum and the other three intestine segments. Jejunum Microbiota metabolic functions > Duodenum > Ileum > CecaD-alanine metabolismP < 0.015P < 0.027P < 0.003Biosynthesis of ansamycines (antibiotics)$P \leq 0.0045$Phosphonate and phosphinate metabolismP < 0.01Purine metabolismP < 0.022P < 0.028Xylene degradationP < 0.006Chloroalkane and chloroalkene degradationP < 0.008Styrene degradationP < 0.0065Dioxin degradationP < 0.0072Translation proteinP < 0.0076Lipid metabolismP < 0.0062Fatty acid biosynthesisP < 0.017Butanoate metabolismP < 0.018Tyrosine metabolismP < 0.019Benzoate degradationP < 0.020Synthesis and degradation of ketone bodiesP < 0.029See also Figs. S5 and S6.Table 10Significant differences in microbiota metabolic functions between the Ileum and the other three intestine segments. Ileum Microbiota metabolic functions > Duodenum > Jejunum > CecaXylene degradationP < 0.004Metabolism of co-factors and vitaminsP < 0.0021D-alanine metabolismP < 0.0015Glutathione metabolismP < 0.015Chloroalkane and chloroalkene degrationP < 0.024Retinol degradationP < 0.024Drug metabolism – cytochrome P450P < 0.026Tyrosine metabolismP < 0.033Dioxin degradationP < 0.033See also Fig. S3.Table 11Significant differences in microbiota metabolic functions between the ceca and the three small intestine segments. Ceca Microbiota metabolic functions > Duodenum > Jejunum > IleumProtein digestion and absorptionP < 0.002P < 0.00016P < 0.0001Steroid hormone biosynthesisP < 0.0006P < 0.0009P < 0.0003Insulin signaling pathwayP < 0.049P < 0.00007P < 0.0008Amino sugar and nucleotide sugar metabolismP < 0.002P < 0.002Glycosphingolipid biosynthesis—globo seriesP < 0.0008P < 0.0006P < 0.002Glycosphingolipid biosynthesis—ganglio seriesP < 0.0005P < 0.0008P < 0.002Other Glycan degradationP < 0.0006P < 0.0013P < 0.001Glycosaminoglycan degradationP < 0.0007P < 0.0017P < 0.001Sphingolipid metabolismP < 0.0015P < 0.0015P < 0.002Alanine, aspartate and glutamate metabolismP < 0.001P < 0.0085P < 0.003Galactose metabolismP < 0.009P < 0.004Glutametergic synapseP < 0.001P < 0.046P < 0.028Restriction enzymeP < 0.001P < 0.001P < 0.001Nitrogen metabolismP < 0.0021Other ion-coupled transportersP < 0.0004Carbohydrate digestion and absorptionP < 0.030P < 0.0004P < 0.007Purine metabolismP < 0.006P < 0.004Zeatin biosynthesisP < 0.011P < 0.010P < 0.009Biotin metabolismP < 0.026P < 0.010P < 0.003Streptomycin biosynthesisP < 0.005P < 0.014P < 0.010Sulfur metabolismP < 0.032P < 0.011Polyketide sugar unit biosynthesisP < 0.017P < 0.009P < 0.011Carbon fixation in photosynthetic organismsP < 0.023P < 0.011Phenylpropanoid biosynthesisP < 0.012P < 0.042Antigen processing and presentationP < 0.046P < 0.0019P < 0.012NOD-like receptor signaling pathwayP < 0.046P < 0.0021P < 0.012Energy metabolismP < 0.026P < 0.013Butirosin and neomycin biosynthesisP < 0.017P < 0.015Secondary bile acid biosynthesisP < 0.042P < 0.017Phosphonate and phosphinate metabolismP < 0.0026P < 0.02Vitamin B6 metabolismP < 0.037P < 0.005P < 0.021Other transportersP < 0.049P < 0.0027P < 0.024Nitrogen metabolismP < 0.0021Biosynthesis of vancomycin group antibioticsP < 0.0026P < 0.0024P < 0.0034Adipocytokine signaling pathwayP < 0.0049P < 0.044Lipopolysaccharide biosynthesis proteinP < 0.016P < 0.040Methane metabolismP < 0.013Isoquinoline alkaloid biosynthesisP < 0.023P < 0.020See also Figs. S2, S3, and S4.
## Discussion
Gut microbiota can be considered as an additional organ due to its vital importance on the physiological, metabolic, immunological, and digestion and nutritional uptake functions of the host45. Gastrointestinal microbiota may include bacteria, protists, yeasts, archaea, and viruses/phages. However, due mostly to their abundance and diversity, and limitations in sequencing techniques, a bacterial focus dominates the research field46. Birds host complex gastrointestinal bacterial communities that facilitate their biological roles, distribution, and diversity. However, the gut microbiota of birds has been poorly studied, especially in wild species under natural conditions. Studies of avian gut microbiota are dominated by research on domestic poultry47. Poultry are unlikely to be representative of all bird species. Birds are diverse and vary in life-history traits such as migratory behavior, flight capacity, diet, mating systems, longevity and physiology, all of which may impact gut microbiota. However, most microbes recovered from birds show little evidence of host specificity. Among the bird species, the flightless orders (ostriches, emus, cassowaries, kiwis, and rheas), and the weak fliers from the related Tinamiformes (tinamous) were among those hosting communities with median specificities approaching those of mammals. The microbiota of these bird orders (constituting the Palaeognathae) were also more likely to occur in mammals48.
## Composition and distribution of emu GIT microbiota
Most studies of avian species have focused on characterizing the microbiota in the ceca due to its large bacterial diversity13–16. However, a few poultry studies have sampled multiple gut regions of the small intestine (SI) which are less rich and diverse than cecal samples49–51. These studies have found differences between the microbial communities across the SI and concluded that a sample taken from one site should not be taken as representative of the small intestine as a whole. Wilkinson, et al.33 found nine phyla of bacteria in the quail gastrointestinal tract (GIT); however, their distribution varied significantly among GIT sections. Our study found that the overall composition of the emu gut microbiota appears similar to the microbiota of other migratory birds27,52. The emu microbiota exhibits significant regionalization among gut regions and the SI microbiota richness and diversity also was significantly less than the cecum. On the other hand, similar to Sage Grouse27, the emu cecum samples displayed lower variability than samples from SI segments. The size of the cecum cluster in the NMDA plot (Fig. 5) was much smaller than the clusters formed by samples from SI.
Similar to most avian species, the emu gut microbiota were dominated by Firmicutes and Proteobacteria, with lower abundance of Bacteroidetes and Actinobacteria. In broiler chickens, several studies showed that Lactobacilli are the major bacterial population in three segments (duodenum, jejunum, and ileum) of the small intestine, whereas *Clostridium and* Bacteroides are dominant in the ceca24,53. In emu, the distribution of these phyla were also different in the different intestinal segments. Firmicutes being most dominant in the jejunum, sharing its dominance with Proteobacteria in the duodenum and ileum and with lowest abundance in the ceca. Proteobacteria were evenly distributed among the intestinal segments but had huge variations among individual birds ranging from $76\%$ to $0.18\%$. Bacteroidetes was most abundant in the ceca and very low abundance in the SI segments. Actinobacteria has low abundance in the SI segments and almost absent in the ceca. A fifth phylum, Fusobacteria, was detected in the emu ceca with fair abundance but not detected in the SI segments.
Firmicutes plays a key role in degradation of fiber into volatile fatty acids that provide energy for the hosts54. An abundance of Firmicutes has been linked to obesity in humans55, and to weight gain in chickens56. There are no studies addressing Firmicutes function in wild birds57, but the positive relationship between Firmicutes abundance and mass gain and immune function in domestic chickens58,59 suggests similar roles of Firmicutes between mammals and birds47.
Proteobacteria are able to grow on a range of organic compounds including protein, carbohydrates, and lipids. Recent studies of the human gut microbiome have shown that despite their relatively lower abundance, Proteobacteria contribute to much of the functional variation60. Within the avian digestive tract, Proteobacterial function remains undetermined61. Never the less, metagenomic sequence data from cats and dogs show that Proteobacteria encode a number of functions that relate to their ability to grow aerobically such as respiration, utilization of propionate as a carbon source, and repair of protein from oxidative damage62. As such, it is postulated that the Proteobacteria contribute to homeostasis of the anaerobic environment of the GIT, and hence, the stability of the strictly anaerobic microbiota62. Among the Proteobacterial classes, α-Proteobacteria are abundant in wild birds ($45\%$), in contrast to only $15\%$ relative abundance in mammalian hosts63. In our study, at the phylum level, about $30\%$ of the emu microbiota were Proteobacteria and they were evenly distributed among the different intestinal segments. However, in chickens, the ceca harbour a microbiota dominated by carbohydrate metabolism with a much lower occurrence of respirational genes64. Birds have fewer obligate anaerobes and more facultative anaerobes than mammals, but flightless birds have more obligate anaerobes than flighted birds as a proportion of their gut microbial communities48. Moreover, large flightless birds harbor more homogeneous microbiomes than smaller passerines52.
*Bacteroidetes* generally produce butyrate, an end-product of fermentation which is thought to have antineoplastic properties and thus plays a role in maintaining a healthy gut65. In emu, Bacteroidetes are found most abundant in ceca (Fig. 2)13. High abundance of *Bacteroidetes is* also found in the ceca of Japanese quail (Coturnix japonica) and ostrich (Struthio camelus)14,66,67, and may support the hypothesis that Bacteroidetes play a specific role in break-down of cellulose and other plant materials68.
Actinobacteria are the fourth most abundant phylum of microbes in the wild bird GIT, but no studies have investigated the function of Actinobacteria in wild or domestic birds47. In human, Actinobacteria are pivotal in the maintenance of gut homeostasis69. Actinobacteria, in particular Bifidobacteria, are involved in the biodegradation of resistant plant-derived carbohydrate starch70. Moreover, Bifidobacteria are suspected to be involved in the transformation of linoleic acid (LA) into conjugated linoleic acids (CLA) which enhance immune functions71. Bifidobacteria are able to produce large quantities of acetate (SCFA), which is crucial for providing energy to gut barrier epithelial cells turnover and for their potent antibacterial activity72. Actinobacteria were not detected in the ostrich ceca14. While not detected in the emu ceca13, they were found in low abundance in the SI segments in our study. Both the ostrich and the emu are flightless ratites that are ranged outdoor. In chickens, the access to range enriched Bifidobacteria in both the ileum and ceca73. However, our emu samples were obtained in November when the birds started to breed and restricted their feed intake. Fasting may affect the composition of microbiota in the ceca74.
In human, Fusobacteria are often studied in the context of pathogenicity. A rich community of Fusobacteria was frequently reported in the guts of carnivorous and omnivorous birds75. Up to one third of the vulture gut microbiota, and over half of the penguin microbiota, can consist of Fusobacteria76–79. Analyses of the vulture microbiota have revealed abundant populations of Fusobacteria that appear to be beneficial to the host bird78. Fusobacteria are also observed at a lower abundance in other carnivorous seabirds and the omnivorous bustard80,81. We have found Fusobacteria only in the emu ceca and not in the SI. Emus are mainly herbivorous and only opportunistic in catching insects and small rodents when plants and fruits are not available. Olsen82 also found Fusobacteria in the Greylag geese’ gut microbiome but this species was considered herbivorous, consuming a diversity of foods that includes leaves, roots, and seeds. Fusobacteria may be more common and play a more important role in the avian gut microbiota than previously thought. Interestingly, our sampling of emu gut microbiota was at the beginning of their breeding season and some of the individuals may have started reducing their feed intake already.
In human and in laboratory mice, males and females were observed to be characterized by a different microbiota community composition83–85. Sex had a significant effect on Japanese quail (C. japonica) ileum microbial community86–88. The male chicken's cecal microbiota indicated a closer relation with glycan metabolism, while in the female chickens it was more related with lipid metabolism89,90. In human83,91, mice92, Japanese quail87 and chickens89, sex difference of gut microbiota can also be affected by diet. Sex difference of gut microbiota in free-living birds has rarely been investigated93. Liu et al.94 has documented sex difference of gut microbiota in wild Great Bustards (Otis tarda). In our study, sex seems to affect the emu SI more so than the ceca. Similar to zebrafish, emu females showed higher abundance of intestinal Proteobacteria than males95. Because of the small sample size, our results should be interpreted with caution and need confirmation in future studies. Given that emus have reversed sex role in incubation and brooding, it would be worthwhile to further study the sex difference of gut microbiota in emus.
## Microbial metabolic functions
Identifying the microbial diversity of the host gut is a necessary first step, but provides only limited information on functional aspects of the microbial community47. It is not only important to understand what microbial species may be present in the intestine, but it is more critical to understand what physiological and metabolic processes are taking place taking the whole microbiome into account.
The avian gut microbiota, and specifically microbiota associated with the crop and ceca, may be involved in detoxification of plant materials and other food compounds. Phenols, resins, and saponins, plant defense compounds against herbivory, are usually associated with plant defenses against herbivory and are usually indigestible or toxic to birds but common in diets of herbivorous birds. The crop is the first region of the gut to process consumed food and is therefore a logical reservoir for detoxifying bacteria3. Emus are predominantly herbivorous but have no prominent crop. The detoxification of plant material and environmental chemicals seem to be performed by SI microbiota, especially those in the jejunum. PICRUSt comparisons indicated that the emu jejunum microbiome has more genes for [chloroalkane and chloroalkene degradation]—from insecticides and a/the toxic component of plants, [styrene degradation]—from plant source, [dioxin degradation]—from environmental contaminants, [xylene degradation]—from plant source, and [benzoate degradation]—from a plant source, than the ceca and other SI segments. The emu SI microbiome also has genes for [atrazine degradation] (duodenum)—herbicide; [D-alanine metabolism] to modulate pathogenic bacterial colonization and host defence (jejunum)96; [Retinol degradation] to regulate protective or pathogenic immune responses in the intestine to prevent colonization by enteric pathogens (ileum)97; [Biosynthesis of ansamycines] (jejunum)—antibiotics]; [Drug metabolism] (ileum)—cytochrome P450 to alter the metabolic outcome of environmental toxicants and heavy metals98. Emus, being free ranged outdoors, would be exposed to more environmental factors associated with foraging and would need stronger detoxification of ingested food and better protective or immune response to enteric pathogens than chickens which are kept indoors and fed a processed diet. The importance of maintaining a stable cecal environment for the commensal bacteria is further evidenced by the predicted metabolic functions of the emu cecal microbiota. PICRUSt comparisons indicated that the emu cecal microbiome has more genes than SI segments for [Glycosphingolipid biosynthesis—globo series], which play a role as receptors in pathogen invasion and also participate in the mechanism of resistance to E. coli F1899; [Zeatin biosynthesis] to induce resistance against pathogen infections100; [Streptomycin biosynthesis]—Streptomycin enhances commensal E. coli and kills competing bacteria101; [Polyketide sugar unit biosynthesis] which has antibacterial, antifungal, antiviral, immune-suppressing, and anti-inflammatory activity102; [Butirosin and neomycin biosynthesis], and [Biosynthesis of vancomycin group antibiotics]—Antibiotics; [Isoquinoline alkaloid biosynthesis]—which has antiviral, antibacterial, and antifungal functions103. Gut microbes can combat microbial pathogens directly through competitive exclusion104 or indirectly by activating the host immune system105. The emu cecal microbiome has more genes than the SI segments for [Antigen processing and presentation]—protein antigen is ingested, partially digested into peptide fragments, and then displayed for recognition by certain lymphocytes such as T cells106; [*Lipopolysaccharide biosynthesis* protein]—gut microbial *Lipopolysaccharide is* thought to be one of the most potent activators of innate immune signaling and an important mediator of the microbiome’s influence on host physiology107; [Secondary bile acid biosynthesis]—A number of molecules either produced (e.g., volatile fatty acids) or transformed (e.g., trimethylamine, secondary bile acids) by gut microbiota is known to operate as signals in the host-microbe interplay and through their cognate receptors influence host metabolism and immunity to prevent gut dysbiosis108. The considerably longer retention time of digesta in the cecum, relative to other gut regions, also permits cecal microbial communities to stabilize and is likely the cause for the reduced variability observed among individuals109.
In chickens, the SI is the site for most digestion and practically all absorption of nutrients109. Sklan et al.110 reported that $95\%$ of the fat was digested in the duodenum. Starch is the main carbohydrate in poultry feed. Starch granules are digested by pancreatic alpha-amylase in the SI111 and are absorbed by active transport. Duodenal microbiota is significantly associated with energy utilization112. It has been demonstrated that absorption of digestion products from fat, starch, and protein113 is to a large extent completed by the end of the jejunum109. The chicken ileum is mainly thought to play a role as a site for water and mineral absorption. It has been shown, however, that it may play a significant role in the digestion and absorption of starch in fast-growing broiler chickens109,114. Most of the undigested protein and fiber would undergo fermentation in the ceca and large intestine115,116.
In emus, microbial digestion and fermentation are mostly in the jejunum and ceca. There were significantly more jejunal microbiome genes than the other SI segments and the ceca for.
[Lipid metabolism]; [Fatty acid biosynthesis]—Short-chain fatty acids (SCFAs), are the main metabolites produced by bacterial fermentation of dietary fibers and resistant starch; [Butanoate metabolism]—The conversion of acetate to other SCFA, such as butyrate may be another regulatory approach for fat absorption and deposition117; [Synthesis and degradation of ketone bodies]—During fasting, a microbiota-dependent, Pparα-regulated increase in hepatic ketogenesis occurs, and myocardial metabolism is directed to ketone body utilization118. The samples were collected at the beginning of the breeding season when some of the individuals may have started fasting; [Tyrosine metabolism] (Jejunum, Ileum)—Bacterial tyrosine decarboxylases efficiently convert levodopa to dopamine. The abundance of bacterial tyrosine decarboxylases at the site of levodopa absorption in the SI had a significant impact on levels of levodopa in the plasma of rats119.
Herd & Dawson120 commented that the emu ileum may be where most of the fibre fermentation is taking place because the ileum had the largest amount of digesta and the emu ceca were small in comparison to other birds that used the ceca and colon for fibre fermentation and digestion. *Bacteroidetes* generally produce butyrate, an end product of fermentation that is thought to play a role in maintaining a healthy gut65. In emu, Bacteroidetes are found most abundant in the ceca. Actinobacteria, in particular Bifidobacteria, are involved in the biodegradation of resistant plant-derived carbohydrate starch, and *Actinobacteria is* more abundant in the emu ileum than other segments of the SI and not detectable in the ceca. Turicibacter has been correlated with butyrate and the consumption of a highly resistant starch diet in rats121. When pigs were fed with a high fibre diet, there was a significant increase in Tericibacter abundance in the ileal lumen122. In emu, Tericibacter were $32\%$ and $20\%$ of the sequence reads in the duodenum and ileum, respectively. In our study, the microbiome in the emu ceca had more genes for fermentation, digestion, and absorption than that of the ileum: [Carbohydrate digestion and absorption]; [Protein digestion and absorption]; [Energy metabolism]; [Nitrogen metabolism]; [Purine metabolism]; [Amino sugar and nucleotide sugar metabolism]; [Galactose metabolism]; [Methane metabolism]—Methane-producing microorganisms can improve fermentation efficiency by consuming any excess hydrogen and formate in the bowel, which subsequently improves acetate production and allows the body to absorb more nutrients and calories123; [Carbon fixation]—*Gas is* an inevitable product of microbial fermentation in the alimentary tract. The majority of bacterially generated gas comprises hydrogen, carbon dioxide, and methane. Gut bacteria can combine carbon dioxide with hydrogen to form carbohydrates (acetogenesis) for further digestion124. In the wild, emus feed largely on succulent herbage, seeds, fruits, flowers, and insects125 and it has been considered to make little use of microbial digestion in early studies126. However, later investigations into the dietary energy and nitrogen requirements indicated that the emus have appreciable digestion of plant fibre127. Emus were able to digest up to $45\%$ of the fibre in their diets. Firmicutes plays a key role in the degradation of fiber into volatile fatty acids that provide energy for the hosts54. In our study, Firmicutes was most dominant in the jejunum, sharing its dominance with Proteobacteria in the duodenum.
We have fulfilled our objective to characterize the intraluminal intestinal bacterial community in the different SI segments. However, our samples were collected at the beginning of the breeding season and was a single time point sampling. In order to approach our long term goal of manipulating gut microbiota for improving emu fat production128, future research should explore seasonal variation of gut microbiome with association to reproductive state, changing diets and fat deposition74, age and sex variation, microbiota associated to the intestinal mucosa129, and the epigenetics of the intestinal mucosa130,131.
## Conclusion
The objective of this study was to characterize the intraluminal intestinal bacterial community in the different SI segments using pyrotag sequencing and compare that with the ceca. We found that the detoxification of plant material and environmental chemicals seem to be performed by SI microbiota, especially those in the jejunum. The emu cecal microbiome has more microbial genes than SI segments involving in protective or immune response to enteric pathogens. Microbial digestion and fermentation was mostly in the jejunum and ceca. This is the first study to characterize the microbiota of different compartments of the emu intestines via gut samples and not fecal samples. Results from this study allow us to further investigate the influence of the seasonal and physiological changes of intestinal microbiota on the nutrition of emus and indirectly influence the fatty acid composition of emu fat.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4. The online version contains supplementary material available at 10.1038/s41598-023-31684-8.
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|
---
title: Molecular cloning, characterization and 3D modelling of spotted snakehead fbn1
C-terminal region encoding asprosin and expression analysis of fbn1
authors:
- Priyanka Sathoria
- Bhawna Chuphal
- Umesh Rai
- Brototi Roy
journal: Scientific Reports
year: 2023
pmcid: PMC10024713
doi: 10.1038/s41598-023-31271-x
license: CC BY 4.0
---
# Molecular cloning, characterization and 3D modelling of spotted snakehead fbn1 C-terminal region encoding asprosin and expression analysis of fbn1
## Abstract
The FBN1 gene encodes profibrillin protein that is cleaved by the enzyme furin to release fibrillin-1 and a glucogenic hormone, asprosin. Asprosin is implicated in diverse metabolic functions as well as pathological conditions in mammals. However, till date, there are no studies on asprosin in any non-mammalian vertebrate. In this study, we have retrieved the spotted snakehead *Channa punctata* fbn1 gene (ss fbn1) from the testicular transcriptome data and validated it. The transcript is predicted to encode 2817 amino acid long putative profibrillin protein. Amino acid sequence alignment of deduced ss profibrillin with human profibrillin revealed that the furin cleavage site in profibrillin is well conserved in C. punctata. Further, differential expression of ss fbn1 was observed in various tissues with the highest expression in gonads. Prominent expression of furin was also observed in the gonads suggesting the possibility of proteolytic cleavage of profibrillin protein and secretion of asprosin in C. punctata. In addition, the C-terminal of the fbn1 gene of C. punctata that codes for asprosin protein has been cloned. Using in silico approach, physicochemical properties of the putative ss asprosin were characterized and post-translational changes were predicted. The putative ss asprosin protein sequence is predicted to consist of 142 amino acid residues, with conserved glycosylation sites. Further, the 3D model of ss asprosin was predicted followed by MD (molecular dynamics) simulation for energy minimization. Thus, the current study, for the first time in non-mammalian vertebrates, predicts and characterizes the novel protein asprosin using in silico approach.
## Introduction
The FBN1 gene in mammals is reported to encode profibrillin protein that is cleaved by activated protease, furin into fibrillin-1 and 140-amino acids-long asprosin1. Furin is a proprotein convertase belonging to a family of subtilisin serine proteases that cleaves at the R-X-K/R-R↓ or R/K-X-X-X-K/R-R↓ motif in the target protein and is ubiquitously expressed2. Fibrillin-1 is a major glycoprotein of extracellular matrix, belonging to the fibrillin family of proteins3. The C-terminal region of FBN1, specifically exons 65 and 66 are responsible for encoding the glucogenic hormone asprosin that was discovered by Romere et al. in 20161.
Asprosin is majorly expressed in the white adipose tissue in mammals1. It is shown to have diverse physiological effects acting through different receptors. This metabolic hormone regulates glucose release from the liver through interaction with G-protein coupled receptor (GPCR), olfactory receptor 734 (OLFR734)4. It also regulates the orexigenic effect via acting on the agouti-related peptide (AgRP) neurons in the brain5. In addition to this, asprosin regulates insulin secretion and inflammation in pancreas6. Not surprisingly, asprosin is implicated in various metabolic disorders including type 2 diabetes7, obesity8,9 and polycystic ovary syndrome10,11. In recent years, the profound effects of asprosin on reproductive functions have also been demonstrated12–15. However, all these studies are confined to mammals and till date the existence of asprosin in non-mammalian vertebrates is obscure.
In view of this lacuna, the present study was undertaken in fish Channa punctata. Teleost are the oldest and most diverse extant vertebrates. In this study, using various bioinformatics tools, fbn1 and furin genes were validated and the furin cleavage site was determined in the fbn1 encoded putative profibrillin protein in spotted snakehead. Further, tissue-dependent differential expression of the fbn1 and furin gene was demonstrated. In addition, an attempt has been made to overexpress ss asprosin in a heterologous bacterial system. Using in silico approach, the physicochemical properties and post-translational modifications have been predicted for ss asprosin. The 3D modelling of the deduced ss asprosin was carried out using I-TASSER followed by energy minimization through MD simulation.
## Identification and validation of fbn1 and furin sequence
The potential longest transcript of fbn1 obtained from testicular transcriptome data of C. punctata contained 8454 base pair long coding sequence and was partial from the 5′-end. Similarly, the best transcript of C. punctata furin containing 1869 base pair open reading frame (ORF) was retrieved and it was partial from the 5′-end.
## Expression analysis of fbn1 and furin gene in Channa punctata
The expression analysis of fbn1 in different tissues of C. punctata revealed the highest expression in gonads followed by heart and different parts of the brain. Among the tissues that expressed fbn1 prominently (testis, ovaries, heart and brain), furin was highly expressed in gonads as observed by semi-quantitative PCR (Fig. 1; Supplementary Fig. S1).Figure 1(a) Tissue distribution of fbn1 gene in different tissues of C. punctata. Real-time quantitative PCR was used to quantify the gene expression in each tissue. Each data point represents the mean ± SEM of tissues collected from 6 fish ($$n = 6$$). Two technical replicates for each sample were used. ( b) Tissue distribution of furin using semi quantitative PCR. *The* gene expression of furin was studied in tissues which exhibited prominent fbn1 expression (testis, ovary, heart, forebrain midbrain and hindbrain) using semi-quantitative PCR followed by resolving into the $1\%$ agarose gel.
## Identification of furin cleavage site
The ss fbn1 transcript encoded 2817 amino acid long putative protein. The multiple sequence alignment of deduced profibrillin of C. punctata with profibrillin of H. sapiens showed the conserved furin cleavage site (R/K-X-X-X-K/R-R↓) (Fig. 2). On the basis of the presence of furin cleavage site, amino acid sequence of asprosin protein was deduced in C. punctata. Figure 2Amino acid alignment of C-terminal region of profibrillin of human and snakehead. The furin cleavage site has been highlighted in green colour and the predicted mature ss asprosin has been highlighted in yellow colour. The ‘*’ represents amino acids that are conserved, ‘:’ indicates amino acids with strongly similar properties, ‘.’ represents amino acids with less similar properties whereas the gaps represent mismatch residues.
For the prediction and analysis of putative peptide sequence encoded by fbn1 gene, ExPASy server (http://ca.expasy.org/)41 was used. The sequence alignment was constructed for the putative partial sequence of profibrillin of C. punctata with the profibrillin of Homo sapiens using Clustal omega. Furin cleavage site (R-X-K/R-R↓ or R/K-X-X-X-K/R-R↓; where ↓ represents cleavage site)2 was manually identified in the aligned sequences near the C-terminal region. The amino acid sequence towards the C-terminus after the furin cleavage site represented the putative ss asprosin protein sequence as mentioned by the Romere et al.1.
## Cloning and expression of ss recombinant asprosin
The 429 base pair sequence of fbn1 gene with one stop codon was cloned in the pProEx-HTc vector, transfected into DH5ɑ and validated through sequencing. After transformation in the BL21 cells, induction using IPTG was carried out. Proteins isolated from the uninduced pellet, induced pellet, uninduced supernatant and induced supernatant samples were analyzed using coomassie staining for presence of recombinant asprosin protein (Supplementary Fig. S2). Significant expression of recombinant asprosin (~ 19 kDa) having six histidine tag was observed in the supernatant of induced sample as compared to uninduced supernatant sample. However, in case of pellet, no protein band was detected around 19 kDa in both uninduced as well as induced sample. Further, using anti-His antibody, recombinant asprosin protein with histidine tag was validated in the induced supernatant sample (Fig. 3, Supplementary Fig. S3).Figure 3Expression analysis of recombinant ss asprosin. ( a) $15\%$ SDS-PAGE showing the protein expression in the uninduced pellet, induced pellet, uninduced supernatant and induced supernatant samples observed using coomassie staining. ( b) Western blotting using anti-His antibody for validation of recombinant asprosin in supernatant of induced and uninduced samples.
## Physicochemical properties
The putative ss asprosin and human asprosin comprises 142 and 140 residues, respectively with approximately 16 kDa molecular weight. The instability index of putative ss asprosin was 33.89 and human asprosin was 37.84. The aliphatic index of the putative ss asprosin and human asprosin was 94.72 and 89.86, respectively. The GRAVY values of putative ss asprosin was -0.585 and human asprosin was − 0.549 (Table 1). The percent identity matrix of the primary sequence of putative asprosin of C. punctata with asprosin of H. sapiens revealed $57.86\%$ similarity. The post-translational modifications such as N-linked glycosylation and phosphorylation were predicted using Motif scan software (Fig. 4). Two conserved N-linked glycosylation sites at position 3 and 37 were predicted in ss asprosin. Phosphorylation sites at 5, 16, 42, 56, 88 and 89 positions were predicted in asprosin of C. punctata. Table 1Physicochemical properties of asprosin protein using ExPASy ProtParam tool. Physicochemical propertiesH. sapiensC. punctataNumber of amino acids140142Molecular weight (in kDa)15.8816.32Total number of negatively charged residues (Asp + Glu)20 ($14.3\%$)25 ($17.6\%$)Total number of positively charged residues (Arg + Lys)16 ($11.43\%$)15 ($10.56\%$)Instability Index37.8433.89Aliphatic Index89.8694.72Grand average of hydropathicity (GRAVY)− 0.549− 0.585Figure 4Sequence alignment of putative ss asprosin and human asprosin showing post-translational modifications. The glycosylation sites are highlighted in green and phosphorylation sites are highlighted in yellow.
## Protein modelling, quality assessment and validation of asprosin
The secondary and tertiary structure of ss asprosin showed the presence of β-strand. In the present study, MD simulation of the 3D structure of ss asprosin protein was carried out for the refinement and conformational dynamics. RMSD analysis showed that the model attained a stable plateau after 85 ns with an average value of 0.95 nm. Fluctuation of Cɑ atoms of every amino acid during energy minimization assessed through RMSF showed no major fluctuations. However, residues at position 5-20 and 115-125 fluctuated with RMSF value of approximately 1 nm. During the initial time period between 1 and 10 ns, Rg pattern was unstable, but after 90 ns simulation time, Rg value showed stable behaviour with an average value of ~ 1.73 nm. The Ramachandran plot for geometry evaluation of ss asprosin protein after MD simulation using PROCHECK module revealed that $81.5\%$ residues lie in the most favoured region, $17.7\%$ residues in additional allowed region, $0.8\%$ residues in generously allowed region and $0\%$ residues in disallowed region (Fig. 5).Figure 5Structural representation and stability parameters of ss asprosin after stimulation. ( a) 3D model of ss asprosin in which arrow represent the β-sheets. ( b) Ramachandran plot of amino acid residues of ss asprosin. ( c) RMSD (Root mean square deviation) of the backbone Cɑ atoms of ss asprosin vs time. ( d) RMSF (Root mean square fluctuation) of each residue of ss asprosin vs time. ( e) Rg (Radius of gyration) vs time.
## Discussion
Since the discovery of asprosin in 20161, there are at present no reports on asprosin in any of the other vertebrate groups. This is surprising as it is shown to play a critical role in metabolism, immunity and reproduction in mammalian species1,6,13–16. In the current study, the partial cDNA sequence for spotted snakehead fbn1 and furin was obtained. ss fbn1 comprises a 8454 bp long coding sequence that is predicted to code for 2817 amino acid long profibrillin protein with conserved furin cleavage site. The ss furin transcript retrieved from the transcriptome of C. punctata had 1869 bp ORF. The constitutive and ubiquitous expression of ss fbn1 was observed in many tissues of C. punctata. Unlike humans, in which the maximum expression of FBN1 has been reported in the white adipose tissue1, the gonads of C. punctata expressed the maximum fbn1. The gonads also show prominent gene expression of the protease furin, thereby indicating the potential production of asprosin in teleosts. Similar expression of FBN1 and FURIN was also observed in the ovaries of beef heifers wherein it has been suggested to play an important role in ovarian functions13. The importance of asprosin in regulation of gonadal activities is further corroborated by a recent report in mice where Leydig and Sertoli cells are found to be immune-positive for asprosin and intratesticular administration of asprosin has been shown to promote steroidogenesis as well as spermatogenesis17. Based on our observations, and studies in mammals, it can be hypothesized that asprosin might be implicated in reproduction in teleosts also.
For the first time in a teleostean model, we have successfully cloned the fbn1 region encoding asprosin protein of C. punctata and expressed it in the bacterial system. The IPTG-induced recombinant ss asprosin protein observed in the supernatant had an approximate molecular weight of 19 kDa. Intriguingly, the recombinant protein was not detected in the pellet which might be due to either the absence, or extremely low amount of inclusion bodies formed of recombinant ss asprosin. Recombinant ss asprosin was validated using anti-His antibody. In the case of human, the constructed recombinant protein has molecular weight of ~ 17 kDa with six histidine residues, however the molecular weight of asprosin found in human plasma is 30 kDa1. The discrepancy in size has been attributed to the lack of several post-translational changes in the recombinant asprosin expressed in the bacterial system as opposed to the asprosin found in the plasma. Indeed, it was shown that when recombinant asprosin is expressed in mammalian cell line, then the molecular weight is almost similar to that found in plasma1. Based on this observation, it can be suggested that the molecular weight of asprosin in *Channa punctata* might be more than 19 kDa and needs to be confirmed in further studies.
The percent identity matrix revealed that putative ss asprosin is more than $55\%$ similar with the human asprosin and comprises 142 amino acids, whereas mammalian asprosin consists of 140 residues1. Interestingly, alignment of ss asprosin with human asprosin reveals that two amino acids at position 13th (Met) and 94th (Ser) exist in ss asprosin but are lost in due course of evolution in human. In silico analysis revealed that the putative ss asprosin and human asprosin were stable as its instability index, a parameter to measure the stability of protein under ex vivo conditions, were lower than the threshold value of 4018. The predicted ss asprosin and human asprosin had a high aliphatic index, thereby indicating its thermostability. The aliphatic index represents the relative volume of the aliphatic side chain (valine, alanine, leucine and isoleucine) occupied in a protein and determines the thermostability of a globular protein with values ranging between 71.13 and 143.54. Higher aliphatic value denotes a thermostable protein19. In addition to this, based on the negative grand average of hydropathicity index (GRAVY), the deduced ss asprosin and human asprosin seems to be hydrophilic in nature. The negative GRAVY value represents that a protein is hydrophilic and globular in nature, while positive GRAVY value implies a hydrophobic and membrane-bound protein20. The hydrophilic nature of ss asprosin implies that it is a soluble protein and might not require binding protein for its transport.
Post-translational analysis revealed the presence of glycosylation sites. Glycosylation is reported to increase the stability, as well as, half-life of proteins21,22. In case of other glycoproteins such as thyroid stimulating hormone, luteinizing hormone and follicle-stimulating hormone, glycosylation in these protein hormones helps in their interaction with the cognate receptor by increasing binding affinity and effecting the signalling23–28. The N-linked glycosylation sites in ss asprosin at position 3 and 37 correspond to the glycosylation sites in human asprosin1 and hence seem to be well conserved and might play a crucial role in the functional aspect of asprosin. Moreover, in ss asprosin, six phosphorylation sites are also predicted. Although phosphorylation plays a critical role in proteins involved in cell signalling, the implication of phosphorylation sites in ss asprosin is yet to be understood.
Currently, lack of NMR/crystal experimental structure of asprosin is a major constraint in understanding the structural aspect of the protein. This study provides the first comprehensive description of structural parameters using in silico approach via prediction of tertiary structure of asprosin in C. punctata. The presence of β-strands in the predicted 3D model of ss asprosin might be involved in strengthening the backbone of the protein and thereby enhancing the stability29. Similarly, tertiary structure of human asprosin predicted using bioinformatics tools also showed the presence of several β-strands30. Molecular dynamics (MD) simulation and energy minimization represent an important tool for the optimization of 3D models and relaxation of geometric chains with the unfavourable bond angle, bond length and torsion angles31,32. MD simulation of 3D model of ss asprosin was carried out for 100 ns and stability parameters revealed that it is stable in the physiological system. The RMSD value determines the protein stability on the basis of conformational changes in the Cα backbone of complete structure from the initial to the final position. The conformation of a protein is relative to the fluctuations occurring during the simulation process. These fluctuations are plotted as RMSD value vs the time during which simulation was in process. The smaller deviations in RMSD values indicate a stable structure33. After 80 s of simulation, the ss asprosin showed stable RMSD value of approximately 0.95 nm. RMSF value determines the fluctuations in the Cα atom of individual residue during MD simulation. The amino acid residues between position 5-20 and 115-125 in the 3D model of ss asprosin include coils and hence show more fluctuations in the RMSF value. Residues involved in the formation of loops, coils and turns and therefore more exposed to solvents are known to have high fluctuations in the RMSF value34. Loops, coils and turns are flexible random structures in the model and play crucial roles in protein function and folding35,36. The ss asprosin showed stable Rg value which is an indicator of globularity and compactness of the protein37. Further, the Ramachandran plot analysis revealed more than $80\%$ residues of ss asprosin in the most favoured region and $0\%$ residues in disallowed regions. Thus, the geometrical evaluation of ss asprosin along with RMSD, RMSF and Rg value attests the robustness of the predicted 3D model of putative ss asprosin protein.
## Conclusion
Asprosin is an important metabolic hormone, playing diverse roles in mammals and implicated in various metabolic disorders. The hormone is encoded by the C-terminal of the FBN1 gene. Interestingly, since its discovery in 2016, there have been no studies on asprosin in any other vertebrate group. Since, teleost are being the most abundant extant vertebrates, we undertook the current study to explore the possibility of the presence of asprosin in a teleost model, Channa punctata. The expression of fbn1 gene and the enzyme furin responsible for cleavage of the profibrillin into fibrillin-1 and asprosin, in various tissues of C. punctata suggest the presence of asprosin in teleost. The conserved furin cleavage site in the profibrillin helped in determining the putative primary ss asprosin sequence. Understanding the physicochemical properties of ss asprosin helped in determining the nature of the putative protein. Although, N-linked glycosylation sites in ss asprosin were found similar to human asprosin, the exact role of glycosylation and phosphorylation in asprosin are yet to be elucidated. The cloning and expression of ss recombinant asprosin has been carried out. In future, the purified ss recombinant asprosin could be used to understand the role played by this hormone in fish physiology, especially in reproductive functions.
## Identification and validation of fbn1 and furin gene transcript
The transcript of fbn1 and furin gene were retrieved from the testicular transcriptome of Channa punctata, previously annotated with Takifugu rubripes, *Oreochromis niloticus* and *Rattus norvegicus* reference protein sequences38. The transcript with longest open reading frame (ORF) and maximum percentage identity was selected from several fbn1 and furin transcripts with alternate ORF (Gene runner version 3.05, Hastings Software, Inc., USA) and using Blastx (http://blast.ncbi.nlm.nih.gov/Blast.cgi), the nucleotide sequence was verified.
Reverse transcriptase polymerase chain reaction (RT-PCR) was used to validate the transcript encoded sequence. Using Clustal omega, multiple sequence alignment was constructed to determine the conserved region for designing gene specific primers for fbn1 and furin (http://www.ebi.ac.uk/Tools/msa/clustalo)39. Table 2 includes the details of the primers. For RT-PCR procedure, the following steps were employed: initial denaturation (95 °C for 5 min), 35 cycles of denaturation (95 °C for 30 s), annealing (60 °C for 30 s) and extension (72 °C for 45 s), followed by final extension (72 °C for 10 min). $1\%$ agarose gel (Himedia, India) was used to resolve the amplified product of desired length with ethidium bromide staining. For elution, Wizard SV Gel and PCR Clean-Up System (Gel extraction kit Cat. No. A9281, Promega, USA) was used and Sanger sequencing was employed for fbn1 and furin nucleotide sequence. Using Clustal Omega, the obtained partial sequence was aligned with the transcript encoded sequence and it exhibited complete similarity. The same partial sequence of fbn1 and furin were submitted to the NCBI GenBank and accession number OP271666 and OP921042 has been assigned. Table 2Primers used for the reverse transcriptase Polymerase Chain reaction (RT-PCR) and qPCR.S. no. Types of primerPrimer sequenceLength1fbn1 (Semi-quantitative)Forward (5′-3′)CTTGGTGGGTGGATACAGGT20Reverse (5′-3′)GTCTTTGTCATGTCTGTCCTCC222fbn1 (qPCR)Forward (5′-3′)CCAAAGAAAGGACGCAAACG20Reverse (5′-3′)TCCTCGACATCCACACTG183furin (Semi-quantitative)Forward (5′-3′)GTAGGACGCAGAGTGAGT18Reverse (5′-3′)CTGAGGGGATTTTCGTTGG19
## Fish procurement and maintenance
Adult spotted snakehead C. punctata weighing 80–100 g was obtained from the wild populations in and around the National Capital Region of Delhi, India and stocked in dechlorinated fresh water tanks (15 fish per tank containing 45 L water) having dimension: 74 cm × 34 cm × 32 cm (L × B × H) under light regimen of 12 L:12 D at 25 ± 2 °C. The water was changed on alternate days and the water temperature was maintained at 24–26 °C. The fish were acclimatized for 3 weeks. The protocol has been approved by the Institutional Animal Ethics Committee (DU/ZOOL/IAEC-R/$\frac{2021}{6}$), Department of Zoology, University of Delhi and experiment was carried out following the relevant guidelines and regulations of the IAEC. The studies involving the live animals follows the recommendations in the ARRIVE guidelines.
## Tissue-specific expression of fbn1 and furin
Healthy spotted snakeheads ($$n = 6$$) were sacrificed with an overdose of 2-phenoxyethanol (5 mL/L water, Sisco Research Laboratories, Mumbai, India). Different tissues including brain (forebrain, hindbrain and midbrain), heart, liver, adipose tissue, gut, gonads and lymphoid organs including head kidney, spleen, skin, gills and trunk kidney were dissected out and stored at − 80 °C until RNA extraction after washing with 1×PBS.
## RNA extraction and cDNA synthesis
Using the TRIzol reagent, total RNA was extracted from tissues following the manufacturer's protocol (Invitrogen). For assessment of RNA integrity, $1\%$ agarose gel was used to observe the band intensities of 28S and 18S rRNA and RNA levels were quantified using a Nanodrop (ND-1000, Nanodrop Technologies, USA). The RNA samples having 1.8–2.0 absorbance ratio at A$\frac{260}{280}$ were selected for the cDNA preparation. In order to remove genomic contamination, RNA was treated with the enzyme DNase I (Thermo Scientific, USA) in 0.2 mL PCR tubes in a standard thermocycler at 37 °C for 30 min, and to terminate the DNase activity, EDTA (Thermo Scientific) was added and reaction was run at 65 °C for 10 min and then 4 °C hold. Using avian myeloblastosis virus (AMV) reverse transcriptase, the treated samples were processed for reverse transcriptional synthesis of single-strand cDNA following manufacturer’s specifications (Cat. No. K1622, Thermo Scientific, USA) in a thermocycler at 65 °C (for 10 min), 25 °C (for 5 min), 42 °C (for 60 min), 72 °C (for 10 min) and 4 °C hold. Through amplification of the 18S rRNA housekeeping gene, the synthesis of cDNA was confirmed.
## Real-time quantitative PCR (qPCR) for fbn1
The partially validated sequence of fbn1 was used for qPCR primers design in order to quantitate the target mRNA transcript. For validation of primers, melt curve analysis was employed. $1\%$ agarose was used to resolve the amplified products of fbn1 gene and using ethidium bromide staining method, single bands were visualized. The qPCR primers percentage efficiency was checked by a standard curve using two-fold serial dilutions of ovarian cDNA. Similarly, using specific primers, 18S rRNA was quantified as a reference gene in each sample. The reaction was carried out in qPCR machine having standard cycle mode: 50 °C (for 2 min), 95 °C (for 2 min), 95 °C (for 15 s), 59 °C (for 15 s) and 72 °C (for 1 min) using power SYBR Green (Cat. No. 4367659, Applied Biosystems, USA) and finally a dissociation step: 95 °C (for 15 s), 60 °C (for 1 min) and 95 °C (for 15 s) for melt-curve analysis.
## Semi-quantitative PCR (RT-PCR) for furin
Tissues expressing high levels of fbn1 such as different parts of the brain (forebrain, midbrain and hindbrain), heart, testis and ovary were selected for studying the expression of furin. Using semi-quantitative primers of furin and 18S rRNA, RT-PCR procedure was employed with the following steps: initial denaturation (95 °C for 5 min), 35 cycles of denaturation (95 °C for 30 s), annealing (60 °C for 30 s) and extension (72 °C for 45 s), followed by final extension (72 °C for 10 min). $1\%$ agarose gel (Himedia, India) was used to resolve the amplified products of desired length with ethidium bromide staining. The gel was observed under ChemiDocTM XRS + Imaging system (Bio-rad).
## Statistical analysis
18S rRNA expression was used to normalize the relative expression of fbn1. In order to calculate the relative fold change, 2− ∆∆CT method40 was employed, wherein the tissue that showed lowest expression, i.e., head kidney was considered as a reference for tissue distribution. For the statistical analysis GraphPad Prism 8.0.1 software (GraphPad Software, La Jolla, CA) was used.
## Cloning
The C-terminal region of ss fbn1 that is predicted to code for asprosin (8026–8454 bp) was cloned in the pProEx-HTc vector, having N-terminal His-tag. To clone the gene region of interest, the sequences of forward and reverse primers were 5′-CCTGGATCCTAAGCACTAACGCAACACACGATGAGC-3′ (carrying a BamHI site) and 5′-CGCAAGCTTTTAATGGAGGATGATCTGCACCCTC-3′ (containing a HindIII site). The amplified product using these primers was digested with the BamHI and HindIII restriction enzymes and the resulting fragment was ligated into the pProEx-HTc plasmid, which was also previously digested with the same restriction enzymes. After ligation, the plasmid containing gene of interest was transformed into the DH5α cells and grown overnight on 100 µg/ml ampicillin resistance agar plates at 37 °C. The white colonies from the plate were picked, grown and maintained in the LB medium supplemented with 100 µg/ml ampicillin at 37 °C overnight with constant shaking (220 rpm). The DH5α cells containing plasmid were pellet down and DNA was extracted using WizardⓇ Plus Minipreps DNA purification system (Cat. No. A7660, Promega, USA). The integrity of plasmid construct was validated through Sanger sequencing.
## Expression
BL21(DE3) cells were transformed with pProEx-HTc vector derivative expressing asprosin protein and inoculated into LB medium containing 25 µg/ml chloramphenicol and 50 µg/ml ampicillin followed by incubation at 37 °C with constant shaking (220 rpm) until A600 reached at OD 0.6. 1 mM Isopropyl 1-thio-d-galactopyranoside (IPTG) was added in the culture for the induction of recombinant protein and culture was grown for an additional 2 h at 37 °C with constant shaking (220 rpm). The cells were pellet down and resuspended in cell lysis buffer containing 50 mM Tris–Cl, pH 8.0, 300 mM NaCl, 1 mM dithiothreitol, 1 mM EDTA, 10 mg/ml lysozyme, $0.1\%$ SDS, 1× protease inhibitor cocktail and 1 mM phenylmethylsulfonyl fluoride and lysed by sonication. The cell lysate was then centrifuged at 12,000 rpm at 4 °C for 30 min. The supernatant and pellet were collected separately and stored at − 80 °C. Further, to check the induction of asprosin protein, protein samples from induced and uninduced cells were mixed with 2× SDS loading dye and loaded on $15\%$ SDS-PAGE for gel electrophoresis and observed using coomassie brilliant blue staining. To validate the recombinant asprosin protein, western blotting was done using anti-His antibody (Anti-PolyHistidine-Peroxidase antibody, mouse monoclonal, Cat No. A7058, Sigma-Aldrich). The uninduced supernatant sample was taken as control. Briefly, for western blotting, protein was resolved in the $15\%$ SDS-PAGE and transferred onto nitrocellulose membrane in transfer buffer (193 mM glycine, 25 mM Tris and $20\%$ methanol, pH 8.5). After transfer, blocking of nitrocellulose membrane was done in $5\%$ BSA for 1 h and subsequently incubated in the primary anti-His antibody (dilution 1:2000) for 1 h. The membrane was washed three times with TBST (50 mM Tris, 150 mM NaCl, $0.1\%$ Tween-20, pH 7.6) and bands were developed using Luminata™ Crescendo Western HRP substrate (Millipore Corporation) and the luminescent image analyzer amersham imager-600 (GE/Biosciences AB) was used for visualization.
## Physicochemical properties and post-translational modifications
To determine the molecular weight, grand average of hydropathicity index (GRAVY) and other physicochemical properties, the deduced primary sequence of asprosin of C. punctata and H. sapiens asprosin were subjected to ProtParam tool (http://expasy.org/cgi-in/protparam)42. Clustal omega was employed to compute the percentage similarity of deduced asprosin of C. punctata with asprosin of H. sapiens. Motifscan software (https://myhits.sib.swiss/cgi-bin/motif_scan) was employed for determining the post-translational modifications such as glycosylation and phosphorylation in the deduced asprosin of C. punctata.
## Modelling and MD simulation
Automated modelling program I-TASSER (Iterative-Threading ASSEmbly Refinement) server43 was employed for modelling 3D structure of putative ss asprosin protein in order to gain insight into asprosin protein. For validation of the obtained 3D model, MD simulation was carried out using GROMACS (Groningen Machine for Chemical Simulations) 2019.2 version for 100 ns with force field Charmm2744. Simulation system was solvated with the TIP4P water model in a triclinic box. 25 Na+ and 15 Cl− ions were added to the system for achievement of electroneutrality by steepest descent energy minimization. For the position restrained simulation, system equilibrium was done in two phases: NVT (constant number of particles, volume and temperature) and NPT (constant number of particles, pressure and temperature) ensemble for 500 ns each. System temperature and pressure was maintained at 300 K and 1 bar during MD runs via Berendsen and Parinello-Rahman methods. For long-range electrostatic interactions and bond length constrain, Particle Mesh Ewald (PME) and LINC algorithm were used45. GROMACS modules such as gmx rms, gmx rmsf and rmx gyrate were performed for protein analysis. After MD simulation, the 3D model of asprosin was subjected to the SAVES (Structural Analysis and Verification Server) and geometry evaluation was done using PROCHECK module (http://servicesn.mbi.ucla.edu/SAVES/)46.
## Ethics declaration
The protocol has been approved by the Institutional Animal Ethics Committee (DU/ZOOL/IAEC-R/$\frac{2021}{6}$), Department of Zoology, University of Delhi and all the methods were performed in accordance with the relevant guidelines and regulations of the IAEC. The studies involving the live animals follows the recommendations in the ARRIVE guidelines.
## Supplementary Information
Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3. The online version contains supplementary material available at 10.1038/s41598-023-31271-x.
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---
title: CRISPR-Cas9 editing of TLR4 to improve the outcome of cardiac cell therapy
authors:
- Yeshai Schary
- Itai Rotem
- Tal Caller
- Nir Lewis
- Olga Shaihov-Teper
- Rafael Y. Brzezinski
- Daria Lendengolts
- Ehud Raanani
- Leonid Sternik
- Nili Naftali-Shani
- Jonathan Leor
journal: Scientific Reports
year: 2023
pmcid: PMC10024743
doi: 10.1038/s41598-023-31286-4
license: CC BY 4.0
---
# CRISPR-Cas9 editing of TLR4 to improve the outcome of cardiac cell therapy
## Abstract
Inflammation and fibrosis limit the reparative properties of human mesenchymal stromal cells (hMSCs). We hypothesized that disrupting the toll-like receptor 4 (TLR4) gene would switch hMSCs toward a reparative phenotype and improve the outcome of cell therapy for infarct repair. We developed and optimized an improved electroporation protocol for CRISPR-*Cas9* gene editing. This protocol achieved a $68\%$ success rate when applied to isolated hMSCs from the heart and epicardial fat of patients with ischemic heart disease. While cell editing lowered TLR4 expression in hMSCs, it did not affect classical markers of hMSCs, proliferation, and migration rate. Protein mass spectrometry analysis revealed that edited cells secreted fewer proteins involved in inflammation. Analysis of biological processes revealed that TLR4 editing reduced processes linked to inflammation and extracellular organization. Furthermore, edited cells expressed less NF-ƙB and secreted lower amounts of extracellular vesicles and pro-inflammatory and pro-fibrotic cytokines than unedited hMSCs. Cell therapy with both edited and unedited hMSCs improved survival, left ventricular remodeling, and cardiac function after myocardial infarction (MI) in mice. Postmortem histologic analysis revealed clusters of edited cells that survived in the scar tissue 28 days after MI. Morphometric analysis showed that implantation of edited cells increased the area of myocardial islands in the scar tissue, reduced the occurrence of transmural scar, increased scar thickness, and decreased expansion index. We show, for the first time, that CRISPR-Cas9-based disruption of the TLR4-gene reduces pro-inflammatory polarization of hMSCs and improves infarct healing and remodeling in mice. Our results provide a new approach to improving the outcomes of cell therapy for cardiovascular diseases.
## Introduction
The inconclusive results of cell therapy trials for heart disease1–3 drive the search for alternative strategies. Mesenchymal stromal cells (MSCs), also known as mesenchymal stem cells, possess immunomodulatory, anti-inflammatory, and reparative properties4–6, either directly or via the release of free cytokines and extracellular vesicles (EVs)7. MSCs have emerged as a viable source for cardiac cell therapy6,8. However, the environment of the diseased heart may drive resident and transplanted MSCs toward a pro-inflammatory phenotype and restrict their survival and reparative effects9–11. In part, this effect is mediated by toll-like receptor 4 (TLR4)9,10, a membranous and endosomal receptor that controls the innate immune system by recognizing a broad spectrum of molecules, such as damage-associated molecular patterns (DAMPs)12,13. The phenomenon of pro-inflammatory MSCs aligns with the new paradigm of MSC polarization14. The primary mechanism of pro-inflammatory polarization is mediated by TLR414.
The introduction of clustered regularly interspaced short palindromic repeats (CRISPR) as a gene-editing tool has revolutionized basic and translational research15–17. CRISPR can be applied to either disrupt, knockout (KO) or knock-in genes18. However, the clinical application of CRISPR technology in vivo has been restricted by efficacy and safety concerns15,18. Here, we aimed to test the hypothesis that ex vivo disruption of the TLR4 gene by the CRISPR-Cas9 system will improve the reparative properties of cardiac MSCs. The strategy of ex vivo gene editing of TLR4 is clinically relevant and can improve the outcome of cell therapy for heart repair.
## Electroporation-based CRISPR-Cas9 TLR4 gene editing
To disrupt the expression of the TLR4 gene in human cardiac MSCs (hMSCs), we developed an improved electroporation protocol. Donor patients’ basic clinical characteristics are available in Supplementary Table 3. We used a TLR4-targeted ribonucleoprotein Cas9. We started by comparing three single guide RNAs (sgRNAs) located in exon I. *The* general yield of these sgRNAs was around $30\%$. To further increase the insertion and deletion (indel) rate and to prevent alternative splicing of the gene, we added another sgRNA to the comparisons that target exon III on the antisense strand of the DNA. This sgRNA was highly efficient and thus used for the rest of the experiments (Fig. 1A)19. Unedited control groups were subjected to the same editing protocol but with scrambled sgRNA. Two days after the procedure, we sequenced the target site and aligned the DNA sequence, comparing edited and unedited cells at the cut site of the DNA (Fig. 1B). Using the tracking of indels by decomposition (TIDE) algorithm20, we calculated the distribution and rate of nucleotide indels in the sequence (Fig. 1C). Further sequencing data analysis showed that adding one nucleotide was the most abundant repair event ($43.9\%$, mostly cytosine, Fig. 1C). Seven replicate reactions showed a $51.9\%$ indel rate that translated into frameshifts in the reading frame (Fig. 1D). A minimal indel rate of > $50\%$ was set for all further experiments. Overall, our electroporation protocol achieved a high, robust percentage of editing in primary human cells in a simple procedure. Figure 1CRISPR-Cas9 TLR4 Editing in Human MSCs. ( A) To edit the TLR4 gene in hMSCs we designed a sgRNA targeting exon III. ( B) To calculate the editing efficiency, we produced genomic DNA from the cells and sequenced the target site with Sanger sequencing, with and without gene editing, marked by the horizontal dotted line. ( C) For each experiment, we calculated the indel rate with indel distribution analysis (TIDE). ( D) Average editing efficiency (%) with cells from seven different patients. We used 4 × 105 cells and read 1 × 105 for each sample. Positive (E–G) and negative (H,I) hMSC surface markers show no differences in the expression levels between the edited and unedited cells after comparison to a matching isotype control, as found by flow cytometry. Statistical analyses by unpaired two-tailed t-test, $$n = 4$.$ ( J) To determine the effect of gene editing on hMSC proliferation and viability, we used a proliferation assay (XTT). hMSCs (5 × 104 per well, in duplicates) were incubated for four consecutive days. Gene editing did not affect cell proliferation. Statistical analysis by the mixed-effects model (REML) with Holm-Šídák's post-test, $$n = 4$.$ ( K) We used a migration scratch assay to determine whether disrupting the TLR4 gene affects hMSC migration. We scratched sheets of cultured hMSCs. Next, we exposed the hMSCs to conditioned media from edited and unedited cells. Gene editing did not affect the cell migration rate. Statistical analysis by two-way ANOVA with Holm-Šídák's post-test, $$n = 4$.$ Abbreviations hMSCs, human mesenchymal stromal cells; indel, insertion and deletion; PBS, phosphate-buffered saline; sgRNA, single guide RNA; XTT, 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide.
## The effect of TLR4 gene editing on hMSC phenotype
To determine the effect of TLR4 gene editing on hMSC phenotype, we assessed the expression of hMSC markers by flow cytometry. Markers for hMSCs were maintained after TLR4 gene editing: CD73+ ($99\%$), CD90+ ($93\%$), and CD105+ ($77\%$), (Fig. 1E–G). In addition, both edited and unedited hMSCs exhibited low expression of hematopoietic lineage markers: CD45− (< $5\%$) and CD34− ($10\%$) (Fig. 1H,I).
Next, we sought to determine the effect of TLR4 editing on the functional characteristics of the edited hMSCs. Gene editing did not affect the hMSC proliferation rate or viability over 4 days, as indicated by the metabolic-based XTT assay (Fig. 1J), nor hMSC migration rate, as indicated by the "scratch" assay (Fig. 1K, Supplementary Figure 1A). Overall, electroporation-based editing of the TLR4 gene using Cas9 RNPs did not affect the expression of hMSC markers and the rate of proliferation and migration.
## TLR4 expression in hMSCs after editing
We used several assays to determine the effect of TLR4 gene editing on TLR4 protein expression. Immunostaining of TLR4 and its primary target, the transcription factor NF-ƙB, in edited compared to unedited hMSCs revealed a $52\%$ decrease in the mean intensity of expression of TLR4 (Fig. 2B, 15.4 vs. 8.1, $$p \leq 0.0012$$) and a $76\%$ decrease in the mean intensity of NF-ƙB (Fig. 2C, 23.4 vs. 19.1, $$p \leq 0.0021$$) (Fig. 2A–C, Supplementary Figure 1B). In contrast to the high abundance of TLR4, we found that by immunostaining the surface, TLR4 was undetectable by flow cytometry in both edited and unedited cells (Fig. 2D). Therefore, we assumed that TLR4 was translocated intracellularly in inflamed hMSCs. The latter finding confirms that other cell types can internalize TLR4 into the cytoplasm21,22. Overall, TLR4 targeting by CRISPR-Cas9 reduced one of the main downstream signaling mechanisms of inflammatory pathways. Figure 2Effect of TLR4 Gene Disruption on Human MSCs. ( A) Representative immunofluorescent images of hMSCs with anti-TLR4 and anti-NF-ƙB antibodies. White arrows indicate edited cells with little expression of TLR4 and no visible NF-ƙB after editing. ( B,C) Quantification of TLR4 and NF-ƙB staining in hMSCs following TLR4 gene editing using ImageJ 1.53c, http://imagej.nih.gov/ij72. Each sample represents one field of view acquired from three replicates in two separate experiments. Analysis of images shows a reduction of TLR4 expression after editing and less NF-ƙB expression. P-values by Mann–Whitney test. ( D) TLR4 expression was measured by FACS and showed no significant signal over isotype read in both groups. Considering that no membrane permeabilization was done, we posit this was likely due to the internalization of the TLR4. ( E) Using western blot quantification, we found a $50\%$ decrease in TLR4 protein levels. Cropped images as well as full raw images of data can be found in Supplementary Figure 2A. (F) Gene expression analysis by qRT-PCR revealed no significant differences in the levels of mRNA with and without editing. Statistical analyses for all TLR4 comparisons by Mann–Whitney test, $$n = 4$.$ Abbreviations hMSCs, human mesenchymal stromal cells; TLR4, toll-like receptor 4; NF-ƙB, nuclear factor-kappa B; FACS, flow cytometry; mRNA, messenger ribonucleic acid; qRT-PCR, quantitative reverse transcription-polymerase chain reaction.
Gene editing reduced the protein level of TLR4 by $56\%$, measured by western blot (Fig. 2E, Supplementary Figure 2A). In contrast, gene editing did not significantly affect the expression of TLR4 mRNA (Fig. 2F). These conflicting findings may reflect the consequence of gene disruption at exon III. The TLR4 gene was translated into mRNA, but the mRNA was insufficient for synthesizing a functional TLR4 protein. Thus, disruption of the TLR4 gene by CRISPR-Cas9 reduced the expression of TLR4.
## TLR4 gene editing modulated hMSC secretome
Paracrine properties are a major determinant of the reparative and immunomodulatory effects of MSCs6,23,24. Thus, we conducted a proteomic analysis to determine the effect of TLR4 gene editing on the hMSC secretome. Proteomic analysis was conducted on conditioned media and EVs from edited and unedited hMSCs from four patients. Nano-tracking analysis showed that the size distribution of EVs was smaller than 200 nm, with smaller EVs derived from edited cells (Supplementary Figure 2B). We found that TLR4 editing decreased the secretion of both soluble (Fig. 3A,C) and EV-encapsulated proteins (Fig. 3B,D). To analyze quantitative data of proteins identified by proteomics, we created a Z-score grade for each protein based on expression level. Comparing proteins secreted into the conditioned media from all four patients’ cell lines showed that all had a similar protein profile before and after editing. Moreover, a significant decrease in the amount of secreted proteins was found after editing (Fig. 3A). Unlike the free soluble proteins, analysis of EV proteomics showed high diversity between patients’ cell lines. Even so, the average change after editing the cells was a significant decrease in the level of secreted proteins (Fig. 3B).Figure 3Proteomics Analysis Indicated that Disrupting the TLR4 Gene Reduced Secretion of Pro-Inflammatory Proteins. To determine differences in the proteome of free proteins in growth media and purified EVs released from edited and unedited hMSCs, we carried out a comparative MS proteomic analysis. Data are available via ProteomeXchange with identifier PXD033253. ( A) Heat map showing the levels of proteins secreted by hMSCs with and without TLR4 gene editing from four patients shown as Z-scores (abundance between -2 and 2). ( B) Heat map of EV-encapsulated proteins in EVs secreted by hMSCs with and without gene editing of cells from four patients shown as Z-scores (abundance between -2 and 2). ( C) To graphically present the quantitative data, we constructed a volcano plot (log2 fold-change edited vs. unedited cells, plotted against log10 of statistical difference). For free-protein secretion, q-value was used to determine the statistical strength of protein identification, with ±1 as the cutoff region for significant changes in secretion after editing. ( D) For EV protein content, the p value was used for statistical strength of protein identification, with ±0.8 as the cutoff region of significant changes. Points above the non-axial vertical line at 0.05 represent proteins with significantly different abundances ($p \leq 0.05$). Results show a significant reduction in the release of proteins involved in immune regulation (red) and extracellular organization pathways (orange). $$n = 4$.$ ( E) The number of validated protein signatures found in each experimental group, separately and combined. The complete list of protein names is specified in Supplementary Table 1. Abbreviations TLR4, toll-like receptor 4; hMSCs, human mesenchymal stromal cells; EVs, extracellular vesicles; FBS, fetal bovine serum; MS proteomics, mass spectrometry proteomics.
To provide further insights, we rearranged the proteomic data on a logarithmic scale according to significance of change (Fig. 3C,D). We found that editing hMSCs reduced the secretion of regulatory proteins from the immune system and the extracellular matrix (ECM) (Supplementary Table 1).
Analysis of EV-encapsulated proteins identified a total of 63 proteins. Of them, 14 were unique to EVs, compared to the conditioned medium (Fig. 3E, Supplementary Table 1). Furthermore, disrupting the TLR4 gene significantly lowered the expression of 13 EV-encapsulated proteins. Amongst them, the protein with the lowest expression after gene editing was the heat-shock protein S100A6 (Fig. 3D), an activator of TLR4 (q < 0.0098)25. Overall, our data showed that disruption of the TLR4 gene affects the hMSC secretome.
Using advanced analysis of protein–protein interaction networks by STRING26,27, we enlisted the 20 most important biological processes that stem from the data of both the growth media and the EVs (Fig. 4A). Analysis of biological processes revealed that TLR4 editing attenuated processes related to inflammation and ECM organization. The immunomodulatory effect of TLR4 editing was apparent in both free and EV-encapsulated proteins. Figure 4Change in Biological Pathways after Gene Editing in hMSCs. To interpret the changes in the proteomic data before and after gene editing, we clustered the proteins from growth media or EVs by the STRING classification system (https://string-db.org). For each protein group, datasets of protein lists were composed based on the significance of the change. Proteins that had a significant decrease ($p \leq 0.05$) were grouped into the list of edited cells group, while proteins with no significant change comprised the second list, representing the unedited cells group. ( A) STRING classification of the 20 most significant biological processes from edited and unedited protein data sets. False-discovery rate according to p values corrected for multiple testing by Benjamini–Hochberg. ( B) Multiplex ELISA analysis of inflammatory cytokine levels in hMSC growth media of edited vs. unedited cells. Results show lower secretion of pro-inflammatory cytokines from edited hMSCs. Media was collected from 2 × 104 cells after incubation for three consecutive days. The ratio was calculated for each patient's edited/unedited cells to find changes in cytokine secretion ($$n = 5$$). ( C) To determine the angiogenic properties of the edited cell conditioned medium, we used HDMEC Matrigel Tube Formation Assay. The results were quantified after 3.5 h. Abbreviations EVs, extracellular vesicles; hMSCs, human mesenchymal stromal cells; ELISA, enzyme-linked immunosorbent assay; HDMEC, human dermal microvascular endothelial cells.
Next, we analyzed inflammatory-related cytokines directly secreted from the cells using ELISA. We found that, consistent with the proteomic results, the release of pro-inflammatory cytokines such as IL-1α, IL-1β, and IFN-γ was decreased, along with VEGF and the pro-fibrotic, anti-inflammatory cytokine IL-10. Gene editing did not affect the secretion of other cytokines with dual effects on inflammation, such as IL-6 and IL-8 (Fig. 4B).
Finally, conditioned medium from edited hMSCs stimulated a pro-angiogenic response as indicated by endothelial cell tube formation assay (by 1.4-fold compared to unedited cells, $$p \leq 0.028$$) (Fig. 4C, Supplementary Figure 2C). Thus, TLR4 editing improved the pro-angiogenic properties of hMSCs in a VEGF-independent manner28. Together, our analysis of edited hMSCs secretome suggests that TLR4 editing in hMSCs activated anti-inflammatory and reparative properties.
## Edited hMSCs improved survival, cardiac remodeling, and function after MI
To test the effect of the edited hMSCs on infarct healing and repair, we subjected mice to MI and cell therapy immediately after infarction. During 28 days of follow-up, edited and unedited hMSCs improved survival ($90\%$ and $80\%$, compared with $70\%$ in saline controls; $$p \leq 0.05$$ for edited cells) (Fig. 5A).Figure 5Cell Therapy after MI Improved Survival and Prevented LV Dilatation. To determine the reparative effects of edited and unedited cells on the infarcted heart, we allocated 48 12-week-old Balb/C female mice to MI and a single intra-myocardial injection of edited, unedited cells, or saline. LV remodeling and function were assessed by echocardiography before (baseline), 1, 8, and 28 days after MI. On day 28 post-MI, hearts were harvested for further analyses. ( A) Survival curve for three treatment groups, 28 days post-MI. Log-rank test for trend analysis demonstrates that mice treated with edited cells had the best survival rate compared with other groups with MI. ( B,C) Serial measurements of systolic and diastolic LV volumes revealed that both edited and unedited cells reduced LV dilatation 28 days after MI, compared with the saline-treated group. This favorable effect was statistically significant for the edited cells. ( D,E) LV contractility, as indicated by EF and FS, was improved by both edited and unedited cells. This favorable effect was statistically significant for the edited cells. Statistical analyses were performed by repeated-measures two-way ANOVA with Holm-Šídák's multiple comparisons test. ( F) To visualize cell engraftment after injection into the infarct, slides were stained with a human mitochondria marker 28 days post-MI. Images acquired with a confocal microscope show batches of human cells adjacent to the viable myocardial islands within the scar in mice treated with edited cells. On the contrary, only sporadic cells were found in mice treated with unedited cells. Abbreviations MI, myocardial infarction; LV, left ventricular; DIC, differential interference contrast; echo, echocardiography; EF, ejection fraction; FS, fractional shortening; hMSCs, human mesenchymal stromal cells.
Serial echocardiography studies showed that edited and unedited cells improved cardiac remodeling, compared with saline control, 28 days after MI, as indicated by left ventricular (LV) systolic and diastolic volumes (Fig. 5B,C). These favorable effects were statistically significant for the edited cells. Both edited and unedited cells improved LV function, as indicated by LV ejection fraction (EF) and fractional shortening (FS), compared with saline control (Fig. 5D,E). Echocardiography variables before and after MI are available in Supplementary Table 2.
Overall, our echocardiography results indicated that edited hMSCs improved cardiac remodeling and function in mice after MI. Despite a small advantage for edited cells, the favorable effects of edited and unedited hMSC were similar.
## Edited hMSCs improved infarct repair and scar formation
To evaluate the outcome of implanted hMSCs, we stained the mouse heart sections for human mitochondria 28 days after MI (Fig. 5F). In hearts treated with edited cells, we identified clusters of hMSCs. The human cells were located in the scar tissue, near islands of viable myocardium (Fig. 5F, upper panel, in green). Contrary to mice treated with edited cells, only infrequent human cells were found in the hearts of mice treated with unedited hMSCs (Fig. 5F, lower panel). Thus, TLR4 editing improved cell engraftment after MI.
Next, to determine the effect of TLR4 editing on infarct repair, we evaluated heart sections 28 days after MI. A unique finding was that islands of viable myocardium were present within the scar tissues of hearts treated with edited cells but not in hearts treated with unedited cells or saline (Fig. 6A). The islands of viable myocardium (upper panel) within the scar tissue are a significant finding, especially when compared to hearts treated with edited hMSCs using manual surface measurements (Fig. 6B). Further histological analysis revealed cell infiltrates at the sites of the scar tissue and particularly in hearts treated with unedited cells, which indicates active inflammation and, most likely, an immune reaction to the human cells (Fig. 6A, middle panel). Significantly, edited, and unedited cells increased scar thickness, as calculated by the average scar thickness divided by the average wall thickness (Fig. 6C). This is a significant outcome because scar thickening reduces wall stress (as defined by the law of Laplace), infarct dyskinesis, and LV dilatation. In this line of evidence, edited hMSCs also markedly reduced the expansion index ([LV cavity area/whole LV area]/relative scar thickness), an indication of adverse LV remodeling, compared with the unedited and control groups (Fig. 6D). Furthermore, treatment with edited hMSCs reduced the incidence of the more severe form of MI, the transmural scar, compared with unedited and saline treatments ($38\%$ vs. $75\%$ and $90\%$; $$p \leq 0.009$$) (Fig. 6E). Overall, our histologic and morphometric analyses indicated that treatment with TLR4-edited hMSCs improved infarct repair and scar formation. Figure 6Edited-Cell Therapy in Mice after MI Increased Viability, Preserved Scar Thickness, and Lowered Expansion Index. ( A) We performed postmortem morphometric analysis 28 days after MI. The slides were stained with hematoxylin–eosin or picrosirius red, photographed, and analyzed with planimetry software (SigmaScan Pro 5, from Systat Software, Inc., San Jose California USA, www.systatsoftware.com). Bar graphs represent: × 20—1 mm; × 40—500 µm; × 200—100 µm; (B–D) We measured the viable myocardium directly by manually circling the surface area within the scar tissue area. We measured LV maximal diameter, defined as the longest diameter perpendicular to a line connecting the insertions of the septum to the ventricular wall, average wall thickness from three measurements of septum thickness, average scar thickness from three measurements of scar thickness, LV cavity area, and whole LV area. Relative scar thickness and expansion index were calculated as: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Relative}}\,{\text{scar}}\,{\text{thickness}} = \frac{Average\,scar\,thickness}{{Average\,wall\,thickness}} \times 100.$$\end{document}Relativescarthickness=AveragescarthicknessAveragewallthickness×100. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Expansion}}\,{\text{index}} = \frac{{\frac{LV\,cavity\,area}{{Whole\,LV\,area}}}}{Relative\,scar\,thickness} \times 100.$$\end{document}Expansionindex=LVcavityareaWholeLVareaRelativescarthickness×100. Measurements show that only edited hMSCs protected viable myocardium within the scar tissue. They also show thicker scar tissue and smaller expansion index in the hearts of mice that received edited cells. P-values were calculated by one-way ANOVA with Holm-Šídák's multiple comparisons test. ( E) Further morphological analysis revealed that edited-cell therapy increased the occurrence of a subendocardial, rather than a transmural, scar. p value by Chi-squared test for trend. Abbreviations MI, myocardial infarction; LAD, left anterior descending; LV, left ventricular; hMSCs, human mesenchymal stromal cells.
## Discussion
Our work provides several new findings. First, we show, for the first time, that CRISPR-based TLR4 disruption in hMSCs improves the function of cells and the outcome of cell therapy after MI. Second, we optimized an improved electroporation protocol to refine CRISPR-based gene editing in human cells. Third, we confirmed and extended upon our previous report10, and showed that TLR4 disruption switched hMSCs from patients with ischemic heart disease to an anti-inflammatory and reparative phenotype. Implantation of the edited hMSCs into the infarcted myocardium of mice ameliorated infarct repair and scar formation. Together, we proved that CRISPR-based gene editing could be used to engineer hMSCs with improved therapeutic properties (Fig. 7).Figure 7Graphical abstract: genome editing to improve the outcome of cardiac cell therapy. Schematic description of the use of genome editing to improve the outcome of cardiac cell therapy. TLR4 disruption can improve the reparative properties of autologous hMSCs in patients with heart disease. Created with BioRender.com.
## Targeting TLR4 to enhance the therapeutic properties of hMSCs
Following MI, dying cells and ECM fragments release DAMPs that activate a family of pattern recognition receptors (TLRs), which in turn activate the innate immune system19,25. In the infarcted myocardium, the cell surface TLR4 has been implicated in the extension of ischemic injury and the development of adverse remodeling29–33. MSCs express functional TLR434,35, and here we have shown that activated TLR4 is localized intracellularly. MSCs can be polarized into a pro-inflammatory and anti-inflammatory phenotype by ligands of TLRs, leading to respective changes in their immunomodulatory and reparative properties36–39.
Though previously assumed that TLR4 in hMSCs acts as a transmembrane receptor19, we found that in hMSCs TLR4 is located intracellularly, at least part of the time. This is consistent with previous reports40–42 of different cell types as well. It is also known that after internalization of TLR4, an LPS signal can still affect inflammatory pathways43,44. We believe that the location of TLR4 does not affect its function, and that the receptor can still be activated and promote inflammatory pathways. With regards to the level of the downstream signals of TLR4, there is a baseline level of activation in the cells. Considering the source of the cells in human patients with ischemic diseases, we have previously shown that the cells are already activated and switched toward a pro-inflammatory state45. Considering this fact, it is not surprising that by deletion of TLR4, the level of NF-kB stimulation decreases. In the present work, we show that TLR4 deficiency in hMSCs improves cell survival and LV function after MI in mice.
A unique finding in our study was the presence of islands of viable myocardium in the scar tissue of hearts treated with edited hMSCs after MI. This finding was associated with increased scar thickness and reduced expansion index. Based on previous reports46–48, it is likely that the edited hMSCs secreted protective factors that improve cardiomyocyte survival and viability at an early stage after infarction.
## Efficient electroporation-based gene editing in hMSCs
CRISPR technology offers new opportunities in the arena of gene editing15. The development of an ex vivo platform that combines precise genome editing in vitro with practical application in vivo can improve the outcome of cardiac cell therapy. Trade secrets cover many of the electroporation protocols for CRISPR-based gene editing. As a result, researchers cannot reproduce, adjust, tune, and perfect existing protocols. This lack of transparency may add to the reproducibility challenges in science, and hinders other researchers from establishing new and innovative research. We report our new and efficient electroporation protocol for gene editing in primary human cells using the Cas9 RNP complex. Supplementary Table 4 shows the steps taken to develop this improved protocol, which is now available and can be reproduced. Using this protocol, we achieved, on average, a relatively high yield of gene disruption ($51\%$) that significantly affected cell function. Together with previous reports49,50, we show that ex vivo electroporation-based gene editing is safe and effective.
## Limitations
We are aware of several limitations of our work. First, our protocol created a heterogeneous population of edited and unedited cells. Second, although edited hMSCs improved LV remodeling and function, many favorable effects were similar to unedited hMSCs. The absence of significant functional advantage for edited over unedited cells may be related to the heterogeneity of the edited-cell group and the fact that nearly $50\%$ of the cells remained unedited. A longer follow-up period may reveal significant differences between the edited and unedited cells. Still, the effects on transmural infarcts, infarct expansion, and myocardial islands were more substantial with edited-hMSC therapy.
Another limitation of this study is the ECM composition. Collagen expression in the edited hMSCs’ conditioned medium was downregulated. We did not analyze sub-types of collagen in the infarcted hearts because we, and others, believe that resident hMSCs/fibroblasts are the major sources of collagen in the infarcted tissue51–55. Resident fibroblasts may be affected by implanted cells, either directly or via modulation of inflammation56.
Furthermore, we believe that late injection may yield different results. However, due to the high mortality associated with a second thoracotomy in mice and difficulties related to the delivery of cells into the thin (< 0.5 mm) scar of a mouse within days after MI, we injected the cells immediately after MI. Another aspect of cell transplantation that can benefit from further research is the change in the level of DAMPs released from resident cells. We did not measure DAMPs following the transplantation of hMSCs. Although DAMP release after MI is well documented57, it is unknown whether hMSC transplantation affects levels of DAMPs. hMSC transplantation early after MI may modulate the levels of DAMPs. Still, the immune response and modulation following MSC transplantation have both been extensively described by us and others58–63. The immune response is highly dependent on the source of cells (healthy or sick tissues)58–63.
In addition, in this study, we focused on LV remodeling and scar formation, and did not assess angiogenesis in the infracted and remote myocardial tissue. As a result, we found conflicting results: while cell editing resulted in a modest downregulation of VEGF secretion, adding conditioned medium from edited hMSCs stimulated endothelial cell tube formation (Fig. 4C). A possible explanation for this is the upregulation of other unidentified angiogenic factors or downregulation of angiogenesis inhibitors such as IL-1064 or sFLT1 (we did not measure this in the present experiment).
Finally, although we implanted human cells in mice, we did not use immunosuppression. The rationale for avoiding immunosuppression was that acute immune response and immunomodulation play central roles in improving LV remodeling and function by MSCs56,61. Therefore, we avoided immunosuppression. Consequently, an immune response against the implanted human cells may compromise hMSC survival. Indeed, we noticed inflammatory infiltrates at the site of cell implantation, particularly around unedited hMSCs. Contrary to unedited cells, edited cells showed high resilience and survival of human cells in the mouse tissue. However, the survival of implanted MSCs in the infarcted myocardium is limited even with the syngeneic model of cell therapy12.
## Summary
We combined the power of genome editing and cell-based therapy to engineer better cells for heart repair (Fig. 7). CRISPR-based disruption of the TLR4 gene in hMSCs facilitates a reparative response in vitro and in vivo. The ex vivo approach to cell editing may generate new, function-modified, personalized cells for heart repair. Autologous edited cells may be relevant in treating elderly patients with massive myocardial damage, as well as patients whose myogenic or angiogenic cells have been depleted or have inadequate reparative potential to prevent LV deterioration and heart failure. A better understanding of molecular and cellular mechanisms underlying heart repair is needed to improve gene editing, efficacy, and safety, identify relevant targets, and create better cells for heart repair.
## Methods
A detailed description of the methods is provided in the online Supplemental Materials. In addition, all data that support the findings are available within the article, in the Supplemental Materials, or upon reasonable request.
## Patients, sample collection, and cell isolation
An institutional Helsinki review board approved the study at Sheba Medical Center and Tel Aviv University. The participants gave written informed consent. All comply with the Declaration of Helsinki.
We obtained samples of tissues from the right atrial appendage and epicardial fat of patients undergoing elective open-heart surgery. We isolated and grew the human cardiac mesenchymal stromal cells (hMSCs), as previously described9.
## Ribonucleoprotein (RNP) complex
We used recombinant Cas9 nucleases, synthetic CRISPR RNA (crRNA), and trans-activating CRISPR RNA (tracrRNA) (Alt-R S.p. Cas9 Nuclease V3, IDT, Coralville, IA, USA). crRNA was designed according to the Benchling Guide RNA Design Tool (https://benchling.com). crRNA and tracrRNA were annealed according to the manufacturer’s instructions. Control groups received non-specific scrambled single guide RNA (sgRNA) (IDT, Coralville, IA, USA).
## Electroporation-based Crispr-Cas9 gene editing
The electroporation setup contained: RNP complex, hMSC electroporation enhancer by IDT, and DMEM medium. A single square wave pulse of 125 V for 5 ms was applied once using an ECM 830 (BTX, Cambridge, UK).
## Sequencing and analyzing results
To determine the efficacy of the gene-editing reaction, we processed and examined the cells for insertion or deletion (indel) rate two days after electroporation. Processing involved the Genomic DNA Extraction Kit (Invitrogen, Carlsbad, CA, USA) and the Platinum SuperFi PCR Master Mix (Invitrogen, Carlsbad, CA, USA) with the Benchling Primer Design Tool65 used for primer design, all according to the manufacturer’s instructions. PCR products were validated with a $2\%$ agarose gel and sent to Macrogen (Amsterdam, Netherlands) for Sanger sequencing. Cell sequencing results were analyzed using the tracking of indels by decomposition (TIDE) assay (https://tide.nki.nl)20.
## Flow cytometry
We used flow cytometry to validate hMSC surface markers on edited and unedited cells. Cluster of differentiation (CD)90, CD105, CD73, CD34, CD45, and TLR4 (BioLegend, San Diego, CA) were used according to the manufacturer's instructions.
## Cell proliferation colorimetric assay
To determine the effect of TLR4 disruption in hMSCs on cell number and growth, we used the Cell Proliferation Kit (2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide, XTT) (Biological Industries, Beit HaEmek, Israel) according to the manufacturer's protocol.
## Fibroblast migration scratch assay
To determine the effect of TLR4 gene disruption on cell migration, we used a fibroblast migration ("scratch") assay66. In short, we used a 10 µL tip to scratch a well of $90\%$ confluence. We then measured the gap between the marginal of the cells every hour until the cells closed the gap.
## hMSC cell culture staining
To stain cultured hMSCs for various markers, we washed, fixed, and immunostained the cells with specific antibodies against their isotype control. ( A full list of antibodies is available in the Supplemental Materials.)
## TLR4 marker by western blot
Anti-human TLR4 marker for hMSCs was analyzed by western blot (bs-20594R, Bioss Antibodies, MA, USA).
## RNA extraction and quantitative reverse transcription PCR (qRT-PCR)
We performed TLR4 gene expression analysis in cultured hMSCs using qRT-PCR. Total RNA was extracted from hMSCs using the RNeasy Mini Kit (Qiagen, Germantown, MD. USA), cDNA library prep, and TaqMan assays (Applied Biosystems, Waltham, MA, USA) were performed according to the manufacturer’s instructions. 2−ΔΔCt values were normalized to GAPDH.
## Purification of EVs by size exclusion chromatography (SEC)
Our methods for the isolation of epicardial fat-derived EVs were guided by the recent position statement of the International Society for Extracellular Vesicles (MISEV2018)67. Accordingly, we isolated EVs by Izon qEV columns (IZON, Oxford, UK)68.
## Nanoparticle tracking analysis (NTA)
To measure the amount and size distribution of isolated EVs, we used NTA using the Malvern NanoSight NS300 (Malvern, Grovewood Road, UK).
## Proteolysis and mass spectrometry analysis
To evaluate proteins secreted from hMSCs directly in conditioned media and indirectly by EVs, we performed Proteomic analysis. We isolated EVs by size exclusion chromatography column (SEC) and analyzed them with a Q-Exactive Plus mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). In addition, we analyzed pathway enrichments with STRING using the PANTHER Pathway keywords. We deposited mass spectrometry proteomics data to the ProteomeXchange *Consortium via* the PRIDE69 partner repository with the dataset identifier PXD033253.
## Cytokine array
To determine the effect of gene editing on cytokine secretion, we used the Q-Plex Human Cytokine Array, 4-Plex, and a custom plate (Quansys Biosciences Multiplex ELISA, West Logan, UT, USA)70. Concentrations of cytokines were determined by Quasys Q-View imaging and a software system.
## Angiogenic tube formation assay
To determine the angiogenic properties of the edited-cell conditioned medium, we used the Human Dermal Microvascular Endothelial Cells (HDMEC) Matrigel Tube Formation Assay71. The number of formed tubes was evaluated after 3.5 h.
## Animal care
This study was performed under the guidelines of the Animal Care and Use Committee of the Sheba Medical Center. This study complies with the ARRIVE guidelines.
## Myocardial infarction in adult mice
To determine the impact of cell therapy on infarct repair, we used a mouse model of MI in 12-week-old female Balb/C mice (Harlan Laboratories, Jerusalem, Israel), as previously described9,56. MI was confirmed by immediate visual blanching and wall motion akinesis distal to the occlusion site, and by echocardiography 24 h after MI.
## Cell therapy
We used our previously reported protocol to deliver hMSCs to the infarcted heart10. Mice were allocated to three experimental groups, using a color code for double-blinding purposes: one control group with Dulbecco's phosphate buffered saline (PBS) and two experimental groups with edited or unedited cells. 100,000 cells in 20 µL of PBS were washed 3 times. Cells were kept on ice until 5 min before use. Then, mice were treated with a single injection of cell therapy to the border of the ischemic zone one minute after coronary artery ligation. The chest was sutured, and mice were placed on a heating pad (37 °C) until recovery.
## Echocardiography to evaluate cardiac function
To assess LV remodeling and function after myocardial injury in mice, we used a small animal echocardiography system (Vevo 2100 Imaging System; VisualSonics, Toronto, Ontario, Canada) equipped with a 22- to 55-MHz linear-array transducer (MS550D MicroScan Transducer). Echocardiographic studies were performed before surgery and on days 1, 8, and 28 after injury and treatment.
## Histologic analysis
To assess myocardial injury, healing, repair, and regeneration after MI and cell engraftment, we harvested the hearts, washed them, and then fixed them on day 29 after MI. Adjacent blocks were embedded in paraffin, sectioned, and stained for further analysis.
## Statistical analysis
Statistical analyses were performed using GraphPad Prism 9.2 for Windows (GraphPad Software, San Diego, California USA, www.graphpad.com). Variables are expressed as mean ± standard deviation (SD). Statistical tests are detailed in the figure legends and in the Supplementary Methods.
## Supplementary Information
Supplementary Information 1.Supplementary Information 2.Supplementary Information 3. The online version contains supplementary material available at 10.1038/s41598-023-31286-4.
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|
---
title: Effects of short-term PM2.5 exposure on blood lipids among 197,957 people in
eastern China
authors:
- Qiao Liu
- Zhan Wang
- Junjie Lu
- Zhongqi Li
- Leonardo Martinez
- Bilin Tao
- Chunlai Wang
- Limei Zhu
- Wei Lu
- Baoli Zhu
- Xiaohua Pei
- Xuhua Mao
journal: Scientific Reports
year: 2023
pmcid: PMC10024762
doi: 10.1038/s41598-023-31513-y
license: CC BY 4.0
---
# Effects of short-term PM2.5 exposure on blood lipids among 197,957 people in eastern China
## Abstract
Globally, air pollution is amongst the most significant causes of premature death. Nevertheless, studies on the relationship between fine particulate matter (PM2.5) exposure and blood lipids have typically not been population-based. In a large, community-based sample of residents in Yixing city, we assessed the relationship between short-term outdoor PM2.5 exposure and blood lipid concentrations. Participants who attended the physical examination were enrolled from Yixing People’s hospital from 2015 to 2020. We collected general characteristics of participants, including gender and age, as well as test results of indicators of blood lipids. Data on daily meteorological factors were collected from the National Meteorological Data Sharing Center (http://data.cma.cn/) and air pollutant concentrations were collected from the China Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/) during this period. We applied generalized additive models to estimate short-term effects of ambient PM2.5 exposure on each measured blood lipid-related indicators and converted these indicators into dichotomous variables (non- hyperlipidemia and hyperlipidemia) to calculate risks of hyperlipidemia associated with PM2.5 exposure. A total of 197,957 participants were included in the analysis with mean age 47.90 years (± SD, 14.28). The increase in PM2.5 was significantly associated with hyperlipidemia (odds ratio (OR) 1.003, $95\%$ CI 1.001–1.004), and it was still significant in subgroups of males and age < 60 years. For every 10 μg/m3 increase in PM2.5, triglyceride levels decreased by $0.5447\%$ ($95\%$ CI − 0.7873, − 0.3015), the low-density lipoprotein cholesterol concentration increased by 0.0127 mmol/L ($95\%$ CI 0.0099, 0.0156), the total cholesterol concentration increased by 0.0095 mmol/L ($95\%$ CI 0.0053, 0.0136), and no significant association was observed between PM2.5 and the high-density lipoprotein cholesterol concentration. After excluding people with abnormal blood lipid concentrations, the associations remained significant except for the high-density lipoprotein cholesterol concentration. PM2.5 was positively correlated with low-density lipoprotein cholesterol and total cholesterol, and negatively correlated with triglyceride, indicating PM2.5 can potentially affect health through blood lipid levels.
## Introduction
Exposure to fine particulate matter (PM2.5) has been linked to a substantial disease burden globally1. Although some research has shown that short-term exposure to PM2.5 is positively correlated with mortality from respiratory diseases as well as an increased risk of cardiovascular disease2,3, the mechanisms and other impacts of PM2.5 exposure on health is still unclear.
Previous studies have reported that PM2.5 exposure may increase the incidence of non-alcoholic fatty liver disease4. A previous study among senior citizens found that individuals exposed to long-term PM2.5 exposure had an increased incidence of dementia5. Other studies found long-term PM2.5 exposure was associated with increased serum triglyceride and decreased high-density lipoprotein cholesterol concentration in elderly males6. Other research in children and adolescents suggests that long-term PM2.5 exposure was positively associated with the total cholesterol concentration and risk of hypercholesterolemia7. Whether PM2.5 exposure and blood lipids are epidemiologically related is debated and few large studies have investigated this relationship at the population-level.
Although there have been several studies on PM2.5 exposure and blood lipids, most of these studies are based on long-term PM2.5 exposure, and few studies have explored the association between short-term PM2.5 exposure and blood lipids. To further the understanding of the relationship between short-term PM2.5 exposure and blood lipids, we collected test results of blood lipid-related indicators through routine physical examinations from a community-based sample of 197,957 residents in Yixing city. We also assessed for a range of environmental factors during the same period.
## Study population
This cross-sectional study was performed in Yixing city, located in eastern China, with a population of approximately 1.3 million. The study population was not selected based on disease status; participants who attended a routine physical examination at Yixing People’s Hospital from 2015 to 2020 were eligible and enrolled in the study. No subjects repeatedly took part in the study. Inclusion criteria: [1] participants who were tested for lipid-related indicators [2] participants were local residents Exclusion criteria: [1] participants who took lipid-lowering drugs [2] participants who were workers exposed to dust. We collected participant characteristics, blood lipid-related indicators, including total cholesterol, triglyceride, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol. Blood samples were obtained from individuals after at least 8 hours overnight fasting. High-density lipoprotein cholesterol and low-density lipoprotein cholesterol was analyzed by the direct assay method. Total cholesterol by cholesterol oxidase method and triglyceride by enzymatic method, using CobasC501, (Roche Diagnostics GmbH, Switzerland).
## Binary and continuous outcomes
According to Chinese guidelines for the prevention and treatment of dyslipidemia in adults8, the normal range of these indicators are: [1] total cholesterol < 6.2 mmol/L; [2] triglyceride < 2.3 mmol/L; [3] low-density lipoprotein cholesterol < 4.1 mmol/L; and [4] high-density lipoprotein cholesterol > 1.0 mmol/L. We calculated the number of participants with normal blood lipid-related indicators separately. Participants with abnormalities in either indicator were defined as having hyperlipidemia.
## Data on meteorological factors and air pollutants
The exposure data were obtained from a fixed monitoring station ((120.35′E, 31.62′N)) for the city. The quality control methods of the monitoring stations include climate limit value check, station extreme value check, time consistency check, space consistency check and manual check. We collected daily average meteorological factors, including atmospheric pressure (hPa), temperature (oC), wind speed (m/s), and relative humidity (%) during January 8, 2015 and December 31, 2020 from the National Meteorological Data Sharing Center (http://data.cma.cn/). Data on daily average air pollutant concentrations, including PM2.5, PM10 (particles of less than 10 μm diameter), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) were collected from the China Air Quality Online Monitoring and Analysis Platform (https://www.aqistudy.cn/). Except that the unit of CO concentration was mg/m3, the unit of other pollutants was μg/m3.
## Statistical analysis
A generalized additive model (GAM) was applied to explore the relationship between short-term ambient PM2.5 exposure and blood lipid-related indicators similar to prior studies9,10. GAMs are useful for evaluating the impact of air pollution on human health11. Among the four indicators, triglyceride were not normally distributed. Total cholesterol, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol were all normally distributed. We performed natural log conversions of triglyceride to achieve a approximate normal distribution. To account for potential confounders, adjusted covariates in the GAM model included day of the week, time, sex, age, and meteorological factors. To address multiple collinearities, Spearman rank correlation coefficients between environmental factors were calculated; the model only included variables with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|\mathrm{r}\right|$$\end{document}r<0.712. We applied a thin plate spline function in order to control for nonlinear effects of meteorological factors13. Minimum Akaike information criterion (AIC) values corresponded to the optimal degree of freedom10. Considering lag effects of PM2.5 on blood lipids, we calculated 2- to 8-day moving averages (lag 0–1 day to lag 0–7 days) of the daily average concentration of PM2.5 to capture cumulative lag effects. For example, if a person attended the physical examination on January 9, we collected the daily average concentration of PM2.5 from January 2 to January 9, and then calculated the average concentration from January 8 to January 9 as the 2-day moving average. The n-day moving average concentration was applied to estimate personal short-term PM2.5 exposure level. Minimizing the AIC value was applied to identify the optimal lag time14–16. We expressed the effects as the estimated changes in blood lipid-related indicators and their $95\%$ confidence intervals (CIs) for a 10 μg/m3 increase in ambient PM2.5 concentration15. We also converted lipid-related indicators into dichotomous variables (normal and abnormal) to calculate risks of hyperlipidemia associated with PM2.5 exposure, and expressed them as the odds ratio (OR) as well as their $95\%$ CIs for 10 μg/m3 rise in outdoor PM2.5 concentration. In addition, we analyzed the relationship between other air pollutions (including PM10, SO2, NO2, O3, and CO) and blood lipids using similar approaches.
We performed two sensitivity analyses to examine the robustness of the associations between PM2.5 and blood lipid-related indicators. First, we constructed single- and multi-pollutant models for PM2.5, respectively. Second, individuals with abnormal indicators were excluded to estimate the effects of PM2.5 among the population with normal indicators. A subgroup analysis was also performed to explore if the effect was modified by sex or age. The heterogeneity effects between subgroups were evaluated using the formula: \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left|{\beta }_{1}-{\beta }_{2}\right|/\sqrt{{{SE}_{1}}^{2}+{{SE}_{2}}^{2}}$$\end{document}β1-β2/SE12+SE22, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{1}$$\end{document}β1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{2}$$\end{document}β2 are the estimated effects, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SE}_{1}$$\end{document}SE1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SE}_{2}$$\end{document}SE2 are their standard errors, respectively. When the value was larger than 1.96, the difference was considered statistically significant15.
All analyses were performed with the “mgcv” and “ggplot2” packages in R software version 4.1.2 (https://www.r-project.org/). The significance level was set at 0.05.
## Ethics statement
This study was approved by ethics committee of Yixing people’s hospital.
## Characteristics of study participants
Of 206,452 participants eligible for the study, 205,945 attended a physical examination. In total, 7988 participants were excluded for various reasons; 1944 ($0.90\%$) participants were excluded because they were not local residents while 4562 participants were not tested for lipid-related indicators. Lastly, 1482 participants were taking lipid-lowering drugs during the study period and were also excluded (Fig. 1). After exclusions, a total of 197,957 people were included in the analysis. Mean age was 47.90 years (± SD, 14.28) and $55.61\%$ of participants were male. The mean values of total cholesterol, low-density lipoprotein cholesterol, and high-density lipoprotein cholesterol was 4.94 mmol/L (± 0.94), 2.73 mmol/L (± 0.67), and 1.31 mmol/L (± 0.31), respectively. The median triglyceride values were 1.35 mmol/L ((interquartile range [IQR], 0.92, 2.04) (Table 1). The number of participants with normal levels was 133,080 for triglyceride, 132,643 for low-density lipoprotein cholesterol, 132,308 for high-density lipoprotein cholesterol and 133,172 for total cholesterol. Figure 1Flowchart of participants enrolled in the study from eastern China. Table 1Characteristics of the study population. VariablesN (%)Median (IQR)Mean (± SD)Sex Male110,090 (55.61) Female87,867 (44.39)Age48.00 (37.00–57.00)47.90 ± 14.28 < 60 years156,195 (78.90)44.00 (34.00–51.00)42.46 ± 10.20 ≥ 60 years41,762 (21.10)66.00 (63.00–72.00)68.27 ± 7.19Hyperlipidemia64,448 (32.56)Low-density lipoprotein cholesterol (mmol/L)2.66 (2.22–3.13)2.73 ± 0.67High-density lipoprotein cholesterol (mmol/L)1.27 (1.08–1.49)1.31 ± 0.31Total cholesterol (mmol/L)4.87 (4.30–5.50)4.94 ± 0.94Triglyceride (mmol/L)1.35 (0.92–2.04)1.72 ± 1.44Meteorological factors Temperature (°C)18.10 (9.60–24.70)17.47 (± 8.97) Atmospheric pressure (hPa)1016.30 (1008.00–1023.40)1016.16 (± 9.35) Wind speed (m/s)2.10 (1.50–2.60)2.14 (± 0.83) Relative humidity (%)74.00 (64.00–83.00)73.40 (± 13.37)Air pollutants PM2.5 (μg/m3)38.00 (26.00–57.00)45.56 (± 28.45) PM10 (μg/m3)67.00 (48.00–95.00)76.64 (± 41.12) SO2 (μg/m3)11.00 (8.00–18.00)13.86 (± 8.81)NO2 (μg/m3)38.00 (28.00–51.00)41.76 (± 17.82) CO (mg/m3)0.90 (0.70–1.10)0.96 (± 0.33) O3 (μg/m3)95.00 (63.00–140.00)103.50 (± 51.59)
## Characteristics of meteorological factors and air pollutants
The median daily average meteorological factors and air pollutant concentrations was 18.10 °C for temperature, 1016.30 hPa for atmospheric pressure, 2.10 m/s for wind speed, $74\%$ for relative humidity, 38.00 μg/m3 for PM2.5, 67.00 μg/m3 for PM10, 11.00 μg/m3 for SO2, 38.00 μg/m3 for NO2, 0.90 mg/m3 for CO, and 95.00 μg/m3 for O3 (Table 1). PM2.5 was positively correlated with atmospheric pressure, PM10, SO2, NO2, and CO, and negatively correlated with temperature and wind speed ($P \leq 0.05$). Because Spearman rank correlation coefficients between PM2.5 and PM10 and CO were larger than 0.7, the above two air pollutants were excluded from the final model. The absolute value of the correlation coefficient between temperature and atmospheric pressure was larger than 0.7, atmospheric pressure was removed from the model (Supplementary Table 1). The df of meteorological factors in the analyze of effects of PM2.5 on blood lipids were shown in Supplementary Table 2.
## PM2.5 and blood lipids in entire population
We applied lag 0–6 days, 0–7 days, 0–5 days and 0–7 days for triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and total cholesterol in the entire population respectively. For a 10 μg/m3 increase in PM2.5, the triglyceride decreased by $0.5447\%$ ($95\%$ CI − 0.7873, − 0.3015), the low-density lipoprotein cholesterol concentration increased by 0.0127 mmol/L ($95\%$ CI 0.0099, 0.0156) and the total cholesterol concentration increased by 0.0095 mmol/L ($95\%$ CI 0.0053, 0.0136) and no significant association was observed between PM2.5 and the high-density lipoprotein cholesterol concentration(Table 2). The associations remained significant of low-density lipoprotein cholesterol and total cholesterol by the subgroups of males, females, age < 60 years and age ≥ 60 years. Of triglyceride, the associations remained significant in the subgroups of females, age < 60 years and age ≥ 60 years, and the effect of short-term PM2.5 exposure on the low-density lipoprotein cholesterol concentration and total cholesterol concentration could be modified by age, the effects was stronger for the subgroup of age ≥ 60 years (Fig. 2, Supplementary Table 3).Table 2Estimated changes in the blood lipids for every 10 μg/m3 increase in PM2.5.IndicatorsComplete study populationaPersons with normal blood lipid levelsaSingle-pollutant modelMulti-pollutant modelbSingle-pollutant modelMulti-pollutant modelbTriglyceride (%)− 0.5447 (− 0.7873, − 0.3015)0.3081 (− 0.0495, 0.6669)− 0.5184 (− 0.7235, − 0.3128)0.2006 (− 0.1024, 0.5045)Low-density lipoprotein cholesterol (mmol/L)0.0127 (0.0099, 0.0156)0.0194 (0.0152, 0.0237)0.0096 (0.0068, 0.0124)0.0134 (0.0093, 0.0175)High-density lipoprotein cholesterol (mmol/L)0.0002 (− 0.0010, 0.0013)− 0.0022 (− 0.0039, − 0.0005)− 0.0002 (− 0.0014, 0.0011)− 0.0017 (− 0.0036, 0.0001)Total cholesterol (mmol/L)0.0095 (0.0053, 0.0136)0.0284 (0.0224, 0.0345)0.0057 (0.0019, 0.0095)0.0209 (0.0153, 0.0265)aPersons with normal blood lipid levels were people with total cholesterol < 6.2 mmol/L, triglyceride < 2.3 mmol/L, low-density lipoprotein cholesterol < 4.1 mmol/L and high-density lipoprotein cholesterol > 1.0 mmol/L. We applied lag 0–6 days for triglyceride, lag 0–7 days for low-density lipoprotein cholesterol, lag 0–5 days for high-density lipoprotein cholesterol, and lag 0–7 days for total cholesterol.bAdjusted for SO2, NO2 and O3.Figure 2Estimated changes ($95\%$ confidence intervals) in the blood lipids for every 10 μg/m3 increase in PM2.5 among the entire population. We applied lag 0–6 days for triglyceride, lag 0–7 days for low-density lipoprotein cholesterol, lag 0–5 days for high-density lipoprotein cholesterol, and lag 0–7 days for total cholesterol.
## PM2.5 and blood lipids in persons with normal blood lipid levels
In persons with normal test results, for a 10 μg/m3 increase in PM2.5, the triglyceride decreased by $0.5184\%$ ($95\%$ CI − 0.7235, − 0.3128), the low-density lipoprotein cholesterol concentration increased by 0.0096 mmol/L ($95\%$ CI 0.0068, 0.0124) and the total cholesterol concentration increased by 0.0057 mmol/L ($95\%$ CI 0.0019, 0.0095). No significant association was observed between PM2.5 and the high-density lipoprotein cholesterol concentration (Table 2). The associations remained significant of low-density lipoprotein cholesterol and triglyceride in the subgroups of males, females, age < 60 years and age ≥ 60 years and remained significant of the total cholesterol concentration in the subgroups of males, age < 60 years and age < 60 years (Fig. 3). After excluding participants with abnormal test results, short-term PM2.5 exposure and its effect on triglyceride could be modified by age, the effects were stronger for the subgroup of age ≥ 60 years (Supplementary Table 3).Figure 3Estimated changes ($95\%$ confidence intervals) in the blood lipids for every 10 μg/m3 increase in PM2.5 among persons with normal blood lipid levels. We applied lag 0–6 days for triglyceride, lag 0–7 days for low-density lipoprotein cholesterol, lag 0–5 days for high-density lipoprotein cholesterol, and lag 0–7 days for total cholesterol.
## The effects of PM2.5 on the blood lipid at different lag days
The associations between PM2.5 and triglyceride, low-density lipoprotein cholesterol concentration and total cholesterol concentration were robust at different lag days. And the effects of PM2.5 exposure on triglyceride, low-density lipoprotein cholesterol concentration, and total cholesterol concentration were strongest at lag 0–4 days, lag 0–7 days, lag 0–4 days and lag 0-7 days. However, no significant association was observed between PM2.5 and the high-density lipoprotein cholesterol concentration at different lag days. ( Table 3).Table 3Estimated changes in the blood lipids for every 10 μg/m3 increase in PM2.5 at different lag days. Indicators and lag daysEntire populationPersons with normal blood lipid levelsaTriglyceride (%) 0–1 days− 0.3718 (− 0.5286, − 0.2147)− 0.2735 (− 0.4070, − 0.1399) 0–2 days− 0.5087 (− 0.6875, − 0.3296)− 0.4713 (− 0.6235, − 0,.3188) 0–3 days− 0.5394 (− 0.7371, − 0.3414)− 0.5895 (− 0.7580, − 0.,4206) 0–4 days− 0.5895 (− 0.8039, − 0.3745)− 0.6790 (− 0.8621, − 0.4956) 0–5 days− 0.5102 (− 0.7400, − 0.2798)− 0.5734 (− 0.7701, − 0.3764) 0–6 days− 0.5447 (− 0.7873, − 0.3015)− 0.5184 (− 0.7235, − 0.3128) 0–7 days− 0.5376 (− 0.7873, − 0.2874)− 0.5297 (− 0.7341, − 0.3185)Low-density lipoprotein cholesterol (mmol/L) 0–1 days0.0055 (0.0037, 0.0073)0.0020 (0.0002, 0.0037) 0–2 days0.0051 (0.0030, 0.0071)0.0011 (-0.0009, 0.0031) 0–3 days0.0064 (0.0041, 0.0086)0.0022 (0.0000, 0.0044) 0–4 days0.0074 (0.0049, 0.0099)0.0038 (0.0014, 0.0062)0–5 days0.0082 (0.0056, 0.0108)0.0050 (0.0024, 0.0076) 0–6 days0.0104 (0.0076, 0.0132)0.0071 (0.0044, 0.0099) 0–7 days0.0127 (0.0099, 0.0156)0.0096 (0.0068, 0.0124)High-density lipoprotein cholesterol (mmol/L) 0–1 days0.0005 (− 0.0003, 0.0013)0.0002 (− 0.0006, 0.0011) 0–2 days0.0004 (− 0.0005, 0.0013)0.0001 (− 0.0009, 0.0011) 0–3 days0.0004 (− 0.0006, 0.0014)0.0001 (− 0.0009, 0.0012) 0–4 days0.0008 (− 0.0003, 0.0019)0.0005 (− 0.0007, 0.0017) 0–5 days0.0002 (− 0.0010, 0.0013)− 0.0002 (− 0.0014, 0.0011) 0–6 days0.0000 (− 0.0013, 0.0012)− 0.0005 (− 0.0019, 0.0008) 0–7 days0.0005 (− 0.0008, 0.0018)− 0.0002 (− 0.0016, 0.0011)Total cholesterol (mmol/L) 0–1 days0.0067 (0.0041, 0.0092)0.0033 (0.0010, 0.0056) 0–2 days0.0073 (0.0044, 0.0102)0.0031 (0.0005, 0.0057) 0–3 days0.0076 (0.0044, 0.0108)0.0032 (0.0002, 0.0061) 0–4 days0.0080 (0.0046, 0.0114)0.0039 (0.0007, 0.0070) 0–5 days0.0083 (0.0045, 0.0120)0.0051 (0.0018, 0.0085) 0–6 days0.0086 (0.0047, 0.0126)0.0054 (0.0017, 0.0091) 0–7 days0.0095 (0.0053, 0.0126)0.0057 (0.0019, 0.0095)Adjusted for time, day of the week, sex, age, temperature, wind speed and relative humidity.aPersons with normal blood lipid levels were people with total cholesterol < 6.2 mmol/L, triglyceride < 2.3 mmol/L, low-density lipoprotein cholesterol < 4.1 mmol/L and high-density lipoprotein cholesterol > 1.0 mmol/L.
## The effects of PM2.5 on the blood lipids in multi-pollutant models
For a 10 μg/m3 increase in PM2.5, the low-density lipoprotein cholesterol concentration increased by 0.0194 mmol/L ($95\%$ CI 0.0152, 0.0237), the high-density lipoprotein cholesterol concentration decreased by 0.0022 mmol/L ($95\%$ CI − 0.0039, − 0.0005) and the total cholesterol concentration increased by 0.0284 mmol/L ($95\%$ CI 0.0224, 0.0345). No significant association was observed between PM2.5 and the triglyceride. In persons with normal test results, for a 10 μg/m3 increase in PM2.5, the low-density lipoprotein cholesterol concentration increased by 0.0134 mmol/L ($95\%$ CI 0.0093, 0.0175) and the total cholesterol concentration increased by 0.0209 mmol/L ($95\%$ CI 0.0153, 0.0265). No significant association was observed between PM2.5 and the triglyceride and high-density lipoprotein cholesterol concentration (Table 2).
## The effects of PM2.5 on hyperlipidemia
We converted lipid-related indicators into binary variables (non- hyperlipidemia and hyperlipidemia) to calculate risks of hyperlipidemia associated with PM2.5 exposure. As a result, when PM2.5 increased 10 μg/m3, the OR ($95\%$ CIs) was 1.003 ($95\%$ CI 1.001, 1.004), and it was still significant in the subgroups of males and age < 60 years (Supplementary Table 4). We also converted lipid-related indicators into binary variables (normal and abnormal) to calculate the OR and $95\%$ CI of blood lipids for every 10 μg/m3 increase in PM2.5. We applied lag 0–6 days for triglyceride, lag 0–3 days for low-density lipoprotein cholesterol, lag 0–5 days for high-density lipoprotein cholesterol and lag 0–7 days for total cholesterol. When PM2.5 increased 10 μg/m3, the OR ($95\%$ CIs) for triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and total cholesterol was 0.998 ($95\%$ CI 0.996, 0.999), 1.001 ($95\%$ CI 1.000, 1.001), 1.002 ($95\%$ CI 1.001, 1.003) and 1.003 ($95\%$ CI 1.001, 1.004) (Supplementary Table 5).
The effects of other air pollutions (including PM10, SO2, NO2, O3, and CO) on the blood lipids are shown in the Supplementary Appendix (Supplementary Tables 6–15).
## Discussion
In this study, we found that PM2.5 was positively correlated with low-density lipoprotein cholesterol concentration and total cholesterol concentration, while being negatively correlated with triglyceride. Findings from our study provide evidence of the potential harmful effects of PM2.5 exposure on blood lipids. To our knowledge, this is the largest population-based study to explore the association between short-term PM2.5 exposure and blood lipids, and will provide new empirical for the effect of short-term air pollutant exposure on health.
Previous studies have been heterogeneous with some showing similar results17,18 while others harmful. Study design and differential exposure may partially explain these differences. For example, long-term exposure to PM2.5 was positively associated with triglyceride concentration in a study in Perth6. Distinct durations of exposure may partially explain this inconsistency and our analysis specifically evaluated short-term effect of PM2.5 exposure while many studies concentrated on longer-term effect of PM2.56. Previous studies in rural areas have demonstrated that short-term PM2.5 exposure was positively associated with triglyceride concentration19 and negatively associated with total cholesterol concentration20, inconsistent with our results. Differences in lifestyle and air quality between rural and urban areas may influence outcomes17,18,21. For example, Omega-3 fatty acids may attenuate cardiovascular effects of short-term exposure to ambient air pollution22. In our study, PM2.5 was negatively correlated with triglyceride in the single pollutant model, and positively correlated with triglyceride in the multi-pollutant model, regardless of the overall population or the population with normal blood lipids. This suggested that other air pollutants may alter the associations between PM2.5 and triglyceride, which requires further research.
In our study, PM2.5 was positively associated with the low-density lipoprotein cholesterol concentration. Most previous studies investigated long-term, rather than short-term, exposure effects of PM2.5 to low-density lipoprotein cholesterol concentration23–25. Our study provides new evidence for the effect of short-term exposure. Long-term PM2.5 exposure was shown to be negatively associated with high-density lipoprotein cholesterol concentration18, inconsistent with our findings, indicating that differential exposure durations (short- versus long-term) may also have an impact on the results.
Previous studies have been heterogenous concerning the effect of PM2.5 on total cholesterol concentration. A study in Shanghai showed no significant association between total cholesterol and PM2.526. However, another study is consistent with our results27, showing PM2.5 exposure was associated with an elevated total cholesterol concentration. The difference of exposure durations may explain the inconsistency because we evaluated the short-term effect of PM2.5, while the study in shanghai explored the long-term exposure. A study among college students20 showed that short-term PM2.5 exposure was negatively associated with total cholesterol concentration, the inconsistency may be attributed to the difference of sample size and age. Recent research showed that long- term PM2.5 exposure was negatively correlated with the risk of hyperlipidemia28, however, in our study, the OR of every 10 μg/m3 increase in PM2.5 for hyperlipidemia population was 1.009, which suggested that short-term PM2.5 exposure was a risk factor for hyperlipidemia. Different life-styles and areas may explain the inconsistency. In our study, the effect of short-term PM2.5 exposure on the low-density lipoprotein cholesterol concentration and total cholesterol concentration could be modified by age and the older were more susceptible to PM2.5 exposure, which may be due to hypometabolism and/or hypoimmunity. Previous studies support these findings29–31.
Our study has several limitations. First, our study was a time-series study, limiting our ability to account for reverse causation or time-specific confounding. Second, the fixed environmental monitoring station was used to estimate personal PM2.5 exposure, which cannot be equated entirely with individual exposure. Lastly, although our dataset was large and community-based, we did not have available several other characteristics which may be associated with PM2.5 exposure and blood lipid-related indicators, such as exercise, smoking, and medical history. Therefore, unmeasured and residual confounding is possible.
## Conclusions
PM2.5 was positively correlated with low-density lipoprotein cholesterol and total cholesterol, and negatively correlated with triglyceride, indicating PM2.5 can potentially affect health through blood lipid levels.
## Supplementary Information
Supplementary Tables. The online version contains supplementary material available at 10.1038/s41598-023-31513-y.
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31. Bell ML, Dominici F, Samet JM. **A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study**. *Epidemiology* (2005) **16** 436-445. DOI: 10.1097/01.ede.0000165817.40152.85
|
---
title: Knowledge on added sugar content in food labels among adult out-patient clinic
visitors at a tertiary care teaching hospital, Riyadh, KSA
authors:
- Kavita Sudersanadas
- Maha Al Turki
- Winnie Philip
- Fawzia Alharbi
- Dalal Almeqbel
- Dalia Alanazi
journal: Bioinformation
year: 2022
pmcid: PMC10024778
doi: 10.6026/97320630018455
license: CC BY 3.0
---
# Knowledge on added sugar content in food labels among adult out-patient clinic visitors at a tertiary care teaching hospital, Riyadh, KSA
## Abstract
Consumption of added sugars is reported as an etiological factor for high prevalence of diet-related diseases. Food labels of food products indicate the presence of added sugars. Knowing the different terms used to describe added sugars helps people to avoid food products rich in added sugars. Therefore, it is of interest to assess consumer knowledge about the added sugar terms on food labels. A study was conducted among 215 visitors of a tertiary care hospital outpatient clinic during July-September 2020. The data for this prospective cross-sectional study was collected by using online Google form. Adult visitors of both genders were selected using a non-probability convenient sampling technique. Demography and knowledge of the added sugars were collected. SPSS version 22 was used for data analysis. Mean (± SD), median, and Inter quartile Range (IQR), and Pearson Chi-square test were used. A "p" value of < 0.05 was considered statistically significant. The majority ($96.7\%$) of the study participants was Saudi nationals with a median (IQR) age of 28 [23, 38]. Most ($68.37\%$) of the respondents were undergraduates. Physical inactivity ($37.21\%$) and incidence of obesity ($25.58\%$), and lifestyle diseases ($15.40\%$) were reported. The chi-square test indicated a significant relationship between gender and knowledge of added sugars (χ2 = 69.85; $p \leq 0.05$). Females ($69.41\%$) have more knowledge about added sugars than males. These findings support the notion that there is a lack of knowledge about added sugar terms on the nutrition labels, which might contribute to the prevalence of obesity and other non-communicable chronic illnesses.
## Background:
Food labels are the prime important and direct means of communicating nutrition information to the customers. The internationally accepted definition of a food label is any tag, brand, mark, pictorial or other descriptive matter, written, printed, embossed or impressed on, stencilled, marked, or attached to, a container of food or foodstuff. Nutrition labelling has emerged as a preferred method for promoting healthy eating. It is viewed as a reliable source of information and a method to manipulate consumer behaviour at the point of purchase [1]. Food Labels help to assist customers in better identifying and using labels [2]. The increased prevalence of diet-related non-communicable diseases is one of the main drivers for nutrition labelling. FDA made food and nutrition labelling mandatory [3] so as to make the consumer aware about the advantages or drawbacks of the food ingredients on their health. Nutrition labelling required labelling all necessary components such as ingredients, carbohydrates, fat, protein, calories, minerals and vitamins [3,4]. Consuming a lot of sugars from drinks and food as well as lifestyle habits, may link to multiple health problems such as obesity, diabetes, dental cavities and heart disease [5]. Sugars are added to any recipe like a component added to recipe [6]. The added sugars make the diets energy dense [7]. The excess consumption of added sugars can be an etiological variable for the incidence of diet related non-communicable diseases such as cardiovascular diseases, stroke, diabetes, hyper cholesteremia, cancer, and obesity and dental caries [8]. The term "added sugar" generally refers to sugars (or ingredients that functionally replace sugars) that are added during preparation or processing to foods and beverages. Most popular added sugars are sugar, sweetener and syrup. The added sugars are also found in cane juice and cane syrup, corn sweeteners and high-fructose corn syrup (HFCS), fruit juice concentrate and nectars, honey, malt or maple syrup, molasses, soft drinks, canned juice, candy and desert [7]. The term added sugars do not include those sugars which found in food naturally. It was suggested that adults and children limit their free intake of sugar to less than $10\%$ of the total daily intake of energy-equivalent to about 12 teaspoons [9]. As per dietary guidelines of Food and Drug Administration (FDA), food labels should include added sugars. In KSA, the responsibility of food labelling is vested on Saudi food and drug administration (SFDA). According to SFDA, all food products in Saudi Arabia must have food labelling to prevent or minimize the prevalence of diet related non-communicable diseases and to help people to make healthy dietary decisions [3]. Therefore, it is of interest the knowledge among adult out-patient clinic visitors of a tertiary care teaching hospital in Riyadh, KSA about the added sugar content on food labels.
## Materials and Methods:
The present study was conducted at the outpatient clinic of a tertiary care teaching hospital, King Abdulaziz Specialist Children's Hospital (KASCH) located in Riyadh city of KSA. Initially the method of data collection was face to face interview method. However, due to COVID 19 pandemic situation, the study was conducted by using GOOGLE forms through online.
## Study design/setting/sample selection:
This prospective cross sectional study was conducted among the patients who visited the hospital during the period from July to September 2020. The contact details of the patients were taken from electronic patient/medical records of the hospital. Non probability convenience sampling technique was used to select the respondents for the study.
## Subjects:
Two hundred and fifteen adults of age 18-50 years from both genders, who are willing to provide informed consent, were participated in the study.
## Data Collection Tools:
The data with respect to demography, health and nutritional status and knowledge of the respondents about added sugars were collected by using validated and reliable online google forms approved by the IRB of King Abdullah International Medical Research Centre. The forms consisted of both closed ended and open ended questions.
## Statistical analysis:
The data was analyzed by using SPSS version 22.*The data* from the Google forms were coded and edited before the data analysis. Categorical variables were analyzed by using frequencies and percentages. Distribution of continuous variables were expressed by mean (± SD) and skewed data were analyzed by using median and Interquartile Range (IQR).Pearson Chi square test was used to find the influence of demographic variables on the awareness of added sugar terms. A p value of < 0.05 was considered statistically significant.
## Results:
About 215 visitors of OP clinic of KASCH, who provided written signed informed consent partaken the study. The demographic characteristics of the respondents are provided in Table 1(see PDF). From Table 1(see PDF), it was observed that majority ($96.7\%$) of the respondents are Saudi Nationalities with a median age of 28 years. Around $66\%$ of the respondents were females. Many of the respondents have either under graduate ($68.37\%$) or post graduate ($10.2\%$) degrees. However, 60.9 % were unemployed and $32.6\%$ of them had a family income ranging from 5001-10000SAR per month. The majority ($37.21\%$) is physically inactive and led a sedentary lifestyle. Figure 1(see PDF) indicates the occurrence of lifestyle diseases among the respondents. It was perceived from the figure that most of the ($54.42\%$) subjects were without any lifestyle diseases whereas $25.58\%$ were obese, $7.96\%$ had hyper cholesteremia and $7.44\%$ were with Non-Insulin Dependent Diabetes Mellitus. The knowledge of the respondents based on nutrition science, about calorie and sugar intake and knowledge from nutrition labels were studied. The Knowledge of the respondents about added sugars based on the answers to knowledge-based questions about added sugars, by the respondents is given in Table 2(see PDF). From the table, it was observed that majority ($91.16\%$) of the subjects' defined added sugar correctly. However, the awareness about recommendations of added sugars from various organizations such as AHA and WHO was less among the respondents. In this regards, the correct response was noted among 15.51 % and 13.49 % respectively for recommendations of WHO and AHA. Among the respondents, $17.39\%$ aware about whose recommendation to reduce the added sugar intake. The data related to knowledge of respondents were categorized into three such as knowledge based on nutrition science, knowledge about sugar and calorie intake and knowledge based on nutrition labels and the results are presented in Table 2(see PDF). As depicted in Table 2(see PDF), female respondents have more knowledge about added sugars based on nutrition science($70.70\%$),knowledge about calorie and sugar intake($73\%$) and knowledge based on nutrition labels ($64.83\%$) than their male counterparts. The study also showed that around $71\%$ of the respondents are reading the nutrition labels however, 98.9 per cent of the respondents lack knowledge about variables related to calorie and sugar intake and 75.62 per cent of them deficient in knowledge based on nutrition science. The chi square test indicated that there was a significant relation between gender and knowledge of added sugars (χ2 = 69.85; $p \leq 0.05$).The females have more knowledge about added sugars than males.
## Discussion:
Females formed the major proportion of the subjects of the study. Most of them were undergraduates. The family income of the majority of the subjects ranged from 5001-10000 SAR. It was found that most of the respondents were physically inactive (37.21 percent). High inactivity among the Saudi population was reported earlier also [10]. Earlier studies reported that, it is essential to be physically active to control the increased prevalence of NCD such as obesity, diabetes mellitus, cardio vascular diseases and cancer [11,12 and 13]. In addition, 40.98 % of respondents were self-reported to be suffering from lifestyle diseases. World Health Organization and American Heart Association recommend reducing the intake of free sugars [14,15]. However, the awareness about such recommendations was very low among the respondents of the study. Majority of the participants reported no awareness about the WHO ($84.49\%$) and AHA ($86.51\%$) guidelines. Females ($70.4\%$) scored more than the males ($29.6\%$).Such gender difference in the awareness about WHO guidelines about added sugars was also reported earlier [1]. The study also found that, $88.90\%$ of the subjects had no knowledge about calorie and sugar intake. However, 71.47 % of the study participants can read and have certain information about added sugars given on the nutrition labels of the food products. The study, found that majority of the study participants (61.02 %) had no knowledge about added sugars as per the WHO and AHA guidelines, their calorie contribution as provided on the food labels. The study points out the need for nutrition education for different strata of the Saudi population. However, the current study was conducted on a convenient sample and hence, it is not a representative of the total population. Moreover, over representation of female ($66.5\%$) and undergraduates ($68.37\%$) are formed limitations for the study. Interestingly, even though the great majority of the participants were with higher levels of education, the proportion of participants with awareness about added sugars is very less. Hence, it can be assumed that the section of the people with lower levels of education had poor understanding about added sugars. Earlier studies reported that those from lower socioeconomic class have lower knowledge about nutrition [16]. Hence we recommend follow up study with a representative sample of the population. Moreover, a nation-wide educational program to help improving knowledge about added sugars and nutrition labels are to be initiated as an attempt to reduce the prevalence of chronic medical disease associated with added sugars.
## Conclusion:
Knowledge about the presence of added sugar to help consumers avoid unhealthy produces. This helps in reducing the prevalence of some of the common chronic medical disease conditions. Data documents awareness about added sugars was lower even among those who are undergraduates. The awareness about WHO and AHA guidelines was also found low. Hence, to translate and propagate the guidelines may be beneficial for the consumers. We suggest that public education programs targeting all individuals to improve the public awareness about the way to read food labels, how to identify and reduce the intake of added sugars. Such programs help to enable to reduce the prevalence of chronic medical disease associated with high intake of added sugars.
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|
---
title: 'Assessment of Pulmonary Functions and Dysfunctions in Type II Diabetes Mellitus:
A Comparative Cross-Sectional Study'
journal: Cureus
year: 2023
pmcid: PMC10024785
doi: 10.7759/cureus.35081
license: CC BY 3.0
---
# Assessment of Pulmonary Functions and Dysfunctions in Type II Diabetes Mellitus: A Comparative Cross-Sectional Study
## Abstract
Background *Diabetes mellitus* causes microvascular complications in the eyes and kidneys as well as the nervous system, among other parts of the body. Lungs are a potential target organ for diabetic microvascular complications and remain the least researched among diabetic patients. The aim of this study was to explore whether there is any difference in pulmonary functions in patients with diabetes mellitus compared to those without.
Methodology A comparative cross-sectional study was conducted on 50 participants each with and without type II diabetes mellitus. Pulmonary function parameters, including forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1 as a percentage of FVC in percentage (FEV$1\%$), peak expiratory flow rate in L/second (PEFR), forced expiratory flow rate in L/second in $25\%$ of FVC (FEF$25\%$), forced expiratory flow rate in L/second in $50\%$ of FVC (FEF$50\%$), forced expiratory flow rate in L/second in $75\%$ of FVC (FEF$75\%$), forced expiratory flow rate during 25-$75\%$ of expiration (FEF25-$75\%$), and maximal voluntary ventilation (MVV), of both groups were analyzed using the NDD Large True Flow (Easy One) spirometer (NDD Meditechnik AG., Switzerland). A fully automated chemistry analyzer and linear chromatography were used for glycemic control measurements.
Results All pulmonary function test parameter values were lower in participants with diabetes mellitus compared to those without, except FEV$1\%$ and PEFR, which indicates a mixed pattern of lung dysfunction. FVC had a significant negative correlation with the duration of diabetes (r = -0.299, $$p \leq 0.034$$).
Conclusions Type II diabetes mellitus patients had significant dysfunction in pulmonary functions with early involvement of restrictive parameters which can be monitored/diagnosed by regularly following up patients by measuring pulmonary functions, and, hence, can be taken care of.
## Introduction
Type II diabetes mellitus (type II DM) is a metabolic disorder characterized by hyperglycemia and is caused by insulin resistance and a relative deficiency of insulin. Diabetes affected around 537 million adults (aged 20-79 years) in 2021. In developing countries, the majority of people with diabetes are in the age range of 45-64 years. The overall number of individuals living with diabetes is anticipated to reach 643 million by 2030 and 783 million by 2045. Three out of every four individuals with diabetes reside in low- and middle-income countries [1-3]. The burden of diabetes is especially high in Asian countries, including India, China, and Pakistan. There is also an alarming increase in comorbidities among Asians along with an increase in the incidence and prevalence of diabetes [4]. It is well known that type II DM is the leading cause of morbidity and mortality due to blindness, end-stage renal failure, non-traumatic limb amputations, and cardiovascular complications. The morbidities associated with diabetes mainly stem from macrovascular and microvascular complications [5]. Chronic hyperglycemia in diabetes results in the formation of advanced glycation end products (AGE). This induces pro-inflammatory changes and cross-linking of collagen in the endothelial basement membrane which leads to vascular complications. Microvascular disease-related complications are most profound in the retina, kidneys, and peripheral nerves [6]. Routine investigations are performed in diabetic patients not only to control the disease but also to evaluate for complications in these organs. Lungs, being a highly perfused vital organ, possess extensive microvasculature which remains unexplored, and lung involvement results in cardiovascular morbidities. These may be ignored due to the absence of early clinical signs [7]. Furthermore, if detected early, adequate therapy can be administered to sustain respiratory reserve and slow the onset of problems. Studies have described pulmonary function in type II DM patients with inconsistent results [8]. In the elderly, respiratory problems and diabetes are very common and frequently occur in the same patients. About $20\%$ of people with chronic bronchitis or chronic obstructive pulmonary disease (COPD) also have diabetes, and nearly half of these patients also have concurrent metabolic syndrome [9]. Diabetes has been linked to COPD, although the opposite association has also been recorded. These two illnesses have lately been combined into a single generic condition known as chronic systemic inflammatory syndrome because they both exhibit low-grade chronic inflammation. In patients with asthma, abnormalities in glucose metabolism have also been identified [10]. Therefore, there is an urgent need to examine the pulmonary functions of type II DM patients to act as a future guide for the periodic evaluation of the same. It was hypothesized that the microvasculature of the lung should also undergo described pathophysiological changes and there will be some alterations in pulmonary functions of patients with type II DM. Pulmonary complications of DM have been poorly characterized and understood. Hence, periodic screening for pulmonary complications is not performed in the clinical scenario. However, the possibility of lung involvement, owing to the extensive microvascular circulation, cannot be ruled out. Therefore, this study aims to assess the alterations in pulmonary functions in type II DM in comparison to participants without DM and to explore whether there is a correlation between alterations in pulmonary functions, duration of disease, glycemic control, and demographic factors in type II DM.
## Materials and methods
Study setting and design This comparative cross-sectional study was conducted in collaboration with the Diabetes Clinic and the Department of Physiology, All India Institute of Medical Sciences, Bhopal (AIIMS Bhopal).
Study population The study was conducted on patients who were diagnosed with type II DM (study group) by treating clinicians as well as their relatives (control group) who visited the AIIMS Bhopal Diabetes Clinic. A total of 50 individuals with type II DM identified by the treating doctor were chosen at random from the patients attending the clinic between the ages of 40 and 64. Controls were chosen from the diabetics’ caretakers.
Exclusion criteria Participants with a history of acute or chronic pulmonary disease, smoking, chronic illness, cardiorespiratory illness, anatomical abnormalities related to the thorax such as scoliosis or kyphosis, ascites, or any occupational exposure were excluded from the study.
Study procedure Participants with type II DM who were between the ages of 40 and 64 were randomly selected. The sample size was 100, comprising 50 cases (previously diagnosed with type II DM) and 50 controls (non-diabetic relatives of type II DM patients). After obtaining written informed consent from both cases and controls, a detailed history was collected and a general examination was undertaken. All patients were given a questionnaire seeking thorough personal and medical background information.
Data collection After recording the age (in years), sex, and duration of disease in both groups, the height was measured without shoes using a wall-mounted measuring tape. Weight was measured in kg using an electronic weighing machine after removing the shoes. Body mass index (BMI) was calculated as body weight (kg) divided by the square of height in meters (m2) according to the WHO guidelines.
Measurement of pulmonary function test For the pulmonary function test (PFT), an NDD Large True Flow (EasyOne) spirometer (NDD Meditechnik AG., Switzerland) was used. PFT was performed at the Department of Physiology, AIIMS Bhopal. The subjects were made familiar with the instrument and the procedure for performing the test. All tests were conducted according to the American Thoracic Society/European Respiratory Society (ATS/ERS guidelines) in a quiet room by trained personnel [11]. The test was performed in a sitting position. The study participants were asked to take full inspiration which was followed by as much rapid and forceful expiration as possible in the mouthpiece. Three consecutive readings were recorded at an interval of 15 minutes, and the best reading among the three was selected for reproducibility and validity of the parameter. PFT parameters were considered acceptable if they fell within and between the maneuver acceptability criteria. Guidelines given in the joint statements on lung function testing of the ATS and the ERS were followed [12,13]. PFT parameters that were studied included forced vital capacity (FVC), forced expiratory volume in one second (FEV1), FEV1 as a percentage of FVC in percentage (FEV$1\%$), peak expiratory flow rate in L/second (PEFR), forced expiratory flow rate in L/second in $25\%$ of FVC (FEF$25\%$), forced expiratory flow rate in L/second in $50\%$ of FVC (FEF$50\%$), forced expiratory flow rate in L/second in $75\%$ of FVC (FEF$75\%$), forced expiratory flow rate during 25-$75\%$ of expiration (FEF25-$75\%$), and maximal voluntary ventilation (MVV). For all these parameters, the percentage of predicted values for the respective age, height, and weight were taken into consideration.
Measurement of glycemic control Nearly 2 mL of venous blood was collected in an ethylenediamine tetraacetic acid vacutainer from all diabetic patients with aseptic precautions. Glycemic control was determined by recording fasting blood sugar (FBS), postprandial blood sugar (PPBS), and HbA1C levels. Blood sugar levels were obtained using the Random Access Fully Automated Chemistry Analyzer (Beckman Coulter Pvt. Ltd.), and HbA1c was measured by linear chromatography. All data were extracted and collected in a data extraction form and then transferred to an Excel sheet by independent data entry operators. Discrepant values were corrected by checking the data extraction form. Clean data were then analyzed statistically.
Statistical analysis *Data analysis* was done using the EpiInfoTM version 7 software. The questionnaire was created in the EpiInfoTM software, and data were interpreted using Microsoft Office Excel 2007. For numerical variables, descriptive statistic measures such as mean and standard deviation were used for summarizing data. For categorical variables, frequency and percentage were used to summarize data. Differences between the mean values of PFT among cases and controls were tested using the unpaired t-test. The correlation between sociodemographic variables and glycemic control using different methods with each PFT variable was estimated by calculating Pearson’s correlation coefficient. Considering the small sample size, we did not perform a linear regression analysis to determine predictors of PFT. Statistical significance was set at p-values <0.05. We used R software with ggplot2, ggpairs, and ggstatsplot packages to create visualizations presented in the study [14].
Ethics and permissions The study protocol was reviewed and approved by the Institutional Human Ethics Committee, AIIMS Bhopal (approval number: IHEC-LOP/2015/STS0059-2015). Every participant was provided a detailed patient information sheet explaining the study procedure, following which their queries, if any, were resolved. Subsequently, participants were enrolled after obtaining written informed consent.
## Results
We enrolled 50 type II DM patients (mean age = 51.58 ± 7.49 years) and 50 participants without diabetes (mean age = 47.94 ± 5.98 years) in this study. The mean duration of diabetes was 6.9 ± 6.4 years. There were more females in both groups. The mean FVC (% predicted), FEV1 (% predicted), FEF$25\%$ (% predicted), FEF$50\%$ (% predicted), FEF$75\%$ (% predicted), FEF25-$75\%$ (% predicted), and MVV (% predicted) of the case group were found to be statistically significantly lower ($p \leq 0.05$) than that of the control group. However, the mean FEV$1\%$ (% predicted) and PEFR (% predicted) of the case group were found to be statistically insignificantly lower than that of the control group (Table 1, Figure 1). The mean values of FBS, PPBS, and HbA1C of diabetic patients (cases) were 143.58 ± 41.88 mg/dL, 229.66 ± 59.04 mg/dL, and 8.05 ± 1.35, respectively.
Disease duration was negatively correlated with FVC, and this relationship was statistically significant ($p \leq 0.05$). Age was negatively correlated with FVC; however, this relationship was not statistically significant ($p \leq 0.05$) among cases and controls. We did not find any specific correlation pattern among glycemic status variables such as FBS, PPBS, or HbA1C and PFT variables such as FVC, FEV1, FEV1/FVC ratio, or PEFR (Figure 2, Figure 3, Table 2).
**Figure 2:** *Scatterplots and correlation matrix for pulmonary function test among study participants.BMI = body mass index; FVC = forced vital capacity; FEV1 = forced expiratory volume in one second; PEFR = peak expiratory flow rate in L/second* **Figure 3:** *Scatterplots and correlation matrix for pulmonary function test and glycemic status among participants with diabetes mellitus.FBS = fasting blood sugar; PPBS = postprandial blood sugar; HbA1c = hemoglobin A1c; FVC = forced vital capacity; FEV1 = forced expiratory volume in one second; PEFR = peak expiratory flow rate in L/second* TABLE_PLACEHOLDER:Table 2 A statistically significant negative correlation was observed between MVV (% predicted) and the age of the case group only. No other statistically significant relationship was noted between MVV (% predicted) and demographic factors or glycemic control parameters (Table 3).
**Table 3**
| Parameter | Parameter.1 | Correlation coefficient | Correlation coefficient.1 |
| --- | --- | --- | --- |
| Parameter | Parameter | R | P-value |
| PEFR (% pred) | PEFR (% pred) | PEFR (% pred) | PEFR (% pred) |
| Age | Cases | -0.0980 | 0.4983 |
| Age | Controls | -0.1658 | 0.2499 |
| BMI (kg/m2) | Cases | 0.3233 | 0.0220 |
| BMI (kg/m2) | Controls | 0.0064 | 0.9648 |
| Duration of disease | Duration of disease | -0.0863 | 0.5511 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | 0.0152 | 0.9163 |
| PPBS (mg/dL) | PPBS (mg/dL) | -0.1563 | 0.2785 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | -0.0534 | 0.7125 |
| MVV (% pred) | MVV (% pred) | MVV (% pred) | MVV (% pred) |
| Age | Cases | -0.3333 | 0.0180 |
| Age | Controls | -0.2370 | 0.0975 |
| BMI (kg/m2) | Cases | 0.2212 | 0.1226 |
| BMI (kg/m2) | Controls | 0.2482 | 0.0822 |
| Duration of disease | Duration of disease | -0.0773 | 0.5936 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | 0.0786 | 0.5873 |
| PPBS (mg/dL) | PPBS (mg/dL) | 0.0309 | 0.8314 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | -0.0122 | 0.9329 |
Age was negatively correlated with FEF$50\%$, and this relationship was statistically significant. Glycemic control was also negatively correlated with FEF$50\%$, but this relationship was not statistically significant. A statistically significant negative correlation existed between the age of both groups and FEF$75\%$. No statistically significant relationship existed between FEF25-$75\%$ and demographic factors or glycemic control parameters (Table 4).
**Table 4**
| Parameter | Parameter.1 | Correlation coefficient | Correlation coefficient.1 |
| --- | --- | --- | --- |
| Parameter | Parameter | R | P-value |
| FEF25% | FEF25% | FEF25% | FEF25% |
| Age | Cases | -0.2527 | 0.0766 |
| Age | Controls | -0.3442 | 0.0144 |
| BMI (kg/m2) | Cases | 0.2168 | 0.1304 |
| BMI (kg/m2) | Controls | -0.0986 | 0.4959 |
| Duration of disease | Duration of disease | -0.0144 | 0.9211 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | -0.1905 | 0.1852 |
| PPBS (mg/dL) | PPBS (mg/dL) | -0.2067 | 0.1497 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | -0.2164 | 0.1311 |
| FEF50% | FEF50% | FEF50% | FEF50% |
| Age | Cases | -0.2912 | 0.0402 |
| Age | Controls | -0.3561 | 0.0111 |
| BMI (kg/m2) | Cases | 0.2977 | 0.0357 |
| BMI (kg/m2) | Controls | -0.0209 | 0.8856 |
| Duration of disease | Duration of disease | -0.0418 | 0.7729 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | -0.1263 | 0.3822 |
| PPBS (mg/dL) | PPBS (mg/dL) | -0.1103 | 0.4456 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | -0.0604 | 0.6768 |
| FEF75% | FEF75% | FEF75% | FEF75% |
| Age | Cases | -0.4826 | 0.0004 |
| Age | Controls | -0.3357 | 0.0172 |
| BMI (kg/m2) | Cases | 0.0268 | 0.8536 |
| BMI (kg/m2) | Controls | -0.1025 | 0.4786 |
| Duration of disease | Duration of disease | 0.0384 | 0.7911 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | -0.0014 | 0.9925 |
| PPBS (mg/dL) | PPBS (mg/dL) | -0.0423 | 0.7707 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | -0.0035 | 0.9805 |
| FEV (25-75%) | FEV (25-75%) | FEV (25-75%) | FEV (25-75%) |
| Age | Cases | -0.0485 | 0.7378 |
| Age | Controls | -0.1049 | 0.4683 |
| BMI (kg/m2) | Cases | 0.2434 | 0.0886 |
| BMI (kg/m2) | Controls | 0.1109 | 0.4432 |
| Duration of disease | Duration of disease | 0.0033 | 0.9820 |
| Glycemic control | Glycemic control | Glycemic control | Glycemic control |
| FBS (mg/dL) | FBS (mg/dL) | -0.0085 | 0.9532 |
| PPBS (mg/dL) | PPBS (mg/dL) | 0.0210 | 0.8849 |
| HbA1C (mg/dL) | HbA1C (mg/dL) | 0.1265 | 0.3814 |
## Discussion
The aim of this study was to assess the pulmonary functions of type II DM patients compared to healthy controls. We hypothesized that PFT parameters would be altered in type II DM patients and the values would be lower than those of participants without diabetes. In the present study, pulmonary function assessment showed that some spirometry values (FVC, FEV1, FEF$25\%$, FEF$50\%$, FEF$75\%$, FEF25-$75\%$, and MVV) were significantly lower ($p \leq 0.05$) in type II DM patients compared to controls. On the other hand, FEV$1\%$ and PEFR were not significantly reduced. However, FEV$1\%$ was within the normal range, reflecting that both FEV1 and FVC were affected in DM in relatively the same proportion so that the ratio did not deviate from the normal. Our hypothesis was supported up to a certain degree by Irfan et al. [ 15].
The ratio of FEV1/FVC was preserved, and the reduction in values of both FVC and FEV was noted. The below-normal percentage predicted values indicate a mixed pattern of lung dysfunction (restrictive along with obstructive). This is consistent with studies by Panpalia et al. and Shah et al. [ 16,17], who also deduced a mixed nature of involvement. Hyperglycemia in DM is associated with the increased formation of AGEs. AGEs cause lung dysfunction through their pro-inflammatory effect, suggesting obstructive lung mechanical dysfunction. The second mechanism is AGE-induced cross-linking in the connective tissue of the lung, suggestive of restrictive lung mechanical dysfunction in diabetes.
MVV is the maximum breathing capacity that is affected by poor respiratory muscle strength. A statistically significant reduction in MVV values in type II DM patients compared to controls may be due to poor skeletal muscle strength caused by increased protein catabolism in type II DM. Some studies have reported similar results as our study, while others have reported contradictory results in some parameters [18,19]. Several studies have reported significant reductions in PEFR in type II DM patients compared to those without DM, which is contradictory to our findings [20-22].
Some studies have also reported insignificant differences in spirometric PFTs between patients with diabetes and normal control subjects [23,24]. The possible reasons for such disparities may be differences in race, age group, duration, and glycemic control of diabetes in the studied population [25].
The other objective of our study was to determine the correlation of PFT parameters with age, BMI, duration of disease, and glycemic control. We found a statistically significant negative correlation of age with FEF$50\%$, FEF$75\%$, and MVV. Other parameters did not show any significant correlation with age, which may be understood by early autonomic neuropathic changes in the lung, as suggested by Williams et al. and Parashar et al. [ 26,27]. Duration of disease was negatively correlated with FVC, and this relationship was statistically significant. Furthermore, FVC is considered a marker for restrictive pattern disease [28].
The mean duration of diabetes in this study was 6.9 ± 6.4 years. The longer duration of diabetes exposes the patient to an increased risk of microvascular damage due to AGEs. However, in the present study, the duration of the disease did not appear to predict lung damage, except for FVC. Further, it may suggest that FVC, being the most sensitive, is affected earlier in patients with type II DM. Glycemic control was not observed to be associated with PFT parameters. This result was consistent with some previous studies [29,30]. Thus, glycemic control, as determined by FBS, PPBS, and glycosylated hemoglobin levels, was not a significant determinant of lung function in diabetes.
Limitations of the study The study had some limitations. First, the sample size was small. We recommend further multicenter studies with a larger sample size. Second, this was a hospital-based study where patients were expected to have more comorbidities, which may have impacted the results. Third, we recruited healthy controls from relatives of the patients. Because relatives might share genetic and environmental exposures, it is likely to bias the study results toward the null hypothesis. Further, this was a comparative cross-sectional study and did not consider the decline from the onset of diabetes, which can be investigated in a longitudinal study design. Moreover, PFT was done at just one point in time which is likely to be biased due to measurement errors.
## Conclusions
Lung functions are likely to be reduced in individuals with type II DM. Mostly, lung dysfunction is mixed in nature (obstructive or restrictive pattern). Because most participants did not have any symptoms related to deranged PFT, we need to follow up if symptoms appear in the long run. We may need to initiate lung-protective strategies in individuals with DM, such as tight control of sugars, tobacco cessation, minimal exposure to air pollutants, and occupational exposure which may add to pulmonary insult, along with close supervision of lung functions in type II DM patients. Likewise, avoidance of drugs that have a potential for chronic lung injury, such as immunosuppressants, may be advocated.
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|
---
title: 'Sound reasons for unsound sleep: Comparative support for the sentinel hypothesis
in industrial and nonindustrial groups'
authors:
- Leela McKinnon
- Eric C Shattuck
- David R Samson
journal: Evolution, Medicine, and Public Health
year: 2022
pmcid: PMC10024786
doi: 10.1093/emph/eoac039
license: CC BY 4.0
---
# Sound reasons for unsound sleep: Comparative support for the sentinel hypothesis in industrial and nonindustrial groups
## Abstract
### Background and objectives
Sleep is a vulnerable state in which individuals are more susceptible to threat, which may have led to evolved mechanisms for increasing safety. The sentinel hypothesis proposes that brief awakenings during sleep may be a strategy for detecting and responding to environmental threats. Observations of sleep segmentation and group sentinelization in hunter-gatherer and small-scale communities support this hypothesis, but to date it has not been tested in comparisons with industrial populations characterized by more secure sleep environments.
### Methodology
Here, we compare wake after sleep onset (WASO), a quantitative measure of nighttime awakenings, between two nonindustrial and two industrial populations: Hadza hunter-gatherers ($$n = 33$$), Malagasy small-scale agriculturalists ($$n = 38$$), and Hispanic ($$n = 1$$,531) and non-Hispanic White (NHW) ($$n = 347$$) Americans. We compared nighttime awakenings between these groups using actigraphically-measured sleep data. We fit linear models to assess whether WASO varies across groups, controlling for sex and age.
### Results
We found that WASO varies significantly by group membership and is highest in Hadza (2.44 h) and Malagasy (1.93 h) and lowest in non-Hispanic Whites (0.69 h). Hispanics demonstrate intermediate WASO (0.86 h), which is significantly more than NHW participants. After performing supplementary analysis within the Hispanic sample, we found that WASO is significantly and positively associated with increased perception of neighborhood violence.
### Conclusions and implications
Consistent with principles central to evolutionary medicine, we propose that evolved mechanisms to increase vigilance during sleep may now be mismatched with relatively safer environments, and in part responsible for driving poor sleep health.
## INTRODUCTION
Sleep is a critical component of human health. Insufficient sleep has immediate effects on cognition and long-term negative consequences for metabolic, immune, and neurological function [1–3]. Chronic sleep insufficiency is linked to higher levels of C-reactive protein (CRP), interleukin-6 (IL-6), and white blood cell count, markers indicating systemic inflammation that are associated with numerous chronic diseases and overall mortality risk [4, 5]. Despite its benefits, sleep is a behaviorally vulnerable state that likely increased the sleeping individual’s risk of predation, conspecific violence, and exposure to temperature fluctuation and inclement weather, with potentially harmful outcomes over the course of human evolutionary history. Evolved behavioral strategies to offset this risk are demonstrated in numerous species that are vulnerable to predation including birds, rats, and non-human primates, where sleep duration, timing, and intensity are observed to change depending on perceived threat of predation [6]. For example, birds have been observed to scan their sleeping sites for predators by briefly opening their eyes periodically during sleep periods, a scanning behavior that is less prominent in larger groups which presumably afford more security through communal vigilance [7]. In addition, humans demonstrate sleep behavior that is thought to reflect evolutionary pressures that necessitated increased vigilance. Sleep segmentation (i.e. sleep divided into periods, with pronounced wakefulness during the night) and staggering of sleep periods in groups may increase vigilance throughout the night, which is a pattern that has been documented in small-scale subsistence societies [8, 9]. However, the extent to which these adaptive responses to potential threat persist and influence sleeping patterns in industrial contexts has not yet been thoroughly explored.
## Terrestrial sleep as a uniquely human behavior
Terrestrial sleep is unusual among primates. For chimpanzee (Pan troglodytes) populations where predation is low, a small proportion of males have been observed to ground-sleep [10]. Large non-human primates, including gorillas (Gorilla gorilla), are observed to sleep both arboreally and terrestrially, depending on predation risk. Physically massive male gorillas often sleep on the ground [11], while female and juvenile gorillas sleep terrestrially or arboreally depending on the presence of a protective silverback gorilla in the group [11]. Yet, of all the primates, only humans habitually sleep terrestrially across age and sex classes. Therefore, it is proposed that during the tree-to-ground transition that occurred around 2 million years ago, hominid sleep underwent profound changes as a result of fully terrestrial sleep; based on morphological changes to post-crania that became exclusively suited to terrestrial movement, these changes in sleep likely became defining human features by the emergence of *Homo erectus* [12, 13]. Despite the loss of safety afforded by arboreal sleeping, humans appear to have evolved mechanisms to minimize the risks inherent to this complete transition to the ground. The controlled use of fire would have offered protection from predators [14], as well as opportunities to augment social cohesion and learning through conversation [15]; yet, reliance on fire can also necessitate periods of awakening throughout the night for its maintenance.
The sentinel hypothesis proposes that the combination of sleeping asynchronously in groups coupled with brief, passive periods of awakening around rapid-eye movement (REM) sleep staging allow for environmental scans within a socially protected sleep site which maximizes the balance between sleep continuity and alertness for danger [16]. Alternation between NREM and REM sleep in human sleep cycles is proposed to serve a protective function, in which consecutive time in high arousal threshold sleep (i.e. “deep sleep”) is short, thereby minimizing time that danger can approach undetected. Deep sleep and REM sleep alternate with light sleep (and not with each other), and it is thought that the return to light sleep allows better signal detection. Awakenings typically associated with the ending of REM phases provide further protection with periodic screening of the environment [17]. Blume and colleagues [18] report that in their electroencephalogram study measuring processing of auditory stimuli during sleep, there is evidence for processing of voice familiarity through all NREM and even REM stages. They propose that during sleep, human brains passively enter a ‘sentinel processing mode,’ in which they are able to evaluate environmental stimuli [18]. Depending on what stimuli are detected in these environmental scans, awakening may be initiated to better respond to potential threats. Based on phylogenetic predictions, human sleep on average is much shorter than expected and is characterized by a higher proportion of REM sleep compared to that of other primates [19]. It is hypothesized that humans evolved this shorter, more efficient sleep in part to minimize time that they are especially vulnerable to environmental threats when in deep, high arousal threshold sleep [19, 20].
The sentinel hypothesis further states that humans and other animals would have learned that sleep is only safe when other individuals in their group remain alert, acting as sentinels [16]. Hunter-gatherers exemplify patterns of human behavioral ecology in the absence of agriculture, therefore providing the closest approximation to ancestral sleep-wake patterns [21]. Samson and colleagues [8] found in a community of Hadza in Tanzania, throughout a 20-day study period, simultaneous sleep accounted for only 18 min in total observation—with a median of eight individuals awake throughout the nighttime period. In other words, between the time when the first person went to sleep and the last person awoke, one or more individuals was awake during $99.8\%$ of sampled epochs. This observed group chronotype diversity (i.e. individual differences in sleep timing preference)—which is not planned or intentional—supports the idea that human sleep is shaped by the need to minimize risk while in a reduced state of alertness by selecting for group sentinel behavior [8].
## Evolutionary medicine perspectives on sleep
Evolutionary medicine proposes that modern human physiology and behavior reflect adaptations to selective pressures characteristic of our recent ancestral environments. However, some of these adaptations may now be mismatched with our environments and are implicated in adverse health outcomes [22]. Evolutionary mismatches refer to traits that were once adaptative but lose their adaptive benefits when the environment changes relatively rapidly, thus becoming maladaptive [23]. Remaining vigilant to potential threats in physical surroundings has likely been selected for over the course of human evolution. In only very recent human history has technological infrastructure and reinforced housing protected us from the numerous sources of threat in our environments, including other humans, animals, and inclement weather. However, our physiological responses to signs of potential threat such as noise, nighttime lighting, or social conflict may not have “caught up” to our relatively safer sleeping environments, meaning that harmless noise from other people, traffic, or animals may in part contribute to a mismatch scenario that leads to sleep disorders.
Is sentinel behavior a universal characteristic of human sleep patterns, or is it expressed differentially depending on environmental cues that signal the need for increased vigilance? The current study addresses this question by testing the sentinel hypothesis in nonindustrial Hadza [24] and Malagasy [9] samples and comparing their sleep to two samples from the United States. The first comparison is with participants from the Midlife in the United States (MIDUS) study, a longitudinal health and wellbeing dataset of majority non-Hispanic White (NHW), middle class Americans [25]. The second comparison is with participants in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) project comprised of individuals of Cuban, Dominican, Mexican, Puerto Rican, Central American, and South American background living in the United States [26].
In testing the sentinel hypothesis, we propose that sentinelization of sleep (e.g. multiple awakenings throughout the night) is a passive behavior that is still expressed in human sleep. We hypothesize that sentinel behavior is an evolved expression of flexible sleep patterns in humans that is more apparent in sleep environments where greater noise exposure leads to activation of a vigilance response, resulting in more sleep disruption. We therefore predict that sentinelization will be highest in Hadza and Malagasy samples due to their relatively less secure sleeping environments and corresponding exposure to stimuli that may prompt awakening such as noise, smoke from fires, and temperature fluctuations, and lowest in the two samples from the United States, reflecting sleep environments that are technologically buffered from environmental stimuli that may lead to a heightened threat response.
## Participants
Data were analyzed using a sample of 1,949 participants in total from Hadza hunter-gatherers [24], Malagasy small-scale agriculturalists [9], Hispanics from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Sueño Ancillary Study, and NHWs from the Midlife in the United States (MIDUS) cohorts.
## Hadza
A total of 33 (12 males, 21 females; mean age = 34.62) Hadza participants were recruited between June 2015 and February 2016. Hadza are equatorial hunter-gatherers from near Lake Eyasi in Northern Tanzania. Their diet consists of hunted game animals, birds, and honey, as well as gathered fruits, nuts, seeds, tubers, and legumes. There is a pronounced sexual division of labor, with men primarily hunting and women primarily gathering [8]. The study protocol followed the Duke University and the University of Nevada, Las Vegas Institutional Review Boards for human subjects research. Verbal informed consent was obtained from all participants, and research was approved by the Tanzanian Commission for Science and Technology (COSTECH) and the Tanzanian National Institute for Medical Research (NIMR).
## Malagasy
Thirty-eight Malagasy participants (19 males, 19 females; mean age = 41.07 years) were recruited from Madagascar in three field seasons (July–August 2015 and 2016 and November–December 2017). Malagasy participants are from Mandena, a rural community in the northeastern part of the country. The community has no electric infrastructure and depends on small-scale, subsistence agriculture, but economic development has prompted increased economic shifts away from this pattern of subsistence [9]. Written informed consent was obtained from all subjects, and followed the protocol outlined by the Duke University Institutional Review Board for human subjects research.
## Non-Hispanic White
NHW, middle-class, American participants ($$n = 347$$; 160 males, 187 females; mean age = 52.67 years) were included from the MIDUS longitudinal cohort. The project was started in 1995–1996 by the MacArthur Foundation Research Network on Successful Midlife Development, with the aim of explaining how behavioral, psychological, and social factors affect age-related health and wellbeing. The project now consists of multiple related studies, including data from over 10,000 individuals between the ages of 24 and 74 living in the United States. Participants throughout the country were interviewed by phone and self-administered questionnaire, answering questions related to demographic variables, health, employment, and psychological factors [25].
Sleep data come from the MIDUS II and MIDUS Refresher datasets. MIDUS II was conducted in 2004 and included follow-up of the data collected in MIDUS I as well as cognitive, neurological, and comprehensive biomarker assessments, and sleep data from subsamples of respondents [27]. MIDUS *Refresher data* were collected between 2012 and 2016 [28]. We excluded participants taking sleep medications more than once or twice a week and those with diagnosed sleep disorders (e.g. insomnia). The protocol for data collection was approved by the Education and Social/Behavioral Sciences and the Health Sciences Institutional Review Boards at the University of Wisconsin-Madison.
## Hispanic
Hispanic participants include 1,531 individuals (566 males, 965 females; mean age = 46.14 years) from the Sueño ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). The HCHS/SOL study investigates health outcomes in self-identified Hispanic/Latinx individuals randomly selected from four communities in the United States (Bronx, New York; Chicago, Illinois; Miami, Florida; San Diego, California). The aim of the study is to examine cardiovascular disease risk factors among American Hispanic/Latinx individuals and to assess the role of social factors related to disease risk. Data were originally collected from 15,079 participants between March 2008 and June 2011 and included a physical exam, blood samples, dental exam, hearing test, pulmonary function, physical activity assessment, and questionnaire data. Questionnaire data yielded information related to demographic details, socioeconomic status, and health history and behavior [26].
Sleep data were obtained from the Sueño ancillary study, which recruited 2,252 total individuals within 30 months of their baseline HCHS/SOL examinations. Sleep monitoring was performed between December 2010 and December 2013. Only participants negative for narcolepsy and severe obstructive sleep apnea (AHI ≥ 50/h), and not using nocturnal positive airway pressure therapy, were recruited [29]. For our analysis, we further excluded participants taking sleep medications more than once per week and those with diagnosed sleep disorders (e.g. insomnia). Data collection followed protocols at the institutional review boards at the field centers and coordinating center institutions.
## Equipment and protocol
All sleep data were obtained with accelerometry using wrist-worn actigraphs. Actigraphs are wearable devices that provide non-invasive measures of sleep and wake activity. Actigraphs are validated to a high degree of reliability against polysomnography with the added advantage of enabling data collection of sleep measures in ambulatory participants within their natural sleeping environments [30, 31]. Activity is measured with a built-in, high-sensitivity accelerometer that logs data over a user-defined interval and translates movement into a binary sleep-wake determination.
## Nonindustrial groups
Hadza and Malagasy participants were informed of the study objectives by local translators, and instructed to wear the watches for the duration of the study. Participants were asked to press an event marker button, which helps in actigraphy scoring for identifying sleep times, wake times, and naps. Sleep was measured in both groups using the CamNtech MotionWatch 8 actigraph (CamNtech, Cambridge, United Kingdom), collected in 60-second intervals. Data were then scored using the CamNtech MotionWare 1.1.15 program [8, 9]. Total nights of actigraphy range from 4-20 nights for the Malagasy sample and 3-20 for the Hadza sample.
## Industrial groups
MIDUS sleep data were obtained using the Mini Mitter Actiwatch-64 (Philips Healthcare, Amsterdam, Netherlands) activity monitor, worn by participants for seven consecutive days. Rest, sleep, and active period data generated by the Actiwatch were used to generate summary statistics. The Actiwatch-64 uses a built-in sensor to detect movement during a 30-s interval. Data were processed using Actiware Software to generate summary statistics of the sleep period for a given day [32].
Biometric sleep data were obtained from HCHS/SOL Sueño participants with the Actiwatch Spectrum actigraph (Philips Respironics, Murrysville, Pennsylvania, United States). Participants were instructed to wear the watches for the seven day duration of the study, which measured sleep-wake activity in 30-s epochs [33]. Upon completion of the study, data were sent to a central reading center where they were scored using a standardized method to evaluate and clean data for analysis. Epoch by epoch sleep/wake status was then generated by the Actiware 5.59 algorithm [29].
## Data analysis
The main sleep variable of interest is average time in hours spent awake after sleep onset across all bouts of awakening (wake after sleep onset; WASO). We chose WASO as the closest proxy for the brief awakenings discussed in the sentinel hypothesis [16]. We also present average time in bed and average hours of sleep per sleep period (sleep duration) to better contextualize general sleep patterns in the comparative populations.
To test the sentinel hypothesis, we modeled data using linear regression. We modeled sleep variables of interest as a function of group membership (i.e. NHW, Hispanic, Hadza, Malagasy) and controlled for sex and age. Models were built using the R programming language [34]. In addition to modeling variation in WASO, we also modeled predictors of time in bed and sleep duration. Previous studies have reported that females may experience longer, higher quality sleep on average [35, 36], so we included an interaction of sex and group to investigate whether sex has a differential effect on sentinelized sleep patterns within each group (e.g. WASO ~ Group*Sex + Age).
## Supplementary analysis
We performed supplemental regression analysis of WASO within the Hispanic sample to further investigate the role of perceived environmental threat on sleep disruption. We used available variables from the HCHS/SOL dataset that asked about neighborhood safety and racism. We primarily focused on the Neighborhood Safety survey items in which participants were asked to “Think about your neighborhood as a whole, then please choose the response for each of the following to show how much a problem each one is in your neighborhood”. We focused on the problems of violence and excessive noise, which were rated on a scale of 1 = Very Serious Problem to 4 = Not *Really a* Problem. We also used Racism/Discrimination Score, in which participants were asked about their lifetime experiences with racial discrimination, with responses on a scale of 1 to 5, with 1 being never, 3 sometimes, and 5 very often. For neighborhood variables, we included the 1,528 participants with responses, and we built a linear regression model to predict WASO as a function of neighborhood violence and excessive noise, controlling for age and sex.
## RESULTS
Descriptive statistics of demographic and sleep characteristics by group are presented in Table 1. Wake after sleep onset (WASO) is highest in Hadza and Malagasy (2.44 and 1.93 h, respectively), and lowest for NHW respondents (0.69 h). At 0.86 h of WASO, Hispanic participants demonstrate less time awake during sleep periods than Hadza and Malagasy, but more than NHW participants. Hadza participants have the longest average time in bed (9.16 h), and NHW participants have the shortest average time in bed (7.14 h). Hispanic participants have the longest average sleep duration (6.67 h), while Hadza have the shortest average sleep duration (6.24 h).
**Table 1.**
| Unnamed: 0 | Non-Hispanic White | Hispanic | Hadza | Malagasy |
| --- | --- | --- | --- | --- |
| | (n = 347) | (n = 1,531) | (n = 33) | (n = 38) |
| Sex | Sex | Sex | Sex | Sex |
| Male | 160 (46%) | 566 (37%) | 12 (36%) | 19 (50%) |
| Female | 187 (54%) | 965 (63%) | 21 (64%) | 19 (50%) |
| Age, years | 52.67 (12.87) | 46.14 (11.91) | 34.62 (13.32) | 41.07 (13.19) |
| WASO (h) | 0.69 (0.31) | 0.86 (0.41) | 2.44 (0.66) | 1.93 (0.50) |
| Time in bed (h) | 7.14 (1.02) | 7.60 (1.11) | 9.16 (0.72) | 9.05 (0.91) |
| Sleep duration (h) | 6.44 (1.01) | 6.67 (1.02) | 6.24 (0.72) | 6.57 (0.95) |
## Group membership as a predictor of WASO
To test the sentinel hypothesis, we fitted a linear model predicting WASO as a function of group membership. Our results show that WASO varies significantly by group. Hadza (Estimate=1.287, SE = 0.132, CI = 1.027 to 1.546), Malagasy (Estimate = 1.036, SE = 0.107, CI = 0.826 to 1.247), and Hispanic (Estimate = 0.241, SE = 0.040, CI = 0.163 to 0.319) group membership predicts significantly greater WASO compared to NHW. Female sex (Estimate = −0.108, SE = 0.047, CI = −0.200 to −0.015) predicts lower WASO values compared to males (see Fig. 1).
**Figure 1.:** *A coefficient plot for wake after sleep onset (WASO). Hadza, Malagasy, and Hispanic group membership predicts significantly greater WASO than non-Hispanic White group membership. Female sex predicts significantly less WASO. The plotted lines show the 95% confidence intervals of each predictor variable. Continuous predictor variables were scaled for comparability of coefficients.*
## Group membership as a predictor of time in bed and sleep duration
Compared to the NHW reference, membership in all groups predicts significantly longer time in bed (Hadza: Estimate = 2.216, SE = 0.324, CI = 1.581 to 2.852; Hispanic: Estimate = 0.549, SE = 0.0978, CI = 0.357 to 0.740; Malagasy: Estimate = 2.355, SE = 0.262, CI = 1.840 to 2.869). Female sex also predicts longer time in bed (Estimate = 0.436, SE = 0.116, CI = 0.209 to 0.663) (Fig. 2). Hispanic (Estimate = 0.270, SE = 0.090, CI = 0.093 to 0.447) and Malagasy (Estimate = 0.572, SE = 0.242, CI = 0.097 to 1.047) group membership and female sex (Estimate = 0.519, SE = 0.107, CI = 0.309 to 0.728) predict significantly longer sleep duration (Fig. 3).
**Figure 2.:** *A coefficient plot for time in bed. Hadza, Malagasy, and Hispanic group membership predicts significantly longer time in bed than non-Hispanic White group membership. Female sex also predicts longer time in bed. An interaction of sex and Malagasy group membership predicts shorter time in bed. The plotted lines show the 95% confidence intervals of each predictor variable. Continuous predictor variables were scaled for comparability of coefficients.* **Figure 3.:** *A coefficient plot for sleep duration. Hadza and Malagasy group membership predicts significantly shorter sleep duration than non-Hispanic White group membership, while Hispanic group membership and female sex predict longer duration. The plotted lines show the 95% confidence intervals of each predictor variable. Continuous predictor variables were scaled for comparability of coefficients.*
## Interaction of sex and group membership
Descriptive statistics of sleep averages by sex within each group are presented in Table S1. WASO, time in bed, and sleep duration significantly vary by sex in NHW and Hispanic groups, but no sex differences are significant in Hadza or Malagasy. In modeling an interaction of sex and group membership, there were no significant interactions between sex and group in predicting WASO (sex*Hadza: Estimate = 0.054, SE = 0.166, CI = −0.271 to 0.379; sex*Malagasy: Estimate = 0.071, SE = 0.150, CI = −0.224 to 0.365; sex*Hispanic: Estimate = −0.033, SE = 0.053, CI = −0.137 to 0.070). In the time in bed and sleep duration interaction models, an interaction of female sex and Malagasy group membership predicts shorter time in bed (Estimate = −0.845, SE = 0.367, CI = −1.566 to −0.125) and sleep duration (Estimate = −0.843, SE = 0.339, CI = −1.507 to −0.178). Full results of regression models are presented in Table 2.
**Table 2.**
| Unnamed: 0 | WASO | WASO.1 | Time in bed | Time in bed.1 | Sleep duration | Sleep duration.1 |
| --- | --- | --- | --- | --- | --- | --- |
| | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI | Estimate (SE) | 95% CI |
| Age | −0.0002 | −0.002, 0.001 | 0.0004 | −0.004, 0.004 | 0.0002 | −0.003, 0.004 |
| | (0.001) | | (0.002) | | (0.002) | |
| Sex (female) | −0.108* | −0.200, −0.015 | 0.436*** | 0.209, 0.663 | 0.519*** | 0.309, 0.728 |
| | (0.047) | | (0.116) | | (0.107) | |
| Group (Hadza) | 1.287*** | 1.027, 1.546 | 2.216*** | 1.581, 2.852 | −0.062 | −0.648, 0.524 |
| | (0.132) | | (0.324) | | (0.299) | |
| Group (Hispanic) | 0.241*** | 0.163, 0.319 | 0.549*** | 0.357, 0.740 | 0.270** | 0.093, 0.447 |
| | (0.040) | | (0.098) | | (0.090) | |
| Group (Malagasy) | 1.036*** | 0.826, 1.247 | 2.355*** | 1.840, 2.869 | 0.572* | 0.0974, 1.047 |
| | (0.107) | | (0.262) | | (0.242) | |
| Sex (female)*Group (Hadza) | 0.054 | −0.271, 0.379 | −0.360 | −1.156, 0.435 | −0.288 | −1.022, 0.446 |
| | (0.166) | | (0.406) | | (0.374) | |
| Sex (female)*Group (Hispanic) | −0.033 | −0.137, 0.070 | −0.193 | −0.446, 0.060 | −0.141 | −0.375, 0.092 |
| | (0.053) | | (0.129) | | (0.119) | |
| Sex (female)*Group (Malagasy) | 0.071 | −0.224, 0.365 | −0.845* | −1.566, −0.125 | −0.843* | −1.507, −0.178 |
| | (0.150) | | (0.367) | | (0.339) | |
## Neighborhood safety and racism predictors of WASO in the Hispanic group
We found that perceived neighborhood violence (Estimate = −0.030, SE = 0.013, CI = −0.056 to −0.005), but not noise (Estimate = −0.004, SE = 0.013, CI = −0.030 to 0.022), is a significant predictor of increased WASO. Controlling for age and sex, higher racism scores are only marginally significant (Estimate = 0.029, SE = 0.015, CI = −0.001 to 0.058). Supplementary regression results are presented in Table S2 and Table S3.
## DISCUSSION
In modeling WASO as a function of group membership, we found that hunter-gatherer Hadza and small-scale agriculturalist Malagasy samples exhibit evidence of greater WASO as a proxy of more sentinel behavior than either of the samples from the United States. Findings of significantly greater WASO demonstrate that the nonindustrial populations are waking more throughout the night. These results are consistent with previous literature suggesting increased vigilance expressed in the sleep of small-scale groups, which is consistent with our hypothesis that groups sleeping in louder, less reinforced housing experience sleep disruption as a response to external cues. Previous results have demonstrated sentinelized sleep in Hadza participants, whose sleep patterns are characterized by a very narrow window during which all members of the group synchronously sleep [8]. This behavior is not planned or intentional, but is instead hypothesized to be a passive behavior that has remained part of human sleep ecology. The current study’s comparison of a Hadza population with a similarly nonindustrial community of Malagasy further supports the hypothesis that sentinel behavior may still be an important aspect of human sleep ecology, stemming from the evolved need to respond to signals during the sleep period.
Interestingly, nightly sleep duration does not appear to be as differentially expressed across socio-ecological contexts as is WASO. Hispanic and Malagasy participants sleep significantly longer than NHW participants, so we do not see clear evidence for industrialization being strongly associated with sleep duration. This consistency across groups suggests that sleep duration could be a more highly regulated biological process than is WASO, which we propose is more sensitive to external environmental stimuli. We found that groups exhibiting higher WASO also have longer time in bed. It is possible that longer time in bed allows more awakenings throughout the night while still achieving the required amount of sleep. It is also possible that greater WASO necessitates longer time in bed to make up for the sleep time that is lost during nighttime awakenings. Both possibilities speak to a relationship between time in bed and WASO, and also suggest flexibility in the mechanisms by which sufficient nightly sleep duration is achieved in diverse socio-ecological settings.
## Sleep and technological infrastructure
Industrial sleeping environments are characterized by varying degrees of continuous technological buffers to the environment (e.g. climate and noise-controlled sleep sites), which protect against noise during the night that could activate a threat response and lead to awakening. It has been found that humans process and evaluate auditory stimuli during sleep [18], so quieter sleeping environments could help to minimize awakening responses. Although industrialized sleeping environments are often characterized by nighttime light and noise from traffic and other people—particularly in urban settings—dwellings are much more insulated and buffered from outside disturbance than the sleeping sites of Hadza and Malagasy (Fig. 4), which are much more exposed to environmental noise, light, and activity throughout the night. Hadza huts are often situated within earshot of multiple other huts, and noise is associated with wakefulness in Hadza camps, likely attributed to campfire activity, ritual ceremonies, and sexual activity [24]. The sleep-disrupting effects of these activities are more pronounced given the low-insulation of Hadza and Malagasy sleeping environments, where sound carries more than it would in a house or apartment in the United States. Furthermore, we hypothesize that unlike the impersonal sounds of nighttime city traffic or neighborhood activity in the United States, the sounds coming from a Hadza or Malagasy camp are likely related to kin or group members and warrant more attention. We propose that these factors may all contribute to activation of vigilance responses in the form of increased WASO.
**Figure 4.:** *Top: Hadza home constructed from branches stuck into the ground (right) and then stuffed with grasses (left). Bottom: Malagasy home constructed from wood. Participant consent for photographs was given to DRS.*
Despite the comfort and safety afforded by industrial infrastructure, the regular use of artificial lights after sunset and exposure to noise pollution that are characteristic of urban settings are often blamed for contributing to poor sleep outcomes [37]. Indeed, higher rates of insomnia and sleep disturbance have been reported in developed countries and metropolitan populations [38, 39]. In small-scale subsistence societies with and without access to artificial lighting, electric infrastructure is associated with shorter habitual sleep duration and later bedtime (e.g [40–42].). The results of the current study are in contrast to these findings, as sleep disruption is greater in nonindustrial populations, with greater time spent awake during the night. These findings are consistent with previous results from nonindustrial populations that have reported lower efficiency and/or shorter sleep duration compared to industrial populations [9, 42, 43]. Overall, our results suggest that the relationship between industrialization and sleep is complex, but that some aspects of technological infrastructure in industrial populations may facilitate feelings of safety that lead to better sleep.
## Sex, gender roles, and sleep patterns
Female sex was a significant predictor of lower WASO and longer time in bed and sleep duration in our regression models. Our results on sleep duration are consistent with previous literature demonstrating that women experience longer sleep when using objective analysis methods (e.g. actigraphy, polysomnography) [35, 36]. Interestingly, when examined by group membership, sex differences were only significant in NHW and Hispanic participants, and not Hadza and Malagasy (see Table S1). It is possible that higher occurrences of co-sleeping and young children in the sleeping environment may disrupt females and males similarly in the nonindustrial groups, so that sex differences in WASO are minimized. In other words, a crying infant in a relatively more crowded, less noise-insulated hut is more likely to wake everyone—females and males alike—in both the same hut and even nearby dwellings. In the full regression models, the interaction of sex and Malagasy group membership was significant in predicting significantly lower sleep duration, suggesting the possibility that culturally constructed gender roles (e.g. childcare, economic responsibilities) may have an influence on the observed sleep patterns.
To date, the majority of research conducted on sex differences in sleep duration and quality are based in Western, industrial contexts, so it is unsurprising that our findings of sleep in the United States populations follow these norms; yet, in small-scale nonindustrial populations they do not. It is possible that gendered labor roles, subsistence strategy, and social norms contribute to these differences by population. For instance, Hadza activities demonstrate strong gendered division of labor, with men the primary hunters and women the primary gatherers [44]. Hadza women breastfeed their infants and children on-demand up to around age two, and breastfeeding women are found to have earlier wake times and lower activity during the day compared to men and non-breastfeeding women [45]. Combining ethnographic and qualitative data on sleep in nonindustrial settings will clarify our understanding of the relative contribution of biocultural factors in shaping sex and gender-based sleep patterns. Overall, while we found that there is more sentinel behavior (i.e. WASO) in males than females, this difference is only significant in the NHW and Hispanic samples, and there is no significant interaction effect of group and sex in predicting WASO in the regression models. Variation in the effect of perceived threat on sentinel behavior should be explored in future work to assess whether there are sex differences in sentinel behavior that are not captured by our analysis.
## Sleep health disparities, social stigma, and discrimination
While our prediction that industrial groups would experience less WASO compared to nonindustrial groups was supported, we found that Hispanic participants have significantly greater WASO than NHW participants. We speculated that this pattern could have to do in part with the effects of social marginalization of individuals of Hispanic heritage in the United States. We investigated this hypothesis through performing supplemental regression analysis of WASO and perceived neighborhood safety and racism within the Hispanic sample. These supplementary analyses, particularly on perceived neighborhood violence and noise, support the idea that in the analyzed sample of Hispanics from the United States, perceived threats of violence are a more significant disruptor of sleep than noise. These findings are consistent with previous research. Neighborhood and home safety have been reported to predict sleep outcomes [46, 47], including in previous analysis using the HCHS/SOL sample in which lower perceived neighborhood safety was found to significantly increase odds of short sleep duration [48].
Surprisingly, Racism/Discrimination Score did not significantly predict WASO in our supplementary analysis. It is possible that this measure does not fully capture the lived experience of participants, as the measure asks about lifetime discrimination experiences and may not reflect actual living and sleeping environments that participants are currently in. Nevertheless, Racism/Discrimination Score was marginally significant in predicting more WASO, and it still warrants discussion given the strong previous evidence for the negative effects of racism and stigma on sleep health. Systemic racism in the United *States is* strongly implicated in driving widespread healthcare inequalities for Americans of color [49], and racial and ethnic sleep disparities are well documented. Alcántara and colleagues [50] found that discrimination based on ethnicity and acculturation stress were associated with increased daytime sleepiness [50]. Numerous studies in the United States have found that compared to NHW participants, Black participants demonstrate shorter sleep duration and lower sleep efficiency compared to NHW Americans [51–54], and Black Americans have been found to be more likely to experience sleep insufficiency [55]. A large study using nationally-representative data from the Behavioral Risk Factor Surveillance System found that Black Americans were significantly more likely to report fewer than 7 h of sleep per night [56]. These sleep health inequities are linked to experiences of racism. In a recent study of self-reported health in 422 African American women, personal, direct experiences with racism—particularly violent experiences—were found to be associated with poor self-reported sleep quality [57]. Given the serious health problems associated with insufficient sleep, racism and discrimination are important for considering sleep outcomes within the broader discussion of health inequities.
Our results suggest that health disparities related to neighborhood safety may in part be related to our evolved vigilance to lethal and non-lethal threats in our surroundings, whether real or perceived. Disrupted sleep may reflect a response consistent with the “smoke detector principle,” which proposes that defense mechanisms that cost less than the potential threat they protect against will often trigger false alarms [58]. In other words, the fitness costs of brief sleep disruptions would have been less than even a small risk of predation in our evolutionary history. From a fitness perspective, overreactions in the awakening response to potential danger would have likely been favorable to underreacting and sustaining serious injury or death. While acute sleep disruption may have increased immediate survival in human evolutionary history, this evolved alertness may now be contributing to health concerns such as increasing reports of chronic sleep disruption and insomnia [20, 59], particularly in communities who experience elevated social stigma.
Consistent with an evolutionary medicine perspective, rather than viewing observed sleep disparities as stemming solely from sleep pathologies, we propose that disrupted sleep may in part be related to evolved vigilance mechanisms that were adaptive in our ancestral environment and that current conditions of discrimination and inequitable housing and neighborhood quality arouse these ancient mechanisms to the detriment of health and wellbeing. Evolutionary medicine explains human response mechanisms that have become maladaptive in our safer modern environments, and also indicates where it may be appropriate to minimize our anxiety, stress, and pain responses without a corresponding decrease in fitness [58]. Although our results suggest that in some cases it may be appropriate to classify modern sleep disturbances as overreactions to our relatively safer environments (e.g. harmless noise from city traffic), we should also be cognizant that our safe modern environments are not equally safe for everyone. Social stigma and discrimination should be considered for their real effects on the safety of sleeping and living environments. Therefore, while improving physical sleeping environments would go some way toward redressing sleep health disparities, it must be combined with addressing racial and ethnic social stigma in order to minimize social threat as well. This would likely improve not only sleep health, but also the general health outcomes that sleep underpins.
## Limitations
There are potential limitations to our findings. First, WASO is most likely driven by a complex interaction of different types of nighttime disturbances, including noise from animals, other people, children crying, co-sleeping, breastfeeding and childcare responsibilities, sexual activity, or other factors that are unrelated to vigilance mechanisms. While our results are consistent with the idea that increased alertness during sleep may have been adaptive in our evolutionary history, it is impossible to disentangle the numerous socio-ecological factors—some unrelated to threat and vigilance—that could be contributing to differential expressions of sleep sentinelization. Further research should also model reported perception of safety as a predictor of WASO, which was not available in the Hadza and Malagasy datasets at the time of our analysis.
Second, our regression analyses did not account for co-sleeping. Previous work with Hadza groups has suggested that number of co-sleepers (but not breastfeeding) is associated with shorter sleep duration and lower sleep quality [45]. To the best of our knowledge, co-sleeping data are not available in the HCHS/SOL dataset, so could not be included in our main regression models. However, we included co-sleeping as a covariate in our exploratory analysis (not shown) of predictors of WASO in Hadza, Malagasy, and the MIDUS II cohort, and found that co-sleeping was not significant. Despite this non-significant finding, we believe that co-sleeping, bedsharing, and childcare may still be playing a greater role in driving WASO than our data are able to show, particularly in hunter-gatherer groups such as the Hadza where women’s activity and sleep patterns are affected by long-term, on-demand breastfeeding of infants and children [45]. It is also worth noting that co-sleeping and/or the presence of infants can be related to WASO apart from nursing or other activities. Sentinelized sleep patterns (i.e. greater WASO) may be exaggerated in the presence of others—particularly vulnerable infants and children—as a way of not only ensuring one’s own safety, but also that of co-sleepers and dependents.
Third, while the use of diverse datasets provided valuable cross-cultural comparisons in our test of the sentinel hypothesis, there are methodological limitations. Sleep data were generated from CamNtech MotionWatch 8 actigraphs (Hadza and Malagasy), Mini Mitter Actiwatch-64 activity monitors (MIDUS), and Actiwatch Spectrum actigraphs (HCHS/SOL). Similarly, sleep data scoring and optimization software corresponded to their respective accelerometers. Data cleaning and optimization were performed by different individuals trained in separate protocols, introducing potential inconsistencies and rater bias. MIDUS data were collected in 30-s intervals, unlike the other three groups in which data were collected in 60-s intervals. Differences in actigraphy results generated from 60-s intervals vs 30-s intervals have been previously found to be insignificant [60], and we addressed the other limitations to the best of our ability by choosing sleep variables that were generated with the most consistent protocols, but they should be considered in the comparability of sleep patterns across the four samples.
## CONCLUSIONS
In summary, this work presents support for the idea that sleep disruption is more common in socio-ecological environments with more frequent night disturbances, a behavior that may have conferred a past adaptive advantage. Increased WASO is observed in small-scale, nonindustrial samples compared to industrial samples from the United States, suggesting that sentinelization and a high degree of nighttime awakenings may have been characteristic of ancestral humans’ sleep. The reduced WASO in the United States suggests that quieter, secure housing can buffer against environmental stimuli that lead to awakenings, and reduce sentinel sleep behavior that could be contributing to disrupted sleep. However, our finding that Hispanic participants experience more awakenings throughout the night, which is associated with perceived neighborhood violence, should encourage further research into the sources of sleep disruption in minority communities in the United States, which could reflect the adverse health effects of neighborhood inequities and health disparities stemming from social marginalization. While sentinelized sleep patterns could in part stem from a behavior that increased immediate survival of our ancestors through increased vigilance, it may now be contributing to insufficient sleep and maintenance of an evolved fear response, which may be associated with increased insomnia [59]. Critically, with increasing reports of sleep insufficiency that are often described as a sleep loss epidemic [61, 62], our results contribute to the field of evolutionary medicine by increasing our understanding of the possible evolutionary explanations for sleep disturbances. Understanding and addressing the environmental and social factors that lead to heightened vigilance during sleep should be prioritized to better understand and improve sleep and overall health.
## FUNDING
This work was supported by the Department of Anthropology, University of Toronto Mississauga and by the Canadian Tri-Council Social Sciences and Humanities Research Council Insight Development Grant award number: 430-2018-00018.
## CONFLICT OF INTEREST
The authors declare no conflicts of interest.
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|
---
title: 'Polycystic Ovary Syndrome and Exercise: Evaluation of YouTube Videos'
journal: Cureus
year: 2023
pmcid: PMC10024815
doi: 10.7759/cureus.35093
license: CC BY 3.0
---
# Polycystic Ovary Syndrome and Exercise: Evaluation of YouTube Videos
## Abstract
Objectives: Polycystic ovary syndrome (PCOS) is a common endocrine disorder in the reproductive female population. These young patients often and easily watch YouTube videos on the Internet to learn about their condition and find a natural solution. Our goal is to analyze the contents of PCOS exercise videos.
Methods: In July 2022, research data were collected by typing the term "PCOS exercise" in the search tab on the incognito YouTube page. One hundred and ninety eight videos that met the inclusion criteria were analyzed in detail. The basic data of the videos available on YouTube was recorded. In addition, the DISCERN, global quality score (GQS), and video power index (VPI) scoring systems were calculated by two independent researchers.
Results: The profiles of the video uploaders were: health employee 28 ($14.1\%$), nutritionist 25 ($12.6\%$), sports trainer 48 ($24.2\%$), patient 21 ($10.6\%$), undefined 76 ($38.4\%$), and their countries were: India 91 ($46\%$), Europe and England 36 ($18.2\%$), USA and Canada 54 ($27.3\%$), and other countries 17 ($8.6\%$). The distribution of video content was yoga 58 ($29.3\%$), aerobic exercise 38 ($19.2\%$), strengthening exercise 44 ($22.2\%$), and unified 58 ($29.3\%$). The mean values were: video duration (15.27±11.27), total views (3,070,957±16,474,197), likes (48,116±283,308), dislikes (930±4102), VPI (97.82±7.28), GQS (3.89±1.05), DISCERN (33.62±10.42), subscriber counts (985,614±2,222,354), and comment counts (1741±10,689). Europe-England and America-Canada videos were of better quality for DISCERN and GQS scores than those from other countries.
Conclusion: Overcoming PCOS requires a lifestyle change, including exercise and diet. There is no consensus on which type of exercise is better yet. However, the necessity of regular exercise is known. We showed yoga and Indian hegemony in YouTube "PCOS exercise" videos.
## Introduction
Polycystic ovary syndrome (PCOS) was defined by Stein and Leventhal in 1935 [1]. PCOS is seen in prevalences ranging from $4\%$ to $26\%$ [2]. It can affect the mental and physical health, menstrual pattern, and fertility of these women in reproductive age [3]. The *Rotterdam criteria* including oligo-anovulation, hyperandrogenism, and the appearance of polycystic ovaries are helpful in diagnosis. Although there is no cure at present, symptomatic treatments are available.
PCOS has metabolic and cardiovascular effects. It is observed that in the distribution of type 2 diabetes mellitus cases, PCOS patients are 4-5 times more common than those without PCOS [4]. Despite the fact that the exact causes cannot be revealed, there are hormonal imbalances and insulin resistance [3]. Whether underweight or overweight, PCOS patients have intrinsic insulin resistance. As the patient's weight increases, the severity of the resistance may also increase [5]. The increase in obesity, the increase in stress in daily life, and the decrease in physical activity are the leading causes. Although there are drugs to break insulin resistance, these drugs do not provide long-term benefits unless the root cause of the problem is resolved. Modulation of lifestyle by exercising regularly, increasing physical activity in daily life, and consuming conscious foods reduces insulin resistance. It can be ensured that the body remains in shape by losing weight with routine exercises.
Physical activity that increases the body's energy consumption is called exercise when done regularly and repetitively to maintain health or increase physical condition. The basis of the exercise is the number of repetitions, duration, intensity, applicability, and type. Although there are many classifications, stretching (yoga), aerobic, and strengthening exercises can be mentioned as the main exercise types [6].
YouTube is a huge social media platform where millions of videos are uploaded, and so many videos are clicked tens of times almost every day [7]. Even if most people go to the doctor for their complaints, they search for something about their disease on the internet. These researches are mostly carried out on Youtube because it contains visuals and is easily accessible. On this platform, which reflects society on the internet, there is very useful information as well as garbage-value information. Our goal is to analyze these videos to reveal the quality of the video content.
## Materials and methods
Ethics committee approval was not required since publicly accessible information was used on youtube.com, a widely used social media platform, in accordance with the Declaration of Helsinki. This research was done on YouTube in July 2022. A search was made by typing "PCOS exercise" in the YouTube search bar in Google Chrome incognito tabs. Videos are sorted by relevance. All videos were evaluated by two independent, experienced researchers. A total of 627 videos were evaluated. A total of 429 videos were excluded, including 53 that were irrelevant, 6 that were repetitive, 63 that were non-English, 194 that had less than 1000 views, 105 YouTube-short videos, and 8 commercial advertisements.
A total of 198 videos were included in the study and analyzed statistically. Image type, uploader, country, content, upload time, duration, total views, likes, dislikes, number of subscribers, and number of comments were recorded. The exercise types suggested in the video content were divided into four categories: yoga, aerobics, strengthening, and unified, where multiple exercise types are recommended. During the pandemic, when spending more time at home, it was considered essential to upload videos. It was recorded that the narrator of the video talked about insulin resistance, fertility, menstrual patterns, and hormonal balance. Dietary advice and citation to a specific article were also considered.
Parameters showing video quality were calculated. The video power index (VPI) score was calculated with the formula [likes/(likes+ dislikes)] × 100 [8]. The DISCERN and global quality score (GQS) were calculated independently by each researcher, and their means were included in the statistics. DISCERN is a scoring system consisting of three sections and a total of 16 questions. In this scoring system, where high scores indicate quality, the scores vary between 1 and 5 points for each question. The first article of the DISCERN scoring system has over 1500 citations. [ 9]. Scoring for GQS ranges from 1 (poor quality) to 5 (excellent quality) [10].
Statistical analysis The IBM SPSS Statistics (Version 21.0, SPSS, Inc.) program was used for the statistical analyses. Continuous variables were given as mean ± standard deviation, and categorical variables were given as numbers (%). The normal distribution of the data was examined by using the Kolmogorov-Smirnov test. The Mann-Whitney-U test and one-way ANOVA were used for comparisons between groups. p˂0.05 was considered significant in all statistical comparisons.
## Results
The main features of the videos are presented in Table 1. In the analysis of 198 videos included in the study, there were 5 ($2.5\%$) videos with image-type animation, while 193 ($97.5\%$) videos contained real images. Video narrators include sports trainers 48 ($24.2\%$), health employees 28 ($14.1\%$), nutritionists 25 ($12.6\%$), patients 21 ($10.6\%$), and 76 unidentified ($38.4\%$) groups. When a YouTuber's country was examined, India was at 91 ($46\%$), European countries and England (UK) were at 36 ($18.2\%$), the United States of America (USA) and Canada were at 54 ($27.3\%$), and other countries were at 17 ($8.6\%$). In the examination of video exercise contents, yoga 58 ($29.3\%$), aerobic exercise 38 ($19.2\%$), strength training 44 ($22.2\%$), and unified exercise 58 ($29.3\%$), consisting of a combination of at least two of these exercises, were observed. Most of the videos (133-$67.2\%$) were uploaded after the pandemic. Parameters highlighted along with exercise in PCOS exercise videos were: insulin resistance 71 ($35.9\%$), fertility and/or infertility 31 ($15.7\%$), menstrual regulation and/or irregularity 52 ($26.3\%$), hormonal balance and/or imbalance 85 ($42.9\%$), dietary recommendation 74 ($37.4\%$), and citing a scientific article 17 ($8.6\%$).
**Table 1**
| Features | Features.1 | Features.2 | N | % |
| --- | --- | --- | --- | --- |
| Image type | Image type | Real | 193 | 97.5 |
| Image type | Image type | Animation | 5 | 2.5 |
| Uploaders | Uploaders | Health employee | 28 | 14.1 |
| Uploaders | Uploaders | Nutritionist | 25 | 12.6 |
| Uploaders | Uploaders | Sports trainer | 48 | 24.2 |
| Uploaders | Uploaders | Patient | 21 | 10.6 |
| Uploaders | Uploaders | Unidentified | 76 | 38.4 |
| Country group | Country group | India | 91 | 46.0 |
| Country group | Country group | Europe and England (UK) | 36 | 18.2 |
| Country group | Country group | USA and Canada | 54 | 27.3 |
| Country group | Country group | Other countries | 17 | 8.6 |
| Video content | Video content | Yoga | 58 | 29.3 |
| Video content | Video content | Aerobic exercise | 38 | 19.2 |
| Video content | Video content | Strengthening exercise | 44 | 22.2 |
| Video content | Video content | Unified | 58 | 29.3 |
| Loading time | Loading time | Pre-pandemic | 65 | 32.8 |
| Loading time | Loading time | Post-pandemic | 133 | 67.2 |
| Highlighted in the video | Insulin resistance | Yes | 71 | 35.9 |
| Highlighted in the video | Insulin resistance | No | 127 | 64.1 |
| Highlighted in the video | Fertility/Infertility | Yes | 31 | 15.7 |
| Highlighted in the video | Fertility/Infertility | No | 167 | 84.3 |
| Highlighted in the video | Menstrual regulation/irregulation | Yes | 52 | 26.3 |
| Highlighted in the video | Menstrual regulation/irregulation | No | 146 | 73.7 |
| Highlighted in the video | Hormone balance/imbalance | Yes | 85 | 42.9 |
| Highlighted in the video | Hormone balance/imbalance | No | 113 | 57.1 |
| Highlighted in the video | Diet advice | Yes | 74 | 37.4 |
| Highlighted in the video | Diet advice | No | 124 | 62.6 |
| Highlighted in the video | Scientific article cited | Yes | 17 | 8.6 |
| Highlighted in the video | Scientific article cited | No | 181 | 91.4 |
Descriptive information on video scores is shown in Table 2. The video duration mean value is 15.27±11.27 minutes. Videos had a mean value of 3,070,957±16,474,197 views, 48116±283,308 likes, 930±4102 dislikes, 985,614±2,222,354 subscriber counts, 1741±10,689 comment counts. The mean values of the scales showing the video quality were VPI 97.82±7.28, GQS 3.891.05, and DISCERN 33.62±10.42.
Scales showing video quality were compared in Table 3. A professional group ($$n = 101$$) including health employees, sports trainers, and nutritionists was created. A non-professional group ($$n = 97$$) was made with the patients and the unidentified. While all video quality indicators were higher in the professional group, only DISCERN was statistically significantly different (35.85±10.42 vs 31.29±9.95, $$p \leq 0.002$$). Although the number of videos created in the post-pandemic ($$n = 133$$) period was twice as high as before ($$n = 65$$), there was no statistical difference between the quality indicators of video content ($p \leq 0.05$ for DISCERN, GQS, and VPI). Videos originating from India ($$n = 91$$) were almost equal to the sum of USA-Canada and Europe-UK videos ($$n = 90$$). There was a statistically significant difference only for GQS when comparing country groups with an ANOVA ($$p \leq 0.007$$). In addition, the Europe-UK group had a significantly higher score. There was no difference between the groups in the quality analysis of the videos according to the exercise type ($p \leq 0.05$). Two groups were formed according to whether the number of subscribers was more or less than the mean subscribers. As the number of subscribers on YouTube increased, the number of views and the GQS score were also increasing ($$p \leq 0.031$$ and $$p \leq 0.011$$, respectively). DISCERN and VPI are unaffected by subscriber count ($p \leq 0.05$). In addition to the exercise content of the video, the number of views was significantly lower in the videos that emphasized the scientific data of PCOS, while the video quality scores were found to be statistically significantly higher.
## Discussion
Millions of clicks are made on YouTube every day [7]. On YouTube, which serves as a consultant doctor, patients go to do in-depth research on something they have heard about their health. People strive to take the necessary care for their personal development and health. Especially during the time spent at home due to the COVID pandemic, home-based exercises have become even more popular to protect physical health [11]. Thus, both the number of videos and the number of views increased. Our study found that the number of videos doubled after the pandemic. In fact, we have seen that there are not enough animation videos about exercise, which is an area where animation videos can be easily published. While the DISCERN scores of the videos of the professional video uploaders were statistically significantly higher, there was no difference in other scores compared to the non-professional ones. Professionals using more scientific language can be effective in this regard.
PCOS workout videos could be called a crowded Indian family. Although there was no difference between the DISCERN and VPI scores, we showed that the Europe-UK-sourced videos were of good quality according to the GQS score. The Europe-UK and USA-Canada videos were above the mean value for DISCERN and GQS. In studies conducted in India, the prevalence of PCOS was found to vary between $3.7\%$ and $22.5\%$ [12]. Despite the prevalence of PCOS being similar to other countries, we found that Indian YouTubers are more interested in PCOS exercise video sharing than other countries. In addition, there are articles in the literature that argue that there is a rapid increase in prevalence, especially due to the increase in obesity [13,14]. One of the reasons why both Indian and yoga videos are in the majority, which we found in our study, might be the fact that yoga originated in India [15].
We have mentioned above that there are three (stretching, aerobic, and strengthening) main types of exercise [6]. A traditional method that is a good fighter against stress is yoga. We can say that yoga videos take the lead in the analysis of the videos. Yoga can be classified as a stretching exercise [16]. Yoga regulates blood circulation through breathing and mind exercises. Types of cardiovascular movement such as brisk walking, swimming, running, and cycling that increase heart rate and breathing are called aerobic exercise. Lifting weights, working with resistance bands, and other movements that can be done by yourself are called strengthening exercises. The videos in which these exercises were suggested in combination were quite common. However, a clear exercise has not yet been recommended for PCOS [17], and there was no statistically significant difference in the quality of the PCOS exercise videos in our study.
The long-term effects of PCOS include serious diseases such as heart disease, diabetes, hypertension, infertility, and cancer. Insulin resistance has been reported in almost all patients diagnosed with the *Rotterdam diagnosis* [5]. Insulin resistance gets worse as BMI increases. Obesity in women brings about changes in the endocrine system and blood androgen levels [18]. When this cycle is broken in a positive way, a regression in PCOS symptoms is observed. Although the benefits of regular exercise on PCOS are known, there is no consensus on the type of exercise that is optimal [19]. Regular aerobic exercise helps to lose weight and regulates glucose levels [20].
A good exercise program should be supported by conscious food consumption. The number of views of the videos in which diet advice was given along with exercise was high. DISCERN, GQS, and VPI scores were higher for those with recommended diets, but statistically significantly different for DISCERN. A calorie-restricted diet with a low glycemic index decreased testosterone and insulin resistance levels. An anti-inflammatory, low-glycemic-index, low-fat diet has positive effects on PCOS symptoms [21]. Although a calorie-restricted and low-fat diet style was recommended in the videos we observed in our study, statistical analysis was not performed.
The characteristic features of PCOS patients are a sedentary life with reduced physical activity, chronic hormonal imbalance, unconscious eating habits, and menstrual irregularity. Although the number of views and the VPI of the videos describing these features were low, the DISCERN and GQS scores were statistically significantly higher in our study. There is an increase in testosterone, dehydroepiandrosterone, and its sulphate ester (DHEAS) levels, especially in obese women with PCOS [18]. Excessive androgen production occurs when the concentration of luteinizing hormone (LH) rises relative to follicle-stimulating hormone (FSH), which is more common in women with PCOS [21]. Chronic hormonal imbalance is manifested by numerous cysts and irregular menses. Anovulatory cycles are one of the important causes of female infertility. Approximately one in eight women has fertility problems. $25\%$ of women with fertility problems have trouble with ovulation [22]. Prolonged exercise during the day can also cause anovulation. However, when daily exercise is kept up for 30 to 60 minutes, it reduces the risk of anovulation-induced infertility [23]. One review stated that while aerobic exercise had no effect on LH, yoga reduced LH levels [24].
The lack of detailed analysis of diet programs, which are an integral part of the exercise, is one of the significant limitations of this study.
## Conclusions
PCOS is a common endocrine disorder in which symptoms can regress with exercise. When we saw the application of the videos, we showed that there is no excuse to avoid exercise and that it can be easily applied at home. People who are away from stress, have regular sleep, pay attention to their diet, and exercise regularly are one step ahead of PCOS. We believe that our study will contribute to future studies on PCOS, which still has a great dilemma about its cause, treatment, and even which type of exercise is better.
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|
---
title: 'Modelling of salt intake reduction by incorporation of umami substances into
Japanese foods: a cross-sectional study'
authors:
- Shiori Tanaka
- Daisuke Yoneoka
- Aya Ishizuka
- Megumi Adachi
- Hitomi Hayabuchi
- Toshihide Nishimura
- Yukari Takemi
- Hisayuki Uneyama
- Haruyo Nakamura
- Kaung Suu Lwin
- Kenji Shibuya
- Shuhei Nomura
journal: BMC Public Health
year: 2023
pmcid: PMC10024820
doi: 10.1186/s12889-023-15322-6
license: CC BY 4.0
---
# Modelling of salt intake reduction by incorporation of umami substances into Japanese foods: a cross-sectional study
## Abstract
### Background
Evidence has demonstrated that excess sodium intake is associated with development of several non-communicable diseases. The main source of sodium is salt. Therefore, reducing salt intake in foods is an important global public health effort to achieve sodium reduction and improve health. This study aimed to model salt intake reduction with 'umami' substances among Japanese adults. The umami substances considered in this study include glutamate or monosodium glutamates (MSG), calcium diglutamate (CDG), inosinate, and guanylate.
### Methods
A total of 21,805 participants aged 57.8 years on average from the National Health and Nutrition Survey was used in the analysis. First, we employed a multivariable linear regression approach with overall salt intake (g/day) as a dependent variable, adjusting for food items and other covariates to estimate the contribution of salt intake from each food item that was selected through an extensive literature review. Assuming the participants already consume low-sodium products, we considered three scenarios in which salt intake could be reduced with the additional umami substances up to $30\%$, $60\%$ and $100\%$. We estimated the total amount of population-level salt reduction for each scenario by age and gender. Under the $100\%$ scenario, the Japan’s achievement rates against the national and global salt intake reduction goals were also calculated.
### Results
Without compromising the taste, the $100\%$ or universal incorporation of umami substances into food items reduced the salt intake of Japanese adults by 12.8–$22.3\%$ at the population-level average, which is equivalent to 1.27–2.22 g of salt reduction. The universal incorporation of umami substances into food items changed daily mean salt intake of the total population from 9.95 g to 7.73 g: 10.83 g to 8.40 g for men and 9.21 g to 7.17 g for women, respectively. This study suggested that approximately $60\%$ of Japanese adults could achieve the national dietary goal of 8 g/day, while only $7.6\%$ would meet the global recommendation of 5.0 g/day.
### Conclusions
Our study provides essential information on the potential salt reduction with umami substances. The universal incorporation of umami substances into food items would enable the Japanese to achieve the national dietary goal. However, the reduced salt intake level still falls short of the global dietary recommendation.
## Background
The latest Global Burden of Disease Study 2019 (GBD) highlighted that the global prevalence of non-communicable diseases (NCDs) and inadequate public health efforts to control risk factors may have spurred the pandemic of coronavirus disease 2019 (COVID-19) [1]. In 2013, the World Health Organization (WHO) developed the NCDs Global Monitoring Framework, in which nine NCDs prevention targets were set [2]. Of the nine targets, the only target specifically related to nutrients is a $30\%$ relative reduction in mean population intake of salt/sodium between 2011 and 2025 [2]. Since then, many campaigns aiming at reducing salt, the main source of sodium, have been initiated around the world [3], and the global salt reduction movement has been accelerated [4–6]. However, no country has yet to achieve the $30\%$ reduction goal [7]. In the GBD 2019, high salt intake was listed as one of the top dietary risks contributing to the global burden of disease [8] highlighting the need for an urgent approach.
Japan is one of the countries that are globally recognized for prolonged longevity [9]. However, a high salt intake is a major dietary risk factor for both mortality and morbidity of its population [8, 10]. Japan's nationwide population-based campaign for salt reduction started in 1960s and successfully reduced the population's salt intake and mortality resulting from stroke over time [11]. According to the National Nutrition Survey (NNS), which was renamed to the National Health and Nutrition Survey (NHNS) in 2013, the daily salt intake has steadily decreased from 14.5 g in 1973 to 9.5 g in 2017 [12]. However, the *Japanese* generally consume more salt than people in other countries [13]. For instance, the population average sodium intake in 2010 was 4.89 g/day (12.23 g/day of salt intake) in Japan, whereas those in the United Kingdom (UK) and the United States (US) were 3.61 g/day (9.03 g/day of salt intake) and 3.60 g/day (9.00 g/day of salt intake), respectively [13]. The government aims to reduce the daily salt intake of Japanese adults to 8 g by 2023 in their 10-year national health promotion plan, titled the Second Term of National Health Promotion Movement in the Twenty-First Century, also known as "Health Japan 21 (the second term)" [14]. Another dietary guideline is called the Dietary Reference Intakes for Japanese (DRIs), which proposes reference values for the intake of energy and nutrients to prevent lifestyle-related diseases and extend healthy life expectancy [15]. The DRIs recommend daily salt intake of 7.5 g/day for men and 6.5 g/day for women. However, the average salt intake among Japanese adults remains higher than the recommendations made by both guidelines. The targets set for the *Japanese is* unlikely to be attained if current trends persist [16, 17].
Sodium replacement in foods is one of the most widely used approaches to reduce salt intake. The technical challenge is to ensure that the sodium alternative is palatable and safe to eat [18]. Umami is a common and familiar taste in Japanese cuisine, and perhaps globally better known as the fifth flavour, in addition to the classic four tastes: saltiness, sweetness, bitterness, and sourness, discovered by the Japanese scientist in 1908 [19]. Umami substances, including glutamate or monosodium glutamates (MSG), calcium diglutamate (CDG), inosinate and guanylate, have been proposed as enhancers of savory taste when combined with sodium chloride (NaCl) [20–22]. A large number of studies have suggested the potential use of umami substances as a healthy and natural solution for salt intake reduction [23–25]. In recent years, academic institutions, such as the Institute of Medicine in the United States, have identified umami substances as candidates for practical salt intake reduction alternatives [18]. Wallace et al. [ 2019] estimated that incorporating MSG into a savoury seasoning of processed foods in the United States could reduce salt intake of the population by at least 3 to $8\%$ [26]. However, given the fact that the source of salt intake is highly dependent on the dietary habits and the cooking processes in each country [27], the effectiveness of the umami substances for reducing salt intake at the population level in the context of other cultures is not well known. Therefore, our study aims to investigate the effects of umami substances on the daily salt (NaCl) intake reduction among Japanese adults using the NHNS data.
## Study design and participants
We conducted a cross-sectional study using the de-identified national data from the 2016 NHNS. The NHNS is a nationally representative household survey conducted annually by the Japanese Ministry of Health, Labour and Welfare (MHLW) to collect data on the population's dietary habits, nutrition intake and lifestyle [28]. Residents above the age of one were selected from the census enumeration areas using a stratified single-stage cluster sample design. The 2016 survey, the latest large-scale survey data available from the NHNS at the time of the study, is comprised of 24,187 households randomly selected from 475 districts. The response rate of the survey was $44.4\%$. The NHNS consists of three parts: 1) physical examination, 2) an in-person survey and weighted single-day dietary record of households, and 3) a self-reported lifestyle questionnaire. Details of the survey design and the procedures are available elsewhere [16, 28]. In the present analysis, we included persons aged 20 years or older as Health Japan 21 requires the age group to complete the dietary intake data. We further excluded participants who reported daily consumption of less than 1.5 g of salt, the minimum physiological requirement for survival, assuming that the data may not reflect their diet accurately or may be measurement error.
This study was performed under the Declaration of Helsinki and approved by the Research Ethics Committee of the Graduate School of Medicine, the University of Tokyo (authorization number 11964). The ethical committee waived the need for informed consent because this study conducted a secondary analysis using anonymized data routinely collected by the MHLW.
## Dietary assessment
The dietary intake survey was conducted on a single designated day for household representatives, who were usually responsible for food preparation. Trained interviewers, mainly registered dietitians, instructed household representatives on how to measure the food and beverages consumed by members of the household and checked their compliance with the survey. The proportion of shared dishes, food waste, and foods eaten out were recorded by the household representatives. The nutrient intake and food consumption were estimated by experts using the dietary record and the corresponding food composition list of the Japanese Standard Tables of Food Composition (7th revised edition) [29]. In addition, food intake (g/day) and overall sodium intake (mg/d) were recorded. Salt intake (g) was defined as sodium (mg) × $\frac{2.54}{1}$,000. NHNS did not measure urine sodium.
## Salt intake modelling
We have conducted an extensive review of the scientific literatures, and found that umami substances, such as glutamate or MSG, CDG, inosinate, and guanylate, have been used to reduce salt in various mainstream products. Table 1 shows the percentage reduction of salt intake in each food item estimated by previous studies using one or more umami substances. The food items listed in Table 1 were then matched with the predetermined 13 NHNS food groups, and assumed the salt reduction rate for each NHNS food group to be used in our analyses, in consultation with food and nutrition experts (co-authors). In addition, we assumed that the study participants already consume some low-sodium food items containing umami substances in their diet. Therefore, the market share of low-sodium food products was used as a proxy indicator of the baseline consumption of low-sodium foods [30]. This market share was estimated from data of the total sales and the sales of low-sodium foods acquired from the surveys conducted in 2017 by Fuji Keizai Management Co., Ltd. (a Japanese market research company). Low-sodium food products were defined as products labelled with “reduced salt,” “salt cut,” “salt off,” or “no salt” on the package. The market share of low-sodium food products for each food group was also summarized in Table 1. We considered three scenarios in which consumers could potentially reduce their salt intake with the additional umami substances from the baseline to $30\%$ (30-percent scenario), $60\%$ (60-percent scenario) and $100\%$ (100-percent scenario or universal incorporation scenario).Table 1Percentage reduction of salt intake in food items by incorporation of umami substance ReferencesFood itemsSalt alternativesSalt reduction rate (%)NHNS food groupAssumed salt reduction rate in the study (%)Assumed market share of low-sodium products (%)JPA 01–304860 [31]Salt compositionMSG, inosinate22–43Seasoning salt22–439.2JPA 58–198269 [32]Salt compositionMSG, inosinate, guanylate30JPA 2006–141226 [33]Liquid seasoningMSG40–49Soy sauce40–6128.7Ishida 2011 [34]Soy sauceMSG, inosinate, guanylate60JPA 09–275930 [35]Soy sauceMSG, inosinate, guanylate61Ishida 2011 [34]Miso pasteMSG, inosinate, guanylate15Miso paste15–3512.4JPA5523618 [36]Low-salt bean misoGlutamine acid35Chi 1992 [37]Chicken brothMSG11Other seasonings11–404.1Manabe 2008 [38]Japanese clear soupDried bonito (Rich in inosinate)15Goh 2011 [39]Japanese clear soupSoy sauce (Rich in glutamates)15Huynh 2016 [40]Tomato sauceFish sauce (Rich in glutamates)16Kremer 2009 [41]Tomato soupSoy sauce (Rich in glutamates)17–33Ogasawara 2016 [42]Mentsuyu (Japanese noodle soup)Dried bonito (Rich in inosinate)20Wang 2019 [25]Chicken soupMSG20Leong 2015 [43]Mee soto brothMSG22Jinap 2016 [44]Spicy soupMSG32.5Carter 2011 [45]Chicken brothCDG38Ball 2002 [46]Pumpkin soupCDG40Yamaguchi 1984 [21]Japanese clear soupMSG40Roininen 1996 [47]Minestrone; leek and potato soupMSG, inosinate, guanylate40Rodrigues 2014 [48]Garlic and saltMSG50Spices and other504.1JPA 59–118038 [49]Processed meat and fishMSG, inosinate30–40Processed fish (including salted fish, canned fish, fish boiled in soy sauce and fish sausage)30–503.5de Quadros 2015 [50]Fish burgers (minced fish)MSG50Wooward 2003 [51]SausageCDG17Ham and sausage17–750.3JPA 59–118038 [49]Processed meat and fishMSG, inosinate30–40dos Santos 2014 [52]SausagesMSG75Miller 2014 [53]Minced beefMushrooms (Rich in glutamates)25Beef250.3JPA60-153751 [54]Pickled vegetableMSG55Pickled vegetable550.3Rodrigues 2014 [55]Mozzarella cheeseMSG54Cheese54–1000.4da Silva 2014 [56]Cream cheeseMSGup to 100de Souza 2014 [57]ButterMSGup to 100Butter1000.0Goncalves 2017 [58]MargarineMSG33Margarine330.0Kongsta 2020 [24]Potato chipsMSG30Other confectionery30–570.3Buechler 2020 [23]Chips and rice puffsMSG, inosinate, guanylate57 CDG Calcium diglutamate, JPA Japanese Patent Application, MSG Monosodium glutamate, NHNS National Health and Nutrition Survey
## Statistical analysis
We first constructed a linear regression model with overall daily salt intake (g/day, continuous) as a dependent variable. To estimate the salt intake contribution from the 13 food groups (continued) to the overall salt intake, we included age (continuous), sex (dichotomous) and food intake (g/day) from the 13 food groups and the food items (continuous) in the following model [1].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}=\alpha +{\beta }_{1}{X}_{1i}+{\beta }_{2}{X}_{2i}+\cdots +{\beta }_{13}{X}_{13i}+{Z}_{i}\gamma +{\epsilon }_{i}$$\end{document}Yi=α+β1X1i+β2X2i+⋯+β13X13i+Ziγ+ϵiwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Y}_{i}$$\end{document}Yi and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{ji}$$\end{document}Xji (for $j = 1$, …,13) are the overall daily salt intake and the food intake (g/day) from each of the 13 food groups for the ith individual, respectively. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${Z}_{i}$$\end{document}*Zi is* the covariate vector for the remaining 35 food items, sex and age for the ith individual. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha, {\beta }_{j}, \gamma$$\end{document}α,βj,γ are regression coefficients and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\epsilon }_{i}$$\end{document}ϵi is a gaussian error term. The regression coefficients are estimated by ordinary least squared method.
The food group-specific upper and lower changes in salt intake by umami substance incorporation were estimated using the current market share of low-sodium products for the jth food group (denoted as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${M}_{j}$$\end{document}Mj), the upper and lower salt intake reduction rates for the jth food group (denoted as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${U}_{j}$$\end{document}Uj and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${L}_{j}$$\end{document}Lj, respectively), as well as the scenario-based increased consumption (denoted as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${S}_{k}$$\end{document}Sk= 30, 60 or $100\%$ increase ($k = 1$, 2, 3)), as follows.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Upper\ changes\ in\ salt\ intake\ of\ the\ jth\ food\ group\ under\ the\ kth\ scenario$$\end{document}Upperchangesinsaltintakeofthejthfoodgroupunderthekthscenario\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$={\widehat{\beta }}_{j}-{\widehat{\beta }}_{j}\times {U}_{j}\times \left({S}_{k}-{M}_{j}\right),$$\end{document}=β^j-β^j×Uj×Sk-Mj,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Lower\ changes\ in\ salt\ intake\ of\ the\ jth\ food\ group\ under\ the\ kth\ scenario$$\end{document}Lowerchangesinsaltintakeofthejthfoodgroupunderthekthscenario\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$={\widehat{\beta }}_{j}-\widehat{\beta }_{j}\times {L}_{j}\times \left({S}_{k}-{M}_{j}\right),$$\end{document}=β^j-β^j×Lj×Sk-Mj, The first term indicates the original salt intake contribution of the jth food group to the overall salt intake. In the second term, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${U}_{j}$$\end{document}Uj indicates how much salt intake we can reduce by incorporating umami substances into food groups. Finally, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left({S}_{k}-{M}_{j}\right)$$\end{document}Sk-Mj indicates how much salt intake we can change into that from low-sodium products.
The baseline and reduced salt intakes among consumers of each food group, and the proportion of salt intake from each food group to the total salt intake were estimated for the total population, men, and women in the three scenarios.
The achieving rate of the salt intake reduction goals when umami substances are universally incorporated into all the food groups were then calculated by age groups and sex. Health Japan 21’s dietary goal is defined as a daily mean salt intake of less than 8 g [14], while that of the DRIs is 7.5 g for men and 6.5 g for women [15]. The WHO, on the other hand, recommends a daily salt intake of 5.0 g [59]. We used STATA version 16 for all analyses (Stata Corp LLC).
## Results
A total of 30,820 people joined the NHNS survey in 2016. We excluded ineligible subjects who were younger than 20 years old ($$n = 4$$,595) and consumed less than 1.5 g of salt per day ($$n = 46$$). 4,374 subjects had missing values on dietary intake. Finally, a sample of 21,805 Japanese persons with an average age of 57.8 (standard deviation [SD] 17.6) years were used in our analysis. Overall, the daily mean salt intake among the Japanese population was 9.95 g, which is higher than the daily salt intake recommended by Health Japan 21, the DRIs or the WHO.
The sex- and age-specific daily mean salt intake and the achieving rate of the salt intake reduction goals are shown in Table 2. Salt intake was likely to be higher among older persons than younger persons. Of the total population, $28.7\%$ has already achieved the dietary goal of Health Japan 21, while $15.3\%$ and $2.8\%$ have achieved the dietary goals of the DRIs and the WHO, respectively. Men had higher salt intake than women in all age groups. The daily mean salt intake was the highest among men aged 60–69 years (11.43 g) and women aged 70–79 years (9.72 g), while the lowest among men (10.37 g) and women (8.60 g) both aged 20–29 years. The difference in daily mean salt intake between the highest and the lowest groups was 2.83 g. The rate of achieving the dietary goals was higher in women than men across all age groups. Table 2Daily mean salt intake among the NHNS participants and achieving rates of the salt reduction goals in Health Japan 21, the DRIs and the WHO, by age group and sex, the NHNS 2016Age (years)Number of the NHNS participantsDaily mean salt intake (g/day (SD))Achieving rate of Health Japan 21 goal (%)a Achieving rate of DRIs goal (%)b Achieving rate of WHO goal (%)c Total population 20–291,4809.44 (3.2)35.319.33.9 30–392,5489.52 (3.2)35.020.64.1 40–493,3949.49 (3.1)33.819.63.9 50–593,2549.86 (3.1)29.014.02.5 60–694,94010.52 (3.2)21.510.01.5 70–793,93410.38 (3.3)23.812.52.1 80 + 2,2559.58 (3.1)33.518.63.5 All21,8059.95 (3.2)28.715.32.8Men 20–2970710.37 (3.5)24.219.02.1 30–391,20510.51 (3.4)23.718.01.8 40–491,58110.47 (3.2)23.317.51.8 50–591,48110.77 (3.3)18.713.21.6 60–692,30411.43 (3.4)13.39.00.4 70–791,80211.09 (3.4)17.513.31.2 80 + 89210.32 (3.2)24.219.32.1 All9,97210.83 (3.4)19.414.41.4Women 20–297738.60 (2.6)45.419.55.4 30–391,3438.64 (2.7)45.122.96.1 40–491,8138.65 (2.6)43.021.55.8 50–591,7739.09 (2.6)37.614.73.2 60–692,6369.72 (2.9)28.611.02.4 70–792,1329.78 (3.0)29.211.82.8 80 + 1,3639.10 (3.0)39.618.24.4 All11,8339.21 (2.8)36.516.04.0 DRIs Dietary Reference Intakes, WHO World Health Organization, NHNS National Health and Nutrition Survey, SD Standard deviation aRecommend consumption of no more than 8 g of salt intake a day bRecommend consumption of no more than 7.5 g of salt intake a day for men and 6.5 g for women cRecommend consumption of no more than 5 g of salt intake a day The sex-specific salt intake and potential reduction of salt intake estimated for each food group in three scenarios are presented in Table 3. The consumption of food items in each food group by the participants on any given day during the survey varied. The percentages of the participants who consumed the food items in each food group, i.e., “consumers,” were high for other seasonings ($97.9\%$), soy sauce ($85.9\%$), seasoning salt ($83.1\%$), and miso paste ($69.3\%$), and low for beef ($24.71\%$), cheese ($17.3\%$), butter ($14.9\%$), margarine ($14.4\%$) and other confectionery ($15.2\%$). Compared to women, men were more likely to consume salt, soy sauce, spices and other, ham and sausage, beef, and pickled vegetable, and less likely to consume cheese and other confectionery. The consumers of soy sauce, seasoning salt, and miso paste took more than one gram of salt daily from each food group, and all participants consumed food items from at least one food group. Table 3Estimated mean salt intake from the 13 food groups and potential percentage reduction of salt intake by incorporation of umami substances into food items in the 30-, 60-, and 100-percent scenarios by sex, the NHNS 2016Number of the NHNS respondents (%)Estimated mean salt intake from food group (g/day (SD)) a Potential percentage reduction of salt intake in scenarios30-percent scenario60-percent scenario100-percent scenariog/day%b g/day%b g/day%b Seasoning salt Total population18,123 (83.1)1.13 (1.00)0.05–0.104.6–8.90.13–0.2511.2–21.80.23–0.4420.0–39.0 Men8,482 (85.1)1.26 (1.10)0.06–0.110.14–0.270.25–0.49 Women9,641 (81.5)1.02 (0.90)0.05–0.090.11–0.220.20–0.40Soy sauce Total population18,726 (85.9)1.57 (1.30)0.01–0.010.5–0.80.20–0.3012.5–19.10.45–0.6828.5–43.5 Men8,736 (87.6)1.71 (1.40)0.01–0.010.21–0.330.49–0.74 Women9,990 (84.4)1.44 (1.20)0.01–0.010.18–0.280.41–0.63Miso paste Total population15,105 (69.3)1.48 (1.00)0.04–0.092.6–6.20.11–0.257.1–16.70.19–0.4513.1–30.7 Men6,971 (69.9)1.55 (1.00)0.04–0.100.11–0.260.20–0.47 Women8,134 (68.7)1.42 (0.90)0.04–0.090.10–0.240.19–0.44Other seasoning Total population21,341 (97.9)0.34 (0.50)0.01–0.042.9–10.40.02–0.086.2–22.40.04–0.1310.6–38.4 Men9,756 (97.8)0.38 (0.50)0.01–0.040.02–0.090.04–0.15 Women11,585 (97.9)0.31 (0.40)0.01–0.030.02–0.070.03–0.12Spices and other Total5,567 (25.5)0.08 (0.20)0.0113.00.0228.00.0448.0 Men2,707 (27.1)0.08 (0.20)0.010.020.04 Women2,860 (24.2)0.07 (0.10)0.010.020.03Processed fish Total population13,783 (63.2)0.80 (0.80)0.06–0.110.5–0.80.14–0.2312.5–19.10.23–0.3928.5–43.5 Men6,393 (64.1)0.86 (0.90)0.07–0.110.15–0.240.25–0.42 Women7,390 (62.5)0.75 (0.80)0.06–0.100.13–0.210.22–0.36Ham and sausage Total population8,899 (40.8)0.66 (0.50)0.03–0.158.0–13.30.07–0.2917.0–28.30.11–0.4929.0–48.3 Men4,245 (42.6)0.72 (0.60)0.04–0.160.07–0.320.12–0.54 Women4,654 (39.3)0.60 (0.50)0.03–0.130.06–0.270.10–0.45Beef Total population5,380 (24.7)0.19 (0.20)0.017.40.0314.90.0524.9 Men2,717 (27.2)0.21 (0.20)0.020.030.05 Women2,663 (22.5)0.17 (0.10)0.010.030.04Pickled vegetable Total population8,813 (40.4)0.91 (1.00)0.1516.30.3032.80.5054.8 Men4,390 (44.0)0.94 (1.10)0.150.310.51 Women4,423 (37.4)0.89 (0.90)0.140.290.49Cheese Total population3,767 (17.3)0.51 (0.40)0.08–0.1516.0–29.60.17–0.3132.2–59.60.28–0.5153.8–99.6 Men1,551 (15.6)0.52 (0.40)0.08–0.150.17–0.310.28–0.51 Women2,216 (18.7)0.51 (0.40)0.08–0.150.16–0.310.28–0.51Butter Total population3,253 (14.9)0.24 (0.20)0.0730.00.1460.00.24100.0 Men1,397 (14.0)0.24 (0.20)0.070.140.24 Women1,856 (15.7)0.24 (0.20)0.070.140.24Margarine Total population3,144 (14.4)0.45 (0.40)0.049.90.0919.80.1533.0 Men1,320 (13.2)0.49 (0.40)0.050.100.16 Women1,824 (15.4)0.42 (0.30)0.040.080.14Other confectionery Total population3,323 (15.2)0.10 (0.10)0.01–0.028.9–16.90.02–0.0417.9–34.00.03–0.0629.9–56.8 Men1,161 (11.6)0.12 (0.10)0.01–0.020.02–0.040.04–0.07 Women2,162 (18.3)0.10 (0.10)0.01–0.020.02–0.030.03–0.06All foods Total population21,801 (100.0)5.06 (2.70)0.24–0.432.3–4.10.68–1.206.4–11.41.27–2.2212.0–21.1 Men9,970 (100.0)5.57 (2.90)0.26–0.472.3–4.20.74–1.316.5–11.51.39–2.4312.1–21.3 Women11,831 (100.0)4.63 (2.40)0.22–0.402.3–4.10.63–1.106.4–11.31.16–2.0311.9–20.9SD Standard deviation aSalt intake from each food group among consumer, not all participants bThe reduction rate of each food group was the same for men and women. The salt reduction rate for the total population was the average reduction rate among individuals in the percentage decrease of overall salt intake In the universal incorporation scenario where consumers could potentially reduce their salt intake up to $100\%$ with the additional umami substances, the highest amount of expected salt reduction was found in soy sauce (0.45–0.68 g), followed by cheese (0.28–0.51 g), pickled vegetable (0.50 g), ham and sausage (0.11–0.49 g), seasoning salt (0.23–0.44 g) and miso paste (0.19–0.45 g). Negligible reductions in salt intake could be expected for spice and others, beef, and other confectionery (< 0.1 g).
Table 4 presents proportion of salt intake from each food group to overall salt intake, and potential salt intake reduction with the additional umami substances in the universal incorporation scenario by sex. Among all participants, soy sauce ($12.5\%$), miso paste ($9.7\%$), and seasoning salt ($8.9\%$) were the major contributors to the overall daily salt intake. In contrast, spice and others, beef, cheese, butter, margarine, and other confectionery were the minor sources of salt intake (< $1\%$). Although high reduction of salt intake was found in cheese among the cheese consumers (0.28–0.51 g), there was less impact at the population level (0.05–0.09 g) because there were few cheese consumers among the participants. The total daily mean salt intake from all the food groups was 5.06 g for all the participants, resulting in a $48.0\%$ salt intake contribution to the overall salt intake. Thus, by universally incorporating umami substances into the food groups, salt intake could be reduced by an average of 1.27–2.22 g among the total population (with an average reduction rate of 12.0–$21.1\%$). This corresponds to a reduction of 12.8–$22.3\%$ in the average salt intake among the total population (not shown in the table).Table 4Proportion of salt intake from each food group contributed to overall salt intake, and potential percentage reduction of salt intake with the additional umami substances in the universal incorporation scenario by sex, the NHNS 2016All ($$n = 21$$,805)Proportion of salt intake contributed to overall salt intake (%)Reduction in salt intakea g/ day%Seasoning salt Total population8.90.19–0.371.8–3.5 Men9.40.21–0.421.9–3.7 Women8.60.17–0.321.7–3.4Soy sauce Total population12.50.38–0.593.6–5.4 Men12.20.43–0.653.7–5.6 Women12.80.35–0.533.5–5.3Miso paste Total population9.70.13–0.311.3–3.0 Men9.90.14–0.331.3–2.9 Women9.50.13–0.301.3–3.0Other seasoning Total population3.20.04–0.130.3–1.2 Men3.20.04–0.140.3–1.3 Women3.30.03–0.120.3–1.2Spices and other Total population0.20.010.1 Men0.20.010.1 Women0.20.010.1Processed fish Total population4.80.15–0.241.4–2.3 Men4.80.16–0.271.4–2.3 Women4.90.14–0.231.4–2.3Ham and sausage Total population2.60.05–0.200.5–2.0 Men2.50.05–0.230.5–2.1 Women2.80.04–0.180.4–1.9Beef Total population0.50.010.1 Men0.40.010.1 Women0.60.010.1Pickled vegetable Total population3.30.201.8 Men3.20.231.9 Women3.40.181.8Cheese Total population0.90.05–0.090.5–0.9 Men1.10.04–0.080.4–0.8 Women0.80.05–0.100.6–1.1Butter Total population0.40.040.4 Men0.40.030.3 Women0.30.040.4Margarine Total population0.70.020.2 Men0.80.020.2 Women0.60.020.3Other confectionery Total population0.20.00–0.010.0–0.1 Men0.20.00–0.010.0–0.1 Women0.10.01–0.010.0–0.1All foods Total population48.01.27–2.2212.0–21.1 Men47.51.39–2.4312.1–21.3 Women48.61.16–2.0311.9–20.9 aThe scenario in which umami substances were universally incorporated into selected food groups. The salt reduction rate was the average among individuals Figure 1 describes distributions of daily salt intake among the total and sex-specific population before and after the universal incorporation of umami substances into food items. It is obvious that higher proportions of the population in both sexes came to consume less amount of salt intake after the universal incorporation of umami substances into food groups. Table 5 shows sex- and age-specific daily mean salt intake estimated after umami substances are universally incorporated into the food items and their achieving rates of the salt reduction goals set by Health Japan 21, the DRIs and the WHO. While the salt intake still varied by sex and age groups, the difference in the mean salt intake between the highest and the lowest groups was slightly smaller when umami substances were universally incorporated into food items. The daily mean salt intake of all the participants in the universal incorporation scenario was 7.73–8.68 g; thereby, suggesting a possibility to achieve Health Japan 21’s goal of consuming less than 8 g of daily salt intake by 2023. Moreover, the rate of those who achieve Health Japan 21’s dietary goal increased from $19.6\%$ to 31.2–$46.6\%$ for men and $36.9\%$ to 53.6–$70.8\%$ for women under the scenario. While approximately 23.9–$36.7\%$ of men and 25.6–$39.2\%$ of women could achieve the recommended daily salt intake outlined by the DRIs, only 2.2–$3.8\%$ of men and 6.4–$10.8\%$ of women were expected to achieve the WHO’s dietary goal: 5 g of daily salt intake, in the universal incorporation scenario. Fig. 1Distributions of daily salt intake among the total and sex-specific population before and after the universal incorporation of umami substances into food items in (A) the total population, by sex of (B) men, and (C) women, NHNS 2016. The grey bars indicate the distributions of daily salt intake before the universal incorporation of umami substances into food items. The blue bars indicate the distributions of daily salt intake after the universal incorporation of umami substances into food itemsTable 5Estimated salt intake and achieving rates of the dietary goals with the additional umami substances in the universal incorporation scenario by sex and age groups, the NHNS 2016Age (years)Estimated salt intake (g/day)Achieving rate of Health Japan 21 goal (%)a Achieving rate of DRIs goal (%)b Achieving rate of WHO goal (%)c Total population 20–297.40–8.3449.5–68.031.1–45.76.1–10.0 30–397.46–8.4148.9–63.930.3–44.35.9–10.6 40–497.42–8.3649.4–65.029.6–44.16.1–10.1 50–597.69–8.6444.3–60.924.1–38.63.9–7.0 60–698.13–9.1335.1–52.418.1–29.72.7–4.8 70–798.02–8.9838.5–54.720.9–33.13.4–5.4 80 + 7.43–8.2949.2–65.029.8–43.25.7–9.6 All7.73–8.6843.4–59.724.8–38.14.4–7.6Men 20–298.12–9.1536.9–54.229.8–44.43.1–5.1 30–398.19–9.2535.2–50.327.7–39.92.4–5.0 40–498.15–9.2035.2–50.227.1–41.43.0–4.8 50–598.38–9.4331.1–46.922.6–36.52.6–3.9 60–698.79–9.9024.3–39.217.7–29.11.0–2.1 70–798.58–9.6128.9–44.021.8–34.41.8–2.9 80 + 8.00–8.9237.3–53.330.8–42.83.0–5.2 All8.40–9.4531.2–46.623.9–36.72.2–3.8Women 20–296.75–7.6061.1–80.632.3–47.08.9–14.5 30–396.80–7.6461.2–76.032.5–48.29.0–15.7 40–496.79–7.6261.8–77.931.7–46.48.8–14.7 50–597.11–7.9755.3–72.625.4–40.45.0–9.6 60–697.54–8.4544.5–63.818.5–30.24.1–7.2 70–797.54–8.4546.6–63.820.2–32.04.8–7.6 80 + 7.06–7.8857.0–72.729.1–43.47.5–12.5 All7.17–8.0453.6–70.825.6–39.26.4–10.8 SD Standard deviation, DRIs Dietary Reference Intakes, WHO World Health Organization aRecommend consumption of no more than 8 g of salt intake a day bRecommend consumption of no more than 7.5 g of salt intake a day for men and 6.5 g for women cRecommend consumption of no more than 5 g of salt intake a day
## Discussion
Excess salt intake reduction is now a global public health challenge [60]. Reducing salt intake has been identified as one of the most cost-effective measures to improve population health outcomes [59]. High sodium intake is a crucial risk factor for chronic diseases, and it has posed a high burden in Japan for decades [8, 10]. The current daily mean salt intake in Japan exceeds public recommendations across all ages and in both sexes. This study shows that it is possible to reduce the Japanese population’s salt intake by up to 2.22 g ($21.1\%$) on average without compromising the taste of food by substituting salt with umami substances, which corresponds to a $22.3\%$ reduction in the average salt intake of the population. In addition to reducing the salt intake among consumers, this study demonstrates that the universal incorporation of umami substances into the some foods can effectively reduce salt intake at the population level.
The previous study, using the data from the National Health and Nutrition Examination Survey 2013–2016 in the US, focused solely on MSG as a solution for salt reduction [26]. However, global recognition of MSG as an effective and practical solution for salt intake reduction remains a major challenge. In a widely reported study published in 1968, MSG in Chinese food was suggested to be the cause behind numbness and palpitations in the neck and arms and linked to various health problems, known as the Chinese restaurant syndrome [61]. Following this study, several studies also reported the association between MSG and various health effects, including asthma, urticaria, atopic dermatitis, dyspnea, tachycardia, metabolic syndrome, obesity and blood pressure increase [62–66]. However, other studies, including a double-blind placebo-controlled trial, have evaluated the reported reactions to MSG and confirmed a lack of plausible evidence between MSG intake and the development of such symptoms [67–70]. Furthermore, major scientific committees and regulatory bodies, such as the Joint FAO/WHO Expert Committee on Food Additives (JECFA), the European Commission Scientific Committee on Food (SCF), and the U.S. Food and Drug Administration (FDA), have assessed the safety of MSG and all separately came to a conclusion that MSG is safe to consume at a normal intake level and there is no evidence linking the use of MSG to long-term medical problems for the general public [71]. The more recent evidence-based safety reviews of MSG also came to the same conclusions, addressing that some studies speculatively linked animal pharmacology to human food use of MSG, and many are based on excessive dosing that does not meet with levels normally consumed in food products [72, 73].
The previous US study focusing solely on MSG reported that the overall salt intake reduction among the population was $7.3\%$ [26]. Meanwhile, reduction of sodium can also be achieved with sodium-free glutamates, such as CDG, inosinate, and guanylate [74]. Accordingly, the scope of our study has been expanded from MSG to the wider range of umami substances. As such, our findings suggested that umami substances could potentially make a greater impact on reducing salt intake than MSG in the previous study. On the other hand, we selected food items that are widely consumed by the Japanese, such as seasoning salt, soy sauce, miso paste, other seasonings, and processed fish. Indeed, soy sauce is one of the most highly consumed food items in Japan, and this study showed the largest impact of daily salt reduction in soy sauce, up to 0.68 g among its consumers and 0.37 g among the total population. On the other hand, cheese, spices and other, beef, margarine, and other confectionery had less impact on reducing salt intake at the population level because they are less consumed in Japan.
To reduce the Japanese population’s daily salt intake, the Japanese government took steps to enforce the new food labelling system and the nutrition labelling system in April 2020 [75, 76]. These systems made it mandatory for food companies to disclose the amount of salt/sodium in their products to ensure that their consumers are aware of the nutritional contents in their foods. However, these measures alone may not be sufficient in addressing the problem because reducing salt intake is not a priority among consumers [77]. Furthermore, reducing the salt in foods may lower the quality of food. For example, a $75\%$ reduction of salt in sausages decreases the sausages' hardness, chewiness, and cohesiveness [52]. Hence, food companies often provide low-sodium alternatives that give their consumers the taste and the quality they seek without the harmful amounts of sodium [78]. Potassium chloride, calcium chloride, and magnesium sulfate are also commonly used as substitutes for table salt. However, their bitter taste has repelled the consumers and resulted in their limited use. In contrast, umami substances, which are naturally present in various foods, are widely accepted by consumers [79]. As umami substances enhance the original flavor in foods, incorporation of umami substances into food items will reduce the salt intake more effectively [18, 80].
The food industry should take action to raise consumer awareness on the benefits of eating low-sodium foods while reducing the salt in their products, so that consumers can adapt to the changes in the taste over time [74]. Accordingly, the food industry's role is essential in reducing the daily salt intake of Japanese population and reducing their health risks [11]. Moreover, reducing salt intake through food science and technological advance is an appropriate method to make the most impactful salt intake reduction at the population level [60]. Our study has provided the essential data on the distribution of the selected food consumers, the market shares of the selected food items with low-sodium alternatives, and its impact on public health by showing the potential salt intake reduction. This information may instruct and inspire the food industry to develop more low-sodium products and distribute them in the market.
This study has some strengths. This is the first study to show the impact of salt reduction by replacing NaCl with umami substances in the selected Japanese food products. The use of the nationally representative data has guaranteed the study's generalisability to the Japanese population. The modelling assumptions of salt reduction were made based on scientific evidence, consultation with food scientists and consideration of market distributions of low-sodium products.
This study is subject to similar limitations found in other studies concerning dietary patterns [81, 82]. First, the dietary data we used in our analysis may have some biases. Because the dietary data from the NHNS were based on the weighted single-day dietary record, the analysis may not have captured the long-term dietary patterns. In dietary surveys, participants' self-reports tend to be associated with social desirability and recall bias. Moreover, reliance on dietary intake records made by household representatives may lead to biased estimates of dietary intake, particularly for those meals taken outside the home. Additionally, NHNS's stratified two-cluster sampling design may have caused selection bias, leading to biased estimates. Second, the data on food-specific salt intake were not publicly available. Therefore, an individual's food-specific salt intake was estimated by regression method which may not have accurately reflected the actual amount of salt intake from each food item. Third, age-specific preferences of food which may affect the potential overall salt reduction were not considered [83]. Fourth, we did not consider possible changes in food intake as a result of umami substance incorporation to reduce salt intake. Umami flavour may increase overall food intake or decrease vegetable intake as the previous studies suggested that vegetable intake is associated with salt intake [84–86]. The changes in food intake could change the effects of umami substances on salt intake reduction. Fifth, we did not include the low-sodium products using sodium replacers, other than umami substances, such as potassium chloride, mineral salts and yeast extracts in Japan [87–89] in our modelling, as we assessed the effects of umami substances on salt intake reduction. Thus, we may have underestimated the market share of low-sodium products. Finally, we did not consider the Japanese population’s current MSG intake and its association with health outcomes due to unavailability of data. Hence, caution is needed when the salt intake reduction is pursued by using MSG which may cause the long-term health effects on the population.
## Conclusions
Our study has suggested that the incorporation of umami substances into the selected food items could potentially reduce the average daily salt intake of the Japanese population by $22.3\%$, which is equivalent to 2.22 g of daily salt reduction. The universal incorporation of umami substances into the selected food items might enable the Japanese to achieve the national dietary goals. However, the level of salt intake reduction still falls short of 5.0 g/day recommended by WHO. Along with the public health efforts, collaboration with experts in food science should be pursued. Further investigation, innovation, and distribution of low-sodium food products are needed to help reduce the adult Japanese population's salt intake and consequently reduce their chances of developing NCDs.
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---
title: Enhanced immune complex formation in the lungs of patients with dermatomyositis
authors:
- Yoshiaki Zaizen
- Masaki Okamoto
- Koichi Azuma
- Junya Fukuoka
- Hironao Hozumi
- Noriho Sakamoto
- Takafumi Suda
- Hiroshi Mukae
- Tomoaki Hoshino
journal: Respiratory Research
year: 2023
pmcid: PMC10024827
doi: 10.1186/s12931-023-02362-0
license: CC BY 4.0
---
# Enhanced immune complex formation in the lungs of patients with dermatomyositis
## Abstract
### Background
Interstitial lung disease is frequently comorbid with dermatomyositis and has a poor prognosis, especially in patients with the anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody. However, the pathogenesis of dermatomyositis-related interstitial lung disease remains unclear.
### Methods
We examined 18 and 19 patients with dermatomyositis-related interstitial lung disease and idiopathic pulmonary fibrosis (control), respectively. Lung tissues obtained from these patients were semi-quantitatively evaluated by immunohistochemical staining with in-house anti-human MDA5 monoclonal antibodies, as well as anti-human immunoglobulin (Ig) G, IgM, IgA, and complement component 3(C3) antibodies. We established human MDA5 transgenic mice and treated them with rabbit anti-human MDA5 polyclonal antibodies, and evaluated lung injury and Ig and C3 expression.
### Results
MDA5 was moderately or strongly expressed in the lungs of patients in both groups, with no significant differences between the groups. However, patients with dermatomyositis-related interstitial lung disease showed significantly stronger expression of C3 ($p \leq 0.001$), IgG ($p \leq 0.001$), and IgM ($$p \leq 0.001$$) in the lungs than control. Moreover, lung C3, but IgG, IgA, nor IgM expression was significantly stronger in MDA5 autoantibody-positive dermatomyositis-related interstitial lung disease ($$n = 9$$) than in MDA5 autoantibody-negative dermatomyositis-related interstitial lung disease ($$n = 9$$; $$p \leq 0.022$$). Treatment with anti-MDA5 antibodies induced lung injury in MDA5 transgenic mice, and strong immunoglobulin and C3 expression was observed in the lungs of the mice.
### Conclusion
Strong immunoglobulin and C3 expression in the lungs involve lung injury related to dermatomyositis-related interstitial lung disease. Enhanced immune complex formation in the lungs may contribute to the poor prognosis of MDA5 autoantibody-positive dermatomyositis-related interstitial lung disease.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12931-023-02362-0.
## Introduction
Idiopathic inflammatory myopathies (IIMs) are rare autoimmune diseases [1]. Dermatomyositis is an IIM subtype presenting with proximal skeletal muscle weakness and muscle inflammation. Dermatomyositis is characterized by skin disorders, including Gottron’s sign and the heliotrope sign, with some patients showing definite cutaneous manifestations of dermatomyositis along with slight or clinically non-significant myopathy, which is defined as clinically amyopathic dermatomyositis (CADM) [2].
Interstitial lung disease (ILD) is relatively common among dermatomyositis patients and affects their prognosis. Specifically, patients with CADM are at a high risk of developing rapidly progressive interstitial lung disease (RP-ILD) with a poor prognosis due to resistance to immunosuppressive therapy [1]. Although various autoantibodies are involved in dermatomyositis [3, 4], the detailed mechanism underlying the immune response in dermatomyositis-related ILD (DM-ILD) remains unclear.
The anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody [5] is among the main autoantibodies in DM-ILD. Anti-MDA5 autoantibodies have been often detected in high titers in patients with CADM and can be used as a biomarker in the clinical diagnosis of dermatomyositis; moreover, the presence of anti-MDA5 autoantibodies is associated with a poor survival prognosis, especially in DM-ILD patients [6, 7]. We have previously shown that low serum titers of anti-MDA5 autoantibody (< 100.0 IU/mL) or the absence of autoantibodies improved the survival rate of patients with DM-ILD and RP-ILD [8]. However, the role of anti-MDA5 autoantibodies in respiratory failure development in patients with DM-ILD remains unclear.
Several studies have reported a relationship between autoimmune disease and MDA5 [9]. A genome-wide association study revealed a significant association of single-nucleotide polymorphisms of MDA5 with resistance to type 1 diabetes [10] and the Aicardi–Goutières syndrome [11]. Gateva et al. reported that human MDA5 single-nucleotide polymorphisms (A946T) are risk variants for systemic lupus erythematosus (SLE) [12]. Robinson et al. reported that this single-nucleotide polymorphism was a gain-of-function mutation in SLE [13]. In particular, SLE is a leading cause of the third type of hypersensitivity in the Gell–Coombs classification (type III hypersensitivity), which causes deposition of immune complexes including complement and immunoglobulins in tissues throughout the body. Such type III allergic reactions may be closely related to dysfunction of MDA5.
We hypothesized that the third type of hypersensitivity in the Gell–Coombs classification (type III hypersensitivity) [14–16] is present in the lungs of patients with DM-ILD, especially in patients with anti-MDA5 autoantibodies. This study aimed to investigate the expression of immunoglobulins (IgG, IgM, IgA) and complement component 3 (C3) in the lungs of patients with DM-ILD and those with idiopathic pulmonary fibrosis (IPF) as control. We also have successfully produced in-house anti-human MDA5 polyclonal and monoclonal antibodies and used these antibodies to examine the expression of MDA5 protein in the lungs of DM-ILD and IPF patients. Using the human surfactant promoter SPC, we also established transgenic mice overexpressing full-length human MDA5 protein in the lungs.
## Study participants
This retrospective study included 18 patients diagnosed with DM-ILD at our institutions between 1997 and 2020. The patients met the diagnostic criteria for polymyositis and dermatomyositis as reported by Bohan and Peter [17, 18] or the diagnostic criteria for CADM reported by Sontheimer [2], while patients met the diagnostic criteria of RP-ILD described by Kondoh et al. [ 19] Additionally, we investigated 19 patients, as control, histologically diagnosed with IPF, including nine patients showing acute IPF exacerbation. All patients were diagnosed as having IPF and/or IPF-AE by multidisciplinary discussion based on the official global guidelines for IPF [20, 21]. The sample size of this comparative group was determined using Student’s t-test with a detection power and significance level of $80\%$ and $5\%$, respectively, based on the results of a preliminary study. We examined a single section of lung specimens showing obvious lung injury and obtained through surgical lung biopsy (SLB), lung transplantation, or autopsy. In addition, specimen paraffin blocks for each case were retrieved from the archives of the participating institutions. We also collected the patients’ background characteristics; information regarding the applied diagnostic criteria of IIMs; and anti-MDA5 antibody test results. Additionally, as controls for immunohistochemistry staining, we investigated two cases without any underlying respiratory disease that involved lung resection for lung cancer.
## Establishment of an anti-human MDA5 polyclonal and monoclonal antibodies
Full-length human MDA5 cDNA (GenBank Accession No. AF095844) with a 6X His tag, GST, and turbo 3C protease cleavage site of Leu-Glu-Val-Leu-Phe-Gln-Gly-Pro at the N-terminus was subcloned into the pPSC8 expression vector (Protein Sciences Corporation, Meriden, CT, USA); subsequently designated as pPSC8/human MDA5. Recombinant human MDA5 protein was isolated from SF9 cells co-transfected with baculovirus AcNPV and pPSC8/human MDA5. *To* generate antiserum, specific pathogen-free (Japanese White) rabbits were immunized with recombinant human MDA5 protein Purified antibody was generated from the antisera using a protein G column (Cytiva, Tokyo, Japan), as reported previously [22, 23]. An anti-human MDA5 monoclonal antibody (mAb) clone H27 (mouse IgG1) and H46 (mouse IgG2b) was established by fusing mouse myeloma cell line X-63·Ag$\frac{8}{653}$ with spleen cells isolated from a BALB/c mouse immunized with the recombinant human MDA5 protein, as reported previously [22, 23].
## Generation of transgenic (Tg) mice constitutively overproducing human MDA5 in the lung and a mouse model of lung injury caused by anti-human MDA5 antibody
Male and female C57BL/6N (B6) and B6D2F1 (BDF1) mice were purchased from Charles River Japan (Yokohama, Japan) and bred in our laboratory. Transgenic (Tg) mice were generated as described previously [24]. Briefly, full-length human MDA5 cDNA (GenBank Accession No. AF095844) was generated and subcloned into the SalI site of a 3.7SPC/SV40 vector containing the SP-C promoter, the SV40 small T intron, and a polyA signal (kindly provided by Dr. Jeffrey A. Whitsett, Cincinnati Children’s Hospital Medical Center, OH), and was designated as SPC-MDA5. The NdeI- and NotI-digested linear DNA fragments were injected into fertilized eggs of BDF1 mice. Hemizygous Tg mice were generated by mating founder mice with B6 mice. *We* generated three lines of the human MDA5 transgenic mice (line no. 32, 55, and 116. In this study, we mainly used line no. 55 of human MDA5 transgenic mice. The lung injury model was established by treating human MDA5 transgenic mice and wild-type BDF1 mice ($$n = 4$$ to 7 per group) with 0.5 mL of rabbit anti-human MDA5 polyclonal antibodies (antisera) or normal rabbit sera 4 times (at day 0, 7, 14, 21) or eight times (at day 0, 7, 14, 21, 28, 35, 42, 49). Mice were sacrificed at day 28 or 56 for histological examination. Three repeated experiments were performed. Human MDA5 transgenic mice and wild-type BDF1 mice were also treated with 1 mg of purified rabbit anti-human MDA5 polyclonal antibodies or 1 mg of rabbit IgG (Sigma-Aldrich, Tokyo, Japan).
## Immunohistochemical staining and RNA in situ hybridization
Immunohistochemistry staining in human specimens was performed as reported previously [25]. Briefly, tissues were fixed with $10\%$ buffered formalin and embedded in paraffin wax. Serial sections (4-μm-thick) were cut from the paraffin-embedded tissues and placed on poly-l-lysine-coated slides. Deparaffinized sections were autoclaved for 3 min in 10 mM citric acid buffer (pH 6.0). The sections were incubated in $0.3\%$ H2O2 for 10 min to block endogenous peroxidase activity; subsequently, they were stained using mouse anti-human MDA5 mAb (H27; 1.2 μg/mL), rabbit anti-human IgG (P0214; Agilent, Palo Alto, CA, USA; × 100), rabbit anti-human IgM (P0215; Agilent; × 50), rabbit anti-human IgA (P0216; Agilent; × 100), and rabbit anti-human complement C3c (A0062, Agilent; × 5000). Mouse IgG1 (BioLegend, Tokyo) was used as the control. The sections were treated with these antibodies at room temperature for 2 h. Positive reactivity was identified using goat anti-mouse and anti-rabbit immunoglobulins (Ig) conjugated to peroxidase-labeled polymer (EnVision Dual Link system-HRP, Agilent) and Liquid DAB + Substrate Chromogen System Liquid (Agilent).
We also investigated the expression of human MDA5, Ig, and complement protein in human MDA5 transgenic mice and its lung injury model by immunohistochemistry. Immunohistochemistry staining was performed for Ig and complement protein utilizing standard methods with the following antibodies: anti-mouse IgG rabbit polyclonal antibody (A90-117A; Bethyl Laboratories, Montgomery, TX, USA; × 2000), anti-mouse IgA rabbit polyclonal antibody (A90-104A; Bethyl Laboratories; × 2000), anti-mouse IgM rabbit polyclonal antibody (A90-102A; Bethyl Laboratories; × 2000), and anti-C3 rabbit monoclonal antibody clone (EPR19394; Abcam, Cambridge, UK; × 2000). Immunohistochemistry staining of anti-human MDA5 mouse mAb was performed as follows. Deparaffinized sections were autoclaved for 10 min in 10 mM citric acid buffer (pH 6.0). The sections were incubated in $0.3\%$ H2O2 for 15 min to block endogenous peroxidase activity; subsequently, they were stained with anti-human MDA5 mouse mAb (H27 and H46; 10 µg/mL). The sections were treated with these antibodies at room temperature overnight. Positive reactivity was identified with a labeled streptavidin biotinylated antibody 2 kit (K0675; Agilent).
To evaluate the efficacy of two anti-human MDA5 monoclonal antibodies (clone H27 and H46; 3.0 μg/mL), we performed ribonucleic acid (RNA) in situ hybridization in lung samples from two patients with DM-ILD. We analyzed human MDA5 messenger RNA (mRNA) in formalin-fixed paraffin-embedded samples using an RNA in situ hybridization kit (RNAscope® 2.5HD Reagent Kit-RED; Advanced Cell Diagnostics, CA, USA), as reported previously [25]. Positive signals for fast red were analyzed using a fluorescent microscope (KEYENCE, Osaka, Japan). We compared slides showing immunohistochemistry staining with mouse anti-human MDA5 mAbs and in situ hybridization slides. We also confirmed that both slides were equally positive for injured alveolar epithelium (Fig. 1). On the basis of these results, we evaluated MDA5 expression in the alveolar epithelium by immunohistochemistry staining and used the in-house anti-human MDA5 mAb clone H27 for evaluation via immunohistochemistry staining. Fig. 1Comparative analysis of melanoma differentiation-associated gene 5 (MDA5) RNA in situ hybridization and immunohistochemical (IHC) staining using anti-human MDA5 monoclonal antibody (mAb) (clone H27) in a patient with dermatomyositis-related interstitial lung disease (DM-ILD). A Low-power view of IHC staining using the anti-MDA5 mAb clone H27. B Mid-power view. C High-power view. Damaged and foamy pneumocytes, alveolar macrophages, and fibrin were strongly positive. D High-power view of MDA5 RNA in situ hybridization. Damaged pneumocytes were positive (red arrows)
## Pathological assessment
Two pathologists (YZ and JF) who were blinded with clinical data performed semi-quantitative immunohistochemistry scoring in human patient samples. They independently examined the pathological slides, recorded their impressions in a blinded manner, and discussed their evaluation of severity based on immunohistochemistry staining in each case. We assessed the immunohistochemistry staining intensity in the alveolar epithelium, but not in the fibrin or macrophages within the alveoli. Slides were rated based as follows: score 0, negative as in controls; score 1, weakly positive; score 2, moderately positive; and score 3, strongly positive. The histological photographs showing the scores for each immunohistochemistry staining were presented in Additional file 1: Fig. S1 and Additional file 1: Fig. S2. In addition, we investigated the expression locations of MDA5 in human MDA5 transgenic mice and of Ig and complement proteins in a model of lung injury using anti-human MDA5 polyclonal antibodies by immunohistochemistry.
## Statistical analysis
Numerical data for patients’ characteristics are presented as median values with a 25–$75\%$ interquartile range; between-group comparisons were performed using the Mann–Whitney U test or Fisher’s exact test, as appropriate. Data for semi-quantitative immunohistochemistry scoring are presented as means ± standard deviations, with between-group comparisons using the Mann–Whitney U test. Statistical significance was set at $p \leq 0.05.$ All analyses were performed using JMP software (version 14.0; SAS Institute, Cary, NC, USA).
## Ethical issues
This study was conducted in accordance with the tenets of the Declaration of Helsinki and approved by the Institutional Review Board of our institute (approval date, July 31, 2019, No. 19090). All procedures were approved by the Committee on the Ethics of Animal Experiments, Kurume University (Approval No. 2022-083, 2022-084, 2022-085). Animal care was provided in accordance with the procedures outlined in the “Principles of laboratory animal care” (National Institutes of Health Publication 86-23, revised 1985). All efforts were made to minimize the suffering of animals used in this study.
## Patient characteristics
Table 1 presents the patient characteristics. We evaluated 18 and 19 patients showing DM-ILD and IPF, respectively. The DM-ILD group included younger patients ($$p \leq 0.001$$) and more females ($$p \leq 0.001$$) than the IPF group. In the DM-ILD group ($$n = 18$$), six, one, and eleven cases involved autopsy, lung transplant, and SLB, respectively. Nine ($50\%$) of the 18 DM-ILD cases showed seropositivity for the anti-MDA5 antibody, while six and three cases involved autopsy and SLB, respectively. Thus, all six autopsy cases showed seropositivity for anti-MDA5 antibodies. Seven of the nine anti-MDA5 antibody-seropositive DM-ILD cases showed a clinical course of RP-ILD. Moreover, the lung tissues of all nine seropositive DM-ILD cases showed a diffuse alveolar damage (DAD) pattern. Among the nine cases of anti-MDA5 antibody-seronegative DM-ILD were positive for anti-Aminoacyl-tRNA Synthetase antibodies. Additionally, two, one, and six cases showed DAD, usual interstitial pneumonia (UIP), or nonspecific interstitial pneumonia (NSIP) patterns, respectively. Table 1Patient characteristicsDM-ILD groupIPF groupP valueNumber1819Age54 (46–63)65 (62–67)0.001Sex: male6 ($33\%$)18 ($95\%$)0.001RP-ILD or IPF-AE9 ($50\%$)9 ($47\%$)Tissue collection method SLB11 ($61\%$)10 ($53\%$) Transplant1 ($6\%$)0 ($0\%$) Autopsy6 ($33\%$)9 ($47\%$)Dx of IIMs DM12 ($67\%$)– CADM6 ($33\%$)–Histological pattern DAD11 ($61\%$)9 ($47\%$) NSIP6 ($33\%$)0 ($0\%$) UIP1 ($6\%$)10 ($53\%$)Treatment for autopsy and lung transplant cases Corticosteroid7 ($100\%$)8 ($89\%$) Calcineurin inhibitors6 ($86\%$)2 ($22\%$) Cyclophosphamide5 ($71\%$)3 ($33\%$)Cause of death in autopsy cases Respiratory failure (including IPF-AE or RP-ILD)6 ($100\%$)6 ($67\%$) Lung cancer0 ($0\%$)3 ($33\%$)AE acute exacerbation; CADM clinical amyopathic dermatomyositis DAD diffuse alveolar damage; DM dermatomyositis; Dx diagnosis; IIMs idiopathic inflammatory myopathies; ILD interstitial lung disease; IPF idiopathic pulmonary fibrosis; NSIP nonspecific interstitial pneumonia; RP-ILD rapidly progressive interstitial lung disease; SLB surgical lung biopsy; UIP usual interstitial pneumonia In the IPF group (control, $$n = 19$$), nine ($47\%$) patients experienced acute exacerbations (AEs) and were histopathologically diagnosed as showing DAD patterns at autopsy. The remaining 10 ($53\%$) patients showed a chronic course and were diagnosed as showing a UIP pattern through SLB.
All seven cases ($100\%$, six autopsies and one lung transplant) and eight of nine cases ($89\%$) in the DM-ILD and IPF groups, respectively, were treated with corticosteroids. Additionally, calcineurin inhibitors and cyclophosphamide were used in six and five DM-ILD cases and in two and three IPF autopsies, respectively (Table 1). Tests for significance for these differences were not calculated as the usage of steroids and immunosuppressive agents in DM-ILD and IPF groups were different.
Among the 15 autopsies (6 DM-ILD and 9 IPF cases), 3 were of patients in the IPF group who died from lung cancer. The remaining 12 patients died due to progressive respiratory failure, including acute exacerbation of interstitial lung disease. Additionally, one patient in the DM-ILD group underwent lung transplantation because of progressive respiratory failure.
## MDA5 overexpression in lungs of patients with DM-ILD and IPF
Immunohistochemistry analysis revealed moderate ($$n = 15$$) or strong ($$n = 2$$) MDA5 expression in the lungs of $\frac{17}{18}$ DM-ILD patients and weak expression in one patient. No correlations were observed among the histopathological pattern, tissue collection method, and MDA5 expression. Notably, all 19 IPF cases showed moderate ($$n = 14$$) or strong ($$n = 5$$) MDA5 expression in the lungs. Strong MDA5 expression was observed in the lungs of both DM-ILD and IPF groups (Fig. 2b, d). However, no significant different between two group was observed in the expression intensity ($$p \leq 0.156$$) (Table 2).Fig. 2Histopathological findings in the lungs of patients with DM-ILD and idiopathic pulmonary fibrosis (IPF). A Hematoxylin & eosin (H&E) staining of a patient with DM-ILD. This patient showed an exudative diffuse alveolar damage (DAD) pattern. B IHC staining with anti-MDA5 monoclonal antibody (mAb) (clone H27) in a patient with DM-ILD. MDA5 is extensively and strongly expressed in the alveolar epithelium. C H&E staining in a patient with IPF. This patient showed an exudative DAD pattern, as well as a usual interstitial pneumonia (UIP) pattern in the background. D IHC staining with anti-MDA5 mAb (clone H27) in a patient with IPF. There was strong MDA expression in the alveolar epitheliumTable 2Evaluation of immunohistochemical stainingExpression intensity$\frac{0}{1}$/$\frac{2}{3}$(mean ± SD)DM-ILD($$n = 18$$)IPF($$n = 19$$)P valueC3c$\frac{3}{7}$/$\frac{6}{2}$(1.389 ± 0.916)$\frac{17}{2}$/$\frac{0}{0}$(0.105 ± 0.315) < 0.001IgG$\frac{0}{4}$/$\frac{12}{2}$(1.889 ± 0.583)$\frac{4}{10}$/$\frac{5}{0}$(1.053 ± 0.705) < 0.001IgM$\frac{7}{10}$/$\frac{1}{0}$(0.667 ± 0.594)$\frac{17}{2}$/$\frac{0}{0}$(0.105 ± 0.315)0.001IgA$\frac{5}{11}$/$\frac{0}{2}$(0.944 ± 0.873)$\frac{4}{9}$/$\frac{6}{0}$(1.105 ± 0.737)0.323MDA$\frac{50}{1}$/$\frac{15}{2}$(2.056 ± 0.416)$\frac{0}{0}$/$\frac{14}{5}$(2.263 ± 0.452)0.161Expression intensity score 0, negative staining; score 1, weakly positive; score 2, moderately positive; and score 3, strongly positive. Details are described in the “Methods” sectionDM dermatomyositis; ILD interstitial lung disease; IPF idiopathic pulmonary fibrosis; MDA5 melanoma differentiation-associated gene 5
## Strong expression of complement and ig in the lungs of DM-ILD but not IPF patients
Figure 3 shows strong expression of complement C3c and IgG in the lungs of DM-ILD patients. Immunohistochemistry staining for complement C3c showed positive results in $\frac{15}{18}$ cases, of which eight cases showed moderate or greater intensity. All 18 cases showed positive results for IgG, while 11 cases showed positive results for IgM. Interestingly, $\frac{13}{18}$ cases showed positive results for IgA; however, only two cases showed strongly positive results, while 11 cases showed weakly positive results (Table 2).Fig. 3IHC staining with complement protein and immunoglobulins (Ig) in the lungs of a patient with DM-ILD. This case shows strong expression of complement C3c and IgG, as well as weak expression of IgM and IgA, in alveolar cells Figure 4 shows the expression of complement C3c and Ig in the lungs of patients with IPF. Quantitative immunohistochemical analysis for IgG and IgA showed double-positive results in $\frac{15}{19}$ IPF cases. IgG expression was weak ($$n = 10$$) or moderate ($$n = 5$$) in the lungs of 15 IPF cases. IgA expression was weak ($$n = 9$$) or moderate ($$n = 6$$) in the lungs of 15 IPF cases. Complement C3c and IgM were absent in $\frac{17}{19}$ IPF cases (Table 2, Additional file 3: Fig. S3).Fig. 4IHC staining with complement protein and Ig in the lungs of a patient with IPF. This patient was negative for complement C3c and Ig In comparison with the IPF group, the DM-ILD group showed a significantly higher expression of complement C3c ($p \leq 0.001$), IgG ($p \leq 0.001$), and IgM ($$p \leq 0.001$$) (Table 2, Figs. 3 and 4). Further, we performed separate statistical analyses for acute and chronic clinical courses (Table 3). We compared 9 DM-ILD patients who met the criteria for RP-ILD (acute course) and 9 IPF patients showing AEs; the DM-ILD group showed significantly higher expression of complement C3c ($p \leq 0.001$) and IgG ($$p \leq 0.011$$). Compared with IPF with a chronic clinical course ($$n = 10$$), DM-ILD with a chronic clinical course ($$n = 9$$) showed significantly higher expression of C3c ($$p \leq 0.011$$), IgG ($$p \leq 0.037$$) and IgM ($$p \leq 0.001$$).Table 3Subset analysis of immunohistochemical staining performed for clinical courseExpression intensity$\frac{0}{1}$/$\frac{2}{3}$(Mean ± SD)Acute courseaChronic coursebDM-ILD($$n = 9$$)IPF($$n = 9$$)P valueDM-ILD($$n = 9$$)IPF($$n = 10$$)P valueC3c$\frac{0}{3}$/$\frac{4}{2}$(1.889 ± 0.782)$\frac{8}{1}$/$\frac{0}{0}$(0.111 ± 0.333) < $\frac{0.0013}{4}$/$\frac{2}{0}$(0.889 ± 0.782)$\frac{9}{1}$/$\frac{0}{0}$(0.100 ± 0.316)0.007IgG$\frac{0}{2}$/$\frac{5}{2}$(2.000 ± 0.707)$\frac{3}{4}$/$\frac{2}{0}$(0.889 ± 0.782)$\frac{0.0110}{2}$/$\frac{7}{0}$(1.778 ± 0.441)$\frac{1}{6}$/$\frac{3}{0}$(1.200 ± 0.632)0.037IgM$\frac{5}{3}$/$\frac{1}{0}$(0.556 ± 0.726)$\frac{7}{2}$/$\frac{0}{0}$(0.222 ± 0.441)$\frac{0.2862}{7}$/$\frac{0}{0}$(0.778 ± 0.441)$\frac{10}{0}$/$\frac{0}{0}$(0.000 ± 0.000)0.001IgA$\frac{1}{6}$/$\frac{0}{2}$(1.333 ± 1.000)$\frac{3}{2}$/$\frac{4}{0}$(1.111 ± 0.928)$\frac{0.7794}{5}$/$\frac{0}{0}$(0.556 ± 0.527)$\frac{1}{7}$/$\frac{2}{0}$(1.100 ± 0.568)0.051MDA$\frac{50}{0}$/$\frac{8}{1}$(2.111 ± 0.333)$\frac{0}{0}$/$\frac{7}{2}$(2.222 ± 0.441)$\frac{0.5390}{1}$/$\frac{7}{1}$(2.000 ± 0.500)$\frac{0}{0}$/$\frac{7}{3}$(2.300 ± 0.483)0.203Expression intensity score 0, negative staining; score 1, weakly positive; score 2, moderately positive; and score 3, strongly positive. Details are described in the “Methods” sectionDM dermatomyositis; ILD interstitial lung disease; IPF idiopathic pulmonary fibrosis; MDA5 melanoma differentiation-associated gene 5aAcute course: rapidly progressive ILD (RP-ILD) in patients with DM-ILD ($$n = 9$$) vs. Acute exacerbation (AE) in patients with IPF ($$n = 9$$)bChronic course: non-RP-ILD in patients with DM-ILD ($$n = 9$$) vs. Non-AE in patients with IPF ($$n = 10$$) Of the 18 patients (9 acute and 9 chronic courses) in the DM-ILD groups showed that the expression of C3c, and not IgG, IgM, IgA, or MDA5, was significantly ($$p \leq 0.022$$) higher in the acute course compared to that in the chronic courses. In contrast, in the 19 patients (9 acute and 10 chronic courses) in the IPF groups, there was no significant difference in the expression of C3c, IgG, IgM, IgA, and MDA5 in the acute course compared to the chronic courses. Additionally, there was no significant difference in the histological patterns in terms of the expression of C3c, IgA, IgG, IgM, or MDA5 between cases with DAD pattern ($$n = 11$$) and those with NSIP pattern ($$n = 6$$) in the DM-ILD group. Notably, the UIP pattern was only observed in one case (Table 1).
## Subset analysis: DM-ILD patients with and without anti-MDA5 antibody
Subsequently, we performed a subset analysis of DM-ILD patients who showed positive ($$n = 9$$) and negative ($$n = 9$$) results for anti-MDA5 antibodies (Table 4). In comparison with DM-ILD patients without anti-MDA5 antibodies, those showing anti-MDA5 antibody significantly ($p \leq 0.001$) showed a histological DAD pattern in tissue samples collected at autopsy ($$p \leq 0.002$$). Notably, in comparison with DM-ILD without anti-MDA5 antibody, cases with the anti-MDA5 antibody showed significantly higher C3c expression in the lungs ($$p \leq 0.015$$). However, no significant between-group difference was observed in the expression of IgG, IgA, IgM, and MDA5.Table 4Subset analysis of DM-ILD with and without anti-MDA5 antibodyWith MDA5 AbWithout MDA5 AbbP valueNumber99Age55 (48–66)53 (44–63)0.659Sex: male5 ($56\%$)1 ($11\%$)0.131Clinical course: RP-ILD7 ($78\%$)2 ($22\%$)0.057Tissue collection method0.002 SLB3 ($33\%$)8 ($89\%$) Transplant0 ($0\%$)1 ($11\%$) Autopsy6 ($67\%$)0 ($0\%$)Dx of IIMs0.620 DM5 ($56\%$)7 ($78\%$) CADM4 ($44\%$)2 ($22\%$)Histological pattern < 0.001 DAD9 ($100\%$)2 ($22\%$) NSIP0 ($0\%$)6 ($67\%$) UIP0 ($0\%$)1 ($11\%$)IHC staininga C3c$\frac{0}{3}$/$\frac{4}{2}$(1.889 ± 0.782)$\frac{3}{4}$/$\frac{2}{0}$(0.889 ± 0.782)0.022 IgG$\frac{0}{2}$/$\frac{5}{2}$(2.000 ± 0.707)$\frac{0}{2}$/$\frac{7}{0}$(1.778 ± 0.441)0.458 IgA$\frac{1}{6}$/$\frac{0}{2}$(1.333 ± 1.000)$\frac{4}{5}$/$\frac{0}{0}$(0.556 ± 0.527)0.060 IgM$\frac{5}{3}$/$\frac{1}{0}$(0.556 ± 0.726)$\frac{2}{7}$/$\frac{0}{0}$(0.778 ± 0.441)0.315 MDA$\frac{50}{0}$/$\frac{8}{1}$(2.111 ± 0.333)$\frac{0}{1}$/$\frac{7}{1}$(2.000 ± 0.500)0.586CADM clinical amyopathic dermatomyositis; DAD diffuse alveolar damage; DM dermatomyositis; Dx diagnosis; IHC immunohistochemistry; IIMs idiopathic inflammatory myopathies; ILD interstitial lung disease; MDA5 melanoma differentiation-associated gene 5; NSIP nonspecific interstitial pneumonia; RP-ILD rapidly progressive interstitial lung disease; SLB surgical lung biopsy; UIP usual interstitial pneumoniaExpression intensity score 0, negative staining; score 1, weakly positive; score 2, moderately positive; and score 3, strongly positive. Details were described in the “Methods” sectionaIHC staining items are indicated by intensity scores of $\frac{0}{1}$/$\frac{2}{3}$ and mean ± SDbPatients without anti-MDA5 antibody were all positive for anti-Aminoacyl-tRNA Synthetase antibodies
## Lung injury in human MDA5 transgenic mice
*We* generated three lines (lines no. 32, 55, 116) of human MDA5 transgenic mice with overexpression of human MDA5 proteins in the lungs and treated them with anti-MDA5 polyclonal antibodies (antisera) to create a new lung injury model. We also administered normal rabbit sera to transgenic mice and used them as control mice and administered antisera or normal rabbit sera to wild-type (BDF1) mice. Treatment with antisera or rabbit sera did not induce severe lung injury in wild-type BDF1 mice. In contrast, treatment with an anti-MDA5 polyclonal antibodies induced lung injury in all three lines of MDA5 transgenic mice. In the transgenic mice treated with antisera, the alveolar septum was strongly infiltrated with lymphocytes, and many of the alveoli had collapsed (Fig. 5a). Transgenic mice treated with rabbit sera also showed inflammatory cell infiltration of the alveolar septum, but it was more reduced than that observed in the lung injury model (Fig. 5b). Transgenic mice treated with purified rabbit anti-MDA5 polyclonal antibodies also induced lung injury and showed inflammatory cell infiltration of the alveolar septum. In contrast, transgenic mice treated with rabbit IgG did not induce lung injury (data not shown).Fig. 5Histopathological findings in the lungs of lung injury model mouse. A H&E staining of lung injury model mouse (transgenic mice treated with antisera) showing severe inflammatory cells infiltration with alveolar collapse. B H&E staining of control mouse (transgenic mice treated with rabbit sera). This shows only very mild inflammatory cells infiltration. C IHC staining with anti-human MDA5 mAb (clone H27) in lung injury model mouse showing extensive and strong expression in the alveolar epithelium. D IHC staining with anti-MDA5 mAb (clone H27) in control mouse showing very low expression in the alveolar epithelium
## Human-MDA5 expression in human MDA5 transgenic mice
In the lungs of human MDA5 transgenic mice treated with anti-MDA5 antibodies (lung injury model mouse), human MDA5 protein was strongly expressed in alveolar epithelium and macrophages in the alveoli (Fig. 5c, h, e; staining in Fig. 5a). Very low human MDA5 protein expression was also observed in transgenic mice treated with rabbit sera (control mouse) (Fig. 5d, h, e; staining in Fig. 5b). The kidneys of the lung injury model mice showed glomerular atrophy and lymphocytic infiltration. However, control mice rarely showed glomerular atrophy and lymphocytic infiltration (Additional file 4: Fig. S4a, b). Human MDA5 protein was strongly expressed in the renal tubular epithelial cells and glomeruli in lung injury model mice, while control mice showed very weak MDA5 expression in renal tubular epithelial cells and negative expression in the glomeruli (Additional file 4: Fig. S4c, d).
## Strong expression of complement and Ig in human MDA5 transgenic mice
IgG, IgM, IgA, and complement (C3) were all strongly expressed in the lungs of the lung injury model. IgG, IgM, IgA, and C3 were strongly expressed in the alveolar epithelium, where human MDA5 was strongly expressed (Fig. 6). When the human MDA5 lung injury models at 4 and 8 weeks were compared, slightly stronger Ig and C3 expression was observed in the mice grown for 8 weeks (Additional file 5: Fig. S5a, b). Although human MDA5 transgenic mice injected with normal rabbit serum also expressed complement and Ig, the expression was less pronounced than that seen in the lung injury model and stronger than that seen in wild-type mice treated with normal rabbit sera (Additional file 5: Fig. S5c, d). In the kidney of the lung injury model, IgG, IgM, IgA, and C3 were all strongly expressed in the glomeruli, where human MDA5 was strongly expressed, in comparison with the corresponding expression levels in the control mice (Additional file 6: Fig. S6).Fig. 6Immunohistochemical (IHC) analysis for complement protein and Ig in the lungs of the lung injury model mice. A Lung injury model mice (transgenic mice treated with antisera) showed moderate to severe expression of C3 and Ig. B Control mice (transgenic mice treated with rabbit sera) showed moderate expression of IgG but showed very mild expression of the other immunoglobulins and C3
## Discussion
This study showed that C3c, IgG, and IgM, but not IgA, were more strongly expressed in the injured alveolar epithelium of patients with DM-ILD than in patients with IPF (used as control patients), suggesting that C3c, IgG, and IgM are crucially involved in DM-ILD-related lung injury. In the present study, no difference in MDA5 expression was observed between DM-ILD and IPF, indicating that the intensity of MDA5 expression in lung tissue is not involved in the development of DM-ILD. On the other hand, complement and immunoglobulins were expressed much more strongly in DM-ILD than in IPF, suggestive of the formation of immune complexes. Immune complex deposition, which is the pathogenesis of type III hypersensitivity, is a prominent characteristic of several lung-damaging autoimmune diseases, including hypersensitivity pneumonitis (e.g., farmers’ lungs, summer-type pneumonitis), systemic lupus erythematosus, cryoglobulinemia, rheumatoid arthritis, scleroderma, and Sjögren’s syndrome [14–16]. Thus, while type III hypersensitivity reactions can cause lung damage in patients with DM-ILD, the contribution of type III hypersensitivity reactions to lung injury in DM remains unclear. This is the first report outlining the relationship between DM-ILD and type III hypersensitivity reactions.
We also established human MDA5 transgenic mice and anti-human MDA5 antibodies in this study. By administering anti-human MDA5 polyclonal antibody to human MDA5 transgenic mice, we created a new mouse model of autoantibody-induced lung injury. This mouse model showed strong expression of complement and Ig in the alveolar epithelium, as in human specimens. Our in vivo model proved the involvement of type III hypersensitivity reactions in the lung injury associated with DM-ILD.
Funabiki et al. [ 26] reported that a mutant mouse line harboring a single missense mutation G821S in MDA5 spontaneously developed lupus-like nephritis and systemic autoimmune symptoms. These mice presented with chronic inflammation and nephritis, showing Ig and complement deposition, upregulation of inflammatory cytokines and chemokines in the kidney, increased Ig expression, and serum positivity for anti-nuclear and anti-DNA antibodies. Similar observations were obtained in our lung injury model mouse. Taken together, chronic inflammation can occur in various organs, including the lungs, which may explain the chronic course and fibrotic changes in the lungs of some DM-ILD patients.
Myositis-specific antibodies are relevant for the diagnosis and prognosis prediction of IIMs. Several myositis-specific antibodies have been recently reported in patients with IIMs, including transcription intermediary factor 1 gamma (TIF1γ), Mi-2, small ubiquitin-like modifier activating enzyme (SAE), aminoacyl-transfer (t)RNA synthetases (ARS) including anti-Jo-1, and MDA5 [6]. This study showed lung deposition of immune complexes consisting of Ig and complement C3 in patients with DM who showed positive or negative findings for the anti-MDA5 antibody, indicating deposition of immune complexes in patients with DM who are positive for other myositis-specific antibodies for TIF1γ, Mi-2, SAE, and ARS. Further analysis is required to verify this finding.
Some recent studies have shown the effectiveness of plasma-exchange therapy in DM-ILD patients showing RP-ILD [27, 28]. Abe et al. reported that $\frac{6}{10}$ DM-ILD patients showing RP-ILD who were treated with plasma-exchange therapy, as well as corticosteroids and calcineurin inhibitors, had a better prognosis than those who did not undergo plasma-exchange therapy [27]. Moreover, plasma-exchange therapy is a treatment option for SLE caused by type III hypersensitivity reactions that cannot be controlled with corticosteroids or other immunosuppressive therapies [29]. In cases of DM-ILD caused by a type III hypersensitivity reaction, as shown in this study, plasma-exchange therapy could effectively remove complement components, Ig, and inflammatory cytokines. Plasma-exchange therapy could be an alternative therapeutic option for DM-ILD patients showing RP-ILD. A future prospective large-scale clinical study should clarify this hypothesis.
This study had two limitations. First, this was a small cross-sectional study; moreover, tissues were collected using a combination of SLB and autopsy. Thus, histological changes in the agonal stage in autopsy cases could have resulted in a bias in the evaluation. Second, only IHC staining was used to examine the expression of complement proteins and Ig. There was some unreliability in the evaluation of IHC staining, although the staining method was fixed, and the slides were evaluated by two pathologists who were blinded to the study. Future studies, utilizing additional approaches in the analyses will be needed to confirm our hypotheses.
## Supplementary Information
Additional file 1: Figure S1. Semi-quantitative immunohistochemical (IHC) scoring of C3c. We evaluated the staining intensity in the alveolar epithelium over four levels: score 0, negative; score 1, weakly positive; score 2, moderately positive; and score 3, strongly positive. Additional file 2: Figure S2. Semi-quantitative immunohistochemical (IHC) scoring of IgG, IgM, and IgA expression. These images were obtained after IgG staining; IgA and IgM were evaluated similar to the process shown in the pictures. Additional file 3: Figure S3. Results of statistical analysis for scoring of C3c, IgG, IgM, and IgA expression. Additional file 4: Figure S4. Histopathological findings in the kidney of the mouse model. ( A) H&E staining of the lung injury model mice (transgenic mice treated with antisera) showing lymphocyte infiltration and atrophy of the glomerulus. ( B) H&E staining of the control mice (transgenic mice treated with rabbit sera) showing no abnormalities. ( C) IHC staining with anti-MDA5 mAb (clone H27) in the lung injury model mice showing moderate positivity in renal tubular epithelial cells and strongly positivity in glomeruli. ( D) IHC staining with the anti-MDA5 mAb (clone H27) in control mice showing weak expression in renal tubular epithelial cells but no expression in glomeruli. Additional file 5: Figure S5. Expression of C3 and IgG in several mouse models. ( A) The lung injury model grown for 4 weeks. ( B) The lung injury model grown for 8 weeks. ( C) Human MDA5 transgenic mice treated with control rabbit serum. ( D) Wild-type mouse treated with control rabbit serum. Additional file 6: Figure S6. IHC staining with complement protein and Ig in the kidney of mouse model. ( A) The lung injury model showed severe expression of C3 and Ig in the glomeruli. ( B) The control mouse showed almost no C3 and Ig expression.
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|
---
title: 'Estimated glucose disposal rate is associated with retinopathy and kidney
disease in young people with type 1 diabetes: a nationwide observational study'
authors:
- Wedén Linn
- Martina Persson
- Björn Rathsman
- Johnny Ludvigsson
- Marcus Lind
- Mikael Andersson Franko
- Thomas Nyström
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10024828
doi: 10.1186/s12933-023-01791-x
license: CC BY 4.0
---
# Estimated glucose disposal rate is associated with retinopathy and kidney disease in young people with type 1 diabetes: a nationwide observational study
## Abstract
### Aims
The aim of this study was to investigate the association between estimated glucose disposal rate (eGDR), a proxy for insulin resistance, and retinopathy or kidney disease, i.e. micro-, or macroalbuminuria, in young individuals with type 1 diabetes (T1D).
### Material and Methods
Using data from the Swedish pediatric registry for diabetes (SweDiabKids) and the registry for adults (NDR), all individuals with T1D with a duration of diabetes of less than 10 years between 1998 and 2017 were included. We calculated the crude incidence rates with $95\%$ confidence intervals (CIs) and used multivariable Cox regression to estimate crude and adjusted hazard ratios (HRs) for two cohorts: retinopathy cohort or kidney disease cohort, stratified by eGDR categories: < 4, 4 to 5.99, 6 to 7.99, and ≥ 8 mg/kg/min (reference).
### Results
A total of 22 146 (10 289 retinopathy cohort, and 11 857 kidney disease cohort with an overlapping of 9575) children and adults with T1D (median age 21 years, female $42\%$ and diabetes duration of 6 and 7 years, respectively for the cohorts) were studied. During a median follow-up of 4.8 years (IQR 2.6–7.7) there were 5040 ($24.7\%$), 1909 ($48.1\%$), 504 ($52.3\%$) and 179 ($57.6\%$) events for retinopathy in individuals with an eGDR ≥ 8, 7.99 to 6, 5.99 to 4, and < 4 mg/kg/min, respectively. Corresponding numbers for kidney disease was 1321 ($6.5\%$), 526 ($13.3\%$), 255 ($26.8\%$) and 145 ($46.6\%$). After multiple adjustments for different covariates, individuals with an eGDR 7.99 to 6, 5.99 to 4 and < 4 mg/kg/min, had an increased risk of retinopathy compared to those with an eGDR ≥ 8 mg/kg/min (adjusted HRs, $95\%$ CIs) 1.29 (1.20 to 1.40); 1.50 (1.31 to 1.71) and 1.74 (1.41 to 2.14). Corresponding numbers for kidney disease was (adjusted HRs, $95\%$ CIs) 1.30 (1.11 to 1.52); 1.58 (1.25 to 1.99) and 1.33 (0.95 to 1.86), respectively.
### Conclusions
eGDR, a proxy for insulin resistance, is associated with retinopathy and kidney disease in young adults with T1D. The risk of retinopathy increased with lower eGDR. The risk of kidney disease also increased with lower eGDR; however results show no association between the lowest eGDR and kidney disease. eGDR can be helpful to identify young T1D individuals at risk.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01791-x.
## Introduction
Individuals with type 1 diabetes (T1D) are at increased risk of macrovascular and cardiac complications, in which microvascular complications may predict the risk [1]. Intensified glycemic control can lower the risk [2, 3]. However, in spite of good glycemic control individuals with T1D are still at higher risk of cardiovascular complications compared to individuals without T1D [4]. This may be explained by other cardiovascular risk factors besides hyperglycemia such as dyslipidemia, kidney disease and hypertension [5].
The term double diabetes, first described in 1991, was initially defined as an individual with T1D with a family history of type 2 diabetes (T2D) [6]. Today, it lacks a clear definition but is often referred to as T1D with components of the metabolic syndrome, which is typically observed in people with T2D. Individuals with double diabetes often have poorer glycemic control with increased insulin requirements, and components of the metabolic syndrome such as dyslipidemia, insulin resistance, obesity and hypertension [7, 8]. Moreover, double diabetes is linked to both genetic and life-style factors, e.g., poor physical activity, repeated hypoglycemia and peripheral insulin resistance [7–9].
The gold standard for measuring insulin resistance is the euglycemic hyperinsulinemic clamp technique [10]. Since this method is invasive and time consuming it is not suitable for daily clinical practice or larger studies. Estimated glucose disposal rate (eGDR) using standard clinical measures (hemoglobin glycated A1c [HbA1c], hypertension and waist circumference, or body mass index [BMI]) is derived from clamps in young people with T1D, and has been shown to correlate well [11]. On the other hand, independent cohorts trying to validate the above eGDR formula [11] have not come to the same conclusion [12–14]. Although, the eGDR has been shown to be good marker of increased risk for different outcomes [15–17], it is not entirely sure that it reflects insulin resistance. Several studies among individuals with T1D have shown a connection between double diabetes and macrovascular complications independent of glycemic control [18–20]. Recently, in a large nationwide cohort study among individuals with T1D, our group demonstrated a strong association between eGDR and risk of preterm mortality [16].
Not only macrovascular complications are associated with insulin resistance, but also microvascular complications [15]. There is a limited number of large studies that have examined the association between insulin resistance and microvascular complications among young individuals with T1D [17, 21–24]. The aim of this study is to investigate whether eGDR, as a proxy for insulin resistance, is associated with increased risk of retinopathy and kidney disease, i.e. microalbuminuria, or macroalbuminuria, in young people with T1D.
## Study design and study population
The study was a nationwide, observational cohort study. The study was approved by the Swedish Ethical Review Authority (Dnr 977-17).
We used data from the Swedish national diabetes registry for adults (NDR) and the Swedish pediatric registry for diabetes (SweDiabKids), no other registers were used. Participants provided informed consent when entering the registries. NDR defines T1D on the basis of epidemiological data: treatment with insulin and a diagnosis at the age of 30 years or younger, which has shown to be validated in $97\%$ of cases [25]. In children and adolescents, HLA genotype and autoantibodies are determined before diagnosis. The majority of all Swedish individuals with T1D are registered in NDR, or SweDiabKids. Data was extracted from NDR from 1998, and from SweDiabKids from 2000, both until 31st December 2017. Between these time points all children, adolescents and adults in the registries were included if they had a diagnosis of T1D since ten years, or less when first recorded in the registries. The age span in the whole cohort was 0–39 years, i.e., patients were included if they are diagnosed with T1D before 30 years of age and if duration was < 10 years.
Data extracted from the registers were age, sex, diabetes duration, weight, height, BMI, blood pressure, HbA1c, total cholesterol, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, smoking, physical activity, type of diabetes treatment, micro/macroalbuminuria, estimated glomerular filtration rate (eGFR), and result of retinopathy screening. Categorization of the variables from the registers are shown in Tables 1 and 2, also described below (endpoints).Table 1Baseline characteristics of all patients divided in the retinopathy cohort and kidney disease cohort, respectivelyRetinopathy analysisKidney disease analysisNumber10,28911,857Age, yrs21 (19–26)21 (19–26)Males$58.1\%$$57.3\%$Debut age, yrs16 (11–21)15 (10–21) 0–10 yrs$18.3\%$$22.9\%$ 10–15 yrs$26.3\%$$25.7\%$ 15–20 yrs$23.7\%$$21.9\%$ 20–25 yrs$18.9\%$$17.6\%$ 25–30 yrs$12.7\%$$11.9\%$Duration, yrs6 (3–9)7 (3–10)Follow-up time, yrs4.8 (2.6–7.7)5.4 (2.9–8.7)eGDR measurements per individual4 (2–6)4 (2–7)eGDR, mg/kg/min9.0 (8.0–9.8)8.9 (8.0–9.7) < $41.7\%$$1.5\%$ 4 ≤ to < $65.1\%$$5.0\%$ 6 ≤ to < $818.5\%$$19.4\%$ ≥ $874.8\%$$74.1\%$LDL-cholesterol, mmol/L2.43 (1.97–2.96)2.43 (1.98–2.98) < $2.659.1\%$$58.6\%$ 2.6 ≤ to < $3.428.3\%$$28.3\%$ 3.4 ≤ to < $4.18.9\%$$9.3\%$ ≥ $4.13.6\%$$3.8\%$HDL-cholesterol, mmol/L1.4 (1.2–1.7)1.4 (1.2–1.7) < $1.113.1\%$$13.2\%$ ≥ $1.186.9\%$$86.8\%$Total-cholesterol, mmol/L4.4 (3.8–5.0)4.4 (3.8–5.0) < $4.554.5\%$$53.9\%$ ≥ $4.545.5\%$$46.1\%$Triglyceride, mmol/L0.90 (0.62–1.28)0.90 (0.66–1.30) < $1.786.5\%$$86.1\%$ ≥ $1.713.5\%$$13.9\%$HbA1c, mmol/mol60 (51–71)61 (52–72) < $4817.2\%$$16.4\%$ 48 ≤ to < $5824.9\%$$23.8\%$ 58 ≤ to < $7029.7\%$$30.5\%$ ≥ $7028.2\%$$29.2\%$BMI (Kg/m2)*23.6 (21.6–26.2)23.4 (21.3–26.1) Normal$64.5\%$$65.4\%$ Overweight$26.3\%$$25.7\%$ Obese$9.2\%$$8.9\%$Smokers$11.0\%$$11.3\%$Physical activity; daily$18.2\%$$17.6\%$Physical activity; 3–5 times/week$33.2\%$$33.6\%$Physical activity; 1–2 times/week$26.2\%$$26.9\%$Physical activity; < 1 times/week$14.1\%$$13.7\%$Physical activity; never$8.3\%$$8.1\%$Insulin method; injection$81.3\%$$79.5\%$Insulin method; pump$18.7\%$$20.5\%$ASA; yes$0.8\%$$0.7\%$Antihypertensive therapy; yes$3.5\%$$3.0\%$Lipid lowering drug; yes$3.0\%$$2.6\%$Hypertension$5.2\%$$5.0\%$eGFR, mL/min123 (106–145)124 (107–147) < $300.06\%$$0.03\%$ 30 ≤ to < $450.03\%$$0.02\%$ 45 ≤ to < $600.13\%$$0.10\%$ 60 ≤ to < $907.26\%$$6.79\%$ ≥ $9092.53\%$$93.06\%$ASA Acetylsalicylic acid, BMI Body mass index, eGFR Estimated glomerular filtration rate, HbA1c Glycated hemoglobin 1c, HDL-Cholesterol High-density lipoprotein-Cholesterol, LDL-Cholesterol Low-density lipoprotein-Cholesterol. Yrs years*isoBMI was used in individuals < 18 yrsTable 2All categorized covariates that were multivariate adjusted for in the final modelRetinopathyKidney diseaseHazard ratioConfidence intervalp-valueHazard ratioConfidence intervalp-value8 ≤ eGDR, mg/kg/minREFREF6 ≤ eGDR < 8, mg/kg/min1.291.20–1.40< 0.0011.301.11–1.510.0014 ≤ eGDR < 6, mg/kg/min1.501.31–1.71< 0.0011.581.25–1.99< 0.001eGDR < 4, mg/kg/min1.741.41–2.14< 0.0011.330.95–1.860.099MaleREFREFFemale0.990.92–1.060.701.341.17–1.53< 0.001Age at diabetes onset < 15 yrsREF15 ≤ Age < 20 yrs2.271.63–3.15< 0.0013.882.79–5.40< 0.00120 ≤ Age < 25 yrs3.622.62–5.00< 0.0014.092.95–5.66< 0.00125 ≤ Age < 30 yrs3.912.82–5.42< 0.0013.042.15–4.29< 0.00130 ≤ Age < 40 yrs3.622.59–5.05< 0.0013.492.44–5.01< 0.001Physical activity, dailyREFREFPhysical activity, 3–5 times/week1.030.93–1.130.590.780.64–0.940.009Physical activity, 1–2 times/week1.010.91–1.110.881.040.86–1.250.70Physical activity, < 1 times/week1.100.98–1.230.0911.110.90–1.370.34Physical activity, never1.010.88–1.150.931.371.08–1.720.008Non-smokerREFREFSmoker1.311.19–1.44< 0.0011.241.04–1.480.014Insulin method, injectionREFREFInsulin method, pump0.980.91–1.060.660.960.82–1.120.61LDL < 2.6, mmol/LREFREF2.6 ≤ LDL < 4.1, mmol/L1.091.00–1.180.0621.211.02–1.440.0274.1 ≤ LDL, mmol/L1.140.96–1.360.141.361.01–1.830.045HDL < 1.1, mmol/LREFREF1.1 ≤ HDL, mmol/L0.940.85–1.040.210.890.74–1.070.23Cholesterol < 4.5, mmol/LREFREFCholesterol ≥ 4.5, mmol/L0.920.84–1.000.630.970.81–1.160.74Triglyceride < 1.7, mmol/LREFREF1.7 ≤ Triglyceride, mmol/L1.211.10–1.34< 0.0011.431.21–1.69< 0.001ASA, noREFREFASA, yes1.330.99–1.790.0561.711.12–2.600.012Antihypertensive, noREFREFAntihypertensive therapy, yes0.850.72–1.000.0523.963.15–4.98< 0.001Lipid lowering drug, noREFREFLipid lowering drug, yes1.130.99–1.300.0781.020.79–1.320.8790 ≤ eGFR, mL/minREFREF60 ≤ eGFR < 90, ml/min0.850.75–0.970.0131.020.78–1.330.8845 ≤ eGFR < 60, ml/min0.860.38–1.940.726.663.07–14.42< 0.00130 ≤ eGFR < 45, ml/min0.730.23–2.320.609.262.92–29.33< 0.001eGFR < 30, mL/min0.790.11–5.670.825.611.72–18.300.004All covariates are time-varying, but sex. Continuous variables are presented as medians and interquartile range and categorical variables as proportionsASA acetylsalicylic acid, BMI body mass index, eGDR estimated glucose disposal rate, eGFR estimated glomerular filtration rate, HbA1c glycated haemoglobin 1c, HDL cholesterol high-density lipoprotein cholesterol, LDL cholesterol low-density lipoprotein cholesterol. Yrs years In the final data set individuals with at least one registration of examination of retinopathy, or kidney disease was included. They also needed at least one registration of eGDR (consisting of the variables BMI, HbA1c and hypertension yes/no) and at least one registration of each of the covariates that was used in the final model. In total 15 111 individuals were recruited from SweDiabKids and 19 298 individuals were recruited from NDR. 26 786 individuals had T1D with < 10 years duration when entering the registry. After including patients with at least one examination of retinopathy or nephropathy, one eGDR observation and at least one observation for each covariate, 10 289 cases in the retinopathy cohort and 11 857 cases in the nephropathy cohort remained Figure S1 (Additional file 1). The overlap between cohorts included 9575 individuals.
## Excluded patients
Due to lack of data on retinopathy/kidney disease, eGDR and/or a covariate 16 497 individuals ($61\%$) were excluded from retinopathy analysis ($72\%$ from SweDiabKids and $47\%$ from NDR, respectively) and 14 929 individuals ($55\%$) from kidney disease analysis ($62\%$ from SweDiabKids and $39\%$ from NDR, respectively) Figure S1 (Additional file 1). Also, individuals before index date with already known retinopathy, or kidney disease were excluded from the study. Baseline characteristics of excluded patients are shown in Table S1 (Additional file 1).
## eGDR procedures and categorization
eGDRBMI (mg/kg/min) was calculated as previously described [11] based on the following formula: eGDRBMI = 19.02 − (0.22*BMI) − (3.26 * HT) − (0.61*HbA1c) (Personal communication Katherine Williams, MD, MPH, Pittsburgh, PA, US). BMI = body mass index (kg/m2), HT = hypertension (yes = 1/no = 0), and HbA1c = HbA1c (DCCT %).
Hypertension was defined as a blood pressure > $\frac{140}{90}$ mmHg or current use of any anti-hypertensive agents. For individuals < 18 years old, we used IsoBMI [26]. Analyses of HbA1c levels were performed at certified local laboratories and reported according to the International Federation of Clinical Chemistry standard, measured in mmol/mol. We converted all HbA1c values to standard values according to the National Glycohemoglobin Standardization Program [27].
Based on previous studies [16, 23], we categorized individuals with T1D into four groups according to eGDR levels as follows: < 4, 4 to 5.99, 6 to 7.99, and ≥ 8 mg/kg/min. The highest eGDR category (≥ 8 mg/kg/min) was used as the reference category. The individual components of eGDR were assessed using updated means. During the follow up, study participants could change category if their eGDR worsened, or improved during the study.
## Endpoints and index date
Endpoints of retinopathy were classified as any retinopathy, preproliferative diabetic retinopathy (PPDR), or proliferative diabetic retinopathy (PDR). Any retinopathy included any signs of retinopathy, i.e. simplex retinopathy, PPDR or PDR. PDR was defined as evidence of current proliferations or any earlier laser photocoagulation. Endpoints of kidney disease were classified as any microalbuminuria defined as two positive test results from three samples taken within one year with an albumin/creatinine ratio of 3–30 mg mmol−1, or urinary albumin of 20–200 µg min−1 (20–300 mg/L), or macroalbuminuria defined as an albumin/creatinine ratio > 30 mg mmol−1, or urinary albumin > 200 µg min−1 (> 300 mg L−1) [3].
All individuals were followed from the first observation (between 1998 and 2017) when they first appeared in the register, i.e. index date, until the first event of any retinopathy, or any kidney disease (microalbuminuria/macroalbuminuria), or until the end of the study 31st of December 2017, whichever came first. Follow up did not end if mild retinopathy, i.e. simplex retinopathy, or microalbuminuria was registered. If more severe forms were registered, milder forms were ignored, and the first diagnosis of the severe form was included.
## Statistical analysis
Baseline characteristics (clinical data) are based on the first available observation (between 1998 and 2017) from index date and after that with no specific time limit, for each individual in the study. Continuous variables are presented as medians with interquartile range (IQR), due to the contribution of data, and categorical variables as proportions. Crude and adjusted hazard ratios (HRs) and confidence intervals (CIs) were estimated using univariable and multivariable Cox regression models with time until endpoints, that is retinopathy or kidney disease, as response variables and time-varying covariates as exposure variables, that is eGDR, and potential confounders. Exposure to risk of retinopathy and kidney disease starts at diabetes onset, but follow-up starts at first eGDR observation. This means that data are left-truncated as well as right-censored handled accordingly.
Instead, age at diabetes onset was included as a covariate. All covariates which were adjusted for in the final model were time-varying, but sex. The time-varying variables, i.e. numbers and timespan per patient are shown in Table S2 (Additional file 1), and were calculated as follows: at each retinopathy or nephropathy examination, numerical variables were calculated as the mean of each registered value since the last examination or start of the study period. If no values were available during this interval, the value from the previous interval was used. For categorical variables, the mode was used rather than the mean. When no observations of a covariate had been made, the value at the previous examination was used. We did not replace missing values by multiple imputation but chose to exclude patients with missing values.
In the calculation of eGDR, the most recent value for each variable in the formula was used with no time limit. This means that eGDR could be based on variables that were collected at different time points. This was done to increase the number of non-missing eGDR values. As new values were registered, eGDR was continually updated mean, thereby making it possible for individuals to change category over time to reflect the varying insulin resistance.
Since retinopathy and kidney disease most often is symptom-free, these conditions can only be discovered at an examination. This means that the outcome is interval censored; that is, the exact time point of the outcome can occur anywhere between a negative and a positive examination. It was assumed that a milder complication precedes a serious complication even if not registered. Cox regression for interval censored time-to-event is not well defined so we used the following algorithm. First, we simulated 1000 data sets where the survival times were uniformly sampled between the time point of the last negative examination and the time point of the first positive examination. Then, Cox regressions were carried out on all data sets and coefficients and standard errors were summarized to account for both estimation error in each model and censoring error between models [28].
Estimated cumulative risks of retinopathy and kidney disease, respectively, stratified over eGDR categories were calculated using the Kaplan–Meier estimator adjusted for left truncated and interval censored observations [29]. Pointwise $95\%$ confidence intervals were calculated based on estimated variances using Greenwood’s formula [30].
Survival curves are calculated from the simulated data sets and time-varying covariates to reflect the assumptions behind the Cox regressions [31]. Smooth ridge regression was used for the interaction to stabilize results for combinations with few observations.
The relative predictive performance for each variable in the eGDR formula was evaluated using Heller R2 for the explained risk in the proportional hazards model [32].
## Study population and patient characteristics
Baseline characteristics of the study participants in the retinopathy and nephropathy cohorts, respectively, are shown in Table 1, and after stratification into different eGDR categories in Table S3 (Additional file 1). The median age at index date in both cohorts was 21 years. $58\%$ were males in the retinopathy cohort and $57\%$ in the kidney disease cohort. Debut age of T1D was 16 years and 15 years, in the retinopathy and kidney disease cohorts, respectively. The median duration of diabetes before entering the study was 6 years and 7 years, respectively. The median eGDR was 9 mg/kg/min in both cohorts and in both cohorts the distribution of individuals in the different eGDR categories was very similar with a majority (around $74\%$) in the reference category eGDR ≥ 8 mg/kg/min. In both cohorts median HbA1c was approximately 60 mmol/mol, and median BMI was 23 kg/m2. $26\%$ of individuals were overweight (BMI > 25 kg/m2) and $9\%$ were obese (BMI > 30 kg/m2), both cohorts. Finally, $5\%$ of individuals had hypertension. All covariates, multivariable adjusted for in the final model, are shown in Table 2.
## eGDR and retinopathy
The estimated retinopathy crude cumulative risk curves illustrating the accumulated risk for retinopathy are shown in Fig. 1A. Median follow up time for the retinopathy cohort was 4.8 (IQR 2.6–7.7) years. The event rate of any retinopathy in each eGDR categories, and the relative risks (HRs) between eGDR and retinopathy are shown in Table 3. After adjustment for age and sex, and all covariates in Table 1, lower eGDR was associated with an increased risk of retinopathy (Table 3). The presence of kidney disease was not significant ($$P \leq 0.21$$) when included as a covariate, indicating no competing risk between retinopathy and kidney disease (Additional file 1: Table S4).Fig. 1Estimated crude cumulative risk curves illustrated the accumulated estimated risk of retinopathy (A) and kidney disease (B) based on these observed time intervals in young people with type 1 diabetes (eGDR = estimated glucose disposal rate). The shaded are represents the $95\%$ confidence interval of the estimated crude curvesTable 3Number of events, event rate and relative risks for any retinopathy, i.e. mild to severe retinopathy, and kidney disease, i.e. microalbuminuria or macroalbuminuria, in children and adults with type 1 diabetesExposure mg/kg/minEvents n (%)Event rate 100 person-yrsCrude HRHR adjusted for sex and ageHR adjustedRetinopathy eGDR ≥ 85040 (24.7)4.11 (3.99–4.22)REFREFREF 6 ≤ eGDR < 81909 (48.1)7.47 (7.13–7.80)1.61 (1.53–1.70) < 0.0011.47 (1.40–1.55) < 0.0011.29 (1.20–1.40) < 0.001 4 ≤ eGDR < 6504 (52.3)8.85 (8.08–9.62)1.91 (1.74–2.09) < 0.0011.57 (1.43–1.72) < 0.0011.50 (1.31–1.71) < 0.001 eGDR < 4179 (57.6)9.63 (8.22–11.05)2.28 (1.96–2.66) < 0.0011.73 (1.49–2.02) < 0.0011.74 (1.41–2.14) < 0.001PPDR/PDR/Laser photocoagulation vs. non/simplex eGDR ≥ 8233 (1.1)0.17 (0.15–0.19)REFREFREF 6 ≤ eGDR < 8181 (4.6)0.57 (0.49–0.66)2.56 (2.10–3.11) < 0.0012.33 (1.92–2.84) < 0.0011.71 (1.32–2.22) < 0.001 4 ≤ eGDR < 652 (5.5)0.73 (0.53–0.92)3.06 (2.26–4.14) < 0.0012.52 (1.86–3.42) < 0.0011.53 (1.00–2.34) 0.051 eGDR < 423 (7.4)1.05 (0.62–1.48)4.58 (2.98–7.05) < 0.0013.45 (2.24–5.32) < 0.0011.66 (0.90–3.07) 0.11PDR/Laser photocoagulation vs. non/simplex/PPDR eGDR ≥ 882 (0.4)0.06 (0.05–0.07)REFREFREF 6 ≤ eGDR < 860 (1.5)0.19 (0.14–0.24)2.45 (1.75–3.44) < 0.0012.19 (1.56–3.07) < 0.0011.45 (0.93–2.25) 0.10 4 ≤ eGDR < 621 (2.2)0.29 (0.17–0.41)3.50 (2.16–5.68) < 0.0012.82 (1.74–4.58) < 0.0011.13 (0.56–2.25) 0.73 eGDR < 411 (3.5)0.49 (0.20–0.79)6.06 (3.21–11.46) < 0.0014.45 (2.35–8.43) < 0.0010.91 (0.34–2.40) 0.85Kidney diseaseeGDR ≥ 81321 (6.5)0.92 (0.87–0.97)REFREFREF 6 ≤ eGDR < 8526 (13.3)1.53 (1.40–1.66)1.64 (1.48–1.82) < 0.0011.53 (1.38–1.69) < 0.0011.30 (1.11–1.52) 0.001 4 ≤ eGDR < 6255 (26.8)3.20 (2.81–3.59)3.45 (3.01–3.94) < 0.0013.07 (2.68–3.52) < 0.0011.58 (1.25–1.99) < 0.001 eGDR < 4145 (46.6)5.72 (4.79–6.65)6.19 (5.21–7.36) < 0.0015.21 (4.36–6.21) < 0.0011.33 (0.95–1.86) 0.097Microalbuminuria vs. non-albuminuria eGDR ≥ 81251 (6.1)0.88 (0.83–0.93)REFREFREF 6 ≤ eGDR < 8489 (12.3)1.45 (1.32–1.58)1.62 (1.46–1.80) < 0.0011.52 (1.37–1.69) < 0.0011.34 (1.15–1.58) < 0.001 4 ≤ eGDR < 6227 (23.8)2.90 (2.52–3.28)3.25 (2.82–3.75) < 0.0012.94 (2.55–3.40) < 0.0011.57 (1.23–1.99) < 0.001 eGDR < 4129 (41.5)5.39 (4.46–6.32)6.08 (5.07–7.30) < 0.0015.28 (4.38–6.36) < 0.0011.39 (0.99–1.95) 0.060Macroalbuminuria vs. non-albuminuria eGDR ≥ 8129 (0.6)0.09 (0.07–0.10)REFREFREF 6 ≤ eGDR < 884 (2.1)0.23 (0.18–0.28)2.59 (1.96–3.41) < 0.0012.14 (1.63–2.83) < 0.0011.34 (0.91–1.95) 0.13 4 ≤ eGDR < 669 (7.3)0.78 (0.60–0.96)8.53 (6.35–11.45) < 0.0016.21 (4.62–8.36) < 0.0011.61 (0.97–2.70) 0.068 eGDR < 439 (12.5)1.34 (0.92–1.76)14.57 (10.16–20.89) < 0.0019.15 (6.34–13.21) < 0.0011.00 (0.48–2.06) 1.00eGDR estimated glucose disposal rate, PDR proliferative diabetic retinopathy, PPDR preproliferative diabetic retinopathy
## Comparison of different degrees of retinopathy in relation to eGDR
We further assigned individuals to different degrees of retinopathy to compare the risk for severe retinopathy with milder degrees: PPDR/PRD/laser photocoagulation vs. non/simplex retinopathy and PRD/laser photocoagulation vs. non/simplex retinopathy/PPDR, respectively, into the same eGDR categories (Table 3).
Number of events and event rates was much less for severe retinopathy compared with mild retinopathy. Crude and adjusted for sex and age relative risk for PPDR/PRD/laser photocoagulation vs. non/simplex retinopathy increased in all eGDR categories below 8 mg/kg/min (Table 3). After multivariable adjustments the relative risks, HR ($95\%$ CI) for severe retinopathy, i.e. PPDR/PRD/laser photocoagulation vs. non/simplex retinopathy were significantly increased in the eGDR categories 8-6, 6-4, but not in the lowest eGDR group, compared with the reference category (Table 3). Corresponding relative risks after multivariable adjustments between higher degrees of retinopathy, i.e. PRD/laser photocoagulation vs. non/simplex retinopathy/PPDR PRD/laser were not statistical different compared to the reference category (Table 3).
## eGDR and kidney disease
The estimated crude survival curves illustrating the accumulated risk for kidney disease, i.e. microalbuminuria, or macroalbuminuria are shown in Fig. 1B. Median follow up time for the kidney disease cohort was 5.4 (IQR 2.9–8.7) years. The event rate of kidney disease in each eGDR categories, and the relative risks between eGDR and kidney disease are shown in Table 3.
After adjustment for age and sex, and all covariates in Table 1, lower eGDR was associated with an increased risk of kidney disease (Table 3). The adjusted HRs ($95\%$ CI) for kidney disease was associated with lower eGDR, however not apply to the lowest category, compared to the reference category (Table 3). The presence of retinopathy was not significant ($$P \leq 0.14$$) when included as a covariate, indicating no competing risk between kidney disease and retinopathy (Additional file 1: Table S4).
## Comparison of different degrees of kidney disease in relation to the eGDR categories
We further assigned individuals to different degrees of kidney disease, i.e. comparison of microalbuminuria and macroalbuminuria vs. non-albuminuria respectively. Number of events and event rates was much less for macroalbuminuria compared with microalbuminuria (Table 3). Crude and adjusted for sex and age relative risk for microalbuminuria and macroalbuminuria, respectively, were increased in all eGDR categories below 8 mg/kg/min (Table 3). After multivariable adjustments the relative risk, HR ($95\%$ CI), for microalbuminuria was significantly increased in the eGDR categories 8-6 and 6-4, without reaching statistical significance in the lowest eGDR category, compared to the reference category of eGDR (Table 3). Corresponding relative risks after multivariable adjustments for macroalbuminuria, did not reach any statistical difference between eGDR categories, compared to the reference category of eGDR (Table 3).
## eGDR estimated with ISO-BMI, or BMI, respectively
The reason for using ISO-BMI, instead of BMI, is that this formula “correct” BMI, especially for the youngest children [26]. Since the eGDR formula has not been validated for the use of ISO-BMI, we further investigated our outcome of interest only by BMI. The results for retinopathy did not change much, whereas association for kidney disease somewhat increased for the lowest eGDR. However, there were no large changes between analysis (Additional file 1: Table S5).
## Explained variance of the variables for retinopathy and kidney disease in the eGDR formula
The estimated explained relative risk (R2 ± SD) for each variable in the eGDR formula for the risk of retinopathy was highest for HbA1c (0.0242 ± 0.0049), followed by BMI (0.0012 ± 0.0011), and hypertension (0.0006 ± 0.0010). Corresponding explained relative risk for kidney disease was for HbA1c (0.0389 ± 0.0103) followed by BMI (0.0163 ± 0.0064), and hypertension (0.0067 ± 0.0059).
## Discussion
This nationwide, observational study shows that eGDR, a proxy for insulin resistance, associates with the risk of retinopathy and kidney disease in young individuals with T1D. The risk of retinopathy increased with lower eGDR. Risks of kidney disease also increased with lower eGDR, however not for the lowest eGDR category (< 4 mg/kg/min).eGDR has been proven a tool for the measurement of insulin resistance in people with T1D [11] and has emerged as a predictor of cardiovascular complications and mortality [16, 20]. Since microvascular complications often proceed cardiovascular complications [1], it is important to curb the progress of microvascular complications at an early stage to prevent further organ damage and macrovascular complications [33]. Poor glycemic control and hypertension are both well-established risk factors for the development of retinopathy and kidney disease. In the present study, eGDR, as a proxy for insulin resistance, was associated with significantly increased relative risks of retinopathy of different severity and macroalbuminuria in the crude model. However, in the fully adjusted model, the relative risks were no longer statistically significantly increased. This was most likely due to the low number of events of the more severe forms of retinopathy ($$n = 174$$ for PDR and laser photocoagulation) as retinopathy develops over time [34], and that our study population was young. For microalbuminuria there was a significantly increased risk with lower eGDR level, except for the lowest eGDR category. Again, this was most likely due to the low number of events in this category.eGDR is calculated from a few clinical measures, which can all individually contribute to our results. In the present study, about a quarter of study participants were overweight (BMI ≥ 25 kg/m2) and $9\%$ were obese (BMI ≥ 30 kg/m2) according to the World Health Organization’s definition. The prevalence of obesity in childhood T1D populations is increasing. In the Nordic countries the obesity rate was $18.5\%$ in children with T1D under 15 years of age during the period 2008–2012, which was higher than for the Swedish reference population [35]. Risk factors for obesity in this group are longer diabetes duration, higher insulin dose, pump treatment, experiencing frequent severe hypoglycemia, and low HbA1c [35]. We adjusted for several of these covariates in the final model, demonstrating that eGDR might be an important risk factor for microvascular complications in young people with T1D [35, 36].
The impact of obesity and microvascular burden in patients with T1D is not fully established [5]. The prevalence of obesity, i.e. BMI ≥ 30 kg/m2, in the population in the DCCT/EDIC study increased from 1 to $31\%$ over the course of 12 years [5]. One possible explanation could be that intensive glycemic control can be associated with weight gain [7, 8, 37]. Another cause might be that obesity is increasing in people with T1D [36, 38], as in the general population. People with T1D and excessive weight gain also have changes in lipid levels and blood pressure similar to those changes seen in insulin resistance syndrome, and a greater central fat distribution [37]. This may contribute to the risk of retinopathy and kidney disease [39]. However, after adjustment for blood lipids there was still an increased risk in people with low eGDR, which was also recently observed by others [23].
Hypertension, often coexist with insulin resistance, is a well-known risk factor for micro- and macrovascular complications [5]. In the current cohort study, the median age was low (21 years) and only $5\%$ had a diagnosis of hypertension and $3\%$ used antihypertensive drugs. The prevalence of hypertension in our study is in line with a previous study on children and adolescents with T1D reporting a rate of hypertension of $5.9\%$ [28]. In the Coronary Artery Calcification (CACTI) study, the prevalence of antihypertensive drugs among individuals with T1D (median age 45 years) was $43\%$ versus $15\%$ in age and sex-matched controls [40]. In the FinnDiane cohort $40\%$ of individuals with T1D were on antihypertensive medication versus $14\%$ of controls [41]. This highlights that the prevalence of hypertension increases with age, also in a population of individuals with T1D even though hypertension might be diagnosed at an earlier age. The low rate of hypertension in our study is probably one of the explanations why most of the study participants had a high eGDR mean 9 mg/kg/min (low insulin resistance). However, in spite of low numbers of hypertension, we found a linear association between eGDR and retinopathy and kidney disease, suggesting eGDR as an important early marker for insulin resistance associated with microvascular complications. In a recent study from our group we demonstrated that early signs of atherosclerosis was associated with increased insulin resistance (measured by clamp technique) in young T1D people without hypertension [42], supporting that risk assessment in people with T1D might include eGDR [16].
Hyperglycemia is a well-known risk factor for both macrovascular and microvascular complications [2, 3]. Hypertension is a well-established risk factor for microvascular complications [43], and treatment of blood pressure decrease the disease progression. There are also data from large cohorts showing that abdominal obesity increases the risk of kidney disease [44, 45]. These three factors are the basis of the eGDR formula and cannot be adjusted for in our model. By using *Hellers formula* to assess the individual contribution of each variable in the model we simply observed that HbA1c was strongest associated with both retinopathy and kidney disease. It was also recently observed in a cohort of people with T1D, with different age compared to the present cohort study, that eGDR was strongly associated with both microvascular and macrovascular complications, regardless of HbA1c levels [23]. In a recent large cohort study of adult people with T1D, our group show that there was a strong association between eGDR and preterm all-cause mortality [16]. Individuals with eGDR > 8 mg/kg/min had the same expected survival as an age- and sex-matched control group, although HbA1c was 61 mmol/mol [16]. This finding suggests that increased HbA1c is not the sole predictor of micro-, and macrovascular complications and mortality in patients with T1D.
The main strength of this study is the large nationwide population of individuals with T1D with a long follow up. The results show an association between eGDR and risk of retinopathy and kidney disease. We were able to adjust for several important confounders. However, there are limitations to this study. The eGDR formula, including hypertension, has not entirely been validated in youth [46]. Different formula of eGDR may work well as a proxy for insulin resistance [12], but is has also been demonstrated a poor correlation [12–14] between the original eGDR formula by Williams et al. [ 11], as was used in our study. Although, the eGDR has been shown to be a good marker of increased risk for different outcomes in many cohorts [15–17, 23], we cannot be sure that it really reflects insulin resistance. Despite a long time span there were few individuals with retinopathy, or kidney disease in the lowest eGDR groups during childhood. It was therefore not possible to obtain reliable results in sub-cohort analysis between children and adults. A large number of individuals were excluded ($61\%$ in retinopathy cohort and $55\%$ in kidney disease cohort) and we cannot rule out that their prognosis differed from that of the individuals in the study cohort. Most of the excluded individuals had no registration of examination of retinopathy or kidney disease, probably due to new onset of T1D. However, based on the characteristics and risk factors of our excluded cohort it is unlikely that rates of undiagnosed retinopathy and kidney disease were high. Furthermore, there is an inability to adjust for HbA1c as a confounder since it is a part of the eGDR formula, although this formula is created as a proxy for insulin resistance in people with T1D [11]. By using Hellers attributable fraction we demonstrated how much the different components of the eGDR formula contribute to our result, in which HbA1c was the strongest factor in our cohort. Despite this, eGDR, except for the highest HbA1c levels, was associated with retinopathy and kidney disease regardless of HbA1c. Also, there are potential, as in any observational study, known residual confounding factors, i.e. insulin dosing and frequency of hypoglycemia, and unknown residual confounding factors that could have affected our results.
In conclusion, the current cohort study shows that young people with T1D with low eGDR have higher risk of developing both retinopathy and kidney disease, which indicates that insulin resistance increases the risk of these complications. Since microvascular complications are predictors for cardiovascular disease, and earlier observation demonstrates the association between eGDR and cardiovascular death and mortality, in individuals with T1D, further studies are much needed to explore insulin resistance as a risk factor for micro-, and macrovascular complications in people with T1D.
## Supplementary Information
Additional file 1: Table S1. Baseline characteristics of all patients divided in the retinopathy and kidney disease cohort and excluded patients, respectively. Table S2. Number of measurements and timespan per patient, median and (range). Table S3. Baseline characteristics of all patients according to eGDR categories. Table S4. Interaction analysis between the retinopathy cohort (10 289 individuals) and the kidney disease cohort (11 857 individuals) with an overlap of 9575 individuals. Number of events, event rate and relative risks for any retinopathy and kidney disease in children and adults with type 1 diabetes. Table S5. eGDR based only on BMI (ISO-BMI excluded). Number of events, event rate and relative risks for any retinopathy and kidney disease in children and adults with type 1 diabetes. Figure S1. Flowchart for the studied group. The overlap between cohorts included 9575 individuals. Proportion of excluded patients between the registers was for the retinopathy cohort $70\%$ vs. $47\%$ (SwedDiabKid vs. NDR) and for the kidney analysis corresponding proportional numbers were $62\%$ vs. $39\%$ (SwedDiabKid vs. NDR), for the two registers. eGDR, estimated glucose disposal rate; NDR, National Diabetes Register.
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|
---
title: Multi-omics to predict acute radiation esophagitis in patients with lung cancer
treated with intensity-modulated radiation therapy
authors:
- Xiaoli Zheng
- Wei Guo
- Yunhan Wang
- Jiang Zhang
- Yuanpeng Zhang
- Chen Cheng
- Xinzhi Teng
- Saikit Lam
- Ta Zhou
- Zongrui Ma
- Ruining Liu
- Hui Wu
- Hong Ge
- Jing Cai
- Bing Li
journal: European Journal of Medical Research
year: 2023
pmcid: PMC10024847
doi: 10.1186/s40001-023-01041-6
license: CC BY 4.0
---
# Multi-omics to predict acute radiation esophagitis in patients with lung cancer treated with intensity-modulated radiation therapy
## Abstract
### Purpose
The study aimed to predict acute radiation esophagitis (ARE) with grade ≥ 2 for patients with locally advanced lung cancer (LALC) treated with intensity-modulated radiation therapy (IMRT) using multi-omics features, including radiomics and dosiomics.
### Methods
161 patients with stage IIIA−IIIB LALC who received chemoradiotherapy (CRT) or radiotherapy by IMRT with a prescribed dose from 45 to 70 Gy from 2015 to 2019 were enrolled retrospectively. All the toxicity gradings were given following the Common Terminology Criteria for Adverse Events V4.0. Multi-omics features, including radiomics, dosiomics (including dose−volume histogram dosimetric parameters), were extracted based on the planning CT image and three-dimensional dose distribution. All data were randomly divided into training cohorts ($$n = 107$$) and testing cohorts ($$n = 54$$). In the training cohorts, features with reliably high outcome relevance and low redundancy were selected under random patient subsampling. Four classification models (using clinical factors (CF) only, using radiomics features (RFs) only, dosiomics features (DFs) only, and the hybrid features (HFs) containing clinical factors, radiomics and dosiomics) were constructed employing the Ridge classifier using two-thirds of randomly selected patients as the training cohort. The remaining patient was treated as the testing cohort. A series of models were built with 30 times training–testing splits. Their performances were assessed using the area under the ROC curve (AUC) and accuracy.
### Results
Among all patients, 51 developed ARE grade ≥ 2, with an incidence of $31.7\%$. Next, 8990 radiomics and 213 dosiomics features were extracted, and 3, 6, 12, and 13 features remained after feature selection in the CF, DF, RF and DF models, respectively. The RF and HF models achieved similar classification performance, with the training and testing AUCs of 0.796 ± 0.023 ($95\%$ confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 ($95\%$ CI [0.73, 0.76]) and 0.801 ± 0.022 ($95\%$ CI [0.79, 0.81]) ($$p \leq 0.74$$), respectively. The model performances using CF and DF features were poorer, with training and testing AUCs of 0.573 ± 0.026 ($95\%$ CI [0.56, 0.58])/ 0.509 ± 0.072 ($95\%$ CI [0.48, 0.53]) and 0.679 ± 0.027 ($95\%$ CI [0.67, 0.69])/0.604 ± 0.041 ($95\%$ CI [0.53, 0.63]) compared with the above two models ($p \leq 0.001$), respectively.
### Conclusions
In LALC patients treated with CRT IMRT, the ARE grade ≥ 2 can be predicted using the pretreatment radiotherapy image features. To predict ARE, the multi-omics features had similar predictability with radiomics features; however, the dosiomics features and clinical factors had a limited classification performance.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-023-01041-6.
## Introduction
Lung cancer is one of the most common types of malignant tumor [1, 2]. ( Chemo)-Radiotherapy (CRT) is the standard treatment for locally advanced lung cancer. In radiotherapy, side effects still present great challenges in treatment management, while high-energy X-rays are killing the tumor tissues [3–6]. For example, acute radiation esophagitis (ARE), which occurs within 6 months after the first radiotherapy (RT) completion, is one of the major debilitating toxicities in patients with lung cancer following CRT [7]. The incidence rate of Common Terminology Criteria for Adverse Events (CTCAE) V4.0 grade ≥ 2 ARE ranges from 30 to $50\%$ [8], and is greater for higher radiation doses and the use of concurrent CRT (CCRT). ARE causes throat pain, dysphagia, and severe cases even cause complete obstruction, ulceration, or fistula formation in the esophagus [8]. Consequently, those symptoms, if not managed well, could seriously reduce the patient’s quality of life, incurring a large financial burden and a deteriorating prognosis [9]. More significantly, multiple studies have shown that the severe ARE contributes negatively to overall survival [10, 11]. Therefore, the pretreatment identification of the ARE using predictors will help physicians to better manage at-risk patients.
The reported key predictors to identify ARE in patients with lung cancer are inconsistent among studies. Palma et al. [ 12] found that the volume with the minimum dose of 60 Gy (V60) of the esophagus was a key dosimetric factor to predict ARE. Several dose−volume histogram (DVH) dosimetric parameters of the maximum dose, average dose, the dose with a volume of 5 cc (D5 cc) and the volume received dose larger than 20 Gy, 30 Gy, 35 Gy, and 40 Gy (that are V20, 30, 35, 40) of the esophagus [13] were recognized as predictors of ARE. Other studies also provided discrepant predictors from the esophagus DVH dosimetric parameters, such as V50, the equivalent doses, and D2 cc [14–18]. One explanation for these discrepancies involves the different DVH factors believed to be associated with ARE, for example, D5 cc, 10 cc used by Nieder et al. [ 13], and equivalent doses (EUD) adopted by Butof et al. [ 18]. Another explanation is the different three-dimensional (3D) dose distributions delivered by different RT techniques, for example, 3D conformal radiation therapy (3D-CRT) in the studies of Palma and Butof [12, 13, 18, 19], and intensity-modulated radiation therapy (IMRT) [14, 16, 20, 21]. Moreover, patients involved in each of the above-studied cohorts were treated with either an obsolete RT technique (i.e., 3D-CRT) or heterogenous RT techniques (i.e., 3D-CRT or IMRT). Therefore, patient cohorts using a uniform RT technique, especially advanced RT techniques of IMRT and volumetric modulated arc therapy (VMAT), are recommended to investigate ARE.
In terms of dosimetric predictors, the DVH or DVH-based parameters only characterize the dose information of the whole volume of interest (VOI), rather than representing the dose spatial pattern of the VOI. The dose spatial pattern is determined by the RT technique. To determine the dose spatial pattern, new features to describe the 3D-dose distribution, known as dosiomics, were proposed to predict radiation pneumonitis, and showed potential in clinical application [22]. In contrast, radiomics has recently been studied to investigate the intrinsic organ treatment response by characterizing the image-derived (magnetic resonance imaging (MRI)/computed tomography (CT)/positron emission tomography (PET), etc.) heterogeneous information of the VOI [23–27]. Overall, these three types of features, including DVH dosimetric, radiomic, and dosiomics features, capture comprehensively the heterogeneity of the VOI in dosimetry and imaging. Accordingly, several studies using a combination of two or three features have demonstrated an improvement in predictive model performance [28–30].
Despite the potential of using combination features, multi-omics features, including radiomics, dosiomics, and DVH dosimetric features, have been applied in a few studies to investigate ARE in patients with lung cancer treated with radiotherapy. Bourbonne et al. surveyed ARE in patients with lung cancer treated with RT VMAT by adopting the combined features, integrating the clinical, DVH dosimetric parameters, and radiomics [26]. To date, however, ARE in patients with lung cancer has not been investigated sufficiently with respect to treatment using IMRT.
The present study aimed to construct an integrated pretreatment ARE prediction model for patients with locally advanced nonsmall-cell lung cancer (LA-NSCLC) treated only with IMRT by adopting multi-omics features, based on the pretreatment planning CT image, RT structures, and the RT 3D dose distribution. Meanwhile, the complementary predictabilities of the three types of features were investigated by comparing the performances of the machine learning models constructed from single-mode features and multi-mode features.
## Materials and methods
In this study, model construction is consist of four parts (Fig. 1): (a) data collection, including images and clinical data; (b) feature extraction, multi-omics features (radiomics and dosiomics features) were extracted for the esophagus region based on the pretreatment planning CT image and the RT 3D dose distribution; (c) feature selection, a portion of the features were screened out using unsupervised and supervised methods, considering feature redundancy and relevance; and (d) modeling and evaluation, a classification model for ARE was built using the selected features and the regression classification algorithm; the model performance in the training and testing cohorts was evaluated using two metrics. The more details are shown below. Fig. 1The framework of the classification model construction
## Patient data
The data for all patients with stage IIIA−IIIB LA-NSCLC treated in our institution from 2015 to 2019 were retrieved retrospectively in the study, using the following inclusion criteria: (a) age > 18 years old at treatment, [2] diagnosed with lung cancer (nonsmall cell or small cell) with pathological findings, [3] treated with (chemo)-radiotherapy using 6 MV X-ray photon, [4] received IMRT in a curative manner, and [5] followed up for at least 6 months after treatment. The clinical factors, including patient’s age, gender, smoking status, TNM stage, pathology, RT prescription dose and fraction, treatment technology, and using chemotherapy or not, were collected. For chemotherapy, patients were given sequential or concurrent prescriptions. This study was approved by the ethical committee of the Affiliated Cancer Hospital of Zhengzhou University.
## Radiation toxicities
The gradings of the acute radiotherapy toxicity, ARE, for all the patients were assigned by experienced physicians (≥ 5 years’ experience) following the CTCAE V4.0 protocol. The details of the grading criteria of CTCAE V4.0 protocol from grades 1 to 5 are as follows: (a) grade 1: asymptomatic, clinical or diagnostic observations only; intervention not indicated; (b) grade 2: symptomatic; altered eating/swallowing; oral supplements indicated; (c) grade 3: severely altered eating/swallowing; tube feeding, TPN or hospitalization indicated; (d) grade 4: life-threatening consequences; urgent operative intervention indicated; (e) grade 5: death. Acute toxicity was defined as toxicity events occurring within 6 months from the first radiotherapy treatment. All patients with their grading were summarized in the Table 1. In this study, we attempted to predict the severe ARE events with a grade ≥ 2.Table 1The patient’s number with each gradeGrade12345Number11148300
## Image acquisition
All patients were immobilized with a vacuum cushion in a supine position, and underwent computer tomography (CT) scans using a 16-slice Brilliance Big Bore CT (Philips Medical System, Cleveland, OH, USA). The scanning parameters were as follows: voltage = 120 kV, X-ray tube current = 321 mA, thickness = 3 mm, spacing = 1.152 × 1.152 mm and with 512 × 512 pixels. In addition, the volume of interest (VOI) of the esophagus volume was segmented by physicians with at least 5 years of experience following the Radiation Therapy Oncology Group (RTOG) 1106 report [31]. It should be mentioned that the esophagus volume was contoured using mediastinal windowing on CT to correspond to the mucosa, submucosa, and all muscular layers out to the fatty adventitia. Besides, the esophagus contour begins at the level of the cricoid cartilage and continues on every CT slice including the gastroesophageal junction, until it ends at the stomach. To ensure the correction of segmentation, two physicians with at least 5 years of experience were involved in contouring esophagus volume, one physician for segmentation and another one for review and correction. The average volume of the esophagus in our data sets is about 37 cc.
## Feature extraction
Before feature extraction, CT images were resampled to a voxel size of 1 × 1 × 1 mm3. The types of radiomic features involved in the study were described in detail in the previous publication [32]. The only difference was in the bin counts, with the setting of [20, 30, 40, 50, 80, 100, 150, 200, 250, 300]. Besides, a threshold for the Hounsfield Unit (HU) for the range of [− 150, 180] was used to eliminate the nonesophagus region, such as air cavities. In total, 8990 radiomics features were extracted from the planning CT images within the esophagus volume.
In this study, dosiomics features consisted of three parts: (a) scale-invariant 3D dose moments [33, 34], 3rd order was chosen for three dimensions, resulting in 64 possible combinations. Except for the order of [0,0,0] with a constant value of 1, the other 63 combinations were contained in the study; b) DVH parameters [35, 36], i.e., Vx and Dx from the DVH curve, where Vx was the volume or % volume receiving a dose larger than x Gy, and Dx was the dose (Gy) to a relative volume of the esophagus; c) radiomics based on the 3D dose distributions [22], the original image type was used to extract radiomics features containing the first-order and high-order features. In total, 213 dosiomics features were acquired from the RT planning 3D dose distributions within the esophagus volume. In the feature calculation, we adopted our in-house developed Python-based platform based on the Python package Pyradiomics [37].
## Model construction
In this study, we constructed three classification models to predict whether a patient would develop severe ARE after IMRT up to the end of the follow-up period. The three classification models were generated using the clinical factors (CF model (CFM)), radiomics features (RF model (RFM)) only, dosiomics features (DF model (DFM)) only, and the hybrid features (HF model (HFM)), which combined both the clinical factors, radiomics and dosiomics features. All the model training and evaluations were performed using Scikitlearn in Python [38].
All the models were constructed following a standard procedure demonstrated in Fig. 1: [1] Features with high outcome relevance and low redundancy were selected under random patient subsampling; [2] *All data* were randomly divided into a training cohort ($\frac{2}{3}$) and a testing cohort ($\frac{1}{3}$) using 30 independent repetitions. Notably, the stratified sampling approach was used to keep the same event distribution between the training and testing cohorts. A series of classification models were trained in the training cohort using the selected features. [ 3] The final model was obtained with a comprehensive analysis of model performances in both training and testing under the condition of 30 repetitions.
The process of feature selection was first proposed in a previous study [39]. The features were selected using a 100-time patient bootstrapping down-sampling method (see Fig. 2a). At each sampling iteration, $70\%$ of the entire patient cohort were randomly sampled and some features were filtered out using criteria of variance = 0, and $p \leq 0.1$ (F test). In the rest of the features, the $10\%$ most frequent features (with a minimum feature number of 10) that were selected in the 100 down-sampling iterations were screened out. After that, the Pearson-R correlation test was used to remove correlated features using a threshold of 0.5 [40]. The final selected features were determined by the best performance of fivefold stratified validation with 20 repetitions among varied feature combinations. Fig. 2a The flowchart of feature selection. b The process of model construction All the models were trained using the selected features and the Ridge classifier, as shown in Fig. 2b. At each training iteration, the optimal model hyper-parameter was determined by grid-searching under tenfold cross-validation. In addition, easy ensembling with the $\frac{2}{3}$ bootstrapping method was used to reduce the model bias from the imbalance of positive and negative cases. After that, the area under the curve (AUC) and accuracy (ACC) were used to evaluate the performance of each model in both the training and testing cohorts for each training–testing-split iteration. Finally, the average value and standard deviation of the AUC and ACC of the model series for 30 training–testing splits were calculated to characterize the overall predictability of the selected multi-model features.
In addition, a combined model was generated using the 30 models from the training–testing splits by means of an easy ensemble method. A nomogram using the optimal feature group were constructed for visualizing the classification model using a combined model.
## Data and features
Following the inclusion criteria, a total of 161 patients with LA-NSCLC diagnosed from 2015 to 2019 were included in the study. The patients’ characteristics are summarized in Table 2, and all of the characteristics showed a significant difference between the endpoints and the clinical factors. Table 2The overall characteristic information of all patientsCharacteristicsOverall [161]ARE [51]p valueGenderp < 0.001 Male (N/%)142/$88.2\%$47/$33.1\%$ Female (N/%)19/$11.8\%$4/$21.1\%$Age, median (range)62 (29–83)–$p \leq 0.001$Pathologyp < 0.001 SCC (N/%)104/$64.6\%$37/$35.6\%$ ADC (N/%)51/$31.7\%$13/$25.5\%$ Others (N/%)6/$3.7\%$1/$16.7\%$RT Dosep < 0.001 Median (range) \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<$$\end{document}< 60 Gy60 (45–70) Gy N/%81/$50.3\%$24/$29.6\%$Smokingp < 0.001 Activity or former (N/%)123/$76.4\%$38/$30.9\%$ Never (N/%)38/$23.6\%$13/$34.2\%$T Stagep < 0.001 T1 (N/%)10/$6.2\%$1/$10.0\%$ T2 (N/%)70/$43.5\%$24/$34.3\%$ T3 (N/%)35/$21.7\%$11/$31.4\%$ T4 (N/%)46/$28.6\%$16/$34.8\%$N Stagep < 0.001 N0 (N/%)9/$5.6\%$2/$22.2\%$ N1 (N/%)4/$2.5\%$1/$25.0\%$ N2 (N/%)85/$52.8\%$33/$38.8\%$ N3 (N/%)63/$39.1\%$15/$23.8\%$TNMp < 0.001 IIIA (N/%)54/$29.2\%$20/$37.0\%$ IIIB (N/%)107/$70.8\%$31/$29.0\%$Treatment technologyp < 0.001 SCRT (N/%)65/$40.4\%$17/$26.2\%$ CCRT (N/%)87/$54.0\%$31/$35.9\%$ RT (N/%)9/$5.6\%$3/$33.3\%$ ARE (N/%)51/$31.7\%$––SCC *Squamous carcinoma* cancer, ADC Adenocarcinoma cancer, SCRT Sequential chemoradiotherapy, CCRT Concurrent chemoradiotherapy As shown in the table, ARE toxicity rate in the whole dataset was approximately $31.7\%$. The average age was approximately 62 years old, with a standard deviation of 9.5 years, showing that almost all patients are from the senior group. Besides, we noticed that almost $66\%$ of the patients had squamous carcinoma cancer. In addition, the majority of patients received the treatment comprising chemoradiotherapy.
After feature selection, 3, 6, 12, and 13 features were involved in the three models, using features of CF, DF, RF, and HF determined following the previously published procedure, as shown in Additional file 1: Table S1. As shown in the table, three clinical factors in the CFM are treatment technology, using chemotherapy or not. In addition, five dose features in the DF model belonged to spatial texture features, describing the spatial dose distribution. The other feature, V0.99, was from the DVH metric. The majority of the selected features in the RF model are related to the gray level matrix from the wavelet filter and log sigma [41], and only one selected feature was calculated based on the original CT images. In the HF model, all the selected features were from the radiomics features, i.e., none of dosiomics features and clinical factors were kept in the final optimal feature group. Thus, the eight selected features in the HF model were also adopted in the RF model.
## Model performance
In the 30 training–testing splits mode, the average AUCs in the training and testing cohorts are shown in Fig. 3 and Table 3. From the figure, we observed that the models using the radiomics and hybrid features, i.e., RF and HF, achieved similar classification performance in training and testing, with AUCs of 0.796 ± 0.023 ($95\%$ confidence interval (CI [0.79, 0.80])/0.744 ± 0.044 ($95\%$ CI [0.73, 0.76]) and 0.801 ± 0.022 ($95\%$ CI [0.79, 0.81]) ($$p \leq 0.74$$), respectively. The model performance using CF and DF features showed a poorer predictive performance, with the training and testing AUCs of 0.573 ± 0.026 ($95\%$ CI [0.56, 0.58])/ 0.509 ± 0.072 ($95\%$ CI [0.48, 0.53]) and 0.679 ± 0.027 ($95\%$ CI [0.67, 0.69])/0.604 ± 0.041 ($95\%$ CI [0.53, 0.63]) compared with the above two models ($p \leq 0.001$), respectively. In addition, the receiver-operating characteristic curves (ROC) of three models were plotted in Fig. 4, and the nomogram using the combined models and the optimal feature group were shown in Additional file 1: Fig. S1. The probability of ARE can be read easily while the values of RadScore was calculated by the formula as shown in Additional file 1: Table S2. The points and total points in the Additional file 1: Fig. S1 are the normalization value in 0 to 100 using the RadScore. Two points can help user read the probability of ARE.Fig. 3The comparison of four models using CF, RF, DF, and HF in the training and testing cohorts. The red and blue solid lines are the training and testing AUC, respectively. The shadow shows the standard deviation (STD), which the narrower of the shadow, the smaller of the STD. The upper and lower subfigures are the results of AUC and ACC, respectivelyTable 3The model performance in the multiple train−test splits modelModelMetricTraining cohort (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu \pm \sigma$$\end{document}μ±σ)$95\%$ CITesting cohort (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu \pm \sigma$$\end{document}μ±σ)$95\%$ CICFMAUC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.573\pm 0.026$$\end{document}0.573±0.026[0.56, 0.58]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.509\pm 0.072$$\end{document}0.509±0.072[0.48, 0.53]ACC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.571\pm 0.041$$\end{document}0.571±0.041[0.56, 0.58]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.538\pm 0.071$$\end{document}0.538±0.071[0.51, 0.57]DFMAUC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.679\pm 0.027$$\end{document}0.679±0.027[0.67, 0.69]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.604\pm 0.068$$\end{document}0.604±0.068[0.58, 0.63]ACC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.630\pm 0.049$$\end{document}0.630±0.049[0.61, 0.65]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.552\pm 0.037$$\end{document}0.552±0.037[0.54, 0.56]RFMAUC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.756\pm 0.023$$\end{document}0.756±0.023[0.79, 0.80]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.744\pm 0.044$$\end{document}0.744±0.044[0.73, 0.76]ACC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.740\pm 0.024$$\end{document}0.740±0.024[0.73, 0.75]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.695\pm 0.045$$\end{document}0.695±0.045[0.68, 0.71]HFMAUC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.801\pm 0.022$$\end{document}0.801±0.022[0.79, 0.81]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.747\pm 0.041$$\end{document}0.747±0.041[0.73, 0.76]ACC\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.744\pm 0.030$$\end{document}0.744±0.030[0.73, 0.75]\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.696\pm 0.041$$\end{document}0.696±0.041[0.68, 0.71]ACC accuracy, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μ average, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma$$\end{document}σ standard deviation, CI confidence intervalsFig. 4ROC curves for each model in the training and testing cohorts. The orange, blue, green, and red solid lines represent the results of CFG, DFG, RFG, and HFG The same results were found using the evaluation metric of accuracy, as shown in Fig. 3 and Table 3. The models using RF and HF have a similar performance with the training and testing ACC of 0.740 ± 0.02 ($95\%$ CI [0.73, 0.75])/0.695 ± 0.045 ($95\%$ CI [0.68, 0.71]) and 0.744 ± 0.030 ($95\%$ CI [0.73, 0.75])/ 0.696 ± 0.041 ($95\%$ CI [0.68, 0.71]), respectively. The CF and DF models had a poorer classification result, with the training and testing ACCs of 0.571 ± 0.041 ($95\%$ CI [0.56, 0.58])/ 0.538 ± 0.071 ($95\%$ CI [0.51, 0.57]) and 0.630 ± 0.049 ($95\%$ CI [0.61, 0.65])/ 0.552 ± 0.037 ($95\%$ CI [0.54, 0.56]), respectively.
## Discussion
In the present study, we investigated the radiation toxicity of the esophagus for patients with LA-NSCLC treated with CRT using the IMRT technique. Several models using single-omics (i.e., radiomics and dosiomics) and multi-omics (the combination of radiomics and dosiomics) were established to predict ARE with a grade ≥ 2. The model performance revealed that ARE could be predicted using the pretreatment image and dose factors.
Using the single-omics feature, the performance of the model using radiomics was much better than that using dosiomics, under the multiple training–testing splits, with training average AUCs of 0.801–0.679 and testing average AUCs of 0.747–0.604 ($p \leq 0.001$). The same situation occurred for the model stability, with the SD of the training AUC being 0.023–0.027 and the SD of the testing AUC being 0.44 to 0.068, respectively. However, the multi-omics features could not improve the prediction performance for ARE compared with the radiomics features, showing similar classification results using RF and HF ($$p \leq 0.74$$). On the one hand, these results revealed that the radiomics features have an overwhelming correlation with ARE compared with dosiomics. These results were consistent with those of a previous study [26], in which all selected features in the model using multi-omics features (combining radiomics, dosiomics, and clinical factors) were chosen from among the radiomics features to predict ARE in patients with lung cancer treated with VMAT. None of the dosiomics features were adopted in the HF model. On the other hand, it might reflect the fact that the dosiomics features have limited predictability for ARE comparing to the radiomics features.
In the DF model, the majority of selected dosiomics features belonged to the spatial texture feature, except for Esophagus_V0.99, which is a one-dimensional dosimetric factor from the DVH. This is inconsistent with the previous studies using DVH metrics of Dmax, mean, 5 cc, 20, 30, 35, 40,50, 60 and V20, 30, 40, 50, 60 [12, 14, 17, 18] to predict ARE. In both the RF and HF models, all utilized features were radiomics features obtained by characterizing the spatial texture information of the esophagus. This might reflect the fact that the radiosensitivity of esophagus tissue dominates the occurrence of acute radiation esophagitis, but there was no contribution was from the dose information. In addition, clinical factors were also investigated in our study, which is agreed with the findings of a previous study [42] that clinical factors had poor predictability for ARE. Besides, this study [42] also investigated the correlation between DVH dosimetric parameters (Dmean,max, V40,50,60 of esophagus) and ARE, and demonstrated very limited classification performance, with an AUC range from 0.46 to 0.56 [42].
Our dataset showed radiation toxicity (i.e. ARE) of $31.7\%$ when using IMRT radiotherapy only, which falls into the previously reported range of 30–$55\%$ [12, 13, 18, 26, 43] for ARE grades ≥ 2. Apart from this, the incidence rates of ARE with a grade ≥ 3 were about $3.7\%$ in our dataset, which was lower than that of the previous studies ($11.4\%$ and $10.3\%$) [14, 42]. This might be caused using either a higher prescription dose [42] with a median dose of 66.6 Gy, or the traditional radiotherapy technique [14] of 3D-CRT. Both techniques result in a higher received dose for organs at risk, including the esophagus, in comparison with our study with a lower prescription dose (median dose of 60 Gy in our data set) or using the RT technique of IMRT. This demonstrated that the lower the dose received by the esophagus, the lower the incidence rates for severe acute radiation esophagitis ≥ grade 3. Hence, even though the dose features were not adopted in the HF model or achieved poor prediction in the DF model, decreasing the dose in the esophagus region still can benefit the management of radiation esophagitis. Therefore, further investigation of other effective dose features is warranted in the future.
To verify the study’s conclusion [8], we also analyzed the incidence rate of ARE in different prescription dose regions and patients with or without the treatment of CCRT. We divided patients into three groups based on the prescription dose level: (a) low-dose group (prescription dose < 60 Gy) with 52 patients; (b) median-dose group (prescription dose = 60 Gy) with 81 patients; and (c) high-dose group (prescription dose > 61 Gy) with 28 patients. ARE incidence rates being $30.8\%$, $29.6\%$, and $39.3\%$ in the low-dose, median-dose, and high-dose groups, respectively. The ARE rate in the high-dose group is higher than in the other two groups. However, there was no statistical difference among these three groups, with p values of 0.37 and 0.46 for high-median and high-low groups, respectively. Besides, we also separate all patients into two groups following the treatment with or without CCRT, a) the CCRT group (treatment using CCRT); (b) the other group (using the other treatments). ARE incidence rates are $35.6\%$ and $24.2\%$ for the CCRT and other groups, respectively. Even though there was a higher rate in the CCRT group, there was no statistical difference between the two groups ($$p \leq 0.24$$). Therefore, our data set agreed with the study’s conclusion [8] that the incidence rate of ARE is greater in higher radiation doses and the use of CCRT. Considering the statistical analysis results and the feature selection procedure, two factors of dose and treatment technology were not chosen in the final selected feature set.
In our study, the pretreatment radiotherapy image data were adopted for prior prediction of ARE. ARE prediction before RT treatment can aid clinical management by allowing more clinical care for patients at high risk. Clinical care could attenuate the side effects, i.e., ARE, and thus can improve the quality of life of patients in the mid-treatment and post-treatment phases to some extent. Consequently, it can benefit the overall survival of patients with locally advanced lung cancer [11].
The present study still has several limitations. First, we only adopted single-center retrospective small sample size radiotherapy data to construct the prediction model. It would be worth carrying out a comprehensive investigation of ARE using large cohort multi-center prospective medical information to verify our findings. Second, the study only employed the CT images and the RT 3D dose distributions. It is worth noting that multiple modality images, obtained by considering the MR, PET, and cone−beam CT (CBCT) images, have the potential for ARE prediction. Alam et al. [ 43] reported the potential predictability using CBCT and MR images by evaluating the esophagus volume changes in ARE. Another study demonstrated that the SUVpeak of PET images correlated significantly with the ARE [44]. Finally, the robustness of the radiomics features was not considered to improve the model generalizability. Several studies [45, 46] have investigated feature robustness using the image perturbation method, and the results showed that only some of the radiomics features in NSCLC cohorts are robust.
## Conclusions
Acute radiation esophagitis ≥ grade 2 can be predicted using pretreatment RT image features for patients with lung cancer treated with IMRT. To predict ARE, the multi-omics features have similar predictability to radiomics features; however, dosiomics features have a limited classification performance.
## Supplementary Information
Additional file 1: Table S1. The selected features for the three feature groups. Table S2. The coefficient and interpolation of each feature for the nomogram. Figure S1. The nomogram of the easy ensemble model shows the ARE probability estimation.
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|
---
title: 'Efficacy of antihyperglycemic therapies on cardiovascular and heart failure
outcomes: an updated meta-analysis and meta-regression analysis of 35 randomized
cardiovascular outcome trials'
authors:
- Masashi Hasebe
- Satoshi Yoshiji
- Yamato Keidai
- Hiroto Minamino
- Takaaki Murakami
- Daisuke Tanaka
- Yoshihito Fujita
- Norio Harada
- Akihiro Hamasaki
- Nobuya Inagaki
journal: Cardiovascular Diabetology
year: 2023
pmcid: PMC10024854
doi: 10.1186/s12933-023-01773-z
license: CC BY 4.0
---
# Efficacy of antihyperglycemic therapies on cardiovascular and heart failure outcomes: an updated meta-analysis and meta-regression analysis of 35 randomized cardiovascular outcome trials
## Abstract
### Background
Effects of antihyperglycemic therapies on cardiovascular and heart failure (HF) risks have varied widely across cardiovascular outcome trials (CVOTs), and underlying factors remain incompletely understood. We aimed to determine the relationships of glycated hemoglobin (HbA1c) or bodyweight changes with these outcomes in all CVOTs of antihyperglycemic therapies.
### Methods
We searched PubMed and EMBASE up to 25 January 2023 for all randomized controlled CVOTs of antihyperglycemic therapies reporting both major adverse cardiovascular events (MACE) and HF outcomes in patients with type 2 diabetes or prediabetes. We performed meta-regression analyses following random-effects meta-analyses to evaluate the effects of HbA1c or bodyweight reductions on each outcome.
### Results
Thirty-five trials comprising 256,524 patients were included. Overall, antihyperglycemic therapies reduced MACE by $9\%$ [risk ratio (RR): 0.91; $95\%$ confidence interval (CI) 0.88–0.94; $P \leq 0.001$; I2 = $36.5\%$]. In meta-regression, every $1\%$ greater reduction in HbA1c was associated with a $14\%$ reduction in the RR of MACE ($95\%$ CI 4–24; $$P \leq 0.010$$), whereas bodyweight change was not associated with the RR of MACE. The magnitude of the reduction in MACE risk associated with HbA1c reduction was greater in trials with a higher baseline prevalence of atherosclerotic cardiovascular disease. On the other hand, antihyperglycemic therapies showed no overall significant effect on HF (RR: 0.95; $95\%$ CI 0.87–1.04; $$P \leq 0.28$$; I2 = $75.9\%$). In a subgroup analysis based on intervention type, sodium-glucose cotransporter-2 inhibitors (SGLT2i) conferred the greatest HF risk reduction (RR: 0.68; $95\%$ CI 0.62–0.75; $P \leq 0.001$; I2 = $0.0\%$). In meta-regression, every 1 kg bodyweight reduction, but not HbA1c reduction, was found to reduce the RR of HF by $7\%$ ($95\%$ CI 4–10; $P \leq 0.001$); however, significant residual heterogeneity ($P \leq 0.001$) was observed, and SGLT2i reduced HF more than could be explained by HbA1c or bodyweight reductions.
### Conclusions
Antihyperglycemic therapies reduce MACE in an HbA1c-dependent manner. These findings indicate that HbA1c can be a useful marker of MACE risk reduction across a wide range of antihyperglycemic therapies, including drugs with pleiotropic effects. In contrast, HF is reduced not in an HbA1c-dependent but in a bodyweight-dependent manner. Notably, SGLT2i have shown class-specific benefits for HF beyond HbA1c or bodyweight reductions.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s12933-023-01773-z.
## Introduction
People with type 2 diabetes are at high risk of developing cardiovascular events, including cardiovascular death, coronary heart disease, stroke, and heart failure (HF) [1]. To date, various clinical trials have investigated the efficacy of antihyperglycemic therapies on cardiovascular outcomes, and some have provided evidence of a significant reduction in the risk of major adverse cardiovascular events (MACE) and/or hospitalization for HF in patients with type 2 diabetes or prediabetes [2]. Although it is presumed that the cardiovascular protection conferred by antihyperglycemic therapies is attributable to various variables, it is unclear which variables affect the development of cardiovascular diseases in chronic glycemia management.
Antihyperglycemic therapies lower blood glucose levels through various mechanisms to mitigate hyperglycemia symptoms and diabetes-related complication risk. Many cardiovascular outcome trials have shown a significant difference in glycemic control between the intervention (medication or intensive care) and control (placebo or standard care) groups during the observation period, even when they were designed to achieve “glycemic equipoise” between trial arms. Moreover, the effects of antihyperglycemic therapies on total bodyweight have varied substantially across previous clinical trials.
Thus far, various pooled analyses of cardiovascular outcome trials have been reported that have examined the risk modulation of cardiovascular and HF outcomes conferred by alterations in blood glucose levels and bodyweight. For instance, in a previous meta-analysis of 30 large-scale cardiovascular outcome trials that was published in 2020, various glucose-lowering drugs or strategies that significantly reduced the glycated hemoglobin (HbA1c) level (> $0.01\%$) also reduced MACE risk, but they did not ameliorate HF risk compared with standard care or placebo [3]. In that study, the meta-regression analysis showed that bodyweight reduction was associated with HF risk reduction. In other meta-analyses of cardiovascular outcome trials involving newer antihyperglycemic medications (such as dipeptidyl-peptidase-4 inhibitors [DPP-4i], glucagon-like peptide-1 receptor agonists [GLP-1RA], and sodium-glucose cotransporter-2 inhibitors [SGLT2i]), significant associations were observed between improvements in glycemic control and the reduction of MACE risk [4–7], whereas no association was identified between glycemic improvement and the risk of HF [5–7]. Additionally, despite the acknowledged association between obesity and an increased risk of atherosclerotic cardiovascular disease (ASCVD) [8], the meta-analysis of cardiovascular outcome trials involving GLP-1RA revealed no correlation between bodyweight reduction and the risk of MACE [4]. However, whether and to what extent blood glucose lowering and bodyweight reduction are associated with cardiovascular and HF benefits has not been comprehensively studied and updated to include all trials of antihyperglycemic therapies completed before and after the establishment of the Food and Drug Administration (FDA) guidelines in 2008 [9]. The trials completed before the establishment of the FDA guidelines include the United Kingdom Prospective Diabetes Study (UKPDS), and those completed after the establishment of the FDA guidelines include newer studies of DPP-4i, GLP-1RA, and SGLT2i.
Recently, the results of more cardiovascular outcome trials with newer antihyperglycemic medications have become available. Therefore, we performed a comprehensive, updated meta-analysis and meta-regression analysis of 35 large-scale cardiovascular outcome trials of antihyperglycemic drugs reporting MACE and HF outcomes published both before and after the establishment of the FDA guidelines in 2008. We aimed to explore the relationships between blood glucose lowering or bodyweight reduction and MACE and HF risks, thereby delineating the potential contribution of blood glucose lowering or bodyweight reduction per se to cardiovascular and HF benefits.
## Search strategies and selection criteria
The protocol of this study has been registered at the International Prospective Register of Systematic Reviews (PROSPERO) (registration number: CRD42022299075). This study was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines [10]. We followed the eligibility criteria previously established [3]: (i) randomized controlled trials with an enrollment of a minimum of 1000 adults with type 2 diabetes or prediabetes, (ii) compared an antihyperglycemic drug, an intensive glycemic control strategy, or a lifestyle intervention strategy with controls (placebo, standard care, or an active control agent), (iii) reported MACE and HF as outcomes of interest, (iv) a follow-up period of at least one year, and (v) achieved an HbA1c difference greater than $0.01\%$ between trial arms. Trials were excluded if they reported an achieved HbA1c difference of ≤ $0.01\%$, or if they did not report the difference in achieved HbA1c between the trial arms. We also excluded trials if a multifactorial intervention or non-glycemic medication were examined.
We conducted a literature search on PubMed and EMBASE databases from their inception until 25 January 2023 without language restriction to find relevant studies using the search strategies as follows: (type 2 diabetes OR prediabetes) AND (randomized OR randomly) AND (cardiovascular OR macrovascular OR MACE OR heart failure) AND (antihyperglycemic OR antidiabetic OR intensive glucose control OR intensive blood glucose control OR intensive blood-glucose control OR intensive glucose lowering OR lifestyle intervention OR biguanide* OR sulfonylurea* OR glinide* OR meglitinide* OR peroxisome proliferator-activated receptor* OR thiazolidinedione OR α-glucosidase inhibitor* OR dipeptidyl peptidase 4 OR glucagon-like peptide 1 OR glucagon-like peptide-1 OR sodium-glucose cotransporter 2). We also manually searched the reference lists of previous meta-analyses of cardiovascular outcome trials to identify potentially relevant studies. The PRISMA flow diagram is shown in Fig. 1.Fig. 1Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) flow diagram for study selection
## Data extraction and quality assessment
From the eligible trials, we extracted data on study characteristics, baseline characteristics of participants, antihyperglycemic regimens used, control regimens used, mean differences in the achieved HbA1c and bodyweight levels between trial arms, and outcomes of interest that included risk ratios (RRs) with $95\%$ confidence intervals (CIs). We extracted data from primary trial results and their accompanying supplementary materials as the primary data source. To determine the achieved differences in HbA1c or bodyweight levels between the trial arms, we used the time-weighted least squares mean difference over the course of follow-up, at the end of follow-up, or at one year of follow-up. We used the first available follow-up data if none of these values were reported. We used version 2 of the Cochrane Risk of Bias Tool to evaluate the risk of bias in the eligible studies [11]. MH and YK independently conducted the literature search and data extraction. The results were compared, and any discrepancies were resolved by consensus or with input from a third independent reviewer (SY).
## Data analysis
The primary outcome was the efficacy of antihyperglycemic therapies on HF and MACE risks (defined as a composite of cardiovascular death, non-fatal myocardial infarction [MI], or non-fatal stroke). We determined the relationships between HbA1c reduction or bodyweight change and HF or MACE risk using meta-regression. If the trials did not report MACE according to our aforementioned definition, we used one of the following alternative definitions: death from cardiovascular or undetermined causes, non-fatal MI, or non-fatal stroke; cardiovascular death, non-fatal MI, or non-fatal ischemic stroke; all-cause death, non-fatal MI, or non-fatal stroke; cardiovascular death, non-fatal MI, non-fatal stroke, or other atherothrombotic events; all-cause death, non-fatal MI, non-fatal stroke, or other atherothrombotic events; fatal and non-fatal MI or stroke; or fatal and non-fatal MI. Detailed definitions of HF and MACE outcomes in each trial are displayed in Additional file 1: Table S1.
For the meta-analysis, pooled RRs with $95\%$ CIs for MACE and HF outcomes were calculated using a random-effects model with the inverse variance method [12]. The between-trial variance was estimated using the DerSimonian–Laird estimator [13]. Heterogeneity among trials was evaluated using Cochran’s Q test and Higgins’s I2 statistics [14]. Thresholds defining the magnitude of heterogeneity based on the I2 index were low (≤ $25\%$), moderate (26–$50\%$), and high (> $50\%$) [15]. Subgroup random-effects meta-analysis was performed based on a type of intervention (an intensive glycemic control strategy or each drug class). Additionally, we evaluated publication bias by funnel plots and Egger’s test [16].
For the meta-regression analysis with a mixed-effects model, we analyzed the association between the differences in the achieved HbA1c or bodyweight difference and the corresponding estimated log RR [Ln(RR)] of MACE and HF outcomes. To obtain the relative RR reduction of each outcome for every $1\%$ HbA1c reduction or every 1 kg bodyweight reduction, we used the following formula with the regression coefficient (slope) in the meta-regression (Additional file 1: Fig. S1):\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Relative RR reduction }}\left({\text{\% }} \right){\text{ for every 1\% HbA1c reduction or 1 kg bodyweight reduction}}\,{ = }\,(1 \, {-}e^{{{\text{slope}}}}) \times 100$$\end{document}Relative RR reduction\%for every 1\% HbA1c reduction or 1 kg bodyweight reduction=(1-eslope)×100 We used the restricted maximum likelihood as an estimator [17]. If a significant association was identified in the primary meta-regression analyses, we conducted trial-level subgroup meta-regression, stratified by [1] type of intervention (an intensive glycemic control strategy or each drug class) and [2] baseline prevalence of ASCVD (≥ $70\%$ vs. < $70\%$). Trials without reported baseline proportions of patients with ASCVD were excluded from the latter subgroup analysis. Sensitivity meta-regression was performed to adjust for trial-level mean age, proportion of female participants, and person-years (calculated as the number of participants multiplied by the median follow-up duration in years, divided by 1000) to assess the consistency of the significant association found in the primary analysis.
Results with two-sided P-values less than 0.05 were considered significant for the pooled RR meta-analysis and meta-regression analysis. P-values less than 0.05 in Cochran’s Q test were also considered significant. All analyses were performed using R statistical software (version 4.1.2; the R Foundation for Statistical Computing, Vienna, Austria) with the ‘meta’ (version 4.18.2) and ‘metafor’ (version 3.02) packages.
## Search results and baseline study characteristics
The initial search identified 1661 trials after removing duplicates. Screening for eligibility using titles and abstracts yielded 44 trials for detailed assessment, of which nine trials did not meet the inclusion criteria and were excluded (Fig. 1, Additional file 1: Table S2). Trials excluded through the full-text assessment included the CAROLINA trial, which compared linagliptin with glimepiride and showed no significant difference in both the achieved HbA1c and the MACE/ HF outcomes [18]. Therefore, 35 trials comprising 256,524 patients were included in the meta-analysis [19–53]. Among the 35 included trials, four assessed an intensive glycemic control strategy, one assessed intensive lifestyle intervention focusing on weight loss, one assessed insulin glargine (long-acting insulin analog), one assessed acarbose (α-glucosidase inhibitor), eight assessed peroxisome proliferation-activated receptor (PPAR) agonists, five assessed DPP-4i, nine assessed GLP-1RA, and six assessed SGLT2i. The main characteristics of the included trials are shown in Table 1. The mean follow-up duration was 1.3–10.0 years, and the average age of the patients was 53.3–69.0 years. A total of 164,276 of 248,306 ($66.2\%$) assessable patients had established atherosclerotic cardiovascular disease, and 30,708 of 229,343 ($13.4\%$) assessable patients had a history of HF at baseline. Although the trials designed as open-label had a high risk of bias in the domain of deviations from intended interventions because of the inability to blind the intervention, the evaluation of eligible trials showed no obvious risk of bias in most other domains. The risk of bias of each eligible trial is summarized in Additional file 1: Table S3. A visual inspection of funnel plots and the results of Egger’s test for outcomes of interest both indicated no evidence of publication bias (P Egger’s test = 0.42 and 0.79 for MACE and HF outcome, respectively); however, the ADOPT and DREAM trials, both of which investigated the efficacy of rosiglitazone, were observed as outliers in the funnel plot for MACE (Additional file 1: Fig. S2) [21, 22]. DREAM was also identified as an outlier in the funnel plot for HF (Additional file 1: Fig. S2) [22].Table 1Key characteristics of the included trialsTrialYearParticipants, nInterventionControlMedian follow-up, yearsMean age (SD), yearsMean diabetes duration, yearsFemale, nMean BMI (SD), kg/m2Baseline CVD, nBaseline HF, nMean baseline HbA1c (SD), %Mean HbA1c reduction, %Mean bodyweight change, kgUKPDS 3319983867IGC with sulfonylurea or insulin (target fasting blood glucose < 6 mmol/L)Standard care (diet; target fasting blood glucose < 15 mmol/L)10.053.3 (8.6)0.0a1508 ($39\%$)27.5 (5.2)NRNR7.1 (1.5)0.90 + 3.10PROactive20055238PPAR agonists (pioglitazone 15 to 45 mg/day)Placebo2.961.7 (7.7)8.0a1775 ($34\%$)30.8 (0.5)5238 ($100\%$)NR8.0 (NR)0.50 + 4.00ADOPT20064351PPAR agonists (rosiglitazone 4 to 8 mg/day)Metformin (500 to 2000 mg) or glyburide (2.5 to 15 mg)4.056.9 (10.1)1.51840 ($42\%$)32.2 (6.4)NR0 ($0\%$)7.4 (1.0)0.13 (vs. metformin); 0.42 (vs. glyburide) + 6.90 (vs. metformin); + 2.50 (vs. glyburide)DREAM20065269PPAR agonists (rosiglitazone 4 to 8 mg/day)Placebo3.054.7 (10.9)0.03120 ($59\%$)30.9 (5.6)0 ($0\%$)0 ($0\%$)NR0.50 + 2.20ACCORD200810,251IGC (target HbA1c < $6.0\%$)Standard care (target HbA1c 7.0–$7.9\%$)10.062.2 (6.8)10.03952 ($39\%$)32.2 (5.5)3608 ($35\%$)497 ($5\%$)8.3 (1.1)1.10 + 3.10ADVANCE200811,140IGC with gliclazide and other drugs as required (target HbA1c < $6.0\%$)Standard care (target HbA1c 7.0–$7.9\%$)5.066.0 (6.0)7.94735 ($43\%$)28.0 (5.0)3590 ($32\%$)NR7.5 (1.6)0.67 + 0.70BARI 2D20092368Insulin-sensitization therapy with oral treatmentInsulin-provision therapy5.362.4 (8.9)10.4701 ($30\%$)31.7 (5.4)2368 ($100\%$)156 ($6.6\%$)7.7 (1.6)0.50− 1.80RECORD20094447PPAR agonists (rosiglitazone 4 to 8 mg/day)Metformin (at a maximum dose of 2550 mg) and sulfonylurea (glibenclamide at a maximun dose of 15 mg or equivalent for different preparations)5.558.4 (8.3)7.12154 ($48\%$)31.5 (4.8)772 ($17.4\%$)21 ($0.5\%$)7.9 (0.7)0.27 + 4.70VADT20091791IGC (treatment absolute difference in HbA1c ≤ $1.5\%$)Standard care5.660.4 (9.0)11.552 ($3\%$)31.2 (3.5)723 ($40\%$)NR9.4 (2.0)1.50 + 4.05ORIGIN201212,537Insulin glargine (target fasting blood glucose < 5.3 mmol/L)Standard care6.263.5 (7.9)5.44386 ($35\%$)29.9 (5.3)7378 ($59\%$)NR6.4 (NR)0.30 + 2.10EXAMINE20135380DPP-4i (alogliptin 25 mg/day)Placebo1.561.0 (10.0)7.2a1729 ($32\%$)28.3 (NR)5380 ($100\%$)1501 ($28\%$)8.0 (1.1)0.36 + 0.06Look AHEAD20135145Intensive lifestyle interventionStandard care9.658.8 (6.9)5.0a3063 ($60\%$)35.9 (5.9)714 ($14\%$)NR7.3 (1.2)0.22− 4.00SAVOR-TIMI 53201316,492DPP-4i (saxagliptin 5 mg/day)Placebo2.165.1 (8.5)10.3a5455 ($33\%$)31.2 (5.6)12,959 ($79\%$)2105 ($13\%$)8.0 (1.4)0.20− 0.10AleCardio20147226PPAR agonists (aleglitazar 150 μg/day)Placebo2.061.0 (10.0)8.61966 ($27\%$)28.7 (NR)7226 ($100\%$)759 ($11\%$)7.8 (1.7)0.60 + 3.70ELIXA20156068GLP-1RA (lixisenatide 20 μg/day)Placebo2.159.9 (9.7)9.21861 ($31\%$)30.1 (5.6)6068 ($100\%$)1358 ($22\%$)7.7 (1.3)0.27− 0.70EMPA-REG OUTCOME20157020SGLT2i (empagliflozin 10 or 25 mg/day)Placebo3.163.1 (8.6)$57\%$ > 10 yearsb2004 ($24\%$)30.6 (5.3)7020 ($100\%$)706 ($10\%$)8.1 (0.8)0.57− 2.00TECOS201514,671DPP-4i (sitagliptin 100 mg/day)Placebo3.065.5 (8.0)11.64297 ($29\%$)30.2 (5.6)10,863 ($74\%$)2643 ($18\%$)7.2 (0.5)0.29− 0.05IRIS20163876PPAR agonists (pioglitazone 30 mg/day)Placebo4.863.5 (10.6)0.01338 ($35\%$)29.9 (10.5)3876 ($100\%$)0 ($0\%$)5.8 (0.4)0.06 + 0.00LEADER20169340GLP-1RA (liraglutide 1.8 mg/day)Placebo3.864.2 (7.2)12.83337 ($36\%$)32.5 (6.3)7598 ($81\%$)1667 ($18\%$)8.7 (1.6)0.40− 2.30SUSTAIN-620163297GLP-1RA (semaglutide 0.5 or 1.0 mg/week)Placebo2.164.6 (7.4)13.91295 ($39\%$)32.8 (6.2)2735 ($83\%$)777 ($24\%$)8.7 (1.5)0.85− 3.61OMNEON20174202DPP-4i (omaligliptin 25 mg/week)Placebo1.863.6 (8.5)12.11254 ($30\%$)31.3 (5.5)4202 ($100\%$)641 ($15\%$)8.0 (0.9)0.30− 0.08CANVAS Program201710,142SGLT2i (canagliflozin 100 or 300 mg/day)Placebo2.463.3 (8.3)13.53633 ($36\%$)32.0 (5.9)6656 ($66\%$)1461 ($14\%$)8.2 (0.9)0.58− 1.60EXSCEL201714,752GLP-1RA (exenatide 2 mg/week)Placebo3.261.9 (9.4)13.15603 ($38\%$)32.7 (6.4)10,782 ($73\%$)2389 ($16\%$)8.1 (1.0)0.53− 1.27ACE20176522α-GI (acarbose 50 mg three times/day)Placebo5.064.3 (8.1)0.01762 ($27\%$)25.4 (3.1)6522 ($100\%$)69 ($1\%$)5.9 (0.7)0.07− 0.64TOSCA.IT20173028PPAR agonists (pioglitazone 15 to 45 mg/day)Sulfonylurea (glibenclamide 5–15 mg or gliclazide 30–120 mg or glimepiride 2–6 mg)4.862.3 (6.5)8.51254 ($41\%$)30.3 (4.5)335 ($11\%$)0 ($0\%$)7.7 (0.5)0.24 + 3.10HARMONY Outcomes20189463GLP-1RA (albiglutide 30 or 50 mg/wek)Placebo1.564.1 (8.7)14.12894 ($31\%$)32.3 (5.9)9463 ($100\%$)1922 ($20\%$)8.7 (1.5)0.52− 0.83DECLARE-TIMI 58201917,160SGLT2i (dapaglifrozin 10 mg/day)Placebo4.263.9 (6.8)11.86422 ($37\%$)32.1 (6.0)6974 ($41\%$)1724 ($10\%$)8.3 (1.2)0.42− 1.80CARMELINA20196979DPP-4i (linagliptin 5 mg/day)Placebo2.265.9 (9.1)14.72589 ($37\%$)31.4 (5.4)4081 ($58\%$)1873 ($27\%$)7.9 (1.0)0.36− 0.15CREDENCE20194401SGLT2i (canagliflozin 100 mg/day)Placebo2.663.0 (9.2)15.81494 ($34\%$)31.3 (6.2)2220 ($50\%$)652 ($15\%$)8.3 (1.3)0.25− 0.80REWIND20199901GLP-1RA (dulaglutide 1.5 mg/week)Placebo5.466.2 (6.5)10.54589 ($46\%$)32.3 (5.7)3114 ($31\%$)853 ($9\%$)7.3 (1.1)0.61− 1.46PIONEER 620193138GLP-1RA (oral semaglutide 14 mg/day)Placebo1.366.0 (7.0)14.91007 ($32\%$)32.3 (6.5)2695 ($85\%$)288 ($12\%$)8.2 (1.6)0.70− 3.40VERTIS CV20208246SGLT2i (ertugliflozin 5 or 15 mg/day)Placebo3.064.4 (8.1)13.02477 ($30\%$)31.9 (5.4)8246 ($100\%$)1958 ($24\%$)8.2 (1.0)0.50− 2.40SCORED202110,584SGLT2i (sotagliflozin 200 or 400 mg/day)Placebo1.369 (63–74), 69 (63–74)cNR4754 ($45\%$)31.9 (28.1–36.2), 31.7 (28.0–36.1)c5144 ($49\%$)3283 ($31\%$)8.3 (7.6–9.3), 8.3 (7.6–9.4)c0.42− 1.16AMPLITUDE-O20214076GLP-1RA (efpeglenatide 4 or 6 mg/week)Placebo1.864.5 (8.2)14.91344 ($33\%$)32.7 (6.2)3650 ($90\%$)737 ($18\%$)8.9 (1.5)1.24− 2.60FREEDOM-CVO20224156GLP-1RA (continuously infused exenatide 20 μg/day)Placebo1.363 (58–68), 63 (57–68)cNR1525 ($37\%$)32.4 (28.8–36.6), 31.9 (28.6–36.1)c2036 ($49\%$)d668 ($16\%$)8.0 (7.2–9.3), 8.0 (7.2–9.2)c0.84− 4.24BMI body mass index, CVD cardiovascular disease, DPP-4i dipeptidyl-peptidase-4 inhibitor, GLP-1RA glucagon-like peptide-1 receptor agonist, HbA1c glycated hemoglobin, HF heart failure, IGC intensive glycemic control, NR not reported, PPAR peroxisome proliferator-activated receptor, SGLT2i sodium-glucose co-transporter-2 inhibitoraMedian valuebApproximately $57\%$ of the participants had more than 10 years of diabetes durationcContinuous data (baseline age, BMI, and HbA1c) of SCORED and FREEDOM-CVO are separately presented as median (interquartile range) in intervention group and control group, respectivelydHistory of coronary artery disease
## Major adverse cardiovascular events
Overall, 25,475 patients ($9.9\%$) experienced MACE outcomes during the follow-up period. In the pooled analysis of 35 trials, antihyperglycemic therapies decreased MACE risk by $9\%$, with moderate heterogeneity between trials (RR: 0.91; $95\%$ CI 0.88–0.94; $P \leq 0.001$; I2 = $36.5\%$) (Fig. 2). In a subgroup random-effects meta-analysis based on the type of intervention, intensive glycemic control strategies (RR: 0.90; $95\%$ CI 0.83–0.97; $$P \leq 0.008$$; I2 = $0.0\%$), PPAR agonists (RR: 0.91; $95\%$ CI 0.84–0.97; $$P \leq 0.006$$; I2 = $25.2\%$), GLP-1RA (RR: 0.87; $95\%$ CI 0.81–0.94; $$P \leq 0.001$$; I2 = $53.3\%$), and SGLT2i (RR: 0.88; $95\%$ CI 0.82–0.94; $P \leq 0.001$; I2 = $28.1\%$) conferred a significantly lower risk of MACE to a similar extent, whereas the others showed null effects on MACE risk (Additional file 1: Fig. S3).Fig. 2Efficacy of antihyperglycemic drugs on the risk of major adverse cardiovascular events (MACE). UKPDS 33, ACCORD, ADVANCE, VADT: trials comparing an intensive glycemic control strategy with standard care; Look AHEAD: a trial comparing intensive lifestyle intervention for weight loss with standard care; ORIGIN: a trial comparing insulin glargine with standard care; ACE: a trial comparing acarbose (α-glucosidase inhibitor [α-GI]) with placebo; PROactive, ADOPT, DREAM, BARI 2D, RECORD, AleCardio, IRIS, TOSCA.IT: trials comparing peroxisome proliferation-activated receptor (PPAR) agonists with placebo or active control drug; EXAMINE, SAVOR-TIMI 53, TECOS, OMNEON, CARMELINA: trials comparing dipeptidyl-peptidase-4 inhibitors (DPP-4i) with placebo; ELIXA, LEADER, SUSTAIN-6, EXSCEL, Harmony Outcomes, REWIND, PIONEER 6, AMPLITUDE-O, FREEDOM-CVO: trials comparing glucagon-like peptide-1 receptor agonists (GLP-1RA) with placebo; EMPAREG-OUTCOME, CANVAS-Program, DECLARE-TIMI 58, CREDENCE, VERTIS CV, SCORED: trials comparing sodium-glucose cotransporter-2 inhibitors (SGLT2i) with placebo The univariate meta-regression analysis revealed a significant association between the HbA1c reduction from baseline and the Ln(RR) of MACE (slope: − 0.15; $95\%$ CI − 0.27 to − 0.04; $$P \leq 0.010$$; variance explained: $52\%$) (Fig. 3A). Accordingly, every $1\%$ greater reduction in HbA1c was associated with a $14\%$ ($95\%$ CI 4–24) relative reduction in the RR of MACE. In contrast, bodyweight change from baseline was not significantly associated with the Ln(RR) of MACE (slope: − 0.006; $95\%$ CI − 0.020 to 0.008; $$P \leq 0.41$$) (Fig. 3B).Fig. 3Association between the risk of major adverse cardiovascular events (MACE) and A HbA1c reduction or B bodyweight change. The thicker line shows meta-regression with $95\%$ CI as shading. The circle size of each trial reflects the study weight. HbA1c glycated hemoglobin, CI confidence interval, Ln(RR) estimated log risk ratio We further evaluated the robustness of the relationship between HbA1c reduction and the risk of MACE through subgroup and sensitivity meta-regression analyses. In the stratified meta-regression by type of intervention, trials involving intensive glycemic control strategies, DPP-4i, and GLP-1RA showed a trend of reducing the Ln(RR) of MACE in association with a decrease in HbA1c; however, the relationship between HbA1c reduction and MACE risk was not statistically significant for all intervention types (Additional file 1: Table S4). In the stratified meta-regression based on the baseline prevalence of ASCVD (≥ $70\%$ vs. < $70\%$), HbA1c reduction was associated with a decrease in the Ln(RR) of MACE for both subgroups. Notably, the decrease in MACE risk associated with HbA1c reduction was significant and greater in trials with ≥ $70\%$ of patients with ASCVD at baseline (Additional file 1 Fig. S4). On the sensitivity meta-regression analysis with adjustment for multiple confounders (trial-level age, sex, and person-years), HbA1c reduction was significantly associated with the risk reduction of MACE, in agreement with the results of the main univariate analysis (slope: − 0.15; $95\%$ CI − 0.26 to − 0.04; $$P \leq 0.008$$; variance explained: $89\%$).
## Heart failure
Overall, 9163 patients ($3.6\%$) experienced HF outcomes during the follow-up period. In the pooled analysis of 35 trials, antihyperglycemic therapies conferred no overall significant effect on HF risk with high heterogeneity across studies (RR: 0.95; $95\%$ CI 0.87–1.04; $$P \leq 0.28$$; I2 = $75.9\%$) (Fig. 4). In a subgroup analysis based on the type of intervention, GLP-1RA (RR: 0.90; $95\%$ CI 0.83–0.98; $$P \leq 0.019$$; I2 = $0.0\%$) and SGLT2i (RR: 0.68; $95\%$ CI 0.62–0.75; $P \leq 0.001$; I2 = $0.0\%$) significantly reduced HF risk with a greater reduction of risk with SGLT2i. PPAR agonists significantly increased HF risk by $38\%$ (RR: 1.38; $95\%$ CI 1.19–1.60; $P \leq 0.001$; I2 = $53.0\%$). The others showed neutral effects on HF risk (Additional file 1: Fig. S5). Notably, SGLT2i lowered HF risk more than any other type of intervention, as indicated by the non-overlapping CI.Fig. 4Efficacy of antihyperglycemic drugs on the risk of heart failure (HF). UKPDS 33, ACCORD, ADVANCE, VADT: trials comparing an intensive glycemic control strategy with standard care; Look AHEAD: a trial comparing intensive lifestyle intervention for weight loss with standard care; ORIGIN: a trial comparing insulin glargine with standard care; ACE: a trial comparing acarbose (α-glucosidase inhibitor [α-GI]) with placebo; PROactive, ADOPT, DREAM, BARI 2D, RECORD, AleCardio, IRIS, TOSCA.IT: trials comparing peroxisome proliferation-activated receptor (PPAR) agonists with placebo or active control drug; EXAMINE, SAVOR-TIMI 53, TECOS, OMNEON, CARMELINA: trials comparing dipeptidyl-peptidase-4 inhibitors (DPP-4i) with placebo; ELIXA, LEADER, SUSTAIN-6, EXSCEL, Harmony Outcomes, REWIND, PIONEER 6, AMPLITUDE-O, FREEDOM-CVO: trials comparing glucagon-like peptide-1 receptor agonists (GLP-1RA) with placebo; EMPAREG-OUTCOME, CANVAS-Program, DECLARE-TIMI 58, CREDENCE, VERTIS CV, SCORED: trials comparing sodium-glucose cotransporter-2 inhibitors (SGLT2i) with placebo In meta-regression analyses, the Ln(RR) of HF was not significantly associated with HbA1c reduction (slope: − 0.12; $95\%$ CI − 0.42 to 0.19; $$P \leq 0.46$$) (Fig. 5A), in contrast to the results from the meta-regression analysis of MACE outcomes. Instead, bodyweight reduction was significantly associated with the Ln(RR) of HF (slope: − 0.07; $95\%$ CI − 0.10 to − 0.04; $P \leq 0.001$) (Fig. 5B). Accordingly, every 1 kg greater reduction in bodyweight was associated with a $7\%$ ($95\%$ CI: 4–10) relative RR reduction of HF. However, significant residual heterogeneity ($P \leq 0.001$) was observed despite the variance explained ($52\%$) was comparable with the variance explained by HbA1c for MACE ($52\%$). Notably, all trials involving SGLT2i did not align well with the regression slope. Fig. 5Association between heart failure (HF) risk and A HbA1c reduction or B bodyweight change. The thicker line shows meta-regression with $95\%$ CI as shading. The circle size of each trial inversely reflects the study weight. HbA1c glycated hemoglobin, CI confidence interval, Ln(RR) estimated log risk ratio We assessed the consistency of the relationship between bodyweight loss and the risk of HF through subgroup and sensitivity meta-regression analyses. In the stratified meta-regression by type of intervention, trials involving intensive glycemic control strategies, PPAR agonists, DPP-4i, and GLP-1RA showed a trend towards reducing the Ln(RR) of HF in association with a decrease in bodyweight; however, the association between bodyweight reduction and HF risk was not statistically significant for all intervention types (Additional file 1: Table S5). In the stratified meta-regression based on the baseline prevalence of ASCVD (≥ $70\%$ vs. < $70\%$), bodyweight reduction was significantly associated with a decrease in the Ln(RR) of HF for both subgroups, in line with the overall results (Additional file 1: Fig. S6). In the sensitivity meta-regression analysis with adjustment for multiple confounders (trial-level age, sex, and person-years), bodyweight reduction was significantly associated with the risk reduction of HF consistent with the results of the main univariate analysis (slope: − 0.07; $95\%$ CI − 0.10 to − 0.03; $P \leq 0.001$; variance explained: $48\%$).
## Discussion
The current meta-analysis with meta-regression enrolled 256,524 patients with type 2 diabetes or prediabetes from 35 large-scale cardiovascular outcome trials and explored the associations between glycemic improvement or bodyweight change and MACE and HF risks. To our knowledge, this is the largest meta-analysis and meta-regression analysis of randomized controlled trials of antihyperglycemic therapies reporting MACE and HF outcomes. With respect to MACE outcomes, antihyperglycemic therapies significantly reduced MACE risk in the random-effects model meta-analysis, and the HbA1c reduction, not bodyweight reduction, was significantly correlated with a decline in the Ln(RR) of MACE in the univariate and multivariate meta-regression analyses; every $1\%$ additional reduction in HbA1c was associated with a $14\%$ relative reduction in MACE risk. The subgroup analysis further demonstrated that the relationship between HbA1c reduction and MACE risk reduction was more pronounced in patients with advanced atherosclerosis. These findings indicated potential effect modifications of MACE outcomes through glycemic control and reiterated the utility of HbA1c reduction as a marker of MACE risk reduction.
Regarding HF, antihyperglycemic therapies demonstrated a trend towards reducing HF risk compared with controls, with a RR of 0.95 ($95\%$ CI 0.87–1.04) in the meta-analysis, whereas the results showed high heterogeneity across trials. Contrary to MACE, bodyweight change, not HbA1c reduction, was associated with the Ln(RR) of HF in the univariate and multivariate meta-regression analyses. However, residual heterogeneity was high, and all trials involving SGLT2i did not align well with the regression slope; these findings indicated that while bodyweight reduction can partially contribute to HF risk reduction, other factors still influence HF risk modulation. In particular, the data on SGLT2i suggest a greater influence of residual contributors beyond the reduction in bodyweight and HbA1c on HF risk amelioration.
## Glycemic control, bodyweight reduction, and major adverse cardiovascular events
In our study, antihyperglycemic therapies reduced MACE risk by $9\%$, which is almost identical to the MACE risk reduction reported by the previous meta-analyses of cardiovascular outcome trials with intensive glycemic control [54] and those with newer antihyperglycemic medications conducted after the establishment of the FDA guidelines in 2008 [5]. The significant association between HbA1c decline and attenuation of the MACE risk indicates that blood glucose lowering would proportionally decrease the risk of MACE, in agreement with the observations of the post hoc studies of the LEADER and REWIND trials, which suggested that HbA1c was a major and significant mediator of the cardiovascular benefits [55, 56]. Moreover, the findings in those mediation analyses that bodyweight was not a significant mediator of cardiovascular benefits for GLP-1RA are consistent with the non-significant association between bodyweight change and MACE risk in our meta-regression analysis [54, 55]. Additionally, most of the cardiovascular benefits associated with SGLT2i were presumed to be attributed to HbA1c reduction in three large cardiovascular outcome trials (EMPA-REG OUTCOME, CANVAS Program, and DECLARE-TIMI 58 trials) [57]. The previous meta-analysis results also corroborate the significant association between glycemic improvement and reduced risk of MACE [4–7]. Our finding supports the hypothesis that HbA1c reduction can be a useful clinical marker of MACE risk reduction across a wide range of antihyperglycemic therapies. Although high hypoglycemia risk associated with antihyperglycemic therapies may dilute the cardiovascular benefit conferred by glycemic reduction [58], blood glucose lowering remains a crucial aspect of cardiovascular risk management and is likely to contribute significantly to reducing MACE risk, as supported by a recent causal directed acyclic graphs study [7]. However, we note that the observed HbA1c reduction can be a marker representing multiple factors, including pleiotropic effects, rather than a single marker of improvement in glycemic control. Pleiotropic effects may include mitigation of endothelial dysfunction and oxidative stress, as observed with GLP-1RA and SGLT2i administration [59, 60]. Further studies are required to disentangle the contribution of glycemic and non-glycemic effects.
## Glycemic control, bodyweight reduction, and heart failure
Regarding HF, antihyperglycemic therapies numerically but not significantly reduced HF risk by $5\%$, and both primary analysis and subgroup analysis showed high heterogeneity across studies. Contrary to MACE risk, HbA1c reduction was not associated with HF risk. Subgroup analyses show differing effects on HF based on the type of intervention, with SGLT2i and GLP-1RA reducing risk and PPAR agonists increasing it. This suggests glycemic control may not be critical for short-term (< 10 years) prevention or treatment of HF in dysglycemia.
In agreement with our meta-regression analysis results, the previous meta-regression analysis results showed that bodyweight reduction was significantly associated with a reduced HF risk [3]. This is theoretically reasonable because the two drugs that reduced HF risk (SGLT2i and GLP-1RA) decrease bodyweight through specific mechanisms [61], and PPAR agonists, which increased HF risk, increase bodyweight via fluid retention [62]. Given the favorable hemodynamic effects, such as ameliorated high blood pressure and fluid congestion, associated with bodyweight loss [63], the significant association we discovered between bodyweight loss and decreased risk of HF is biologically plausible. However, high residual heterogeneity ($P \leq 0.001$) and disproportionate reduction of HF risk by SGLT2i (Fig. 5B) suggest the involvement of other important factors in reducing HF risk. Considering that multiple post hoc analyses of the trials involving SGLT2i revealed that changes in markers of volume status and hemoconcentration (e.g., hematocrit), but not in bodyweight, are the most important mediators of cardiovascular death and HF, the clinical markers of the plasma volume status might be the more reliable markers of HF benefits [64–67]. This hypothesis is supported by an observational study reporting that lower hematocrit levels are associated with an increased risk of hospitalization for patients with HF [68].
## Strengths and weaknesses
The main strength of our study is the inclusion of the largest number of cardiovascular outcome trials investigating various antihyperglycemic therapies conducted both before and after the establishment of the FDA guidelines in 2008, thereby allowing the most comprehensive evaluation of the contribution of blood glucose and bodyweight control to MACE and HF risks. Our study has important clinical implications–we highlight the utility of HbA1c and bodyweight changes as useful surrogates for cardiovascular and HF benefits, respectively, and also show class-specific benefits of SGLT2i beyond HbA1c or bodyweight reduction.
However, this study has several limitations. First, we did not use individual participant data, thus precluding our ability to adjust for some potential confounders. A meta-analysis performed with individual participant data could illustrate the independent effect of potential mediators of HF risk reduction, such as plasma volume, vascular resistance, and ketone bodies [59, 60, 64, 69]. Second, we did not evaluate the relationship of MACE or HF with conventional cardiovascular risks such as hypertension [70] and dyslipidemia [71] due to the limited availability of these data. Third, the included trials varied in their design, population, controls, and definitions of MACE and HF outcomes (Table 1, Additional file 1: Table S1); therefore, the pooled effects have to be interpreted with caution. However, the inclusion of a wider variety of trials, many of which represent the basis for the international clinical practice guidelines, allowed for more robust insights into the relationship between HbA1c or bodyweight change and cardiovascular outcomes. Fourth, it is essential to exercise caution in interpreting the results of the stratified meta-regression by type of intervention (Additional file 1: Tables S4 and S5), as a limited number of trials in each subgroup analysis increases the risk of overfitting and magnifies the variability of individual trial results, including any random error.
## Conclusions
The updated meta-analysis and meta-regression analysis of 35 cardiovascular outcome trials show that glycemic control conferred by a wide range of antihyperglycemic drugs decreases MACE risk in an HbA1c-dependent manner, and the degree of HbA1c reduction is a useful surrogate of cardiovascular benefits. Contrary to MACE risk reduction, HF risk modulation was not associated with HbA1c reduction but was associated with bodyweight reduction. However, high residual heterogeneity suggests the contributions of other factors. Importantly, SGLT2 inhibitors reduced the risk of HF more than could be explained by HbA1c or bodyweight reduction, highlighting the drug class-specific benefits for HF.
## Supplementary Information
Additional file 1: Table S1. Definition of heart failure and major adverse cardiovascular events of the included trials. Table S2. Excluded trials through detailed full-text assessment. Table S3. Risk of bias of the included trials. Table S4. Univariate meta-regression analyses of HbA1c reduction and the estimated log risk ratio of major adverse cardiovascular events based on intervention type. Table S5. Univariate meta-regression analyses of bodyweight change and the estimated log risk ratio of heart failure based on intervention type. Figure S1. Derivation of formula to obtain the relative risk ratio reduction of outcomes with meta-regression results. Figure S2. Funnel plots for assessing publication bias of major adverse cardiovascular events (MACE) and heart failure (HF) outcomes. Figure S3. Efficacy of antihyperglycemic therapies on the risk of major adverse cardiovascular events (MACE) in each subgroup. Figure S4. Association between the risk of major adverse cardiovascular events (MACE) and HbA1c reduction stratified by the baseline prevalence of ASCVD. Figure S5. Efficacy of antihyperglycemic therpies on the risk of heart failure (HF) in each subgroup. Figure S6. Association between heart failure (HF) risk and bodyweight change stratified by the baseline prevalence of ASCVD.
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|
---
title: The localization of molecularly distinct microglia populations to Alzheimer's
disease pathologies using QUIVER
authors:
- Ryan K. Shahidehpour
- Abraham S. Nelson
- Lydia G. Sanders
- Chloe R. Embry
- Peter T. Nelson
- Adam D. Bachstetter
journal: Acta Neuropathologica Communications
year: 2023
pmcid: PMC10024857
doi: 10.1186/s40478-023-01541-w
license: CC BY 4.0
---
# The localization of molecularly distinct microglia populations to Alzheimer's disease pathologies using QUIVER
## Abstract
New histological techniques are needed to examine protein distribution in human tissues, which can reveal cell shape and disease pathology connections. Spatial proteomics has changed the study of tumor microenvironments by identifying spatial relationships of immunomodulatory cells and proteins and contributing to the discovery of new cancer immunotherapy biomarkers. However, the fast-expanding toolkit of spatial proteomic approaches has yet to be systematically applied to investigate pathological alterations in the aging human brain in health and disease states. Moreover, post-mortem human brain tissue presents distinct technical problems due to fixation procedures and autofluorescence, which limit fluorescence methodologies. This study sought to develop a multiplex immunohistochemistry approach (visualizing the immunostain with brightfield microscopy). Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization (QUIVER) was developed to address these technical challenges. Using QUIVER, a ten-channel pseudo-fluorescent image was generated using chromogen removal and digital microscopy to identify unique molecular microglia phenotypes. Next, the study asked if the tissue environment, specifically the amyloid plaques and neurofibrillary tangles characteristic of Alzheimer's disease, has any bearing on microglia's cellular and molecular phenotypes. QUIVER allowed the visualization of five molecular microglia/macrophage phenotypes using digital pathology tools. The recognizable reactive and homeostatic microglia/macrophage phenotypes demonstrated spatial polarization towards and away from amyloid plaques, respectively. Yet, microglia morphology appearance did not always correspond to molecular phenotype. This research not only sheds light on the biology of microglia but also offers QUIVER, a new tool for examining pathological alterations in the brains of the elderly.
## Introduction
Histology on preserved human brain specimens is a robust method to visualize pathology for clinical diagnosis and experimental investigations of neuropathological changes in Alzheimer’s disease (AD). A rapidly expanding body of evidence demonstrates the central role of microglia in neurodegenerative diseases [1–4]. A consensus statement also highlighted the need to consider the importance of the brain environment when describing microglia phenotypes and not an over-simplified assessment of microglia (i.e., M1 vs. M2 or resting vs. activated) [3]. Therefore, integrating microglial cells’ morphological, molecular, and spatial phenotypes is needed to advance the field.
The recent development of spatial proteomics has revolutionized the study of tumor microenvironments in oncology by providing multidimensional single-cell (and subcellular structural) level analyses of protein expression while maintaining the spatial context of the microenvironment [5, 6]. Defining immune cell spatial interactions in the tumor microenvironment provides emerging prognostic and predictive biomarkers for cancer immunotherapy [7–10]. As the technique matures, it is predicted to be a clinically important tool for cancer and will be essential for understanding pathology in neurodegenerative diseases [5, 6].
Techniques such as flow cytometry, single-cell mass cytometry (cytof), and single-cell RNA sequencing (scRNA-seq) provide remarkable depth in characterizing cellular status for microglia and other cell types [11, 12]. However, these tissue-level techniques lose much of the single-cell level information of microglia’s interactions within the brain microenvironment and the morphological appearance of the cell. A conventional microscopy method, on the other hand, provides spatial information but does not allow visualization and quantification of cells classified according to complex phenotypic marker combinations. To advance the understanding of microglial function in the aged human brain, approaches such as the spatial proteomics used to define immune profiles in the tumor microenvironment are needed to characterize cellular inter-relationships in brains and positional proximities with neuropathological changes.
Commercial methods of spatial proteomics have been recently developed, such as Lunaphore, Akoya biosciences Phenocycler (CODEX), or NanoString’s GeoMx, which provides high-plex spatial imaging of 100+ RNA or protein markers [6, 13]. However, these methods may require expensive specialty antibodies (rare mental conjugated antibodies), specialty equipment or microscopes, and novel methodologies requiring specialized training, and limited compatibility with formalin-fixed and paraffin-embedded (FFPE) tissue. In addition, subcellular spatial proteomic methods have been described, which build on the well-established immunostaining technique [5, 6, 14]. In these techniques, an iterative approach is used if many proteins are to be visualized where the same tissue is stained, imaged, and then re-stained with these techniques using successive antibody or label detection rounds. Then computational methods are applied for image co-registration and visualization of the staining using colorimetric and fluorescence visualization methods [5, 6]. Colorimetric visualization methods are useful in postmortem human brain tissue, because of the high levels of autofluorescence in the tissue.
This project’s goal was to develop a multiplex immunostaining method that could spatially profile proteins, specifically in FFPE human brain tissue. Our focus was on the microglial cells’ molecular signature in relation to neurodegenerative disease pathology. *We* generated the Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization (QUIVER) method to overcome challenges of immunostaining in human FFPE tissue, including autofluorescence and limitation of the same primary antibody host species. With QUIVER, we sought to use conventional immunohistochemistry (IHC) techniques and equipment to expand the reach of spatial proteomics to more researchers. Within, the results of QUIVER using a nine-antibody panel provide a spatial image analysis of microglia subpopulations in the presence of amyloid and tau pathology.
## Human subjects
Human brain tissue samples comprising the superior mid-temporal gyrus (Brodmann areas $\frac{21}{22}$) were acquired from the University of Kentucky Alzheimer’s Disease Research Center (UK-ADRC) biobank [12]. Samples were formalin-fixed and paraffin embedded. All patient identifiers were removed, and investigators were blind to case information. Sex of subjects was unknown. Sections in FFPE blocks were cut on a microtome at a thickness of 6 μm and mounted on Superfrost Plus microscope slides (Fisher, 22-037-246) and incubated in a 37° C oven overnight to dry. For digital quantification and analysis, the tissue was compared between entire adjacent sections of sMTG (Figs. 1, 2, 4, and 5). To compare changes in antigenicity, eight cortical punches measuring 1 mm in diameter were taken from various cortical regions in serially sectioned TMA slides. Finally, for quantification and characterization of microglial phenotypes, four sub-regions from throughout the human sMTG were compared (Figs. 7, 8, 9).Fig. 1Comparison of two mIHC protocols for use in human FFPE tissue. ( A) The multiple interactive labeling by antibody neodeposition (MILAN) method uses β-mercaptoethanol and sodium dodecyl sulfate (βME + SDS) to strip the antibody complex. ( B) The multiplexed immunohistochemical consecutive staining on a single slide (MICSSS) uses ethanol (EtOH) to wash out the chromogen. Heat-induced epitope retrieval (HIER) is predicted to elute the antibody partially. ( C, D) GFAP and IBA1 were stained on serial sections of human FFPE brain tissue. Following the MILAN or MICSSS procedure the slide were re-imaged. ( E) $20\%$ of GFAP staining and $90\%$ of IBA1 staining were found across the entire tissue section following the MILAN method. ( F) The MICSSS method effectively reduced the re-development of GFAP and IBA1 to less than $0.1\%$Fig. 2Blocking step optimization to limit antibody cross-reactivity. ( A) Experimental workflow for antibody cross-reactivity test: [1] Indirect IHC is completed using an ethanol-soluble chromogen. [ 2] The digital slide is generated using a slide-scanning microscope. [ 3] The chromogen is removed using the chemical de-staining method. [ 4] The slide is then visually inspected to confirm the de-staining was ~ $100\%$ efficient. [ 5] Additional blocking steps are added to limit cross-reactivity. [ 6] The tissue is stained following step 1, omitting the primary antibody. [ 7] A digital slide is created, and [8] digital pathological tools are used to quantify the percentage of cross-reactivity by co-registration and direct comparison of the image from step 2 to the image in step 8. ( B) Avidin and biotin (A&B) blocking conditions were tested (Table 2). The photomicrographs show a comparison of the GFAP staining between the best A&B block condition versus the omission of the A&B blocking step. Digital neuropathological quantification of the area fraction of GFAP+ staining show that approximately $35\%$ of the staining remains if the A&B blocking step is omitted. ( C) The effect of varying the FAB blocking conditions (Table 2) were tested while holding the best A&B blocking condition from B constant. A high degree ($46\%$) of GFAP+ staining was seen in samples lacking the FAB blocking step following re-staining whit the secondary antibody alone. By area fraction digital quantification, the degree of re-development of the GFAP staining was further blocked beyond the optimized A&B conditions using a higher concentration of FAB a incubating the samples in a greater volume of the blocking solution
## Immunohistochemistry
Sections were deparaffinized and rehydrated in xylene followed by decreasing concentrations of alcohol. Heat-induced antigen retrieval (HEIR) was completed on tissue using pre-warmed citrate buffer (Diva Decloaker (10×), BIOCARE Medical) in a microwave (6 min (power setting 3, 500 watts) followed by cooling for 15 min. Once sections were cooled, they were rinsed in running water. Following antigen retrieval, tissue was permeabilized using $0.4\%$ triton- × 100 for 30 min. Endogenous peroxidases were then quenched in $0.3\%$ H2O2 in methanol for 30 min. Tissue sections were then incubated for 60 min in blocking buffer ($10\%$ normal goat serum, with $0.2\%$ Triton-X 100, in tris buffer saline) at room temperature prior to antibody addition. Sections were incubated in primary antibody overnight in 4 °C. Antibody list may be found in Table 1. A biotinylated secondary antibody specific to each host species was amplified using an avidin–biotin substrate (ABC solution, Vector Laboratories, catalog no. PK-6100), followed by color development in ImmPACT® AMEC Red Substrate (Vector Laboratories, SK-4285). Stained sections were counterstained in hematoxylin and covered using aqueous mounting media (VectaMount® AQ Aqueous Mounting Medium, H-5501-60, (Vector Laboratories). Throughout the staining process, tissue was stained using the Sequenza staining rack (Thermo Fisher #73310017)—a device that uses a capillary action to irrigate mounted sections. For each wash and incubation step, 1 ml of reagent was added to the slide. During the blocking steps, in addition to the Sequenza, slides were also fully submerged in a lockmailer jar that contained 12 ml of reagent and put on a shaker during incubation steps. Table 1Antibodies used during multiplex stainingAntibodyRRIDManufacturerCat. numberHost speciesConcentrationP2Y12AB_2669027AtlasHPA014518Rabbit$\frac{1}{500}$TMEM119AB_2681645AtlasHPA051870Rabbit$\frac{1}{500}$FerritinAB_259622Thermo fisherF5012-2MLRabbit$\frac{1}{1}$,500CD45AB_2750582Thermo fisherM070101-2Mouse$\frac{1}{200}$CD68AB_2661840AgilentGA609Mouse$\frac{1}{100}$Iba1AB_2493179Synaptic systems234–004Guinea Pig$\frac{1}{1}$,000GFAPAB_10013382DakoZ0334Rabbit$\frac{1}{5}$,000PHF-1–Gift from Dr. Peter DaviesMouse$\frac{1}{500}$AβAB_2533317Thermo fisher37–4200Mouse$\frac{1}{10}$,000
## Antibody stripping
To also test the ability to remove primary antibodies from tissue, antigen removal using β-mercaptoethanol/sodium dodecyl sulfate (βME + SDS) was completed as published [15, 16]. Briefly, βME + SDS removal was performed by mixing 20 ml $10\%$ SDS with 12.5 ml 0.5 M Tris–HCl, pH 6.8, and 67.5 ml ultra-pure water. 0.8 ml of βME was added to solution and sections incubated in pre-warmed βME + SDS on shaker for 30 min. Sections were then washed in diH2O followed by TBS-T washes and standard blocking steps before immunostaining.
## Chromogen removal
Tissue was stained using the ABC indirect method and developed using the ImmPACT AMEC Red Substrate kit (Vector Laboratories). Following staining and slide scanning at 20× magnification, the coverslips were removed in water and residual mounting medium was removed by washing sections in 1× PBS for 3× 10 min. To wash out the ImmPACT AMEC Red chromogen (Vector Laboratories), sections were incubated in $50\%$ ethanol (1×, 2 min), $70\%$ ethanol (with $1\%$ HCL) (1×, 2 min), $100\%$ ethanol (1×, 5 min), $70\%$ ethanol (1×, 3 min), and running water (5 min). Following these steps, a coverslip was placed over the rehydrated section and the chromogen removal was evaluated at 20× magnification using a brightfield microscope to confirm a lack of residual staining. The coverslip was floated off the section in PBS and staining protocol commenced.
## Cyclic multiplex immunohistochemistry
Iterative rounds of immunostaining started with the first round of immunohistochemistry, as described above. After the microscope slide scanning, the coverslip was removed by soaking the slide in water, typically overnight on a shaker, until the coverslip fell off. Next, the chromogen was removed as described above. Heat-induced antigen retrieval was then completed as described previously, followed by a series of blocking steps. First, the slides were incubated for 30 min in $0.4\%$ Triton-X 100. Then endogenous peroxidases were quenched with $0.3\%$ H2O2 in methanol for 30 min. The samples were then incubated for 60 min in blocking buffer ($10\%$ normal goat serum, with $0.2\%$ Triton-X 100, in tris buffer saline) at room temperature. Residual avidin and biotin was blocked by incubating in avidin (Vector Laboratories) diluted in blocking buffer for 1 h, washed three times in blocking buffer, and then incubated in biotin (Vector Laboratories) diluted in blocking buffer for 1 h. If the same species primary antibody was used, the samples were incubated in fragment antigen-binding region (FAB) (Jackson Immuno Research labs) overnight at 4 °C. For the remaining steps, we used the immunohistochemistry protocol described above. For each round of staining a no primary antibody control and positive control samples were included.
## Slide scanning and image registration
A Zeiss Axio Scan Z.1 slide scanner was used to image the slide in its entirety at 20× magnification creating a single high-resolution image. After scanning, chromogenic images were loaded into HALO software (Indica labs, version 3.4), and stains were registered and deconvolved to make a single pseudo-fluorescent image. This was achieved by separating the chromogenic stain from the hematoxylin stain using the HALO deconvolution algorithm, which uses color selection and thresholding to create a single-channel image. Then using the HALO Serial Registration module, the multiple rounds of staining were merged into a pseudo-fluorescent image.
## Digital pathological investigations
The object colocalization algorithm in HALO version 3.4 was used to quantify colocalization and the number of cells in stained tissue. By thresholding, the pseudo-fluorescent image for each channel, the Object Colocalization module calculated the number of cells. The computer-generated markup image was used to confirm the specificity of the algorithm. The HALO proximity analysis was used to determine the distance of microglia from Aβ plaques and PHF1+ cell bodies. A size exclusion of 100 μm was used when defining the Aβ plaques and PHF1+ cell bodies to avoid detecting small diffuse Aβ or PHF1+ neurites.
## Results
Two promising methods of iterative colorimetric multiplex IHC (mIHC) were previously described [9, 15]. The multiple iterative labeling by antibody neodeposition (MILAN) method used a striping technique to elute the primary antibody before re-staining using β-mercaptoethanol and sodium dodecyl sulfate (βME + SDS) (Fig. 1A). Removal of the staining antibody complex is an advantage of the MILAN technique, as this would allow for successive rounds of staining with an antibody raised in the same species and eliminates concerns regarding steric hindrance [15]. However, the βME + SDS used to strip the antibody may damage antigenicity for future rounds of staining. In contrast, the multiplexed immunohistochemical consecutive staining on a single slide (MICSSS) uses a method that involves washing out the chromogen using ethanol (EtOH) without antibody elution (Fig. 1B) [9]. Between each round of MICSSS protocol, heat-induced antigen (epitope) retrieval (HIER) is completed, which may partially elute the antibody. Yet, it is known to be ineffective at fully stripping the antibody complex [9]. Thus, the MICSSS approach has a potential limitation of cross-reactivity when the antibody is raised in the same species used in consecutive rounds of staining. While steric hindrance remains a concern with MICSSS, prior evidence suggest that it is uncommon, and may provide useful information regarding neighboring epitopes [9].
Our first approach was to directly compare the MILAN vs. the MICSSS protocols, as neither method has been used in human brain FFPE tissue. We used two antibodies known for robust and reproducible staining in human brain FFPE tissue. The first antibody targets glial fibrillary acidic protein (GFAP), a protein highly expressed by astrocytes [17]. The second antibody targets ionized calcium binding adaptor molecule 1 (IBA1), a pan microglia/macrophage marker [18]. Given the widespread pattern of staining throughout the brain and that staining for both markers is increased with ADNC, we anticipated that these two markers would challenge the two de-staining methods.
To test the MILAN vs. MICSSS protocols serial sections of FFPE human brain tissue were stained with GFAP or IBA1 and imaged using slide scanning microscopy (Fig. 1C, D). The MILAN protocol uses βME + SDS to strip the antibody complex and not soluble chromogen stain. However, we found that after the MILAN protocol ~ $20\%$ of the GFAP staining and ~ $90\%$ of IBA1 staining was still present (Fig. 1E), showing limited efficiency of MILAN protocol for removal of the antigen/antibody complex in human brain sections. In contrast, the MICSSS method which uses an ethanol soluble chromogen was highly effective at with less than $1\%$ of the original stain present after EtOH-mediated chromogen removal (Fig. 1F).
## Refinement of MICSSS blocking steps for use in human brain FFPE tissue
The MICSSS was selected as the framework for our mIHC protocol. An important first step was addressing cross-reactivity issues when the same species-antibodies are used in consecutive rounds of staining. To minimize cross-reactivity in the mIHC protocol, we used an iterative process of staining with rabbit anti-GFAP, de-staining following the MICSSS protocol, and re-staining while omitting the primary antibody and using the same anti-rabbit secondary antibody (Fig. 2). Determining how much staining was present at the end of this procedure would indicate potential cross-reactivity if antibodies raised in the same host were used in successive rounds of staining.
First, we began by optimization of avidin and biotin (A&B) blocking. The omission of the A&B blocking step resulted in extensive (~ $35\%$) cross-reactivity (Fig. 2A). By digital quantification, the area of staining that remained when A&B blocking was less than $1\%$. However, we found that the blocking was not uniform across the tissue. More cross-reactivity was seen in areas with very intense GFAP staining, which is problematic as reactive cells could be incorrectly phenotyped because of varied expression patterns of antibody staining. Therefore, we further optimized the A&B blocking step by varying the concentration of A&B in the blocking solution, increasing the volume of the blocking solution, and increasing the incubation times (Table 2). With the most rigorous A&B blocking method, we observed the greatest degree of blocking and minimal cross-reactivity. However, by the digital pathological quantification, all the conditions were equally effective (Fig. 2A, Table 2).Table 2Blocking step optimizationTrialA&B (drops/ml)DeviceTime (min)FAB (ug/ml)DeviceTime (hr)Area fraction (%)Qualitative evaluation10––20S134.305++++ 24S1520S10.066+36L1520S10.009+46S6020S10.001–56L1520S10.005+66L6020S10.003–710S1520S10.245+++86S600––46.380++++96S6020S10.004+106S6040S10.007+116S6040S180.003–126S6040L10.006+136S6040L180.009–146S6060S10.098+156S60100S10.133++During the incubations slides were in either Sequenza (S) or lockmailer (L) device. The HALO area fraction module was used to determine the % of stained tissue. An observe blind to the experimental conditions also rated the slides as robust residual staining (++++), some cells present throughout the tissue (+++), a few cells present in select regions (++), faint profiles for staining still observable (+), or no observable staining (–) Next, we optimized the fragment antigen-binding region (FAB) blocking step. The omission of the FAB blocking step resulted in ~ $46\%$ remaining cross-reactivity (Fig. 2B). As with A&B blocking, increasing concentrations of FAB increased blocking efficacy up to a concentration of 40 mg/ml (Table 2). When assessed with unbiased digital methods (HALO), there were few differences between blocking methods, regardless of incubation time or device used. However, visually lightly stained cells were present with shorter incubation and low reagent volume compared to the longer incubation times and higher volume reagents.
## Test of refined MICSSS protocol on antigenicity in human FFPE brain tissue
Remark et al. [ 9] previously showed, using serial sections of FFPE colorectal tumor tissue, that following even seven rounds of de-staining, there was no observable loss of antigenicity. Moreover, they found that they could stain for up to four macrophage markers with no observable steric hindrance. We tested if brain tissue and microglia markers would also be resistant to loss of antigenicity from the de-staining procedure or steric hindrance. Using serial sections from a tissue microarray (TMA) of human brain FFPE tissue, we stained the tissue with microglia markers IBA1 and P2Y12. While IBA1 is a pan-marker of microglia and macrophages, P2Y12 is a marker of homeostatic microglia. Therefore, we anticipated that most cells would be double positive for P2Y12 and IBA1. However, some fraction of the IBA1 positive cells should be P2Y12 negative. TMA slides were stained with IBA1 or P2Y12, followed by MICSSS, and then stained with the other antibody (Fig. 3A). As predicted, most of the stained cells were double positive for IBA1 and P2Y12. However, regardless if IBA1 was the first stain or second stain in the series, a fraction of the IBA1 positive cells were P2Y12 negative (Fig. 3A). Digital quantification of the number of cells using the object colocalization algorithm (HALO) showed variability in the number of IBA1 cells in the TMA brain sections from the ten different individuals, which would be expected as the cases had different degrees of pathology. However, for the same case, the number of IBA1 positive cells was steady between the first and second round of antibody staining (Fig. 3B). The P2Y12 antibody also stained a steady number of cells between rounds of staining (Fig. 3C), and a paired t-test showed no statical difference for IBA1 (Fig. 3B) or P2Y12 (Fig. 3C). These results provide additional evidence that the MICSSS protocol can work in brain FFPE tissue without loss of antigenicity. Fig. 3Effects of mIHC protocol on antigenicity following repeated rounds of staining. ( A) Serial sections of a Tissue microarray containing human brain samples from individuals with ADRC-NC were stained with rabbit-anti-P2Y12 or guinea pig-anti-IBA1 for the first round (1°) of staining. Digitalizing the side was followed by the refined MICSSS protocol and a second (2°) round of staining. Arrows indicated IBA1+P2Y12– cells. The number of cells in each case on the TMA was quantified using the object colocalization algorithm (HALO 3.4). By a paired t-test, no statistical difference was seen for between 1° or 2° rounds of staining the number of IBA1+ cells (B) or P2Y12+ cells
## Spatial relationship of microglia to Aβ plaques and PHF-1+ tangles using multiplexed single-cell analysis
AD lesions, including neurofibrillary tangles (NFT) comprised of abnormally phosphorylated tau protein and extracellular plaques containing amyloid-beta (Aβ) proteins, provide an excellent test to define the heterogeneity of microglia populations using histo-cytometry. We defined a panel of nine antibodies to test the spatial heterogeneity of microglia in relation to Aβ plaques and PHF-1-positive NFTs (Fig. 4). We showed that each antibody could each be de-stained and the cross-reactive was effectively blocked using the refined MICSSS protocol. Moreover, there was no apparent loss of antigenicity from the first to the ninth round of staining (Fig. 5).Fig. 4Panel of antibodies used from mIHC. FFPE human brain tissue was stained with antibodies used for the mIHC. The primary stain (1°) is shown, along with the image of the same section following the MICSSS de-staining, and then re-staining omitting the primary antibody. The order of antibody from left to right shows the order used on the mIHC panel. Using a HALO Area Quantification algorithm across the entire tissue section found, less than $0.03\%$ of the primary stain, or other background noise, was detected in the de-stained and redeveloped tissue for all markers. Scale bar = 50 μmFig. 5Comparison of single and sequential histological staining in glia-associated markers. Representative photomicrographs in similar regions from neighboring FFPE sections of the same sMTG tissue block, show comparative staining for the selected glial-associated stains. During each round of staining, tissue was stained alone as a positive control and sequentially using the multiplex staining method to show there is little to-no loss in antigenicity or stainability in subsequent rounds of staining. The difference in the area quantification of staining between the multiplex vs. single stain was $0.5\%$ or less. The single stain and multiplex stain analysis was done on serial sections of the same sMTG tissue block; however, there may be a separation of up to 100 μm in the z dimension between sections. Photomicrographs were captured at × 10 magnification. Scalebar is 50 μm After validating that the antibodies in the panel could be effectively de-stained and antibody cross-reactivity could be blocked, we deconvolved the multiplex slide and registered the images using HALO software. Figure 5 shows the results of the spatially registered multiplex image. PHF-1 and Aβ were the last two stains completed in the multiplex protocol and clearly show microstructures with the expected morphological appearance of tangles and plaques, respectively (Fig. 6A−B).Fig. 6QUIVER image registry and cell identification. ( A) Pseudocolored images were created from deconvolved single-channel IHC images. ( B) The images were aligned using HALO software and generating a ten-channel image (nine antibodies and hematoxylin). ( C) Cell/object count data, including marker co-expression, were generated using the object colocalization algorithm. Label colors coincide with representative color in markup image and merged image. Yellow dashed oval highlights a representative amyloid plaques and pink doted oval highlights a representative of tau tangles shown in all images The multiplex panel included six well-characterized antibodies representing different microglia/macrophage functional states. IBA1 (Fig. 6A) was included as a pan microglia/macrophage marker, although there are reports of IBA1-negative microglia [19, 20]. In this study, we used CD45 (Fig. 6A) to identify reactive microglia and macrophages. However, CD45 is a pan leukocyte marker, thus, is not specific to microglia/macrophages. P2Y12 (Fig. 6A) and TMEM119 (Fig. 6A) were included as markers for homeostatic microglia [21]. CD68 (Fig. 6A) and ferritin (Fig. 6A) were included as functional state markers associated with phagocytosis and iron storage, respectively [21]. Finally, GFAP (Fig. 6A) was included as a control marker to ensure that microglia/macrophage-marker-positive cells were not GFAP-positive. The merged imaged (Fig. 6B) illustrates the excellent registration of nine digital slides.
The next step in the spatial analysis workflow was to create histo-cytometric counts of the cells/objects in the mIHC image. *To* generate object counts, we used the Halo object colocalization algorithm to generate object counts (Fig. 6C). Size exclusions (40μm2) were included, so the algorithm only counted larger-size objects (i.e., cell bodies and plaques) to avoid counting PHF-1+ neurites and oblique cuts of the microglia process as a cell/object. Figure 6C shows the algorithm's results and highlights the heterogeneity in microglia in the human brain tissue.
Five unique IBA1+ microglia/macrophage phenotypes based on antibody marker expression were identified. The most prevalent cell type was the IBA1+ cells that express the homeostatic microglia marker P2Y12 (Fig. 7A, B). Somewhat unexpectedly, the second most abundant phenotype was cells expressing only IBA1 and none of the other microglia/macrophage markers (Fig. 7A, C). Finally, cells that expressed markers of macrophage/reactive microglia accounted for approximately a third of all IBA1+ cells. Interestingly, this third of the IBA1+ cells could be subdivided into the cells that expressed ferritin (Fig. 7A, D), ferritin and CD68 (Fig. 7A, E), and only CD68 (Fig. 7A, F).Fig. 7Identifying of microglia/macrophage phenotypes in human FFPE brain tissue using QUIVER. ( A) Microglia/macrophages were grouped into one of five phenotypes based on unique marker expression. Human gray matter was analyzed to determine how many cells showed those phenotypes on average using the HALO object colocalization algorithm. ( B–F) The pseudo-fluorescent images were created from deconvolved single-channel IHC images. IBA1 (yellow), P2Y12 (green), ferritin (blue), and CD68 (magenta) were included in the pseudo-fluorescent images. The white box indicates a cell that expressed the different marker classes, as determined by the HALO object colocalization algorithm. The other cells in the micrograph may not share the same cell phenotype. The original brightfield image of the IBA1 IHC is shown for the cell highlighted in the box to highlight differences and similarities in the IBA1+ cellular morphology among the molecular distinct microglia/macrophage phenotypes. Scale bar = 25 μm TMEM119 and CD45 were found not useful markers for identifying molecularly unique microglia populations. Most TMEM119+ cells were also P2Y12+, but not all the P2Y12+ cells expressed TMEM119. In contrast, CD45 was present in most of the IBA1+ cells. However, the level of CD45 expression was low on P2Y12+ cells and high on amyloid-associated microglia. Including CD45 low vs. high did not help define a unique microglia population.
We next asked if the spatial distribution of the five microglia/macrophage phenotypes was a function of the cell’s proximity to a PHF-1+ cell or an amyloid plaque. We used the HALO proximity analysis algorithm to determine the spatial distribution of the five microglia/macrophage phenotypes within a 100 μm radius from the PHF-1+ cell or Aβ+ plaque. As Aβ plaques and tangles are often in proximity to another plaque or tangle, the 100 μm radius was set to try to limit spatial overlap. The lowest density of microglia/macrophages occurred nearest to the PHF-1+ cell (Fig. 8A). Nearest to the PHF-1+ cell, few cells expressed homeostatic microglia markers (TMEM119 and P2Y12) (Fig. 8B). In contrast, most cells closest to the tangle expressed all the macrophage/reactive microglia markers (CD68 and ferritin) (Fig. 8B). Macrophage/microglia populations immunoreactive for IBA1 and Ferritin; IBA1, Ferritin, and CD68; IBA1 and Cd68 (further described as reactive macrophage/microglia phenotypes) showed little change in the number of cells at different distances from the PHF-1+ cells (Fig. 8C). In contrast, microglia not expressing CD68 and ferritin dramatically increased at a greater distance away from the PHF-1+ cells (Fig. 8C).Fig. 8Digital proximity analysis of microglia/macrophage phenotypes to PHF-1+ tangles and Aβ+ plaques. ( A) The Halo software generated markup shows the few cellular profiles within 60 μm of the PHF-1+ cell. ( B) The Halo proximity analysis defined the relative percentage of the five microglia/macrophage phenotypes at each distance interval from the PHF-1+ cell. ( C) The average number of cells at the distance intervals away from the PHF-1+ cell. ( D) Near an Aβ+, plaque there is a high density of cells. ( E) Nearest the plaque the IBA1 + Ferritin + CD68+ cell, and the IBA1+ phenotypes account for the majority of the plaque-associated cells. ( F) The number of microglia/macrophage at the distance interval shows the polarization of marker expression that occurs around 50 μm from the plaque. The results are for 942 and 890 IBA1+ cells for figures A-C and D-F, respectively While cell density was lowest near the PHF-1+ cells, Microglia/macrophages were at the highest density around amyloid-plaques, and the density decreased nearly proportional to the distance from the plaque (Fig. 8D). Nearest the plaque, a high percentage of the cells expressed CD68 and ferritin (Fig. 8D). The increase in cells expressing the reactive markers, were proportional to the decline in homeostatic microglia (Fig. 8D). Interestingly, while few cells expressed only the homeostatic microglia markers within 40 μm of a plaque, this population rebounded beyond 60 μm from the plaque (Fig. 8F).
While the IBA1+Ferritin+CD68+ cells were most strongly associated with Aβ plaques, the IBA1+CD68+ cells did not have a clear spatial preference for plaque or tangle pathology. Therefore, we visually inspected each of the IBA1+CD68+ and IBA1+Ferritin+CD68+ using the mIHC image and the co-registered IBA1 brightfield IHC image (Fig. 8).
In agreement with the proximity analysis, IBA1+CD68+ cells (Fig. 9A) and the IBA1+Ferritin+CD68+ cells (Fig. 9B) were often associated with Aβ plaques. The IBA1+Ferritin+CD68+ cells were found touching an Aβ plaque $76\%$ of the time, while $40\%$ of the time, the IBA1+CD68+ cells were touching an Aβ plaque. IBA1 morphology was remarkably similar between these two cell types, showing both small cell bodies and thin branches (Fig. 9A, B).Fig. 9Spatial characterization of molecularly distinct microglia/macrophage phenotypes in relation to pathology and vascular profiles. A representative example of the nine-color multiplex IHC and single-color IBA1 IHC for IBA1+CD68+ (A, C, E) and IBA1+Ferritin+CD68+ (B, D, F) cells associated with Aβ plaques (A, B) non-plaque or PHF-1 associated (C, D), or vascular associated (E, F). The percent of the cells associated with pathology, non-pathology, or blood vessels is indicated on the image. A total of 1382 IBA1+ cells were included in the analysis A subset of the IBA1+CD68+ cells (Fig. 9C) and the IBA1+Ferritin+CD68+ cells (Fig. 9D) were not adjacent to Aβ plaques, or PHF-1 staining. Approximately half ($44\%$) of the IBA1+CD68+ cells were not pathology associated. However, it was rare ($12\%$) to find IBA1+Ferritin+CD68+ cells not near a plaque or tangle. Morphologically, there was no distinction between the two cell phenotypes, providing further evidence that morphology alone misses molecularly distinct microglia phenotypes.
We discovered that a significant proportion of plaque-associated and plaque-unassociated cells showed tight connections with vascular profiles during inspection. Therefore, we next counted the number of vascular-associated cells. $50\%$ of IBA1+CD68+ cells (Fig. 9E) and $40\%$ of IBA1+Ferritin+CD68+ cells (Fig. 9D) were border-associated macrophages.
## Discussion
Within, we report a new tool called QUIVER (Quantitative multiplex Immunohistochemistry with Visual colorimetric staining to Enhance Regional protein localization). With QUIVER, we sought to use conventional immunohistochemistry (IHC) techniques and digital pathological tools to expand the reach of spatial proteomics to more neuroscience researchers. We detail a multiplex IHC strategy using FFPE-preserved human brain tissue with neurodegenerative pathology. This technique successfully distinguishes between five subsets of microglia/macrophages by antibody marker expression and describes the relationship between these five phenotypes and amyloid plaques, NFT pathology and vasculature. We demonstrated a plausible transitional stage in IBA1+ cells, which lack homeostatic microglia but do not express the reactive markers CD68, and ferritin. A subset of microglia/macrophages expressing CD45, and ferritin were also identified, primarily associated with plaques. Finally, the work demonstrated that morphology did not strongly associate with molecular phenotypes, providing further evidence microglia morphology provides complementary information regarding cell stated that is distinct from molecular phenotype. These results demonstrate this technique’s power but are only a starting point. The multiplex IHC tool can move beyond defining well-known cell-type specific markers and evaluate pathways shared by multiple cells in the brain microenvironment. An example would be looking at the activation of signal transduction pathways in multiple kinds of cells. Although microglia were the primary focus of this investigation, the tools we report can be applied to any cell or protein target, including the interaction of multiple misfolded proteins within one cell.
Human brain tissues (particularly if extensive adjunct data are available) provides a unique resource that enables hypotheses to be tested that are directly relevant to clinical disease. Although FFPE-preserved tissue is the most widely available human biobanked tissue, it is not an unlimited resource. Therefore, identifying methods that can maximize the knowledge gained, while using the least possible amount of tissue, will have an outsized impact on expanding access to these valuable scientific specimens. Therefore, we set out to make a multiplexed chromogen-based IHC staining assay that can be easily used in most labs to meet the need for high-dimensional analysis of microglia in the context of neurodegenerative disease pathology. QUIVER uses digital pathology tools and does not require any extra equipment for staining or imaging. This makes it comparable in terms of cost and quality as standard IHC. These advancements will make it easier to categorize microglia into different phenotypes and providing additional levels to the microglia analysis in studies of neurodegenerative diseases and beyond.
One of the biggest challenges of any multiplex immunostaining of postmortem human FFPE brain tissue, particularly that from individuals that suffered an acute brain injury or from older individuals, is extensive autofluorescence. For example, lipofuscin, a normally occurring autofluorescent lipopigment, emission spectrum presents in a wide range of wavelengths, ranging from 400 to 700 nm thus interfering with the most commonly used fluorescent wavelengths [22–24]. While there are a number of reagents available (such as, sudan black or true black), that can quench the autofluorescence signal, the quenching is often incomplete. Lipofuscin autofluorescence is particularly problematic when the real staining is expected to be intracellular, as lipofuscin can erroneously be included as the antibody specific staining. For proteins of low abundance, it is often difficult to amplify the immunofluorescence signal over tissue background autofluorescence, even with the use of autofluorescence blocking reagents, and methods to amplify immunofluorescence staining, including the tyramide signal amplification system [25]. Finally, computational methods provide additional way to overcome autofluorescence signal [26]. Immunofluorescence staining is also not the standard method used in clinical pathology. QUIVER was developed as an approach that could use chromogen-based multiplexing to overcome many of these limitations.
A second major potential limitation of high-plex immunostaining methods is the limited number of donor host species for the primary antibody. Often, this means using suboptimal or less well-validated antibodies to avoid the same primary host antibody interaction. Alternatively, using primary antibodies directly conjugated to a reporter molecule is a common approach used in multiplex immune-staining methods to overcome the same primary antibody host limitation. Directly conjugated antibodies are widely available and used extensively (for example) in flow cytometry. It is, however, common for antibodies optimized for flow cytometry to be incompatible with FFPE tissue. Custom labeling antibodies is costly and requires a significant amount of starting material. Optimization would need to be completed on the custom antibody to determine the concentration needed for the staining. Alternatively, chemical antibody elution provides a method to remove the antibody, so an additional round of antibody staining can be applied. Our attempts at antibody stripping using the MILAN method were not successful. Some antibody elution is predicted by the HIER in the MICSSS method. However, we still found robust redevelopment if we did not include FAB or A&B blocking steps in the protocol, suggesting that the extent of antibody elution following HIER is minor.
Our research allowed using multiple same-species antibodies on tissue by blocking cross-reactivity with FAB and A&B blocking reagents at saturating concentrations. We also found that adjusting concentration, volume, and time of blocking reagents was critical to eliminate cross-reactivity. Also important is validating the lack of cross-reactivity in positive control tissue with robust / maximum expected expression of the antigen. After establishing our FAB and A&B blocking conditions on the anti-GFAP antibody, we found the same conditions were effective for all of our other stains. However, we also found for antibodies that did not stain as strongly as anti-GFAP, shorter blocking time and lower blocking reagent concentrations were also effective. Therefore, it is possible to save money and time by optimizing the blocking conditions of each antibody.
When designing the multiplex panel, it is important to consider the order that antibodies are applied. Steric hindrance and loss of antigenicity following multiple rounds of staining are possibilities. However, we and others, did not find loss of staining because of steric hindrance or loss of antigenicity. The most important consideration for the order of antibodies is any specialized antigen retrieval step. For instance, we completed the amyloid staining last, as we found the formic acid treatment used in the protocol destroyed the antigen for subsequent microglia membrane proteins. Therefore, confirming the compatibility of antigen retrieval step is critical for an effective multiplex IHC experiment.
Even though we specifically avoided immunofluorescence staining in the present set of experiments, others have described multiplex immunofluorescence methods using human FFPE tissue [27, 28]. Immunofluorescence staining is advantageous as it can drastically reduce the number of rounds of staining. The PICASSO method of ultra-multiplexed fluorescence imaging used spectral unmixing. In only three rounds of iterative staining, this approach achieved a remarkable 45-color image of the mouse brain [29]. However, the current version of the PICASSO method is computationally demanding, making it difficult to incorporate into most laboratories. In contrast, the iterative bleaching extends multiplexity (IBEX) method uses an approach of chemical bleaching of the fluorophore using LiBH4 [30, 31], similar to our methods making the transition between the mIHC and IBEX workflow seamless. A mixed approach of mIHC and IBEX- immunofluorescence could be particularly useful to increase throughput, where IBEX- immunofluorescence could be used for highly expressed antigens, while mIHC could be used for low expressed proteins that may masked by autofluorescence.
In addition to standard methods and tools that are widely available in many labs, this study used the HALO imaging suite as a unified tool for spatial analysis, visualization, and image registration. This proprietary software package's strength is its intuitive graphical user interface, which can be used by anyone, regardless of prior experience with programming. The HALO software environment is used currently in many basic sciences and clinical pathology labs. However, spatial proteomics is a rapidly developing field. Over twenty open-source imaging programs have been reported in the past five years. CellProfiler [32], histoCAT [33], CytoMAP [34], QuPath [35], and many others are examples of such programs. As a result, these rapidly developing open-source tools are driving innovations in spatial analysis. At the same time, proprietary software suites like HALO will likely need to continuously catch up in adopting the most recent advancements. Yet, while open-source software has driven innovation in genomics, it has created a bottleneck in data analysis.
In conclusion, we report a method for a refined multiplex IHC technique that can be used in biobanked human FFPE tissue. Using HALO digital pathological tools, we show the potential of spatial analysis to define unique subsets of microglia, which can be defined by proximity to pathology and marker expression, but not necessarily by the cellular morphology. The QUIVER will be a tool useful for better understanding the biologic implications of both the microglia transcriptomic data and the single-cell proteomic data. Through the use of conventional, low-cost reagents, this QUIVER may be broadly useful for the neuroscience community.
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|
---
title: 'Evaluation of trabecular changes following advancement genioplasty combined
with or without bilateral sagittal split osteotomy by fractal analysis: a retrospective
cohort study'
authors:
- Gökhan Çoban
- Taner Öztürk
- Süheyb Bilge
- Emin Murat Canger
- Ahmet Emin Demirbaş
journal: BMC Oral Health
year: 2023
pmcid: PMC10024858
doi: 10.1186/s12903-023-02860-z
license: CC BY 4.0
---
# Evaluation of trabecular changes following advancement genioplasty combined with or without bilateral sagittal split osteotomy by fractal analysis: a retrospective cohort study
## Abstract
### Background
It is aimed to investigate whether there was a difference in radiographic changes in the operational areas between genioplasty alone and genioplasty combined with mandibular advancement and to evaluate the fractal dimension (FD) to assess trabecular changes after genioplasty surgery.
### Methods
Preoperative-(T0) and postoperative-(T1) panoramic radiographs of 26 patients without any complications who underwent genioplasty combined with bilateral sagittal osteotomy and mandibular advancement or genioplasty alone were selected. In the panoramic radiographs of both groups, the genial segment, mandibular angulus, and surgical osteotomy line were examined using FD. The box-counting method was used for FD evaluation.
### Results
It was determined that FD values before and after treatment were similar in both groups for all regions where measurements were made. After surgery, the FD values of the middle region of the genial segment were found to be significantly lower than the other regions. At T1, the FD values at the osteotomy area were found to be significantly higher than those in the middle region of the genial segment.
### Conclusion
Trabecular structure does not differ in patients undergoing genioplasty alone or in combination with mandibular advancement osteotomy. The middle region of the genial segment heals later than other regions.
## Background
Complex geometric structures consisting of curves, points, and surfaces that cannot be defined by shapes (such as square, triangle, or circle) that have the property of appearing independently of each other are called fractals [1, 2]. The method that expresses these structures numerically (fractal dimension; FD) and reveals the structure complexity is fractal analysis [3]. It has previously been described that FD increases with increasing complexity and decreases with decreasing complexity of an object or structure [4, 5]. Although there are various methods to calculate the fractal size of a complex structure with fractal analysis, which is easily accessible, simple to implement, and not affected by projection geometry or radiological features, currently, the most used method is the box-counting method [1, 3, 4]. Notably, the human body can be considered a fractal structure that is like itself and consists of complex structures such as bronchial tubes, blood vessels, brain folds, and bone structures [6–8]. In recent years, this method has become very common in the examination of bone structures in the medical field, and especially in dentistry, in which it is used to reveal the trabecular structure in the jaw bones and evaluate the changes [8, 9]. In the field of dentistry, FD examinations are frequently used in problems that cause changes or differences in the structure of the alveolar bone, such as periapical lesions, bruxism, implant surgery, temporomandibular joint dysfunctions, orthognathic surgical treatments, and periodontitis [4–6, 10–15]. The density of the trabecular bone, where metabolic activity can be observed more clearly compared to the cortical bone, and the arrangement of the trabeculae show the mechanical properties of the bone. As the trabeculae in the structure increases, the complexity increases, and FD values follow this [5–16].
Skeletal class II malocclusions, which are mostly characterized by the mandibular retrognathia, cause functional problems such as aesthetic, respiratory and occlusion, and can be treated with various appliances and surgical-orthodontic procedures [17, 18]. There are various (surgical or non-surgical) methods for the correction of skeletal structures in disharmonic patients that affect the dentofacial appearance and functions (breath, mastication etc.) [ 6, 12, 19–21]. In individuals who do not have mandibular asymmetry or position problems, genioplasty alone is applied to adapt the chin projection to the facial profile for aesthetic reasons [20]. Mandibular advancement with bilateral sagittal split osteotomy (BSSO) is applied to improve dentofacial aesthetics in adults with a skeletal class II relationship originating from mandibular retrognathia. In addition, depending on the patient’s aesthetic complaint, mandibular advancement with BSSO can be combined with genioplasty simultaneously [22]. Clinical signs, which are often subjective, are used to evaluate the bone healing process after orthognathic surgical treatment [12]. Radiography methods can be used for objective evaluation, as stated in a few studies [6, 12, 15]. In cases in which the genial area is taken forward with genioplasty, there is very little improvement in recovery. In the 1988 report by Storum et al. in which monkeys were studied, it was shown that an improvement in the osteotomy line started in the fourth week [23]. Gianni et al., on the other hand, examined the neurosensory change in individuals who had only genioplasty or had genioplasty combined with BSSO and reported that the combined application had a more negative effect on recovery [24]. However, there are still uncertainties about bone healing.
The aim of this study is to examine the radiographic changes in the cancellous bone in the osteotomy line of the genial region (chin) after the genioplasty procedure, which is surgically taken forward in different vertical dimensions with or without mandibular orthognathic surgery (BSSO), and to evaluate them quantitatively by fractal analysis to compare them with other regions of the mandible.
## Sample
This retrospective cohort study was approved by the Erciyes University Clinical Research Ethics Committee prior to initiation (Approval code: $\frac{2020}{485}$; Date: $\frac{23}{09}$/2020). Panoramic and cephalometric radiographs with treatment information data from 26 patients who underwent genioplasty for chin correction at the Erciyes University Faculty of Dentistry between January 2016 and January 2020 (18 women (29.94 ± 5.82 years) and 8 men (31.37 ± 11.90 years)) were included in the study (Table 1).
Table 1Demographic characteristicsFemale [N (%)]Male [N (%)]Total [N (%)]Gender18 (69.2)8 (30.8)26 (100.0)Mean Age (year)29.94 + 5.8231.37 + 11.9028.31 + 8.18Movement TypesOnly Forward [N (%)]Forward and Upward [N (%)]Forward and Downward [N (%)]Total8 (30.8)12 (46.2)6 (23.1)Vertical Movement (mm)-4.00 ± 1.543.83 ± 1.94Sagittal Movement (mm)4.79 ± 1.512.75 ± 2.142.42 ± 1.91* Data was presented mean ± SD. mm: Millimeter. N: Number of subjects *Inclusion criteria* were 1- absence of a congenital or developmental craniofacial anomaly or syndrome, 2- patients with pre- and postoperative cephalometric and panoramic radiographs with adequate radiographic quality, 3- adult patients over 18 years of age, and 4- patients who recovered normally without any complications or reoperation after genioplasty. Individuals who had previously undergone genioplasty, had any traffic accident or traumatic injury to the face, or had systemic disease were excluded from the study. Of the 26 patients included in the study, 14 ($53.8\%$) had genioplasty only and 12 ($46.2\%$) had simultaneous mandibular advancement with BSSO combined with genioplasty. The individuals who underwent mandibular advancement with BSSO were adults with a skeletal class II relationship originating from mandibular retrognathia, and they requested genioplasty for aesthetic reasons.
## Surgical procedure for genioplasty
The mucoperiosteal flap was reflected with a cautery incision along the labial sulcus, 5 mm below the keratinized gingiva, between the mandibular right and left canines. Dissection was performed so that the bilateral mental nerve was identified and protected, and the chin was exposed. The planned osteotomy line, 5 mm inferior to the mental foramen and apical to the mandibular anterior teeth, was marked horizontally with a pencil. As planned, osteotomy was performed with an ultrasonic piezo saw and the genial segment was mobilized. The genial segment was repositioned forwardly and downwardly or upwardly, and the defect was grafted with a 5 mm wide otology block graft. Grafts were used in only 6 cases where the separated genial segment was taken forward and downwards. The genial segment was fixed in its new position with a pre-bended 5-hole genioplasty plate and mini screws specially designed for the genioplasty operation, bringing the chin tip forward by average 7 mm [25]. A 30*40 mm collagen membrane was covered on the osteotomy line and graft. First, the mental muscle was sutured with a $\frac{4}{0}$ maxon. The connective tissue and mucosa were then closed with $\frac{4}{0}$ vicryl.
## Measuring the amount of movement of the genial segment on lateral cephalometric radiographs
Lateral cephalometric radiographs taken from individuals for cephalometric examination were performed by the same technician using the same device (Orthoceph OP300, Instrumentarium, Tuusula, Finland) with the Frankfort horizontal plane parallel to the ground. All cephalometric radiographs were evaluated in Dolphin Imaging Software (version 11.3; Dolphin Imaging and Management Solutions, Chatsworth, CA, USA). Measurements were made according to the true horizontal plane drawn 7° to the sella-nasion line in the cephalometric radiograph and the true vertical plane passing through the sella perpendicular to this plane [26]. The perpendicular lengths of the pogonion point to these planes were measured before and after surgery [27]. Analyses of all radiographs were performed on the same day by the same investigator.
## Fractal analysis
All panoramic radiographs used for evaluation were taken with the same equipment (OP200 D; Instrumentarium Dental, Tuusula, Finland; 66–85 kVp, 10–16 mA, 14.1 s exposure time) with the same specifications and by the same technician. The patients were positioned in accordance with the recommendations of the device manufacturer, with the Frankfort horizontal plane parallel to the ground and the sagittal plane aligned with the vertical line on the device. Radiography images were exported in TIFF format with a 2976 × 1536 pixel size and 5.5 LP/mm resolution. All images were analyzed by the same researcher using a 32” Dell LCD screen with a resolution of 1280 × 1024 pixels in a dark room and ImageJ image analysis software (version 1.3; National Institutes of Health, Bethesda, MD, USA) using a Dell Precision T5400 workstation (Dell, TX, USA). Many methods have been used to calculate FD [8]. In this study, the box-counting method recommended by White and Rudolph, which is frequently used in the literature, was used [28]. The steps generally applied in the box-counting method involve plotting on a logarithmic scale with a line drawn according to the values obtained. The slope of the drawn line gives the FD of the structure [3, 13]. In methods that calculate FD based on distance measurement, an edge length of the pixel is used as the unit of length. In methods that calculate the FD according to the volume measurement, the perimeter of the pixel is used as the volume unit. In this method [13], circles of various diameters are randomly placed in the image and the pixels belonging to the image border inside the circles are counted (Fig. 1). Radiographs from patients who underwent panoramic radiographs before surgery and 6 months after surgery were selected. The measurements were obtained from the panoramic radiographs, a square of 50 × 50 pixels: two regions of interest (ROIs) were selected bilaterally on the mandibular angulus area, three ROIs were selected within the genial segment of the mandible, and one ROI was selected on the osteotomy healing area on both radiographs (Fig. 2) [29]. In the present study, the reason for evaluating the angulus regions (ROI 4 and 5) that will not be affected by occlusal forces and surgical procedures is that the regions that are not included in the osteotomy area were also examined for individual control in the literature [4, 29, 30]. In order to ensure the reproducibility of the measurements, except for the osteotomy line measurement, the other measurements were made to be at the same level of the teeth and just above the cortical bone border and at the same size as the first measurements. All radiographic analysis was performed by a dentomaxillofacial radiologist (E.M.C.) with 15 years of experience.
Fig. 1Sequential radiographical presentation of the fractal analysis with box-counting method. ( A) Cropped and duplicated region of interest. ( B) Gaussian blurred image. ( C) Subtraction of the blurred images from the original cropped image. ( D) 128 pixel added version of the image in figure C. (E) Binarized version of the image in figure D. (F) Inverted version of the image in figure E. (G) Eroded version of the image in figure D. (H) Dilatated version of the image in figure G. (I) Skeletonized version of the image in figure G Fig. 2The region of interests (ROIs) was obtained from the panoramic radiographs, a square of 50 × 50 pixels. ROI 1: right region of the separated genial segment. ROI 2: left region of the separated genial segment. ROI 3: middle region of the separated genial segment. ROI 4: region selected in the right mandibular angulus. ROI 5: selected region in left mandibular angulus. ROI 6: healing region between the separated genial segment and mandibular corpus
## Sample size calculation
The sample size calculation for this study was based on the data obtained from the study by Kang et al. in which the changes in the mandibular bone structure before and after orthognathic surgery were examined by FD [7]. It was determined that using a total of 12 samples would be sufficient for this study, according to the results of two-way paired samples t-test power analysis performed using G*Power software (ver. 3.1.9.7, Heinrich Heine University, Duesseldorf, Germany) at $85\%$ power, an alpha level of 0.05, and an effect size (d) of 1.00.
## Statistical analysis
The Sigma Stat software (ver. 3.5, Systat Software, Point Richmond, CA, USA) was used in the statistical evaluation of the data. The Shapiro–Wilk test was used to evaluate the normality of the data. Normally distributed data were evaluated using the paired samples t-test for pairwise evaluations between dependent groups and the Wilcoxon signed-rank test was used for non-normally distributed data. In the evaluation of repeated measurements of the same region at the same time, the Friedman Repeated Measures Analysis of Variance on Ranks test was used for non-normally distributed data. For measurements for which significant differences were revealed in these tests, the Student–Newman–Keuls method was used as a post hoc test in paired comparisons. The Spearman correlation coefficient was used to examine the correlation between the amount of genial segment movement and the FDs of an ROI. For all the tests, the statistical significance level was set at $P \leq 0.05.$
## Reliability analysis
In order to evaluate the intra-observation reliability, 6 of the samples randomly selected by the same researcher were re-performed at least 1 month after the first measurement. Intraclass correlation coefficient (ICC) was used to evaluate the reliability between the two measurements, and the Cronbach-α coefficient was determined as 0.992 (Lower bound 0.905; Upper bound: 0.998). With this result, the measurement reliability was determined as very strong [31].
## Results
There was an average of 7 months (7.77 ± 1.34 months; range: 6–12 months) between radiographs before (T0) and after (T1) surgery. Since there was no significant difference between the FD values of male and female individuals included in the study, all samples were evaluated as a single group. Patients were classified as anterior only forward ($$n = 8$$; $30.8\%$), forward and upward ($$n = 12$$; $46.2\%$), and forward and downward ($$n = 6$$; $23.2\%$) according to the movement performed in the genial segment during genioplasty, and it was determined that there was a significant difference between the movement type groups in terms of sagittal movement amounts of the genial segment. Notably, the amount of movement in the only forward group (4.79 ± 1.51 mm) was significantly higher than in the forward and upward group (2.75 ± 2.14 mm; $P \leq 0.05$; Table 1). However, FD values did not differ between these movement groups. The FD values of these groups did not differ significantly before and after treatment, nor did the difference between the two measurements differ between groups ($P \leq 0.05$; Table 2). Therefore, all individuals were evaluated as a single group and a comparison before (T0) and after (T1) treatment was made.
Table 2Comparison of the FD values between the groups before, after the treatment and difference between the time-pointsOnly Genioplasty ($$n = 14$$)MABSSO combined with Genioplasty ($$n = 12$$)P values*T0ROI 11.445 ± 0.0501.483 ± 0.0720.124ROI 21.426 ± 0.0521.408 ± 0.0540.397ROI 31.455 ± 0.0621.444 ± 0.0940.733ROI 41.451 ± 0.0631.433 ± 0.0780.681ROI 51.450 ± 0.0561.452 ± 0.0630.921T1ROI 11.459 ± 0.0581.436 ± 0.0720.376ROI 21.405 ± 0.0881.365 ± 0.0860.090ROI 31.425 ± 0.0581.447 ± 0.0500.315ROI 41.491 ± 0.0571.443 ± 0.0650.057ROI 51.468 ± 0.0541.452 ± 0.0820.562ROI 61.427 ± 0.0821.416 ± 0.0520.687Diff. ROI 10.014 ± 0.066-0.047 ± 0.0810.051ROI 2-0.021 ± 0.105-0.043 ± 0.1130.681ROI 3-0.029 ± 0.0850.003 ± 0.1060.385ROI 40.039 ± 0.0720.009 ± 0.0810.381ROI 50.019 ± 0.0580.001 ± 0.0520.535Duration of 2 radiographic evoluation (month)8.18 ± 5.446.33 ± 2.310.498Data was given Mean ± Standard Deviation. ROI: Region of Interest. MABSSO: Mandibular Advancement with Bilateral Sagittal Split Osteotomy. T0: Pre-treatment values. T1: Post-treatment values. Diff.: Values of T1 to T0 DifferenceROI 1: right region of the separated genial segment. ROI 2: left region of the separated genial segment. ROI 3: middle region of the separated genial segment. ROI 5: region selected in the right mandibular angulus. ROI 5: selected region in left mandibular angulus. ROI 6: healing region between the separated genial segment and mandibular corpus. * Results of Independent Samples-t test Comparison of the FD changes that occurred with the genioplasty application is presented in Table 3. When the change between T0 and T1 was examined, it was determined that there was no significant difference for any ROI values (Table 3). However, when the ROI values were examined within everyone, no difference was observed at T0, but significant differences were found in the change at T1 ($P \leq 0.001$) and differences between T0 and T1 ($$P \leq 0.030$$). The FD values of ROI 2 performed in the middle region of the genial segment were found to be significantly lower than the other regions after treatment ($P \leq 0.05$). When the changes in the measurement regions were examined, it was determined that the FD values in the right region of the genial segment were like those of the right and left angulus and significantly higher than in the genial middle and left regions ($P \leq 0.05$).
Table 3Comparison of FD values before (T0) and after (T1) treatment and between measurement regionsN= [26] T0 T1 Diff. P valuesMean ± SDMedian ($25\%$ / $75\%$)Mean ± SDMedian ($25\%$ / $75\%$)Mean ± SDMedian ($25\%$ / $75\%$)ROI 11.462 ± 0.0631.471 (1.410–1.509)1.448 ± 0.064 a1.450 ($\frac{1.413}{1.500}$)0.030 ± 0.074 a0.040 (-$\frac{0.030}{0.095}$)0.382 PtROI 21.418 ± 0.0531.413 ($\frac{1.392}{1.447}$)1.386 ± 0.089 b1.392 ($\frac{1.360}{1.438}$)-0.031 ± 0.107 b-0.024 (-$\frac{0.054}{0.043}$)0.334 WrROI 31.450 ± 0.0771.466 ($\frac{1.405}{1.491}$)1.435 ± 0.055 a1.446 ($\frac{1.392}{1.472}$)-0.014 ± 0.094 b-0.014 (-$\frac{0.097}{0.062}$)0.450 PtROI 41.443 ± 0.0691.454 ($\frac{1.399}{1.503}$)1.469 ± 0.064 a1.472 ($\frac{1.425}{1.525}$)0.026 ± 0.076 a,b0.013 (-$\frac{0.026}{0.078}$)0.093 PtROI 51.451 ± 0.0581.433 ($\frac{1.409}{1.512}$)1.460 ± 0.068 a1.474 (1.421–1.510)0.010 ± 0.056 a,b0.002 (-$\frac{0.014}{0.038}$)0.379 PtP values0.146 ($F = 1.744$) *$P \leq 0.001$ ($F = 6.849$) *, ***0.030 (Chi-square = 10.674) **, ***SD: Standard Deviation. ROI: Region of Interest. T0: Pre-treatment values. T1: Post-treatment values. Diff.: Values of T1 to T0 DifferenceROI 1: right region of the separated genial segment. ROI 2: left region of the separated genial segment. ROI 3: middle region of the separated genial segment. ROI 5: region selected in the right mandibular angulus. ROI 5: selected region in left mandibular angulusPt: Result of Paired-Samples-t test. Wr: Result of Wilcoxon Signed Rank test. * Result of One Way Repeated Measures Analysis of Variance test. ** Result of Friedman Repeated Measures Analysis of Variance on Ranks test. *** Results of All Pairwise Multiple Comparison Procedures (Student-Newman-Keuls Method). The same letters in the column indicate that there is no difference in pairwise comparison The comparison of the FD values of the measurement regions in the genial osteotomy line, including the healing line, taken after the treatment is provided in Table 4. In the evaluation, it was found that the lowest FD values were in the middle genial segment (1.386 ± 0.089), followed by the genial osteotomy line (1.422 ± 0.068), and it was determined that other regions had significantly higher FD values than these two regions ($P \leq 0.001$). There was no significant correlation between the amount of sagittal and vertical movement of the genial segment and the FD values of the genial segment regions and the genial osteotomy line (Table 5).
Table 4Comparison of the measurement regions among themselves in the post-treatment (T1) evaluationN= [26]Mean ± SDMedian ($25\%$ / $75\%$)ROI 11.448 ± 0.064 a1.450 ($\frac{1.413}{1.500}$)ROI 21.386 ± 0.089 b1.392 ($\frac{1.360}{1.438}$)ROI 31.435 ± 0.055 a1.446 ($\frac{1.392}{1.472}$)ROI 41.469 ± 0.064 a1.472 ($\frac{1.425}{1.525}$)ROI 51.460 ± 0.068 a1.474 (1.421–1.510)ROI 6 †1.422 ± 0.068 c1.419 ($\frac{1.391}{1.474}$)P value *, **$P \leq 0.001$ (Chi-Square = 21.385)SD: Standard Deviation. ROI: Region of InterestROI 1: right region of the separated genial segment. ROI 2: left region of the separated genial segment. ROI 3: middle region of the separated genial segment. ROI 4: region selected in the right mandibular angulus. ROI 5: selected region in left mandibular angulus. ROI 6: healing area between the separated genial segment and mandibular corpus† This ROI measurement was performed only to evaluate the healing of the area between the mandibular corpus and genial segment at the time-point of the T1. * Result of Friedman Repeated Measures Analysis of Variance on Ranks test. ** Results of All Pairwise Multiple Comparison Procedures (Student-Newman-Keuls Method). The same letters in the column indicate that there is no difference in pairwise comparison Table 5Investigation of the correlation between the genial segment movement amounts and the FD in the measured areasROI 1ROI 2ROI 3ROI 4ROI 5ROI 6Advancement-0.170-0.116-0.116-0.134-0.051-0.165Vertical Movement0.0220.0630.0660.3220.2350.080* Data was presented as Spearman Correlation Coefficients. ROI: Region of InterestROI 1: right region of the separated genial segment. ROI 2: left region of the separated genial segment. ROI 3: middle region of the separated genial segment. ROI 4: region selected in the right mandibular angulus. ROI 5: selected region in left mandibular angulus. ROI 6: healing area between the separated genial segment and mandibular corpus
## Discussion
Bone healing after orthognathic surgery has been examined radiographically in a few studies in the literature [6, 12, 15]. These studies evaluated recovery after the BSSO procedure for mandibular prognathism. However, to our knowledge, there are no studies examining trabecular changes after genioplasty alone and/or genioplasty combined with BSSO. In this study, trabecular changes after genioplasty advancing the genial segment only and genioplasty combined with BSSO and mandibular advancement was evaluated and compared.
In many studies in the literature in recent years, it has been shown that changes in bone structure can be examined with FD in conditions affecting bone structure such as osteoporosis, hyperparathyroidism, osteoarthritis or osteogenesis imperfecta [9, 16, 32, 33]. In these studies, the differences in bone structure between healthy and sick individuals, as well as the effectiveness of treatments applied to sick individuals in some of the studies, were examined by comparing the changes in FD values occurring in the bone structure. Such studies have revealed that FD values can show differences in bone structure. Today, a wide variety of applications (etc. stem cell) are used to support the healing of bone structure during the treatment of changes caused by diseases [34]. It is often not possible to evaluate the success of them with methods that are less invasive than histological methods. However, FD analysis, which is evaluated on radiographs, comes to the fore in this regard. In the field of dentistry, it has been frequently used for detection of pathologies such as temporomandibular diseases, periodontitis and bruxism, and to examine the differences and changes in bone structure after therapeutic applications such as root canal treatment, implant surgery, orthognathic surgery, extraction of third molar or palatal expansion [4–6, 10, 11, 14, 35, 36]. In these studies, it has been reported that dental treatments can cause changes in the alveolar bone structure and that FD analysis can be used in the evaluation of treatment results. Cone-beam computed tomography (CBCT) can give more detailed and clear results, but it causes higher levels of radiation compared to panoramic radiographs in periodic examinations [37]. In the study by Magat and Ozcan Sener, it was stated that in the evaluation of the trabecular structure of the alveolar bone, it is appropriate to use panoramic radiography instead of CBCT, which has low image resolution and high overall radiation dose [38]. Therefore, panoramic radiographs were used in this study. In the literature, there are studies examining the recovery after implant surgery with fractal analysis [14]. When the panoramic radiographs taken before and after implant surgery were analyzed with fractal analysis, it was found that the increase in the fractal size of the bone around the implant correlated with successful osteointegration and healing of the trabecular bone [14]. In addition to implant surgery, recovery stages in orthognathic surgery cases can be followed with FD. Heo et al. published a study of 35 patients with the diagnosis of mandibular prognathism who were scheduled for BSSO [12]. They reported that no significant difference could be detected in the FD values before and one year after surgery. Moreover, they stated that it would be more accurate to examine trabecular changes with FD, which is an objective and quantitative method, compared to visual diagnosis, which is a subjective observation [12]. In the study by Kang et al., it was reported that there was a decrease in the FD values of the bone in the molar and canine teeth region one month after the BSSO and mandibular set-back procedure to treat mandibular prognathism, and that this may accelerate orthodontic tooth movement [6]. In the study by Park et al., in which mandibular prognathism was treated with mandibular set-back using BSSO, the change in mandibular cortical bone thickness was examined with fractal analysis [15]. Accordingly, it was reported that the FD values of the cortical bone were low even at the sixth month after surgery, but the interdental bone did not show any difference. No difference was reported in patients undergoing genioplasty in this study. Moreover, the results of the present study are also consistent with the literature by Çolak et al., that bone healing in horizontal osteotomies at 6 months after orthognathic surgery was similar [29]. In contrast, it was concluded in the present study that genioplasty only and genioplasty combined with BSSO and mandibular advancement did not differ in radiographs taken at least 6 months later. However, even after at least six months after genioplasty alone and/or combined with BSSO, FD values did not yet reach normal in the healing line or in the middle region of the genial segment. In line with this study, adequate stability at 6 months after advancement genioplasty alone or combined with BSSO has been reported in previous study [39], but no data on recovery were found. Findings from this study support the assumption that this stability will be promoted by bone healing after an average of 6 months.
## Limitations
A limitation of the study is that panoramic radiographs that offer two-dimensional observation were used instead of CBCT, which offers three-dimensional observation. However, given that periodic observations were made, using two-dimensional observation allowed less radiation to be applied, and bone healing could be studied successfully. Moreover, in the study, only data related to advancement surgery are presented. Nonetheless, this study fills a gap in the literature. It may be recommended to compare different surgical procedures with a larger sample size in future studies.
## Conclusion
In conclusion, genioplasty alone and combined with BSSO with mandibular advancement do not differ in terms of recovery at least 6 months after surgery. After the surgical procedure, trabecular structure improvement of the middle region of the genial segment is late complete compared to other regions. Likewise, trabecular changes of the genial segment mid-area and surgical osteotomy line after the surgical procedure is late complete compared to other parts of the segment and non-surgical areas. There is no significant relationship between the amount of movement of the genial segment in the sagittal and/or vertical dimensions between bone healings. In cases where histological evaluation is not possible with the obtained results, FD assessment may be useful to evaluate bone healing in addition to clinical and cephalometric radiographic evaluations. Before more detailed analysis, it was foreseen that this method would make an additional contribution to the evaluations.
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|
---
title: 'Marinated oven-grilled beef entrecôte meat from a bovine farm: Evaluation
of resultant physicochemical and organoleptic attributes'
authors:
- Charles Odilichukwu R. Okpala
- Szymon Juchniewicz
- Katarzyna Leicht
- Małgorzata Korzeniowska
- Raquel P. F. Guiné
journal: PeerJ
year: 2023
pmcid: PMC10024902
doi: 10.7717/peerj.15116
license: CC BY 4.0
---
# Marinated oven-grilled beef entrecôte meat from a bovine farm: Evaluation of resultant physicochemical and organoleptic attributes
## Abstract
Understanding the impact that combined action of marination and oven grill processes would have on such meat products as beef entrecôte is crucial from both consumer appeal and product development standpoints. Therefore, different marinated oven-grilled beef entrecôte meat specifically evaluating resultant physicochemical and organoleptic attributes were studied. The beef entrecôte meat was provided by a reputable local bovine farm/slaughter at Wroclaw, Poland. Physicochemical attributes involved antioxidant (2,2′-azinobis(3-ethylbenzothiaziline-6-sulfonate) (ABTS), 2,2-diphenyl-1-picrylhydrazyl (DPPH), ferric reducing antioxidant power (FRAP)), (pH, thiobarbituric acid reactive substance (TBARS), cooking weight loss, L*a*b* color, and textural cutting force). Organoleptic attributes involved sensory (flavour, appearance, tenderness, taste) and texture (hardness, chewiness, gumminess, graininess, and greasiness) aspects. Different marination variants involved constituent $0.5\%$, $1\%$, and $1.5\%$ quantities of cranberry pomace (CP), grape pomace (GP), and Baikal skullcap (BS), subsequently incorporated either African spice (AS) or industrial marinade/pickle (IM). Results showed pH, ABTS, DPPH, FRAP, TBARS, L*a*b* color, cooking weight loss, and textural cutting force, sensory and textural profile with varying range values. Concentration increases of either CP, GP, and or BS may not always go along with ABTS, DPPH, and FRAP values, given the observed decreasing or increasing fluctuations. As oven-grilling either increased or decreased the TBARS values alongside some color and textural cutting force trends, pH variations by difference seemed more apparent at samples involving GP, before CP, and then BS. The organoleptic attributes obtained differences and resemblances from both sensory and textural profile standpoints. Overall, oven-grilling promises to moderate both physicochemical and organoleptic range values of different marinated beef entrecôte meat samples in this study.
## Introduction
The global meat production recorded about 67 million metric tonnes as of 2013, but seemingly less as of 2020 at about 60.57 million metric tonnes (McGlone, 2013; Cook, 2022; FAO, 2022, accessed September 2022). Notably, the EU as of 2016 was positioned as the third largest global beef producer by area that occupied about $11.5\%$ (SustainBeef, 2021). Among the countries in the EU of notable interest, Poland in 2021 obtained a total national cattle population of 6.4 million by the head, which placed this country as the sixth (EU) beef producer (Nieuwsbericht, 2022). But despite this data, the level of (beef) consumption is still considered as below average (Nieuwsbericht, 2022). Among key factors that influence beef cattle (meat) quality in Poland include age, breed, diet, meat production and processing, as well as her accession to the EU in May 2004 (Domaradzki et al., 2017; Żakowska-Biemans et al., 2017). From the commercial/industrial perspective, the method by which beef is processed in Poland would follow this pathway: beef meat products > cutting plants > slaughterhouses > mechanically separated meat, and after slaughter, there would be intermediaries/outlets that direct the delivery/purchase (Szymańska, 2015).
The quality of beef meat products is very important to consumers, distributors, producers, processors, and slaughterers. Also, beef meat quality would reflect four pathways: healthiness (nutritional quality), satisfaction (organoleptic quality), security (hygienic quality), and serviceability (ease of use, ability to be processed, and prices) (Listrat et al., 2016). Besides being a great protein source for human consumption, beef meat comprises typical structural features like connective tissue, muscle fibers, and tendon, typically enriched with bioavailable iron, zinc, and selenium as well as vitamins A, B, and D (Geletu et al., 2021; Open Textbooks, 2022). At post-mortem, the accelerated glycolysis alongside the formation of lipid peroxidation products confronts beef meat, and facilitates quality deterioration (Toldrá & Flores, 2000; Martini et al., 2021). Moreover, overall beef meat value would associate with intrinsic and extrinsic cues, which consumers employ to explain their quality expectations (Domaradzki et al., 2017). When slaughtered, different beef product types do emerge largely dependent on the cut portions. When butchers cut into the bone-in rib-eyes particularly (with the bone) on each side, there would remain about six leftover boneless steaks potentially available from the beef meat. And this particularly happens between each bone-in rib-eye, which is how the entrecôte would emerge (TasteAtlas, 2023). Indeed, the making of a traditional entrecôte from the rib area of a given beef carcass certainly requires some level of specific skillset (Beef2live, 2022). Recent studies involving various quality attributes of beef carcass appear to investigate more on steak, loins, and others (Clinquart et al., 2022; Berger et al., 2018; Santos et al., 2021) much less the entrecôte.
The use of natural agents that possess preservative potentials continues to be of increasing research interest, which has been demonstrated by antioxidant and antimicrobial properties that help maintain meat quality, extend shelf-life and prevent economic loss (Al-Dalali, Li & Xu, 2022; Istrati et al., 2011). Among such natural preservative agents, marinades have been shown in recent years as increasingly applied to meat products (Cheok et al., 2011; Istrati et al., 2011; Sokołowicz et al., 2021). Dependent on the duration as well as technique of the marination process, the meat muscle can take up marinade constituents (Siroli et al., 2020). Typically requiring the immersion of meat products in a slurry/solution mix, the marination process would allow the incorporation of other edible seasonings that improve flavor development. More so, the ingredients employed in the marination process could include the likes of black/regular pepper, herbs/spices, ginger, cranberry pomace, Baikal skullcap, peanut, etc. ( Al-Dalali, Li & Xu, 2022; Cheok et al., 2011; Istrati et al., 2011; Shahidi & Hossain, 2018; Sokołowicz et al., 2021; Martini et al., 2021; Zhang, Wu & Guo, 2016), some of which are enriched with phenols, and flavonoids (Amber et al., 2021), beneficial polyphenols (Roopchand et al., 2013), as well as antimicrobial capacities (Teplá et al., 2013).
To make beef edible, thermal processing of one form or another remains inevitable, which over the decades has advanced, from cook-chill, grilling, ohmic heating, laser-based packaging, etc. ( Richardson, 2004; Viegas et al., 2012). Of increasing interest is grilling, which is among such thermal processes that involve temperatures above 150 °C transferred by conduction, and through direct/radiant dry heat (Schröder, 2003; Ježek et al., 2020). More so, the application of grilling of various types to meat products has been reported by several workers (Farhadian et al., 2010; Kerth, Blair–Kerth & Jones, 2003; Khan et al., 2015; Muga, Marenya & Workneh, 2021; Gómez, Ibañez & Beriain, 2019; Vişan et al., 2021). Whereas Muga, Marenya & Workneh [2021] performed modeling thin-layer drying kinetics of marinated beef submitted to infrared-assisted hot air processing, and Gómez, Ibañez & Beriain [2019] investigated the physicochemical and sensory properties of sous vide meat and meat analog products marinated and cooked at different temperature-time combinations, Vişan et al. [ 2021] studied the marination of Black Angus beef meat subjected to a grilling process. Other cooking methods applied to beef meat, which paved way for examination and prediction of other quality attributes (Kondjoyan et al., 2014; Onopiuk et al., 2021). Despite the published information currently available, relevant information to specific marinated oven-grilled beef entrecôte meat has not be found. Understanding the impact that combined action of marination and oven grill processes would have on such meat products as beef entrecôte is crucial from both consumer appeal and product development standpoints. To supplement existing information, this current work investigated different marinated oven-grilled beef entrecôte meat, specifically the evaluation of resultant physicochemical and organoleptic attributes. The beef entrecôte meat was provided by a reputable local bovine farm/slaughter retailer that supplies the Wroclaw’s Lower Silesia region of Poland.
## Schematic overview of experimental program
The schematic overview of the experimental program, demonstrating the major stages, from the procurement of beef entrecôte meat samples, preparation of marinade variants, oven-grilling activity, to analytical measurements are shown in Fig. 1. To reiterate, this current work was directed to establish how oven-grilling affected different marinated beef entrecôte meat samples specific to their physicochemical (antioxidants, pH and lipid oxidation, cooking weight loss, L*a*b* color, and textural cutting force), as well as organoleptic (sensory = flavour, appearance, tenderness, taste and flavour; texture = hardness, chewiness, gumminess, graininess, and greasiness) attributes. Added that the beef entrecôte meat has been procured from a bovine farm in Poland, the different marination variants involved cranberry pomace, grape pomace, and Baikal skullcap that subsequently incorporated African spice, and Industrial marinade/pickle. Chemicals and reagents used at this work were of analytical grade standard. Additionally, all the laboratory experimentation adhered to the relevant guidelines set out by the Department of Functional Food Product Development, Wroclaw University of Environmental and Life Sciences-Poland.
**Figure 1:** *The schematic overview of the experimental program, showing the key stages, from the procurement of beef entrecôte meat samples, preparation of marinade variants, through oven-grilling activity, subsequently analytical measurements.ABTS, 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate); DPPH, 2,2-diphenyl-1-picrylhydrazyl (radical scavenging activity); FRAP, ferric reducing antioxidant power; thiobarbituric acid reactive substance, TBARS; UPWr, Uniwersytet Przyrodniczy we Wrocławiu (Wroclaw University of Environmental and Life Sciences-Poland).*
## Procurement, and preparation of beef entrecôte meat samples
Freshly processed beef entrecôte meat samples were procured from a reputable local bovine farm/slaughter retailer that supplies the Wroclaw’s Lower Silesia region of Poland. The beef entrecote meat samples (~20 kg) placed in iced packed poly-boxes were transported to the Department of Functional Food Products Development, Wroclaw University of Environmental and Life Sciences., Poland. Upon arrival, the beef entrecôte meat samples were further cut into equivalent pieces of approximate thickness (9 cm × 9 cm × 3 cm). Afterwards, all samples were subject to cold room refrigeration (~2 °C) and ready for subsequent laboratory activities of marination and oven-grilling.
## Preparation of marinades, and marination variants
The preparation of marinades followed the method described by Okpala et al. [ 2023]. This specifically involved Baikal Skullcap (BS), cranberry pomace (CP), as well as grape pomace (GP), that subsequently incorporated constant quantities of either African spice (AS) or Industrial marinade/pickle (IM) (each constituting 4 g), alongside salt (1.6 g). For emphasis, on one hand, the African spice product (Fresh and Tasty Kebab Powder®) had been purchased from Fresh and Tasty Farms Ltd (Accra-North, Ghana), and its preparation followed the quality standards set by Food and Drugs Authority (FDA) Ghana. The label showed that this product comprised such ingredients as peanut, ginger, as well as black/regular pepper. More so, we utilized this AS product for the reason that it is increasingly being used at barbecues across Poland. On the other hand, the industrial marinade/pickle (Marinate do mięs) product had been purchased from Regis® Food Technology (Regis sp. z o.o., Kraków-Poland) and its preparation followed the quality standards set by the International Organization for Standardization (ISO), British Retail Consortium (BRC), and International Food Standard (IFS). The label showed that this product comprised such ingredients as marjoram, oregano, parsley, rosemary, and thyme. More so, we utilized this IM product for the reason that it already has an established reputation in Poland and other parts in the EU.
It is important to reiterate that the ground CP, GP, and BS served as antioxidant additives for this current study, using the method previously described (Okpala et al., 2023). The incremental concentrations of CP, GP, and BS made up $0.5\%$, $1\%$, and $1.5\%$ by volume, which were calculated based on gram per 100 mL. Clean water served as liquid used to make up the marinade. Importantly, the marination variants were implemented as follows: [1] control where the antioxidant additive was not added ($0.0\%$); [2] control with antioxidant additive of $0.5\%$; [3] control with antioxidant additive of $1.0\%$; [4] control with antioxidant additive of $1.5\%$; [5] AS incorporated with no antioxidant additive ($0.0\%$); [6] AS incorporated with antioxidant additive of $0.5\%$; [7] AS incorporated with antioxidant additive of $1.0\%$; [8] AS incorporated with antioxidant additive of $1.5\%$; [9] IM incorporated with no antioxidant additive ($0.0\%$); [10] IM incorporated with antioxidant additive of $0.5\%$; [11] IM incorporated with antioxidant additive of $1.0\%$; [12] IM incorporated with antioxidant additive of $1.5\%$. Following the method described by Sokołowicz et al. [ 2021] with modifications, the immersion method was adapted. In particular, the amount of marinade was considered adequate to completely immerse the beef entrecôte meat samples, and this applied a 1:2 ratio of weight of meat (g) and marinade volume (mL). Additionally, plastic containers approved for contact with food was used to carry out the immersion process. The beef entrecôte meat samples were dipped sufficiently in the marinade variants for 24 h at 4 °C. Subsequently, after the immersion time had completed, the marinated beef entrecote samples were then allowed to drain (5 min), and placed in folded foiled packages ready for oven-grilling activity.
## Oven-grilling procedure
The oven-grilling activity of marinated beef entrecôte meat samples employed an oven facility (CAMRY CR 6018; Serwis Centralny Camry, Warszawa, Poland). The oven-grilling operated with 2,200 W power, and set temperature of 180 °C. The beef entrecôte meat samples were placed evenly spaced in the oven-grill, which remained closed during the cooking process. Importantly, the opening of oven-grill was only when either to remove, or place new samples. Cooking time was kept constant at 5 min. During the cooking period, the internal temperature of the beef entrecote meat samples was routinely checked to ensure it was maintained roughly at about 75 °C. Upon completion of oven-grilling process, the emergent samples were allowed to cool briefly (15 min) at ambient temperature. Afterwards, emergent samples were then placed in foiled packages, submitted to refrigeration (4 °C), and then followed by analytical measurements.
## Determination of antioxidant aspects
Prior to the antioxidant tests, the preparation of meat tissue supernatant followed the method described by Bai et al. [ 2016] with slight modifications. This required about a gram of beef meat entrecote tissue sample subjected to homogenization at 8,000 rpm for 10 s using 9 ml of $0.9\%$ sodium chloride buffer, briefly placed on ice, subsequently centrifuged at 4,000 rpm for 15 min at 4 °C.
The 2,2′-azinobis(3-ethylbenzothiaziline-6-sulfonate) (ABTS+) radical scavenging activity was performed using the method described by Bai et al. [ 2016] with slight modifications. The ABTS+ has been produced by mixing 7 mM of ABTS+ stock solution with 2.45 mM K2S2O8, subsequently incubated in darkness at 25 °C for 12–16 h. Prior to using the reagent, the ABTS+ solution was diluted with ethanol to an absorbance of 0.7000 ± 0.005 at 734 nm. From this, 10 μL of meat tissue supernatant were mixed with 990 μL of ABTS+ solution and subsequently incubated at ambient temperature of ~25 °C for 6 min. The 990 μL of ABTS+ solution mixed with 10 μL EtOH $70\%$ served as the blank. Spectrophotometrically and against a blank, the absorbance was determined at 734 nm. The ABTS+ radical scavenging activity has been expressed by mM Trolox.
The 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity was performed using the method described by Zhang et al. [ 2015] with slight modifications. Specifically, there was an already prepared DPPH solution (0.3 mM) made with ethanol. Briefly, aliquots (20 μL) from meat tissue supernatant were mixed by vortex for 1 min with 200 μL 0.3 mM of ethanolic DPPH radical solution, then allowed to stand at ambient temperature (25 °C) for 30 min in the dark. Spectrophotometrically and against a blank, the absorbance was determined at 517 nm using a UV-Vis Spectrophotometer (GENESYS™ 180; Thermo Fisher Scientific Inc., Waltham, MA, USA), and DPPH radical scavenging activity expressed in mM Trolox.
The ferric reducing antioxidant power (FRAP) measurement was performed using the method described by Lengkidworraphiphat et al. [ 2020] with slight modifications. This required a mixture of FRAP solution containing 10 mM 2,4,6-tripyridyl-s-triazine (TPTZ), 20 mM ferric chloride, together with 300 mM sodium acetate buffer (pH 3.6), at a ratio of 1:1:10 (v:v:v) added to the test specimen, and subsequently incubated for 30 min at 37 °C. The blank comprised 3 mL FRAP reagent mixed with 1 mL EtOH. The absorbance of resultant solution was read against a blank at 593 nm using a UV-Vis Spectrophotometer (GENESYS™ 180; Thermo Fisher Scientific Inc., Waltham, MA, USA) and FRAP value expressed as mM/dm3.
## Determination of pH and lipid oxidation
The pH measurement was performed in triplicate using the method described by Barido & Lee [2022] with some modifications. This was specifically conducted before and after the oven-grilling activity. This required mixing a 5 g sample with 45 mL of distilled water in a homogenizer (PH91; SMT, Chiba, Japan) at 10,000 rpm, for 1 min using a portable pH meter (HI 99163; Hanna Instrument Company, Vöhringen, Germany) technically calibrated by buffer solutions (approximate pH 4.0, 7.0 and 9.0).
The thiobarbituric acid reactive substance (TBARS) measurement was performed in triplicate using the method described by Luciano et al. [ 2011] with slight modifications. This was specifically conducted before and after the oven-grilling activity. With the help of stomacher, the beef entrecôte meat samples (1.0 g) were homogenised with 10 mL of $10\%$ trichloroacetic acid (TCA) for 1 min to precipitate proteins that are present. Subsequently, centrifugation was performed at 4,000× g (MPW-351R refrigerated; MPW Med. instruments Warszawa, Poland), and emergent mix was subject to filtration (Whatman #1 filter paper), from which 2 mL of supernatant was transferred to 2 mL of 0.06 M thiobarbituric acid. Placed in a water bath at 100 °C for 40 min, the reaction mixture was then cooled in ice-water bath (~2 min). The calibration curve was prepared using 1,1,3,3-tetra-ethoxypropane in TCA, as a standard solution. The samples were finally analysed, with absorbance was read against a blank at 532 nm using a UV-Vis Spectrophotometer (GENESYS™ 180; Thermo Fisher Scientific Inc., Waltham, MA, USA). According to the standard curve equation, TBARS values were expressed as mg of malondialdehyde (MDA) per kg of meat sample.
## Determination of color and cooking weight loss
The color measurements were determined using the method described by Kopec et al. [ 2020] with slight modifications. This was specifically conducted before and after oven-grilling by way of CIE L*a*b* scale (L* = darkness; a* = redness/greenness; and b* = yellowness/blueness) using a Minolta CR-40 reflection colorimeter (Konica Minolta Sensing Europe B.V., Nieuwegein, Netherlands). Three individual measurements were taken on different areas on the beef entrecôte meat surface, and the readings display results via the CIE L*a*b* colorimetric system were recorded.
The cooking weight loss measurements were determined using the method described by Ali et al. [ 2007] with slight modifications. Specifically, the samples have been weighed prior to and after oven-grilling. The cooking weight loss depicted cooked sample (B) weight as a percentage of precooked sample (A) weight as shown by the equation below: [1] \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$${\rm Cooking \;loss\; (\%) = {[(A-B)/(A)]\times 100}}$$\end{document}Cookingloss(%)=[(A−B)/(A)]×100
## Determination of textural cutting force
The textural cutting force measurement was performed using the method described by Augustyńska-Prejsnar, Ormian & Sokołowicz [2017] with slight modifications. The specific aim was to measure the force required to cut a piece of beef entrecôte meat. The facility employed to measure cutting force (Fmax) was the Zwick/Roell machine (Zwick GmbH & Co. KG, Ulm, Germany), already equipped with Warner-Bratzler V-blade knife, which moved at a head speed of 100 mm/min and an initial force of 0.2 N. The portions of beef entrecôte meat samples were estimated cross-sectional diameter of 100 mm2 and 50 mm length.
## Organoleptic measurements
Organoleptic measurements of beef entrecôte meat samples comprised sensorial analysis slightly modified from Augustyńska-Prejsnar, Ormian & Sokołowicz [2018], and textural profiling slightly modified from Brambila, Bowker & Zhuang [2016]. Sensory panelists constituted ten [10] staff and graduate students of the Department of Functional Food Products Development, Wrocław University of Environmental and Life Sciences (Poland), who were already familiar with the evaluation criteria set out to differentiate between the levels of the beef entrecôte meat’s flavor, appearance, tenderness, taste, and off-flavor specific to the sensorial profiling, as well as hardness, chewiness, gumminess, and graininess specific to the textural profiling. Importantly, the verbal consent taken prior to the sensory evaluation. Additionally, the panelists’ participation was voluntary, and no names/gender was reported to ensure privacy. The panelists performed the organoleptic evaluation in a well-ventilated and distraction-free environment of neutral color, and with adequate lighting. The organoleptic assessment involved the evenly cut samples already cooled to 20 ± 2 °C placed in coded white plastic plates before each panelist. Importantly, warm water was made available to each panelist to cleanse taste palates between samples. This was conducted to ensure the previous evaluation did not affect the (taste of the) new one, consistent with the work of Çakmakçı et al. [ 2015]. The coded samples were evaluated using five-point scale (one point being the lowest score and five points being the highest) for the sensory aspects, and using 0 to 15 intensity scale for texture profile, modified from the description given by Civille & Thomas Carr [2015].
## Statistical analysis
The data, independently generated from different samples and based on a minimum of two determinations unless stated otherwise, were submitted to analysis of variance (ANOVA). Statistical significance was set at $p \leq 0.05$ ($95\%$ confidence level). The mean differences were resolved post-hoc by way of Turkey’s test. Statistica 13.0 software (StatSoft GmbH, Hamburg, Germany) was used to run the data.
## Changes in antioxidant aspects
Notwithstanding that combination of herbs and spices makes marination to produce a promising antioxidant resource, different processing methods would alter its efficacy (Thomas et al., 2010; Viegas et al., 2012). This circumstance would be particularly applicable from the point when the marinated product has just been prepared, and when it is potentially ready for consumption. In this current work, the changes in ABTS, DPPH, and FRAP values of various marinated oven-grilled beef entrecôte meat samples compared to control can be seen in Fig. 2. Across CP, GP and BS incorporating either AS or IM, different ABTS, DPPH, and FRAP ranges (for ABTS: from 2.01 ± 0.14 mM/Trolox at IM+GP $1.5\%$ to 3.58 ± 1.89 mM/Trolox at control +BS $1.5\%$; for DPPH: from 0.09 ± 0.00 mM/Trolox at either AS +BS $1\%$ or control +GP $1\%$ to 0.14 ± 0.00 mM/Trolox at either control or IM+CP $0.5\%$; for FRAP: from 0.21 ± 0.01 mM/lit at control + CP $0.5\%$ to 0.76 ± 0.00 mM/lit at either AS+BS $1.5\%$ or IM+BS $1.5\%$) were found. Whereas the control samples obtained a closer DPPH range (~0.12 to 0.14 mM/Trolox), those of marinated oven-grilled beef entrecote meat samples obtained wider ranged ABTS (~2.14 to ~3.38 mM/Trolox) and FRAP (~0.21 to ~0.46 mM/lit).
**Figure 2:** *Changes in ABTS (A), FRAP (B), and DPPH (C) across the various marinated oven-grilled beef entrecôte meat samples compared to control.ABTS, 2,2′-Azinobis-(3-ethylbenzthiazoline-6-sulphonate; DPPH, 2,2-diphenyl-1-picrylhydrazyl (radical scavenging activity); FRAP, ferric reducing antioxidant power; Error bars shows mean values ± standard deviation (SD). African spice, AS; Industrial marinade/pickle, IM; BS, Baikal Skullcap. Results are expressed as mean ± standard deviation (SD).*
Figure 2 also depicts that ABTS, DPPH and FRAP values of various marinated oven-grilled beef entrecôte meat samples specific to increasing CP, GP, and BS concentrations seems comparable with those that incorporated either AS or IM. Besides, the increasing CP, GP and or BS concentrations may not always go along with ABTS, DPPH and FRAP values given the observed decreasing or increasing fluctuations. Emphasizing the increasing CP, GP and or BS concentrations incorporating either AS or IM likens to a herb mix as strengthening the antioxidant efficacy of the marinade medium (Thomas et al., 2010; Viegas et al., 2012; Zhang et al., 2015), the heat temperatures above 120 °C resembling (oven) grilling should be capable of decreasing the antioxidant activity (Barido & Lee, 2022). It is important to understand that thermal processing like oven-grilling could open up the plant cell wall components and make them to become more sensitive so as to allow the progress of Maillard reaction (Moroney et al., 2015). Capably, the amino acids, essential oils, antioxidants, flavones, phenylethanoids, as well as sterols that are available in such herbs like BS and GP herein should scavenge, for instance, the DPPH radicals, which would help to prevent the progress of rancidity (Kim et al., 2012; Lee et al., 2014; Sáyago-Ayerdi, Brenes & Goñi, 2009).
## Changes in pH and TBARS
Applicable to beef meat quality, the changes in pH has for long been understood to associate with lower quantities of expressed juice, reflectance values, and cooking losses (Purchas, 1990). Besides pH considered as indicative of the acid concentration present, the use of marinades could eventually influence the physicochemical properties of the meat muscle (Oreskovich et al., 1992). In this current work, the changes in pH and TBARS values of the various marinated oven-grilled beef entrecôte compared to control are respectively shown in Figs. 3 and 4, as well as Table 1. Both pH and TBARS data obtained varying values. Specifically, the pH ranged from a minimum of ~5.38 at control GP before oven-grill, to a maximum of ~6.08 at control GP after oven-grill, whereas the TBARS ranged from a minimum of ~9.38 mg MDA/kg at AS+GP $0.5\%$ to a maximum of ~26.36 mg MDA/kg at AS +GP $1\%$, the latter which resembled ($p \leq 0.05$) that of AS +GP $0.5\%$ (~26.27 mg MDA/kg). Further, the oven-grilling seemed to either increase or decrease the TBARS values of some marinated beef entrecôte meat samples. For instance, oven-grilling appeared to noticeably reduce ($p \leq 0.05$) the TBARS of control (from ~17.18 to ~16.91 mg MDA/kg), in contrast to the increase when AS (from ~11.73 to 20.45 mg MDA/kg) and IM (from ~14.09 to ~24.91 mg MDA/kg) were incorporated. The detected pH and TBARS differences, which came from the application of either after oven-grilling and or together with marination variants, would most likely have shelf-life implications.
**Figure 3:** *Changes in pH across the various marinated beef entrecôte meat samples before and after oven-grilling.The number representations for different colour shades are as follows: (1) control (antioxidant additive %= 0.0); (2) control (antioxidant additive %= 0.5); (3) control (antioxidant additive %= 1.0); (4) control (antioxidant additive %= 1.5); (5) AS (antioxidant additive %= 0.0); (6) AS (antioxidant additive %= 0.5); (7) AS (antioxidant additive %= 1.0); (8) AS (antioxidant additive %= 1.5); (9) IM (antioxidant additive %= 0.0); (10) IM (antioxidant additive %= 0.5); (11) IM (antioxidant additive %= 1.0); (12) IM (antioxidant additive %= 1.5). African spice, AS; Industrial marinade/pickle, IM; The antioxidant additives include cranberry pomace (CP), grape pomace (GP), and Baikal skullcap (BS).* **Figure 4:** *Variation of pH by difference across the various marinated oven-grilled beef entrecôte meat samples compared to control.The number representations are as follows: (1) control (antioxidant additive %= 0.0); (2) control (antioxidant additive %= 0.5); (3) control (antioxidant additive %= 1.0); (4) control (antioxidant additive %= 1.5); (5) AS (antioxidant additive %= 0.0); (6) AS (antioxidant additive %= 0.5); (7) AS (antioxidant additive %= 1.0); (8) AS (antioxidant additive %= 1.5); (9) IM (antioxidant additive %= 0.0); (10) IM (antioxidant additive %= 0.5); (11) IM (antioxidant additive %= 1.0); (12) IM (antioxidant additive %= 1.5). African spice, AS; Industrial marinade/pickle, IM; The antioxidant additives include cranberry pomace, grape pomace, and Baikal skullcap.* TABLE_PLACEHOLDER:Table 1 Figure 4 shows variation of pH by difference appears more at different marinated oven-grilled beef entrecote samples especially of GP, before CP, and then BS marination variants. Despite this, the application of oven-grill seemed to generally increase the pH regardless of marination variants. Knowing that pH value reflects the quality of beef meat and its suitability for various processing methods (Korkeala et al., 1986), to keep it reduced promises a positive shelf potential, which should avert off-odor believed to be facilitated by collagenases and other proteolytic enzymes associated with meat tenderisation (Siroli et al., 2020). To employ either increasing concentrations of CP, GP, or BS together with either AS or IM that builds up an herb mix should help to regulate the protein oxidation within the meat muscle (Xiong, 2022). Moreover, higher temperatures that come from oven-grilling should facilitate the release of oxygen, heme, and iron in meat products like beef. This situation might have made the marinated beef entrecôte meat of this current study to appear susceptible to lipid oxidation. If this situation were to progress, however, it would be demonstrated by the induced free radical production followed by undesirable off-odors/flavors (Amaral, Silva & Lannes, 2018). Largely applicable to beef carcasses, the functionality of muscles would corroborate the proportion of either slow-twitch oxidative or fast-twitch glycolytic pathways (Pereira et al., 2017).
## Changes in L*a*b* color, and cooking weight loss
The color stability of beef meat has been attributed to the presence of pigments, which ultimately depends on tissue composition and structure (Hashemi Gahruie et al., 2017). In this current work, the changes in L*a*b* color and cooking weight loss values of the various marinated oven-grilled beef entrecôte compared to control are respectively shown in Table 2 and Fig. 5. Varying range values of L*a*b* color (L*color: from 29.2 ± 2.4 at AS+GP $0.5\%$ after oven-grill to 41.3 ± 1.7 at control +GP $1.0\%$ after oven-grilling; a* color: from 2.15 ± 1.6 at IM +BS $1.5\%$ after oven-grilling to 17.54 ± 0.82 at AS + BS $1.0\%$; b* color: from 3.9 ± 0.1 at control+ CP $1.0\%$ to 15.76 ± 1.49 at IM +BS $0.5\%$) as well as cooking weight loss (for CP = from ~$6.05\%$ at IM with $0.5\%$ antioxidant additive to ~$46.03\%$ at IM with no antioxidant additive; for GP = from ~$29.92\%$ at AS with no antioxidant additive to ~$46.03\%$ at IM with no antioxidant additive; for BS: from ~27.79 at control with no antioxidant additive to ~$46.03\%$ at IM with no antioxidant additive) were found. To establish a clear link when comparing color and cooking weight loss of different marinated oven-grilled beef entrecote samples seems difficult at this study. Other parameters, for instance, the pH and TBARS levels might corroborate the cooking weight loss of different marinated oven-grilled beef entrecote samples at this study. We opine this because, earlier workers like Oreskovich et al. [ 1992], by evaluating how marinate pH influenced texture of beef meat, understood that cooking (weight) losses could reach $45\%$ at pH 4.24 and 5.38, but tended to decrease at pH 6.66 and 8.01.
To a large degree, the high temperature of 180 °C and process duration of 5 min set for the oven-grilling at this current study would most likely be contributing to the cooking weight loss outcomes of the different marinated oven-grilled beef entrecote samples. Probably, this heat treatment kickstarted the fibre contractions within the intramuscular connective tissue/muscle, which might have accounted for the differences in the cooking weight loss (Ježek et al., 2020). Besides oven grilling process to bring about some decreases in cooking weight loss, the physical condition of beef entrecôte muscle might not solely depend on the degree of moisture loss influence, but would include the anticipated infiltration of the marination variants. Alongside cooking weight loss, the application of oven-grilling across CP, GP and BS probably brought about some observable color trends, depicted by either increasing or decreasing L*a*b* values. For instance, whilst the oven-grilling largely increased the L* color, and decreased the a* color, it would largely fluctuate the b* color scales of different marinated beef entrecôte meat samples. An enhanced antioxidant effect should not reflect the decreases in a* value, which would be required to stabilize the color (Libera et al., 2018). Feasibly also, the oven-grilling might have facilitated the range values of L*a*b* color as well as cooking weight to expand towards the extreme values, potentially supplemented by increment concentrations of CP, GP and BS, alongside the incorporation of AS or IM.
## Changes in textural cutting force
Defined by certain homogeneous attributes and often adapted by food processing, meat texture often instrumentally determined explains key human physiological-psychological awareness of key rheological and associated properties (Novaković & Tomašević, 2017). In this current work, the changes in textural cutting force values of the various marinated oven-grilled beef entrecôte compared to control are shown in Table 3. Incorporating either AS or IM, the textural cutting force values showed promising ranges across CP (from 35.2 ± 5.83 N at IM without antioxidant additive, to 84.7 ± 10.28 N at IM +CP $1.5\%$), GP (from 35.2 ± 5.83 N at IM without antioxidant additive, to 83.8 ± 21.14 N at AS +GP $0.5\%$) and BS (from 35.2 ± 5.83 N at IM without antioxidant additive, to 70.3 ± 27.92 N at AS + BS $1.0\%$) samples. Probably, the CP, GP and BS concentrations might be increasing with textural cutting force.
**Table 3**
| nr | Antioxidant additive | Type of marinade | Percentage of antioxidant additive | Beef nutting force (N) |
| --- | --- | --- | --- | --- |
| 1 | CP | Control | 0.0 | 67.5bcde ± 7.2 |
| 2 | CP | Control | 0.5 | 59.7abcde ± 4.8 |
| 3 | CP | Control | 1.0 | 69.5cde ± 11.8 |
| 4 | CP | Control | 1.5 | 57.6abcde ± 15.9 |
| 5 | CP | AS | 0.0 | 68.7cde ± 5.2 |
| 6 | CP | AS | 0.5 | 35.6a ± 4.0 |
| 7 | CP | AS | 1.0 | 37.2ab ± 1.5 |
| 8 | CP | AS | 1.5 | 66.9bcde ± 11.5 |
| 9 | CP | IM | 0.0 | 35.2a ± 5.8 |
| 10 | CP | IM | 0.5 | 59.2abcde ± 24.4 |
| 11 | CP | IM | 1.0 | 81.9de ± 15.3 |
| 12 | CP | IM | 1.5 | 84.7e ± 10.3 |
| 13 | GP | Control | 0.0 | 81.5de ± 23.5 |
| 14 | GP | Control | 0.5 | 70.1cde ± 29.0 |
| 15 | GP | Control | 1.0 | 59.7abcde ± 10.8 |
| 16 | GP | Control | 1.5 | 60.9abcde ± 14.9 |
| 17 | GP | AS | 0.0 | 68.7cde ± 5.2 |
| 18 | GP | AS | 0.5 | 83.8de ± 21.1 |
| 19 | GP | AS | 1.0 | 62.0abcde ± 8.6 |
| 20 | GP | AS | 1.5 | 47.9abc ± 4.3 |
| 21 | GP | IM | 0.0 | 35.2a ± 5.8 |
| 22 | GP | IM | 0.5 | 40.6abc ± 11.8 |
| 23 | GP | IM | 1.0 | 67.5bcde ± 8.0 |
| 24 | GP | IM | 1.5 | 56.6abcde ± 2.6 |
| 25 | BS | Control | 0.0 | 64.9abcde ± 27.4 |
| 26 | BS | Control | 0.5 | 42.6abc ± 8.6 |
| 27 | BS | Control | 1.0 | 66.8bcde ± 10.6 |
| 28 | BS | Control | 1.5 | 68.0cde ± 31.5 |
| 29 | BS | AS | 0.0 | 68.7cde ± 5.2 |
| 30 | BS | AS | 0.5 | 59.6abcde ± 7.9 |
| 31 | BS | AS | 1.0 | 70.3cde ± 27.9 |
| 32 | BS | AS | 1.5 | 53.5abcd ± 6.2 |
| 33 | BS | IM | 0.0 | 35.2a ± 5.8 |
| 34 | BS | IM | 0.5 | 61.9abcde ± 34.9 |
| 35 | BS | IM | 1.0 | 54.3abcde ± 4.4 |
| 36 | BS | IM | 1.5 | 60.7abcde ± 7.8 |
Earlier workers like Oreskovich et al. [ 1992] understood that textural properties of beef meat are more likely to change in acidic compared to alkaline conditions. In this current work, higher textural cutting force values seemed to corroborate with samples that had more acidic-like pH values. Contextualizing this observation with the composition of muscle tissue, the connective aspects like myofibrillar proteins would help to build up the meat tenderness (Migdał et al., 2020). Moreover, any increase in the cutting force could associate with the cracking phenomena, which could happen within the muscle fibers to negatively influence the muscle tenderness (Xia et al., 2012). At slaughter, the meat structure would be affected as muscle glycogen increases with resistance to stress-induced (glycogen) depletion, alongside severe pH decreases (Olsson & Pickova, 2005). Whilst (beef) entrecôte samples comprise fat, connective tissue, as well as exudative juice, the muscle mass comprise between 35–$60\%$ of animal’s total weight (Redefine Meat, 2022), all of which should be among the influential considerations that underpin the textural cutting force values of the various marinated oven-grilled beef entrecôte samples.
## Changes in organoleptic aspects
Among key organoleptic attributes, it is believed that color, flavor and texture show strong influence on consumers’ overall acceptability of meat products (Hashemi Gahruie et al., 2017). In this current work, the changes in organoleptic aspects of various marinated oven-grilled beef entrecôte samples, specifically by way of sensory and textural profiles, are shown in Tables 4 and 5. Either incorporating CP, GP and or BS, and even when involving control, AS and IM, there were minimum and maximum ranges found in sensory (Flavor: from 2.79 ± 1.22 at control+CP $0.5\%$, to 4.50 ± 0.79 at AS +GP $1\%$; Appearance: from 3.14 ± 1.35 at IM without antioxidant additive, to 4.43 ± 0.79 at AS without antioxidant additive; Tenderness: from 1.88 ± 0.90 at control + GP $0.5\%$, to 4.06 ± 0.84 at IM+BS $0.5\%$; Taste: from 2.38 ± 1.11 at control +GP $1.5\%$, to 4.36 ± 0.63 at AS + CP $1.0\%$; Off-flavor: 3.88 ± 1.35 at control +GP $1.5\%$, to 5.00 ± 0.00 at either control+BS $0.5\%$, or AS +BS $1.5\%$), and textural (Hardness: from 3.86 ± 1.35 at control +CP $0.5\%$, to 8.14 ± 1.77 at control +GP $1.5\%$; Chewiness: from 3.75 ± 1.57 at IM+BS $0.5\%$, to 8.00 ± 1.83 at control+GP $1.5\%$; Gumminess: from 3.71 ± 2.36 at IM+GP $0.5\%$, to 6.71 ± 2.06 at control+GP $0.5\%$; Graininess: from 2.43 ± 1.13 at IM +GP $1.5\%$, to 4.57 ± 2.15 at IM+CP $0.5\%$; Greasiness: from 2.14 ± 1.07 at IM+ GP $0.5\%$, to 4.63 ± 1.63 at AS +BS $0.5\%$) profiles.
The organoleptic attributes obtained some statistical differences ($p \leq 0.05$) as well as resemblances ($p \leq 0.05$) from sensory and textural standpoints. Specifically, such resemblances might suggest the panelists were unable to differentiate between some specific samples at this study. The fluctuating values would also suggest the increasing the CP, GP and BS concentrations appears not always going along with some of the sensorial and textural profile attributes. Interestingly, the panelists provided somewhat consistently higher off-flavour scores to most evaluated marinated oven-grilled beef entrecôte samples. The application of marinades is believed to have the ability to influence color of meat, yet not negatively when sensorially evaluated by panelists (Siroli et al., 2020). From the combination of instrumental texture and sensory tenderness acceptability, however, it should be possible to detect when beef meat toughness becomes unacceptable (Schilling et al., 2003). More so, differences in flavor, juiciness, and tenderness detectable by organoleptic evaluation may well suggest the preservative potential of marinades (Kim et al., 2010).
## Conclusion
Different marinated oven-grilled beef entrecôte meat, specifically the resultant physicochemical and organoleptic attributes were investigated. Varying range values in pH, ABTS, DPPH, FRAP, TBARS, L*a*b* color scales, cooking weight loss, and textural cutting force, sensory and textural profile were detected. Moreover, the oven-grilling applied across CP, GP and BS largely produced some major color trends, which was either to increase or decrease the L*a*b* values. Also, increasing the CP, GP and BS concentrations might be help to increase the textural cutting force compared to control. The statistical differences and resemblances at organoleptic attributes were demonstrated by varying ranges, from sensory and textural profile standpoints. Considering that antioxidant values fell below control at some instances, the marinated oven-grilled beef entrecote samples of this work should have shelf promise. This has to be verified using different refrigerated packaging and storage conditions, during which the evaluation of other quality attributes like microbiological, volatile amines, amino acid as well as fatty acid/flavor profiles could be ascertained.
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|
---
title: 'Co-creating an intervention to promote physical activity in adolescents with
intellectual disabilities: lessons learned within the Move it, Move ID!-project'
authors:
- Laura Maenhout
- Maïté Verloigne
- Deborah Cairns
- Greet Cardon
- Geert Crombez
- Craig Melville
- Geert Van Hove
- Sofie Compernolle
journal: Research Involvement and Engagement
year: 2023
pmcid: PMC10024913
doi: 10.1186/s40900-023-00420-x
license: CC BY 4.0
---
# Co-creating an intervention to promote physical activity in adolescents with intellectual disabilities: lessons learned within the Move it, Move ID!-project
## Abstract
Studies show that people with intellectual disabilities are less physically active than the general population. This is a problem, since people with intellectual disabilities experience more health problems, and physical activity might be an important angle to reduce these health problems. However, current interventions to promote physical activity in this target group do not appear to work because they do not match their needs and preferences. Therefore, it is important to develop interventions together with them, in collaboration, what is called "co-creation”. This has not happened much in research with people with intellectual disabilities before (and especially not with adolescents having intellectual disabilities), because researchers often have the perception that they do not have the capabilities to understand and discuss research related topics. This study elaborates on the researchers’ experiences in conducting co-creative research with adolescents and young adults with mild to moderate intellectual disabilities, and formulates 'lessons learned' so that future researchers can start from these findings when they themselves want to engage in a co-creation process with this target group. The results showed that co-creation is feasible with this target group, if co-creation methods are selected that fit the target group (e.g. making use of visuals, asking concrete (non-abstract) questions and providing clear but short instructions). We suggest that (standardized) innovative and creative working methods are needed when conducting co-creation with this target group. Moreover, to be better armed against the enormous flexibility expected of a co-creative researcher, it might be helpful to make an assessment of the group dynamics before conducting co-creation. The presence and contribution of the physical education teacher in these co-creation sessions was seen as an added value for several reasons. By describing this entire process transparently and in detail, this could be a first step in making co-creation an evidence-based methodology, also for vulnerable populations.
### Background
Co-creation is a method to develop acceptable, contextually appropriate and potentially more effective interventions. Adolescents with intellectual disabilities (ID) seldomly participate in research and program development due to the assumption that they lack the capacity to understand and discuss the related topics.
### Objective
This study describes reflections on a co-creation process with adolescents with ID from the point of view of the researchers in developing an intervention to increase physical activity. It was the aim to highlight elements that must be considered when implementing co-creation and consequently formulate important lessons learned.
### Methods
Twenty-three adolescents (14–22 y) with mild to moderate ID participated in six co-creation sessions at their school. The objectives and working methods in each session are described. Inductive thematic analysis was conducted on the researchers' reflection forms, which were completed after each session.
### Results
Seven main themes could be distinguished from the data: experiences related to assistance (i.e., teacher presence) during sessions, the importance of building rapport, co-decision making power, the impact of different group dynamics, the relevance of adapted questioning, the influence of co-creative working methods and required characteristics of a co-creation researcher.
### Conclusion
Seven lessons learned were formulated when preparing and conducting co-creation with adolescents with ID. Innovative, concrete (non-abstract) and creative working methods are highly needed. Describing the entire process transparently could be a first step to turn co-creative research into an evidence-based methodology.
### Supplementary Information
The online version contains supplementary material available at 10.1186/s40900-023-00420-x.
The online version contains supplementary material available at 10.1186/s40900-023-00420-x.
## Background
Intellectual disabilities (ID) are defined as limitations in intellectual functioning (intelligence quotient (IQ) < 70) and adaptive behaviour, with an onset in childhood (< 22 years) [1]. Evidence shows that people with ID face more health problems than their peers without ID, such as higher rates of obesity, constipation, cardiovascular diseases and mental health problems [2–5]. This results in a reduction of about 20 years of life expectancy in comparison to the general population [6]. It is clear that these health inequalities, affecting an estimated 1–$3\%$ of people worldwide [7, 8], must be urgently addressed. One way is to promote a healthy lifestyle in young people with ID, such as the increase of physical activity (PA) [6, 9]. PA is associated with improved physical and mental health among adolescents [10–15]. However, adolescents with ID are less physically active than their typically developing peers [16–21]. Low activity levels furthermore track into adulthood, as adults with ID have also been reported to participate in little or no PA [22, 23]. Despite the need to find ways to promote PA in adolescents with ID [15, 16, 24–26], they are a neglected population in PA research. A systematic review from 2018 showed that only five studies involved adolescents with ID as participants in PA promotion interventions [16]. Moreover, the interventions were found to be mostly ineffective (i.e., $\frac{4}{5}$).
A reason for this ineffectiveness could be the lack of a population-specific approach in the development of the interventions [16, 27]. Adolescents with ID do not connect to PA interventions for the general population because the specific interpretation of influencing PA factors (e.g., social support) among these adolescents differ from those of their peers without ID [19, 20, 28, 29]. It is consequently key to immerse oneself in the world of this target group and look for tailored ways to promote their PA specifically. A co-creational approach is therefore seen as promising for the development of more acceptable, contextually appropriate and potentially more effective interventions [30–42]. Although co-creation is used in many domains (e.g. marketing), the definition of co-creation for public health interventions is used within this paper, which is collaborative public health intervention development by academics working alongside other stakeholders [32, 39], in which the population of interest is one of the most important stakeholders. Co-creation is supported by UN’s Sustainable Development Goals (SDG #17, ‘Partnership for the Goals’) as a necessary approach to reach public values such as citizenship, social justice and well-being [40, 43] and seems especially valuable to learn about and work with vulnerable, disadvantaged or at-risk populations, such as people with ID [31, 44, 45]. Moreover, co-creation seeks to centralise participation, which is one of the corner stones of the International Classification of Functioning, Disability and Health (ICF) framework [46]. Unfortunately, individuals with ID are seldom invited to express their opinions and emotions in research (e.g., on PA promotion interventions) [47]. Parents or professional caregivers are often interviewed on their behalf. This can result in the carers’ views being presented rather than the true participant preferences or experiences. This not only has implications for the reliability of the input obtained, but also excludes the voice of individuals with ID [48].
As part of the Move it, Move ID! project, an intervention was developed in co-creation with adolescents with ID to promote their PA. Although co-creation might lead to more promising interventions, it is often not described in detail and lacks transparency (i.e., what has been done and in what way?). As a consequence, there are few good examples or lessons learned to inspire future researchers. The aims of this study were therefore to highlight elements that must be considered when implementing co-creation and to discuss important lessons learned.
## Participants and recruitment
Previous research has learned that recruiting people with ID for research purposes can be challenging. There is less reliance on, for example, advertising or social media to recruit respondents to participate [49]. It may be more important to use an active and personal approach, and to connect with people or organisations close to them: parents, other family members, teachers, health care providers, etc. [ 49]. For this reason, purposive sampling was used to recruit participants through special needs schools in Flanders, Belgium. In February and March 2021, two physical education (PE) teachers of different special needs schools were contacted. Teachers were asked whether they were interested in being involved with one of their classes in co-creating a PA promotion intervention. Each teacher subsequently suggested one class group to take part in co-creating the intervention. Recruiting in this setting may have several advantages. First, participants know each other, which may facilitate discussion later on during the co-creation process [30, 48]. Second, when the researcher meets the adolescents in their school environment, they or their parents do not have to make the effort to reach a venue (i.e., rely on others to get to a location, transport costs, etc.). Third, the environment is familiar, which can make them feel more at ease. And fourth, in Flanders, children and young people with a disability are classified in different types of special education on the basis of their diagnosis. Approaching adolescents with mild to moderate ID according to the Flemish school system of special education could therefore be explained methodically, instead of testing them or specifically asking for diagnoses.
The participants consisted of two different class groups (group A and B). All adolescents in both class groups agreed to participate. In co-creative literature for the general population, a recommendation of 10–12 co-creators per group is advised, which may also account for dropouts due to the process being conducted over a series of meetings [32, 50]. This guideline was followed as there are currently no guidelines for the specific target group available and the recommendation seemed feasible for this target group as well. Group A comprised 14 adolescents between 17–22 years with a level of mild to moderate ID (Mage = 20.33 ± 1.94, $21.4\%$ girls). Four adolescents had a comorbidity of autism. Group B consisted of nine adolescents between 14–15 years with mild ID (Mage = 14.22 ± 0.44, $66.7\%$ girls). One adolescent had a comorbidity of autism and one adolescent had a comorbidity of attention deficit and hyperactivity disorder. In total, three (of the 23) participants were part of a sports club. No participants dropped out of the study, but not all participants attended every co-creation session (e.g., because they were sick or suspended).
## Procedure
During the period April-June 2021, six sessions were organised in each class group: one introductory session and five co-creation sessions following the Behaviour Change Wheel-framework. The Behaviour Change *Wheel is* a theoretical framework to ensure a scientific and systematic development of an intervention [51], making this development process a combination between applying a theoretical framework and co-creation. The application of the Behaviour Change Wheel and the findings that emerged in the process are discussed in more detail elsewhere (see Maenhout et al., under review). The co-creation sessions took place at school (in the classroom) during two consecutive class hours (i.e., total of 12 h per group). During each session, the PE teacher was present, as were the principal investigator (PI) (LM, PhD) and one or two assistants of the PI (e.g., master students in Health Promotion, intern, colleague). The PI was the facilitator during all sessions, the assistants were co-facilitators. It was the intention to bring the same co-facilitators in all sessions for the sake of structure and recognisability for the target group, but due to practical circumstances (i.e., exams for the students) this was not feasible. The PI has a master’s degree in Special Needs Education and Disability Studies, with training on how to interact with the target group. She has years of experience as a supervisor at summer camps for children/young people with disabilities and previous working experience as an educator with deaf and hearing-impaired adolescents which provided expertise on easy read language and visualisation. Moreover, the PI is currently based at the Department of Movement and Sports Sciences (Ghent University), and more specifically the unit 'Physical Activity and Health' which has a wealth of experience in developing PA interventions for different target groups. Each co-creation session started with a repetition of the purpose of the project, followed by clearly communicating the objectives that would be addressed in each session, helping to place the present meeting within the overall context of the process [32, 50]. Therefore, a visual schedule was created that recurred at the start of each session, although this had to be adaptable depending on how the process evolved [32]. Furthermore, five ground rules of participation were agreed upon by all attendees, which were repeated during every session: 1) everyone gets a chance to speak, 2) we listen to each other, 3) everyone has a different idea and that is okay, 4) we do not laugh with each other and 5) there are no right or wrong answers [48, 50, 52]. Finally, after each session, both the adolescents as well as the researchers completed a process evaluation form based on the tool developed by Dewaele et al. [ 2018] for the general population [50] (e.g., satisfaction with participation, feeling at the session, atmosphere, respectful interactions, etc.) [ 32]. The process evaluation forms for the adolescents were adapted in an easy language. Adolescents marked thumbs (not good—neutral—good) for each statement (see Additional file 1, in the spirit of transparency, the forms were translated in English). They were allowed to do this anonymously to encourage honest answers. The main reason to include these process evaluations was to ensure that the co-creation sessions always took place in a positive, respectful and productive atmosphere by learning how the sessions could possibly be handled differently. When an issue kept cropping up from the process evaluation forms and affected multiple participants (e.g., thumbs down on ‘I understood everything that was said’), the PI tried to adjust subsequent sessions to ensure positive progress. At the end of the co-creation process, all participants, including teachers, received two cinema tickets as an incentive about which they were informed when they decided to participate.
## Ethical considerations and barriers experienced
The participants of the current study were minors and had ID. Consequently, signed informed consents were required from both the adolescents and their parents/legal guardians to allow processing personal data on a legal basis (i.e., GDPR). Unfortunately, receiving signed informed consents from the parents was difficult, as many of them were socially disadvantaged, and lacked the skills and attitude to sign and return the consent form [45, 53]. Moreover, the Covid-19 measures prevented us from actively reaching the parents of the adolescents to verbally explain the purpose and design of the study. To avoid dropout before the start of the study, and make sure that also the most vulnerable adolescents were represented during the developmental process, an alternative strategy was discussed with the Data Protection Officer of Ghent University. Based on an argumentation of the PI, the legal basis (not to be confused with the ethical context), was changed from 'active informed consent' of the parents to one of 'public interest' (i.e., the development of an intervention to improve the physical health and quality of life of this target group fulfils a goal of public interest). Importantly, the use of the ‘public interest’ legal basis did not relieve the researchers of the duty to inform the participants. The participating minors and their parents or legal guardians received accessible and comprehensive information on the design and purpose of the study, as well as on the processing of their data (see consent form of participants with ID in Additional file 2, translated in English). For ethical reasons, active and passive informed consent was obtained from the participants and their parents or legal guardians respectively. The initial introductory session with the adolescents also involved an extensive step-by-step review of the information and consent process with time to discuss, in line with previous studies in the target group [54, 55]. A number of questions were asked to ensure that the participants understood what was being asked of them (e.g., Can you tell me what this research is about? Who decides if you want to participate? What do you do if you no longer want to participate?). The researcher was careful to communicate clearly, using appropriate language for the level of ID and was patient and empathic in her interactions with the adolescents [55, 56]. Going through multiple co-creation sessions with the adolescents also meant that the frequent contact throughout the process allowed the researcher to continuously evaluate whether there was still consent. Adolescents with ID took part voluntarily and could always decide to cancel their attendance.
## Goals and working methods used during the sessions
The facilitators provided working methods adapted to the wide range of knowledge, skills and abilities people with ID have to express themselves [30, 39]. In particular, concise visual materials and interaction were used as both seem to be valued by people with ID [30, 52]. Since no information is yet available in literature on working methods for this specific target group, two sources served as inspiration for preparing the co-creation sessions. Firstly, the Department of Special Needs Education and Disability Studies of Ghent University was contacted to exchange experiences regarding good practices of participatory or co-creative research with participants with ID. They suggested 'Plan P', which is based on the same methodology as co-design, but explained in an accessible way so that people with ID can start their own co-design process [37]. Plan P was developed by 'Onze Nieuwe Toekomst' (‘Our New Future’), a Flemish organisation by and for people with disabilities. Secondly, the online database of De Ambrassade, an expertise centre for everything to do with youth work, youth information and youth policy in Flanders, was consulted. Within this database, different working methods for youth are explained (in Dutch).
The goals of each session and the co-creative methods used to reach those goals are described in Table 1. Pictures visualising methods and materials mentioned in the table can be found in Additional files 3, 4 and 5.Table 1Description of the goals and working methods used in each co-creation sessionSessionGoal(s)Description of session (e.g., working methods)MaterialExamples11) Introduction of the project and the researcher2) Going through the informed consent process together3) Explaining definition of PA, and exploring adolescents’ opinions towards PA1) Explanation of the project and ground rules of participation2) Group discussion in which adolescents introduced themselves3) Statements regarding PA where adolescents were asked to go to the left or the right side of the room depending on their agreement with the specific statements1) PowerPoint2) Informed consents and information letters3) List of statements the researcher could read out loudExample statements: “I think exercise is important”, “I think I exercise enough”, “I have enough time to exercise”, “I have enough opportunities to exercise”, etc21) Identify what adolescents (dis)like about types of PA, and what they already do in terms of PA2) Understanding the facilitators and barriers of PA for adolescents with ID1) Write down or draw all things that came to mind hearing the central theme 'movement and sports'2) Choose activity cards that appealed or did not appeal to them. The chosen cards were then discussed3) Making posters for facilitators and barriers to PA. If this did not come spontaneously, examples were given from the literature using visual cards they could pick if they felt that this applied to them1) Large paper in the centre of the room with several markers2) Activity cards which contained images of different sports or physical activities3) Red and green poster4) Visual cards of facilitators and barriers to PAExample questions using activity cards: Why do you (dis)like (the activity)? Did you already do (the activity)? Can you tell me about the last time you did (the activity)? Do you know where to get information about (the activity)? Who did you do (the activity) with? Where did you do (the activity)? Who chose to do this activity? Where do you do (the activity)?Example questions using posters: What makes it hard for you to do sports/to do PA? What makes it easy for you to do sports/PA?3Explore the most important intervention goals on which the intervention should focus1) Previously mentioned barriers were presented and explained using a micro-meso-macro model2) Possible intervention goals were visualised on a poster. During explanation, adolescents could vote with a green/red sticker whether they thought this goal should be included in the intervention or could be left out3) Adolescents worked together to rank the intervention goals from most important to least important1) Micro-meso-macro model of the identified barriers in session 22) Posters on which the 16 possible intervention goals (based on barriers mentioned in session 2) were visually represented3) Green and red stickers to voteExample of an intervention goal: from 'not knowing what there is about exercise', 'not knowing where I can exercise', 'I need information', 'not knowing what suits me', 'not knowing how much I should exercise', 1 poster was made with the intervention goal 'knowledge provision'4Identify how to implement behaviour change techniques (BCTs) to be accessible and engaging for adolescents with IDFor each selected intervention goal (session 3) a story about a fictitious adolescent was described, so that the participants could empathise with the story. Next, adolescents were asked in which way they thought the person would feel most helped. First, they were asked to brainstorm on this themselves in small groups. Second, concrete examples were given from previous interventions or apps on a PowerPoint slide for the adolescents to vote on with red or green cards1) PowerPoint slides with picture of fictitious adolescent and examples from other interventions or existing apps2) Green and red cardsStorytelling: ‘*This is* Marie [picture]. Marie is 17 years old. Marie does not like to exercise. Marie has already noticed that it is easier for her to exercise when she is encouraged to do so or when movement is part of a game. How could we encourage Marie to exercise?’BCT ‘reward’, examples: Getting coins to dress up an avatar; receive a badge; social rewards through likes; material rewards like a power bank, water bottle, voucher for a (sports) shop; earn coins online so that they can exchange them for a discount at a shop, etc51) Explore which apps adolescents are currently using2) Identify facilitators and barriers to app use3) Identify design preferences1) Group discussion in which it was asked which apps adolescents mostly use in their daily lives, and what they liked about these apps2) Smaller groups received an iPad with some apps they could test. Each group was asked to write down on a green sheet what they liked about the app(s) and on a red sheet what they did not like. Afterwards, the findings were shared with the larger group1) iPads and downloaded apps2) Green and red piece of paperExample questions of group discussion: Which apps do you use? What do you (dis)like about these apps? What makes those apps easy (or difficult) for you to use? What do you (dis)like about the design? When do you find apps not interesting/too difficult? Do you know/use apps that have something to do with PA?Examples of apps that were tested: Fitbit, #LIFEGOALS, SideKick Health, Seven, Zombies Run, etc. Apps were selected on the basis of familiarity with young people (e.g. Fitbit, Zombies Run), previously developed apps within the research group (i.e., #LIFEGOALS [57]) and other apps developed for the target group within research (i.e., SideKick Health [58])61) Explore the opinion of adolescents with ID on a possible intervention idea2) Find out which incentives young people with ID prefer3) Probe how adolescents feel about the use of an accelerometer during an intervention1) Adolescents were divided into smaller groups to reflect on an intervention idea presented by the PI, supervised by a member of the research team2) *Using a* PowerPoint slide, examples of possible incentives were presented whereby adolescents could indicate their preference as to which incentive they liked best3) *After a* general explanation what an accelerometer is, adolescents could test some accelerometers themselves (wrist/waist worn) and give their opinion1) PowerPoint slide with intervention idea2) PowerPoint slide with examples of incentives3) Accelerometers adolescents could test (an Axivity AX3 (with wristband) and an Actigraph GT3X + (with waist belt))Example questions on the intervention idea: *What is* the best way to reach young people and ask them to participate? How to communicate all information about the project?Example questions probing the accelerometers: What do you think about an accelerometer? Would you mind wearing it? Which wearing location would you prefer? What do you think about the length of time you have to wear it? How can we remind you to wear it? Would you keep a diary? Can parents help?
## Measurement instrument
The reflection forms that the researchers (both PI and co-facilitators) completed after each session, formed the basis of this paper. The aim of this form according to the researchers that have developed it is to feed discussion about the co-creation process, and not to be used as mere quantitative measure [50]. The reflection form started with 10 open questions (e.g., “what went well”, “how can we bring out the qualities in the group”, etc.), followed by a table with 19 statements where the degree of agreement could be indicated on a 5-point Likert scale (see Additional file 6). The goal of this reflection form was to stimulate the researchers to reflect as broadly as possible, whereby both positive and working points could be highlighted (i.e., what went well? And what c/should be improved?). In addition, the researchers were free to supplement reflections that were not specifically asked for in the form. Furthermore, any extra information the PE teacher provided before, after or during breaks of the sessions was written down in the reflection forms of the PI. In the reflection forms, the PI also reflected on the results that came from the adolescents' process evaluations after each session. In this way, the adolescents' process evaluations were also included in this paper. Additional file 7 shows a description of the results of those process evaluations per session and per group. The process evaluation forms of the participants with ID were indirectly included in the data analysis through the reflections on them by the PI, but were not directly included in the analysis process.
## Analysis
Researchers’ reflections resulted in a total of 29 forms. Inductive thematic analysis was applied to map the most important results. Thematic analysis is a method of identifying, analysing and reporting on themes and sub-themes within data [59]. Inductive or “bottom-up” thematic analysis codes the data without a pre-existing framework [59]. To code the data, we followed the analysis process described by Braun and Clarke [2006], who divide the process into six separate phases [59].
The first step was to familiarize with the data (i.e., reading through forms several times). As transcription of the material was not needed, the PI (LM) read the reflections and wrote some general findings down. In the second step, the data were read again, and initial codes were generated by two independent coders (LM and SC) using qualitative data analysis software NVivo 12.0. A second researcher (SC)—who was not present during the co-creation sessions—analysed the data separately to keep the analysis of the data as objective as possible. A consensual approach was adopted, in which inconsistencies were discussed between the two analysing researchers. In the third step, codes were brought together in different themes by LM, establishing a first differentiation between main and subthemes. Next, all data was read again but with the identified themes in mind. This was done to check whether the data was well captured by the themes. In the next steps, the researchers LM and SC discussed and defined the themes to finally reach a fully analytical narrative with vivid quotations. Lastly, the results were reviewed by the assistants involved in the co-creation sessions to check whether the identified themes matched their experiences.
The COREQ (COnsolidated criteria for REporting Qualitative research) checklist [60] and GRIPP2 (Guidance for Reporting Involvement of Patients and the Public) reporting checklist [61] were consulted to ensure that the data was reported as broadly as possible and were added in Additional files 8 and 9 respectively.
## Results
Figure 1 provides an overview of the seven main themes that emerged, and which codes contributed to that theme (i.e., what a reader can expect to find in terms of information within a theme). Fig. 1Overview of the main themes and codes that contributed to those themes
## Presence of staff members
The presence and participation of the PE teacher during the sessions was felt to be of added value for several reasons. First, a number of adolescents saw the teacher as a familiar figure and confidant, which made them more inclined to participate. For example, it was noticed that adolescents first whispered an answer to the teacher before mentioning it in front of the whole group, probably to be sure their answer was meaningful. Second, the teachers acted as translators if the question was not entirely clear to the adolescent. Third, they knew the context of the adolescent better than the PI did. Consequently, either questions asked by the researcher or answers given by the adolescents could be contextualised by the teacher so both sides understood each other sufficiently. Moreover, tips could be given to the PI towards adapting questioning. “Within co-creation, the teacher also participates: writes something down now and then (e.g. on a large sheet), participates in discussions,... You notice that pupils are at ease as a result. The teacher's input can also help to put things in context: e.g. what they mainly do at school, why they do not like certain things (e.g. swimming).” ( PI, session 2, group A) Fourth, PE teachers were able to give input from their experiences in physical education lessons. This helped because the teachers thought about the project from a different view, providing a fresh perspective to the topic. “The presence of [teacher name] during the sessions is also a great asset to this group. [ Teacher name] first lets the group answer by itself, but now and then he completes the questions or challenges the group to think 'differently'. E.g.: if everyone puts a green sticker on an intervention goal, he can give an example of a red sticker, which makes the group reflect on it.” ( PI, session 3, group A) Moreover, the teachers were able to contribute to the structure of the sessions, as they knew the group better. For example, each session was planned to begin with a reiteration of the project's purpose, where the session was specifically situated within the process and what the agreements were within a session (see ‘Procedure’). In consultation with one of the two teachers after the fourth session, it was decided not to repeat this anymore the other two remaining sessions because she noticed adolescents quickly lost their focus because of it. The teacher suggested it would be better to jump right in and get them engaged immediately.
## Importance of building rapport
The first session was a real search for the researcher(s): not knowing the group, not knowing the teacher, not knowing the context adolescents live in, etc. Throughout the sessions, the researcher got to know both the group and the teacher better and gradually started to build rapport with the participants. After a few sessions, it was for example noticeable that during breaks, conversations were held that were often outside the scope of the project. Building that bond is necessary to get as close as possible to the perception of the adolescents and to give them the confidence to share their opinion. Throughout several sessions, the researchers could observe that the participants felt increasingly at ease, for example by sharing their openness about more difficult topics such as their home situations. “By creating more trust, the participants were more open about their experiences and feelings. This is something that has to grow throughout the process.” ( masters student, session 2, group B) Moreover, a relationship of trust not only seemed important between the participants and the researcher, but also among participants themselves. “They trusted each other and everyone could speak their minds. They knew a lot about each other and picked up on it.” ( masters student, session 2, group A)
## Co-decision making power
An unprecedented step for the researcher to take was daring to let go of control, and give the co-creators the autonomy and ownership to steer the process. If the PI was in doubt or lost as to which methods worked best for the group, or how best to address the participants, the participants themselves were asked how they thought they could best be approached. Moreover, by means of the process evaluation forms that the adolescents completed, the researchers were able to check how the participants had actually felt during the different sessions, so that appropriate adjustments could be made. These forms gave the participants a voice to guide the course of events. “The main conclusion is that the majority of the adolescents did not understand everything that was being said. In the next sessions, I will have to pay attention to speaking more slowly, more clearly, more concretely.” ( PI, session 1, group B)“Very positive process evaluation. Almost all students gave a green thumbs up to all statements. The arrangement of three separate groups with three facilitators, without any feedback to the whole class, seemed to suit this group best.” ( PI, session 6, group B)
## Group dynamics
The co-creation sessions took place in two different groups, whereby it was immediately apparent that both groups were different and consequently required a different approach, even though the sessions for both were initially set up in a similar way.
In both groups, it was striking that the more articulate individuals quickly took the lead, and the more silent adolescents often just followed them. However, both groups were different in that aspect. Adolescents from the older group (17-22y) seemed to be stronger in having their own opinions and expressing them, even if these did not entirely correspond to the opinions of the more expressive participants or the majority. The youngest group (14-15y) seemed to struggle more with this. Most of them had a withdrawn attitude. It could be inferred that there was a lot of compliance in the input they provided; not compliance towards the researcher(s), but towards their more outspoken peers leading to little interaction between them. The teacher of the youngest group explained that this class group was actually a combination of three different very small class groups, who did not always attend lessons together. The oldest group, on the other hand, had been in the same class for years and seemed to respect each other's opinions despite their individual differences. The different group dynamics that prevailed are closely related to the previous paragraph that indicates that there should be an atmosphere of trust among the participants in order to make co-creation a success. “In the exercise where they had to work together [ranking the intervention goals], there was very little cooperation, but rather everyone working individually. The opinion of the three outspoken girls was followed the most. I don't know if the rest of the group agreed with everything, as there was no discussion. When I asked if everyone agreed or if someone would put an intervention goal in a different place, there was again no response.” ( PI, session 3, group B) To counteract these groups dynamics, the decision was made to divide the youngest group into their usual smaller class groups as much as possible during the course of the sessions. This way, everyone would be motivated to speak up and express their opinions honestly. “There was good interaction and the students cooperated well. The students dared to speak their mind honestly. This went better because of the smaller groups.” ( master student, session 2, group B)“In the small groups, they feel at ease and dare to talk more. When this has to be shared with the larger group, it became quiet again.” ( PI, session 4, group B)
## Adapted questioning matters
How questions were asked proved to have an impact in this target group. First, it was important to present the questions as concretely as possible, with many (visual) examples for clarification. We made a lot of effort to use language appropriate to the target group and to use other, more easy words (i.e., no jargon) to explain concepts if it was felt that the participants did not understand everything (e.g. when there was no response to questions). However, it seemed difficult to assess what participants did or did not understand. For example, due to the division in smaller groups, some participants dared to tell the researcher for the first time that they did not understand while they did not do this, or hardly at all, in (earlier) group discussions. “Words that I have used several times over the course of the sessions are actually unclear to the adolescents. For example, the word 'motivate'. [ Student name] did not understand what I actually meant by this, even though I think I used this word in every previous session. Somehow, this made me a little insecure about the previous sessions, because I have the idea that they did not understand everything I asked them then either.” ( PI, session 5, group B) Therefore, during breaks, the PE teacher was consulted whether the questions were clear enough for the group. Participants often really needed a choice between two options (i.e., ‘do you prefer this or do you prefer that?’). It was therefore experienced difficult for the researcher to find a balance between asking a concrete question and not wanting to fill in too much for the adolescents. Furthermore, adolescents who were more in the background were more encouraged to take part in the conversation when questions were asked more individually. “The whole class took part in the group discussion. If the somewhat quieter participants were asked a question directly, they also spoke openly about their experiences.” ( master student, session 1, group A) It was also important to keep the adolescents’ context in mind when asking questions and the sensitivity in language use in that respect. For example, many participants came from a more socially disadvantaged home situation, where not both parents were present. When asking a question, it was then important to talk about 'parents' or ‘at home’, rather than 'mum' or 'dad' specifically, as this could hurt the feelings of young people where 'mum' or 'dad' was not present, leading to resistance in the co-creation sessions.
## Influence of the co-creative methods
Adolescents in both groups were hesitant when being asked a general question in front of the whole group, for example 'which apps do you use and what do you like/dislike about them'. With such a general question, it usually remained silent in which a wait-and-see attitude was adopted. It was remarkable that the adolescents became more relaxed when offered a very concrete exercise (e.g., statements or test apps on an iPad) and visual materials they could interact with (e.g., cards with pictures or posters they could vote on). Getting creative was an entry point for further discussion: in the statement exercise, adolescents could indicate why they were standing on one side of the room and not on the other side; using the activity cards, they could explain why they had chosen a particular card, or why they had voted for a particular poster; when testing different apps on the iPads, adolescents could give their input on that basis. “[Teacher name] indicated that using the [physical activity] cards was a good method for the adolescents, that she was stunned by some of the young people's cooperation and that she will certainly use this way of working in her lessons in the future.” ( PI, session 2, group B) Moreover, creative/interactive methods were also a way of getting less talkative adolescents to integrate their opinions as well. For example, in the poster exercise where adolescents had to vote on intervention goals, verbally expressing an opinion was difficult for some, but through the sticker-method they could at least indicate whether they agreed the intervention goal should be integrated (green) or not (red).
Some co-creative methods were more successful than others. The session with the visual cards showing examples of barriers and facilitators to PA provided considerable input due to the comprehensibility for the participants. Both teachers were enthusiastic about this method and indicated that it was also interesting for them to discover which methods did or did not work well with their students. One teacher indicated that this was a very good way to better understand young people with ID’s thoughts concerning movement. “Splitting the group in two and making the assignment very visual (with cards: both different types of physical activities and barriers/facilitators to physical activity) really helped the adolescents. In both exercises, the groups participated well and a lot of interesting information came out.” ( PI, session 2) In the session on intervention goals, goals were displayed visually per poster, and adolescents could vote with stickers. This session proceeded smoothly because of the interaction and active participation of the adolescents. When placing a sticker, some adolescents already explained more about why they placed a certain sticker without even being asked. Ranking the intervention goals, however, seemed to be difficult for both groups, but for a different reason. In the youngest group, there was no cooperation: more outspoken participants worked individually, and the others watched. The oldest group could not make a choice which goal they considered more important, and lumped everything together. Only two of the 16 goals were clearly chosen as less relevant. It could have worked better if the larger group was divided into smaller groups for this exercise, increasing discussion.
Getting to test apps on an iPad was a success. The adolescents were curious about the apps, so they often enthusiastically tried out different things. By allowing adolescents to test apps and think aloud, it was possible to learn first-hand what they found important.
A technique that worked less well was the story-telling during the fourth session. Whereas the youngest group was involved in the stories: “do you really know this person, where does he live?”, the oldest group did not react to this. However, it became apparent that this method was flawed due to the abstract nature of the task. Adolescents were asked to think about something that was not there yet, or without knowing what it might look like, leading to the fact that they did not really understand what the exercise was about or what was expected of them. In contrast, the previous sessions included concrete exercises, with clear direction, and adolescents were much more interactive. Due to the struggle with this abstractness, it was decided to skip the BCTs that did not have specific examples (e.g., valued self-identity, identity associated with changed behaviour), as it was noted that there was little or no response. In the last session, we did also not proceed with asking adolescents to think about a possible intervention on their own in small groups, but instead for the researcher to present an intervention idea that was based on the input adolescents provided in previous sessions and ask for their feedback. “This session is difficult for the adolescents because they have little idea of what an app can do to meet their chosen intervention goals.” ( teacher, session 4, group B)“The process evaluation forms mainly showed that the majority of adolescents did not understand everything that was being said. A possible explanation could be that these two rather theoretical sessions (3 and 4) are rather abstract, and that it is difficult for young people to imagine an app that does not actually exist yet.” ( PI, session 4, group B) Furthermore, showing examples on a PowerPoint slide seemed to hinder the imagination of the participants. Using this method, participants were already being pushed in a direction somehow. This became clear with a PowerPoint slide showing possible incentives. Participants gave their opinion on incentives that were presented, but no other input, apart from the slide, was given.
Tasks where participants had to write down certain things also hampered the process. A method in which they can work creatively or interactively suited them better. “Part of the exercise was for the groups to write down what they had come up with. It struck me that writing this down was already a barrier in itself. The young people preferred to remember what they wanted to say, rather than writing it down.” ( PI, session 4, group A) Lastly, the setting in which the co-creation took place could have played a role as well. For example, during the second session in the oldest group, the weather was very nice, so it was decided to go outside with a group, which positively influenced the discussion dynamics.
## Required characteristics of co-creation researchers
By conducting these sessions, it was noted that being a co-creative researcher requires enthusiasm, patience, flexibility and openness. First of all, it was important to remain enthusiastic and generate interest among participants by organising the sessions in an exciting way. It was noted that enthusiasm ignites: by being enthusiastic in front of the group, the group in turn reacted engaging and enthusiastically as well. Moreover, patience was needed. If the group did not understand the researcher or question, it was key to keep searching for ways where a similar language could be found that maximised cooperation, for example by using creativity. This required energy, but there was no point in giving up if something did not go as expected. However, when the co-creative process did not go smoothly, efforts were made to adopt a positive attitude: colleagues reminded the PI that no answer is also an answer. It was perceived helpful to share uncertainties with the research team during the process. Furthermore, despite a good preparation and a clear structure at the beginning of a session, a lot of flexibility was needed from the PI. Usually, a session did not go as planned at all. For example, teachers had to reschedule sessions for various reasons (e.g., excursions, info days at school, teachers were not present themselves due to private reasons, etc.), or there was not enough time to discuss everything that was planned, or vice versa there was too much time left without having prepared another exercise. “The statements ran out pretty quickly. I asked a number of other questions, but felt less prepared.” ( PI, session 1, group B) Moreover, it was noticed that certain methods did not work for one group, whereas they worked for the other group, or that it was just not working for both groups. A session had to be prepared on fairly short notice (i.e., one week) based on the input from the previous session(s). In the context of flexibility, it was important to have a back-up plan ready if it was felt that something was not working, or if there were time constraints. Having a back-up plan ready, made researchers feel more confident. “Co-creation is also about letting go of control and initial plans. It is searching for what works best for the group you have in front of you.” ( PI, session 4, group B) Lastly, it was important to be open to what interests the participants. This required adaptability to letting go of what the PI had in mind based on the literature. Moreover, in the context of openness, the PI also shared something about herself from time to time. This helped to build rapport with the participants, as it was noted that participants were more open and vulnerable when the researcher did the same. In this respect, a balance had to be found for the researcher in how far things of her personal life were shared.
## Principal findings, practical implications and future recommendations
This paper reflects on the co-creation process during the development of the Move it, Move ID!-intervention from the researchers' perspective. Butler et al. [ 2012] already stated a decade ago: ‘‘we would really like other researchers to write about their experiences about doing research together, both the good and the bad, so we can learn from each other’’ ([62], p. 142). Notwithstanding this call for transparency, we remain often in the dark on this topic when reading papers. To address this issue, we described the co-creative process used to develop a PA intervention for adolescents with ID in detail and we formulated seven elements that could be taken into account to make co-creation feasible in this target group, being 1) the presence of staff members, 2) building rapport, 3) co-decision making power, 4) the adaptation to the group dynamics at play, 5) the necessity of adapted questioning, 6) the influence of the co-creative methods and 7) the required characteristics of a co-creative researcher. In what follows, the most striking findings are discussed in more detail. Although the focus of this research team was on developing a PA promotion intervention, we assume that the results regarding lessons learned would not have differed greatly had a different behaviour been central.
The presence of staff members (here the PE teacher) during the co-creation process was seen as an added value for several reasons. Informal talks with the PE teacher(s) during breaks or after the sessions were seen as beneficial to gain a deeper understanding of the health problem being addressed [32]. However, during the sessions it became clear that the teachers were not well informed about their role. In preparing the co-creation sessions, the PI mainly focused on finding ways to involve the target group in the discussions, whereby the role of the teacher was somewhat lost sight of. This only emerged when the teacher asked questions or spontaneously took on this role. Teachers did not know whether they were allowed to participate. It is therefore considered important to clearly communicate their role before the sessions start. Of course, the presence of a teacher could also have a downside: e.g. young people who would prefer not to say certain things because their teacher is present. This was however not noticed within the two groups of this study. Both classes had a good relationship with their teacher. To decide whether to include the teacher in the co-creation process, a pre-observation of the group dynamics could provide answers to this question.
Within this co-creative process, co-decision-making was pursued by having the participants give their opinion on the process through the process evaluation forms after each session, as well as by asking for alternative approaches if a session did not run smoothly. The participants were co-creators, which means that they were on the same level as the researcher. The aim was to strive for an equal relationship, where the researcher’s opinion had no more weight than opinion of the others. A subsequent session was prepared fully based on the input received from the previous session(s). However, the content and flow of the sessions was guided by a theoretical framework, the Behaviour Change Wheel (see Maenhout et al., under review). This guidance was chosen to support the process theoretically, as well as to provide clarification within a project application. However, while trying to follow a guide, a difficult balancing act was noted with full co-decision-making power of participants. Co-creative research often takes the researcher to places that were not expected, in which co-decision-making is established by following the path participants are taking. Nevertheless, researchers cannot free themselves of their theoretical and epistemological commitments [59]. Co-creative research cannot be fitted into pre-defined categories or themes, but data can also not be coded in a vacuum. The use of the reflection forms taught us that it is important to reflect sufficiently as a researcher and to exchange thoughts and findings with, for example, the team or colleagues so as to remain aware that the researcher’s view is only one view of reality. Abma et al. [ 2019] expressed this perfectly by stating that "it is not just about exploring others' frames but also critically reflecting on our own as we deepen the inquiry process" [63]. A recommendation for future research could be to add a person/persons from the target group to the research team (i.e., co-researcher(s)) already at the start of the project to boost co-decision-making. According to Smith et al. [ 2022] this is called equitable and experientially-informed research in which people with relevant lived experience/experiential knowledge are seen as essential to the research [42]. Equitable partnerships between the different contributors are promoted and maintained throughout the entire research process, so a variety of perspectives are included and not just that of the (principal) researcher(s). Two studies could provide inspiration concerning inclusive research with people with ID: 1) a study describing the role, barriers and supports of co-researchers with ID in Chile [64] and 2) a consensus statement on how to conduct inclusive health research with people with ID [47].
When preparing the co-creation process, it was remarkable that there are currently few recommendations on how best to approach co-creation research with this target group. There are studies that make general recommendations but lack practical examples to get started [42, 47, 54]. For example, for the specific working methods, the PI had to fall back on previous experiences she had with the target group, as no information is available in literature on which concrete methods suit our specific population, context and goals best. This study showed that conducting co-creative research with this target group is feasible when adjustments are applied. Adjustments appear to be particularly important in terms of communication (i.e., the necessity of adapting questioning): (young) people with ID have differences in language use; difficulties with abstract thinking and recall; are inclined to give compliant answers; experience difficulties with written information; and are overall an extremely heterogeneous group in terms of communication capabilities [41, 45, 54]. It is therefore important to focus on concrete experiences/tasks, read exercises out loud, provide accessible language and illustrations and even develop communication tools in collaboration with them [47, 54]. In this respect, the use of creative working methods to initiate conversation was extremely valuable [65]. The reflections of the researchers showed which methods worked better than others. For example, a concrete exercise with visual cards worked considerably better than an abstract exercise in which a story about a fictitious adolescent was told. Although these examples of methods have now been applied to this specific target group, they could potentially also be used within the general population to promote creativity and maximise interaction. Currently a lack of participation is often seen as synonymous with apathy, but maybe it can rather be seen as a need to find entry in other ways. It would therefore be of great value for future co-creative researchers to be able to consult a taxonomy of creative methods that can be used. As an example, the book ‘Seldom Heard Voices: The how and why of meaningful collaboration’ [2022] [45] was published after carrying out this co-creative process. This is the first book that shares experiences and examples of service user involvement with communities of seldom heard voices, including a chapter which described facilitation tools to support engagement with people with ID. These facilitation tools can be considered the same as the described working methods in this paper. Each tool has a description and sums up limitations or considerations. Together with the methods described in this paper, this can provide a first starting point for such a taxonomy. Creating a taxonomy of working methods is also one of the objectives of the Health Cascade Project. Health *Cascade is* a Marie Skłodowska-Curie Innovative Training Network funded by the European Union’s Horizon 2020 research and innovation programme aimed at delivering the rigorous scientific methodology to secure co-creation as an effective tool to fight public health problems [66]. It would be interesting if the Health Cascade Project could also focus on specific target groups (e.g., which methods would be recommended in which target group), and therefore would not only make recommendations for the general population, since co-creation is as vital with those vulnerable target groups. In this way, different disciplines can learn from and strengthen each other within co-creation.
The use of various creative working methods required the researcher to be well prepared on the one hand, but also to be flexible and make decisions in the moment on the other hand. How well the PI might have prepared a protocol about how to conduct co-creation, the protocol often had to be abandoned based on the group the researcher had in front of her and the input that was given. This, however, can be difficult because funding bodies often expect a detailed plan before funding for research projects can be provided [42]. This shows the messiness of co-creative research. A possible way of being better secured against this, might be to make an assessment of the group dynamics before conducting co-creation, if possible. For example, when preparing the co-creative research in a class context, it could be an option to first go observe the class group during their usual lessons or to spend more time getting to know each other at the beginning of the process, e.g. first be physically active together and then start with the workshops. Creative methods could subsequently be (better) chosen based on that observation. Some participatory or co-creation studies even describe selecting eligible persons (e.g., persons who had certain prerequisites in mobility, language skills, and ability to concentrate) with the help of people who already know them [41]. The success of co-creation (methods) largely rely on the relationships that exist and emerge. A good working environment in which genuine feelings can be expressed and in which there is a shared understanding is necessary [32, 54]. During this co-creation process, groups only came to discussion when everyone’s input was valued equally (e.g., no formation of hierarchy) [50]. When there was no trust, it was difficult to encourage participants to get involved, leading to no interaction and no flow of ideas and input (i.e., different group dynamics in both groups). In this respect, it is important for researchers to learn how to build relationships of trust (as this is a fragile process in constant need of (re-)negotiation [42]), but also to have the opportunity to collaborate with pre-existing groups or familiar people in which there is a respectful atmosphere [30, 48], which can only be discovered by getting to know the group before the co-creation process starts.
Numerous benefits have been listed in literature in regard to co-creation. For example, co-creation for participants themselves would lead to empowerment; gained skills, confidence and experiences; a sense of contribution and respect; etc. [ 32, 45, 47, 63]. For researchers, it would in turn lead to the development of more appropriate interventions; the identification of appropriate research methods or creating new methods; the generation of novel and conceptually rich knowledge; the advance of innovative theories and new concepts; and the acquisition of new skills [41, 42, 54]. However, also barriers and challenges have been described [42]. For example, co-creation research has an often lengthy timescale. Thus, it is also important to assess whether co-creation actually leads to more acceptable, effective and sustainable interventions that are also cost-effective. There is still too limited information on the (cost) effectiveness of co-creation, which should be addressed by future research.
## Limitation and strengths
The main limitation of this study is the primary focus on the researchers’ reflection forms as data material. An attempt was made to obtain a reflection from adolescents with ID using the process evaluation forms with thumbs up/down, but no direct reflections or quotes from adolescents or PE teachers are available to include within this manuscript. We recommend future researchers to include reflections from different perspectives on the co-creation process within data collection, as it might provide supplementary information for future (co-creation) researchers. Second, the PI (LM) analysed her own reflection forms to be able to write the results of this study, creating bias. This was countered by also analysing reflection forms of co-facilitators, as well as involving a second researcher (SC) to code the data. It can therefore be concluded that the PI may have analysed more deductively as she witnessed the whole process and already had ideas about themes in her head, and the second investigator more inductively. Third, a wide age range of adolescents with ID was chosen to participate (13–22 years old). It was noted and described that the two groups differed greatly both internally (content) and externally (working methods), raising the question how to prioritize different views. Choosing a tighter age limit (e.g. 13–16 years old or 17–22 years old) seems recommended in future co-creation research. A fourth limitation of the study is that the choice was made to work only with adolescents with mild to moderate ID, which means that the findings cannot be extended to the target group of severe or profound ID. Future research needs to focus on the representation of all people with ID in health research, notably how to actively involve people with severe and profound ID directly or by proxy [54]. Last, next to following education in special need schools, adolescents with ID also have the possibility to follow (inclusive) education in mainstream schools in Flanders, Belgium. Nevertheless, the percentages of students receiving education in special need schools remain high (partly due to a lack of (individual) support in general education). Because of practical reasons, we only recruited adolescents from special secondary education for this study and not adolescents with ID from inclusive education. However, within the effect study of our intervention, we will very specifically recruit adolescents with ID from inclusive education as well. The greatest strength of this research is that it is, to the best of our knowledge, the first study that involved adolescents with ID in intervention development aimed at increasing PA. This fits perfectly with the “Nothing About Us, Without Us”-motto which is echoed in the philosophy and history of the Disability Rights Movement. The motto encapsulates a fundamental shift in perspective towards a principle of participation and integration of persons with disabilities. Co-creation enables people with ID to participate and thus to increase control over and to improve their health. Second, this is the first study to provide concrete tools for implementing co-creation with this target group. Third, by having both the facilitator and co-facilitators reflect after each session, an attempt was made to identify different lenses on reality. From this, important lessons could be formulated that might be relevant for other researchers who want to start a co-creation process.
## Conclusion
This study formulates seven elements that could be taken into account to make co-creation feasible within this target group. In particular, the need for innovative, concrete (non-abstract) and creative working methods that allow for the inclusion of those with different communication styles is emphasized. Transparent descriptions of the different steps taken, working methods used and lessons learned could be a first step to turn co-creative research into an evidence-based methodology.
## Supplementary Information
Additional file 1. Process evaluation forms of adolescents with ID.Additional file 2. Informed consent forms of adolescents with ID.Additional file 3. Pictures of co-creation session 2.Additional file 4. Pictures of co-creation session 3.Additional file 5. Pictures of co-creation session 6.Additional file 6. Reflection form of researchers. Additional file 7. Description of the results of the adolescents' process evaluations per session and per group. Additional file 8. COREQ (COnsolidated criteria for REporting Qualitative research) Checklist. Additional file 9. GRIPP2 Reporting Checklist - Long Form.
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|
---
title: Accuracy of Ultrasound Scans as Compared to Fine Needle Aspiration Cytology
in the Diagnosis of Thyroid Nodules
journal: Cureus
year: 2023
pmcid: PMC10024942
doi: 10.7759/cureus.35108
license: CC BY 3.0
---
# Accuracy of Ultrasound Scans as Compared to Fine Needle Aspiration Cytology in the Diagnosis of Thyroid Nodules
## Abstract
Introduction: Thyroid nodules (TNs) are among the more common findings on physical examinations. Due to the fear of the TN harboring malignancy and with the increasing incidence of thyroid cancer, ultrasound (US) scanning is used as an important diagnostic tool in the assessment of a TN. The American College of Radiology's Thyroid Imaging Reporting and Data System (TI-RADS) was established based on specific patterns composed of two or more features. According to the TI-RADS guidelines, a suspicious nodule by US findings should undergo fine-needle aspiration cytology (FNAC), in which results would guide further management.
Objective: This study was carried out to assess the accuracy of US as compared to FNAC in the diagnosis of a thyroid nodule.
Methodology: This retrospective study involved 213 cases that were sent for FNAC after having done a US scan of the thyroid. Data was gathered from all patient files that were referred for FNAC thyroid between $\frac{01}{02}$/2018 and $\frac{30}{06}$/2021 in Al-Ahli Hospital in the state of Qatar. The US scans were interpreted and reported according to the TI-RADS criteria. The FNAC samples were interpreted and reported according to the Bethesda System for Reporting Thyroid Cytopathology. Data were tabulated and analyzed with Excel (Microsoft, Redmond, WA, USA) and SPSS version 25 (IBM Corp., Armonk, NY, USA).
Results: The study showed that US had a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of $73.9\%$, $72.6\%$, $24.6\%$ and $95.8\%$, respectively, with a significant association between the results of US and the results of FNAC (X2 (1, $$n = 213$$) = 20.295, $p \leq .001$) and a significant positive correlation (phi coefficient =.309, $p \leq .001$). In addition, the data showed that the odds for having a positive FNAC were 7.519 ($95\%$ CI: 2.811, 20.112) times greater for cases with positive US compared with cases with negative US. The relative risk of having a positive FNAC when the US was positive was 5.913 ($95\%$ CI: 2.440, 14.332) times greater compared to when the US was negative.
Conclusion: While our results showed that US cannot be solely relied on in diagnosing TNs, they did show that US can reliably rule out a malignancy in TNs. Recent studies have been showing increasing accuracy of US in diagnosing TNs and more studies are needed to explore this topic.
## Introduction
Thyroid nodules (TNs) are solid or fluid-filled lumps that form within the thyroid gland. The estimated prevalence by palpation is $3\%$-$7\%$ in some countries [1]. The prevalence is higher among randomly selected individuals by high-resolution ultrasonography where it may increase to $67\%$ according to one study [2]. Thyroid nodules are always examined due to the fear of it being thyroid malignancy. The incidence of thyroid cancer is on the rise and is now the fifth most common cancer diagnosed in adult women worldwide and the second most common in women over 50 years of age [3,4]. The advancements in diagnostic technologies may be contributing to the increasing prevalence of TNs, but it may be explained by other traditional risk factors such as increasing age, insufficient iodine intake, exposure to radiation, and unhealthy lifestyles. This in turn increases the risk of obesity and metabolic syndrome, which are regarded as risk factors for TNs [5,6].
Several conditions can cause nodules to develop in the thyroid gland, including overgrowth of normal thyroid tissue, thyroid cysts, Hashimoto's disease, multinodular goiter and thyroid cancer [7]. Most TNs aren't serious and don't cause symptoms. However, a small percentage of TNs are caused by thyroid malignancy. This percentage is variable in different countries. A study in the United States found that only one out of every 20 clinically identifiable nodules turns out malignant [8]. Another study found that the proportion of thyroid cancer from TNs may reach up to $15\%$ [9,10].
One of the important diagnostic tools in the assessment of TNs is ultrasound (US). It is currently the most accurate imaging modality for detecting TNs. It provides the best information about the shape and structure of nodules. Furthermore, it is useful as a guide in performing fine-needle aspiration (FNA) if required [11].
Different guidelines were proposed in order to help radiologists and clinicians readily recognize the sonographic patterns and classify nodules into categories. In 2009, the Thyroid Imaging Reporting and Data System (TI-RADS) was established based on specific patterns composed of two or more features. This model offers a standardized and simplified approach for radiologists to follow, with a good diagnostic performance of high sensitivity ($88\%$), negative predictive value ($88\%$) and accuracy ($94\%$) [12]. However, radiologic findings alone are inconclusive. Therefore, according to the TI-RADS guidelines, a suspicious nodule by US findings should undergo FNA cytology (FNAC), in which results would guide further management [12,13].
In 2015, the American Thyroid Association (ATA) constructed new guidelines with a risk stratification model from very low suspicion to high suspicion for malignancy. It utilizes sonographic features based on the TI-RADS criteria. Patients with a TI-RADS score of 2 and 3 are considered low risk and are not routinely aspirated. This resulted in a reduction of the number of unnecessary FNAs [14]. A recent meta-analysis showed that the TI-RADS categories were a promising tool to differentiate between benign and malignant nodules, with a sensitivity and specificity of 0.79 ($95\%$ CI = 0.77-0.81) and 0.71 ($95\%$ CI = 0.70-0.72), respectively [15]. The objective of this study is to build up on this aspect of the literature, assessing the accuracy of thyroid US as compared to FNAC in the prediction of thyroid cancer.
## Materials and methods
This was a retrospective study of 213 cases that were sent for FNAC after a US scan of the thyroid. After gaining ethical approval from Al-Ahli Hospital, Doha, Qatar (EIC number EC2-2022), data was gathered from all patient files that were referred for FNAC thyroid between $\frac{01}{02}$/2018 and $\frac{30}{06}$/2021 at Al-Ahli Hospital. This amounted to 320 files. Of these, 25 cases had FNAC samples reported as inadequate and were therefore excluded from the study, leaving 295 cases. Of these, 82 did not have records of US scans of the thyroid and were therefore excluded from the study. This left us with a sample of 213 cases that had both a US scan and an FNAC thyroid.
The US scans were interpreted and reported by experienced radiologists according to the American College of Radiology's TI-RADS criteria. US features were scored as shown in Table 1 and, accordingly, the TI-RADS score was determined as shown in Table 2 [16].
The FNAC samples were interpreted and reported by experienced histopathologists according to the Bethesda System for Reporting Thyroid Cytopathology. Histological results of the FNAC were classified as shown in Table 3 [17].
**Table 3**
| Score | Interpretation |
| --- | --- |
| I | Unsatisfactory sample |
| II | Benign |
| III | Atypia or follicular lesion of undetermined significance |
| IV | Suspicion of follicular neoplasm |
| V | Suspicion of malignancy |
| VI | Malignant |
For the purposes of this study, TI-RADS 1, 2 and 3 were considered negative US scan results as they are not very suspicious for malignancy, and are not a direct indication for FNAC. These cases are managed according to clinical suspicion, where clinical factors are taken into consideration rather than relying on the US result. On the other hand, TI-RADS 4 and 5 will be considered positive US scans as they hold high suspicion of malignancy and are a direct indication for FNAC, regardless of the clinical picture.
Bethesda I is an insufficient sample and as previously mentioned, these have been excluded from comparison in our study. Bethesda II is reported as benign. Bethesda III and IV are the "borderline" results and are considered for follow-up studies to further evaluate the nodule. However, Bethesda V and VI are considered malignant. Therefore, for the purposes of this study, Bethesda II, III and IV were considered negative and Bethesda V and VI were considered positive.
Various demographic, clinical and sonographic criteria were considered in this research. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated for US scans of the thyroid. Odds ratio and relative risk were also calculated to compare US results to those of FNAC. The association between the various criteria considered in this study with the results of the US and FNAC was analyzed using chi-squared test. Data were tabulated and analyzed with Excel (Microsoft, Redmond, WA, USA) and SPSS version 25 (IBM Corp., Armonk, NY, USA).
## Results
The number of cases with the various criteria considered in the study are shown in Table 4. The age of the patients ranged between 17 and 70 ($m = 42.62$, SD = 10.84, $$n = 213$$). Of these, 174 ($81.6\%$) were females. The number of cases with various features seen on US are shown in Table 5.
There were 213 cases with both US and FNAC results. Of these, nine were positive for both tests and 138 were negative for both tests. Sixty-three cases had a positive US but negative FNAC. Only three cases had a negative US but positive FNAC. A significant association was found between the results of US and the results of FNAC (X2 (1, $$n = 213$$) = 9.6451, $$p \leq 0.0019$$) with a significant positive correlation (phi coefficient = 0.2128, $$p \leq 0.0019$$). The validity statistics for US as a diagnostic test for the diagnosis of thyroid carcinoma are shown in Table 6.
**Table 6**
| Statistic | Value |
| --- | --- |
| Sensitivity | 75.0% |
| Specificity | 68.7% |
| Positive Predictive Value | 12.7% |
| Negative Predictive Value | 97.9% |
In addition, the data showed that the odds for having a positive FNAC were 6.57 ($95\%$ CI: 1.7203, 25.1021) times greater for cases with positive US compared with cases with negative US. The relative risk of having a positive FNAC when the US was positive was 5.913 ($95\%$ CI: 1.6409 to 21.0344) times greater compared to when the US was negative.
## Discussion
As previously discussed, the current global consensus is that thyroid US on its own is insufficient to diagnose thyroid cancer. However, with the constantly evolving and advancing field of radiology, new studies are emerging that attempt to challenge this idea. A study conducted in Turkey in 2021 compared the reliability of the ATA and TI-RADS guidelines for thyroid US with FNAC results. It concluded that both guidelines can accurately predict malignancy, and may in fact eventually lead to a decrease in unnecessary FNAs [18]. Another study comparing the ATA, British Thyroid Association and TI-RADS showed that all three guidelines had sensitivities and NPV of over $90\%$, with ATA being the best at $98\%$ and $95\%$, respectively [19]. In our study, we demonstrated a sensitivity, specificity, NPV and PPV of $75.0\%$, $68.7\%$, $97.9\%$ and $12.7\%$, respectively.
Current literature shows large discrepancies between different studies. Shweel et al. conducted a similar study which showed sensitivity, specificity, NPV and PPV of $76.2\%$, $83\%$, $88.8\%$ and $66.4\%$, respectively [20]. Another study by Trimboli et al. similarly showed a sensitivity of $61\%$, specificity of $83\%$ and NPV of $83\%$ [21]. Xing et al. and Wang et al. both demonstrated high sensitivities of $95.7\%$ and $92\%$, respectively [22,23]. Similarly to our study, the former also demonstrated a very high NPV of $99.7\%$, as compared to the latter’s $63.1\%$. Zhang et al. demonstrated a sensitivity, specificity and NPV of $69\%$, $85\%$ and $89\%$, respectively [24]. All of the aforementioned studies showed positive predictive values between $60\%$ and $66.4\%$, with the exception of Wang et al. which showed a high PPV of $95\%$. Specificities ranged between $61\%$ and $85\%$. The values our study has demonstrated appear to be average compared the current literature except for our very low PPV of $12.7\%$.
Russ et al. conducted a study comparing the efficacy of using US alone as opposed to US with elastography [25]. With the use of US alone, the study demonstrated a sensitivity, specificity, NPV and PPV of $70\%$, $92.4\%$, $87.6\%$ and $80\%$, respectively. However, when combining both US and elastosonography together, the sensitivity and NPV went up to $98.5\%$ and $99.8\%$, respectively. Subsequently, the study concluded that FNACs can be reduced by up to $34\%$ using this combined approach. Shweet et al. and Trimboli et al. both demonstrated similar findings as well. The former demonstrated that the combined approach resulted in better performance, with sensitivity, specificity, NPV and PPV of $95.4\%$, $94.8\%$, $98.8\%$ and $82.3\%$ [20]. The latter showed the combined approach resulted in at least $97\%$ sensitivity and $97\%$ NPV [21]. The findings of these studies, among others, show promise in the possibility of eliminating or reducing the need of using FNACs for thyroid nodules.
The current gold standard for diagnosing thyroid cancer is FNAC. A meta-analysis of the Bethesda reporting system found that the sensitivity, specificity, NPV and PPV were $97\%$, $50.7\%$, $96.3\%$ and $55.9\%$, respectively [26]. Another study comparing the effectiveness of TI-RADS criteria in US to the Bethesda reporting system of FNAC demonstrated that the Bethesda reporting system had a sensitivity, specificity, and accuracy of $90\%$, $94.3\%$ and $91.1\%$, respectively [27]. These values are not significantly superior to the values demonstrated by ultrasonography alone in some of our previously mentioned studies. Furthermore, a study conducted in one center demonstrated a false negative rate of FNAC of $15\%$, concluding that the Bethesda risk stratification system often underestimates malignancy rates [28].
The results of our study should be considered in the context of the following limitations, one being our small sample size. A larger study would produce more reliable data, especially if conducted in a specialist center. A second limitation is the nature of the study itself. Patients are only referred for FNAC if they have suspicious findings on US. Therefore, it is difficult to assess the proportion of false negative cases, who may eventually have positive FNACs despite having negative US scans. Once again, a larger sample size may give more reliable data in light of this issue. Another limitation is the nature of healthcare in the region where this study was conducted, where lots of patients seek private healthcare, often outside of the country. This resulted in a large proportion of missing patient data in the hospital system. This is one of the reasons why so many patients had to be excluded from our study, as previously mentioned.
## Conclusions
US was shown to be a reliable tool in the assessment of TNs. There is a considerable amount of discrepancy between different literatures regarding this topic. Some studies and the current consensus suggest that US cannot be used without FNAC to diagnose TNs. Other studies, however, suggest that the advancement in US quality and techniques have led to US being up to par with FNAC in terms of accuracy. With the constant development and evolution of imaging techniques, we expect US scans to become more and more reliable. Highly specialized radiology centers with modern equipment and adequate experience may soon be able to, without the use of FNACs, achieve results that are on par with our current accepted standard. Therefore, we recommend that large studies be conducted in such centers to assess and compare the reliability of modern US scans in diagnosing TNs to the current gold standard of FNAC.
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|
---
title: Cardiovascular Disease and Its Implication for Higher Catastrophic Health Expenditures
Among Households in Sub-Saharan Africa
authors:
- Folashayo Ikenna Peter Adeniji
- Taiwo Akinyode Obembe
journal: Journal of Health Economics and Outcomes Research
year: 2023
pmcid: PMC10024946
doi: 10.36469/001c.70252
license: CC BY 4.0
---
# Cardiovascular Disease and Its Implication for Higher Catastrophic Health Expenditures Among Households in Sub-Saharan Africa
## Abstract
Background: Cardiovascular diseases (CVDs) impose an enormous and growing economic burden on households in sub-Saharan Africa (SSA). Like many chronic health conditions, CVD predisposes families to catastrophic health expenditure (CHE), especially in SSA due to the low health insurance coverage. This study assessed the impact of CVD on the risks of incurring higher CHE among households in Ghana and South Africa. Methods: The World Health Organization (WHO) Study on Global AGEing and Adult Health (WHO SAGE), Wave 1, implemented 2007-2010, was utilized. Following standard procedure, CHE was defined as the health expenditure above $5\%$, $10\%$, and $25\%$ of total household expenditure. Similarly, a $40\%$ threshold was applied to household total nonfood expenditure, also referred to as the capacity to pay. To compare the difference in mean CHE by household CVD status and the predictors of CHE, Student’s t-test and logistic regression were utilized. Results: The share of medical expenditure in total household spending was higher among households with CVD in Ghana and South Africa. Households with CVD were more likely to experience greater CHE across all the thresholds in Ghana. Households who reported having CVD were twice as likely to incur CHE at $5\%$ threshold (odds ratio [OR], 1.946; confidence interval [CI], 0.965-1.095), 3 times as likely at $10\%$ threshold (OR, 2.710; CI, 1.401-5.239), and 4 times more likely to experience CHE at both $25\%$ and $40\%$ thresholds, (OR, 3.696; CI, 0.956-14.286) and (OR, 4.107; CI, 1.908-8.841), respectively. In South Africa, households with CVD experienced higher CHE across all the thresholds examined compared with households without CVDs. However, only household CVD status, household health insurance status, and the presence of other disease conditions apart from CVD were associated with incurring CHE. Households who reported having CVD were 3 times more likely to incur CHE compared with households without CVD (OR, 3.002; CI, 1.013-8.902). Conclusions: Our findings suggest that CVD predisposed households to risk of higher CHE. Equity in health financing presupposes that access to health insurance should be predicated on individual health needs. Thus, targeting and prioritizing the health needs of individuals with regard to healthcare financing interventions in SSA is needed.
## BACKGROUND
Cardiovascular diseases (CVDs) are defined as a group of heart and blood vessel–related disorders.1 Examples of CVDs include cerebrovascular disease, hypertension, congenital heart disease, myocardial infarction, coronary artery disease or angina pectoris, rheumatic heart disease, cardiac arrhythmias, peripheral vascular disease (aneurysms and peripheral arterial disease), valvular heart disease, and congestive heart failure.2 These diseases impose an enormous and growing economic burden on individuals and households in developing countries. Research has shown that CVDs manifest about 10 to 15 years earlier in low- and middle-income countries (LMICs) when compared with developed countries.3 This earlier onset could lead to a reduction in the productive/working-age population, with serious implications for the economies of these countries.4 In particular, CVDs caused twice the number of deaths related to HIV, malaria, and tuberculosis combined in LMICs from the review of changes in mortality between 1990 and 2001.5 The Global Burden of Disease study estimated that CVDs caused almost 6.4 million deaths of people aged 30 to 69 years in developing countries in 2020.6 Another study estimated that 15.9 million potential productive years of life lost were lost in India and China in 2000 due to CVDs7 and about 2.5 billion, 237 billion, and 558 billion in 1998 International dollars lost in the gross domestic product in Tanzania, India, and China, respectively, between 2005 and 2015.7 As such, reducing morbidity and mortality due to CVDs remains at the center of public discourse and population health priorities in LMICs.8
## Epidemiology of CVD in Sub-Saharan Africa
According to the World Health Organization (WHO) Global Burden of Disease Study, CVD is one of the leading causes of morbidity and mortality in sub-Saharan Africa (SSA), with high systolic blood pressure accounting for $56.6\%$ of disability-adjusted life-years in 2021.9 The number of deaths due to these diseases increased by $50\%$ between 1990 and 2019 in SSA.10 In 2019 alone, CVDs caused nearly 1 million deaths in SSA, contributing about $4.5\%$ of all CVD-related mortality globally.11 Although research evidence suggests few cases of CVDs were recorded in SSA before the 1990s, its prevalence has been increasing in the last 3 decades.12 According to studies conducted in some SSA countries, the emerging public and population health problems posed by CVDs in the subregion can be attributed to aging, rapid urbanization, demographic transition, lifestyle change, and a high prevalence of common risk factors such as hypertension, tobacco use, abdominal obesity, physical inactivity (sedentary lifestyle), alcohol misuse, and diabetes.13 In the INTERHEART Africa study,13 the 5 CVD risk factors modeled in the study were estimated to account for population attributable risk of $90\%$ for acute myocardial infarction. In particular, current and former tobacco use, hypertension, and diabetes were revealed as the risk factors with the highest influence on the odds of a patient suffering a cardiovascular event in the Black African population. Simply put, these risk factors account for the largest percentage of the economic burden of CVD in SSA. The study suggests that the failure to control or prevent risk factors and risk behaviors will have a substantial impact on the economic burden of CVDs in Africa compared with the rest of the world in the near future.13 Some research evidence has shown that several other factors contribute to the risk of CVD events. For instance, higher-income Black Africans were more predisposed to myocardial infarction compared with Whites and non-Black Africans of similar economic status.13 The effects of the prevalence of HIV/AIDS has been linked to generating other CVDs such as rheumatic valvular and cardiomyopathy, which manifests in the form of tuberculous pericarditis.14 In particular, many countries in SSA are experiencing an increase in the prevalence of chronic diseases like CVD.15 For example, *Nigeria is* the most populous country in SSA, and CVDs represent a growing public health threat in the country. According to WHO estimates of the burden of noncommunicable diseases globally, CVDs were responsible for about $7\%$ of deaths in Nigeria in 2014 alone.16 Similarly, several subpopulation studies have revealed that hypertensive heart failure contributes the largest burden of CVDs in Nigeria, constituting nearly $61\%$ of all CVD cases in the country.
The Institute for Health Metrics and Evaluation reported that mortality as a result of stroke and ischemic heart disease increased by $10.6\%$ and $14.6\%$, respectively, in Ghana between 2007 and 2017. In terms of the risk factors that contributed to disabilities in the same period, diabetes increased by $52.8\%$ while the deaths and disabilities caused by high blood pressure increased by $17.0\%$.17 In South Africa, reports show that CVD is second only to HIV/AIDS in terms of the number of deaths caused. This disease condition accounts for almost $17.3\%$ of adult deaths in the country, and more South Africans die as a result of CVDs than of all cancers.18 More importantly, given the weak insurance infrastructure in many SSA countries, CVDs have implications for higher catastrophic health expenditures (CHE), especially among economically less viable households. Compared with developed countries and most other developing regions of the world, SSA countries allocate significantly lower funds (as a percentage of gross domestic product) to providing healthcare services.18 A systematic review of public financing of healthcare services in developing countries between 1995 and 2006 revealed that while the share of government spending on health increased in most regions, it decreased in SSA countries.19 Also, a WHO report on health spending and disease burden in Africa indicated that healthcare remains underfunded in the region. As of 2015, only 6 SSA countries (Liberia, Madagascar, Malawi, Rwanda, Togo, and Zambia) have met the target of $15\%$ of annual national budget set at the Abuja Declaration of 2001. ( The Declaration has been used to refer to the conference held in Abuja, the federal capital territory of Nigeria, where all the members of African Union met and pledged to allocate ≥$15\%$ of the national budget to the healthcare sector yearly.) Consequently, individuals and households in SSA are often predisposed to incurring substantial CHE due to high out-of-pocket (OOP) payments for medical services.20 For the 2 countries used as case studies, a recent study reported that only $13.3\%$ of the South African population had health insurance,21 while another study reported poor coverage of the National Health Insurance Scheme in Ghana,22 which means that the progress in terms of universal health coverage (UHC) has been marginal in the last decade in both countries and, indeed, in many SSA countries.
The extent of CHE experienced by individuals and households vary according to health needs (especially for chronic health conditions like CVD) and the availability of effective financial protection mechanism. Earlier studies in SSA have investigated the incidence of CHE in the general population. By extension, this study examined the differentials in the experience of CHE among households with and without CVD using data from 2 SSA countries, Ghana and South Africa. Evidence in the literature reveals that these countries are in various stages in terms of realizing UHC as articulated in the Sustainable Development Goals. Moreover, both countries have yet to provide adequate financial protection that covers the entire population.23 The implication is that the policy on affordable healthcare through social health insurance has yet to attain its optimal potential in both countries. Therefore, this study is warranted to examine the differentials in the experience of CHE among households with and without CVDs in these 2 countries. Findings from this study will provide evidence-based recommendations to support policies toward UHC in both countries and in other similar countries in SSA.
## Data Source
This study utilized data drawn from the WHO Study on Global AGEing and Adult Health (WHO SAGE), Wave 1, which was carried out between 2007 and 2010. This survey is a nationally representative study implemented in 6 countries: China, Ghana, India, Mexico, Russia, and South Africa. Only the 2 SSA countries covered in the survey were included in this study. The WHO SAGE, Wave 1 sampled 5110 and 4223 adults 50 years and older in Ghana and South Africa, respectively. Also, the study survey sampled a smaller comparative sample of adults 18 to 49 years of age. The study combined household-level and individual-level modules to collect important data sets. The household-level module was used to elicit information such as household healthcare demand and utilization, access to medical insurance, broad categories of household spending, including health expenditure, and household permanent income. The expenditure categories elicited in the data include monthly expenditure on food items; housing and utility; clothing, transportation, recreation, and entertainment; and total monthly healthcare spending. Expenditures on education, durable goods, vehicles, cost of hospitalization, and amount paid on insurance premiums were elicited on an annual basis. Similarly, the individual-level module was used to collect data on variables such as sociodemographics, health risk behavior, and health state description. In the data set, household health expenditure aggregated the monetary outlay toward the outpatient care received from physicians and nurses, purchase of medicines/drugs, diagnostic and laboratory tests, traditional or alternative care, costs associated with hospitalizations, and other medical-related costs. The health conditions covered in the data included arthritis, stroke, angina or angina pectoris, chronic lung cancer, asthma, depression, hypertension, cataracts, oral health, injuries, and cervical and breast cancer (for women only). Based on the standard classification of diseases, angina and hypertension are the 2 CVD conditions captured in the data. Therefore, these 2 heart-related conditions were utilized as CVDs for the purpose of data analysis.
## Measure of Catastrophic Health Expenditure
The conceptualization of CHE relates to medical spending above a predetermined threshold, in relation to household income, that often causes financial distress for individuals and households. This study favored the use of household expenditure as a measure of household income and thus as a proxy for household economic well-being. This is because consumption expenditure is less affected by short-term fluctuations and better reflects the welfare of households.24 Therefore, in this study, CHE is defined as the medical expenditure above $5\%$, $10\%$, and $25\%$ of household total expenditure. Similarly, a $40\%$ threshold was applied to household total nonfood expenditure, which has also been widely utilized in the literature. Household expenditure categories were adjusted for household composition and size. Thus, the level of CHE among households was implemented as follows: CHE=Heheor(he−hfex)≥z% where He = total household health expenditure, he = total household expenditure, hfex = total household food expenditure, he−hfex = capacity to pay/discretionary income, and z% = the predetermined thresholds (which are $5\%$, $10\%$, $25\%$, and $40\%$, depending on the denominator). To adjust for household size, all expenditure categories were divided by the respective household size to generate per capita expenditure categories. The share of health expenditure in household budget was expressed as: (Total Household Health Expenditure(He)Total Household Expenditure(he))×100
## Data Analysis
For comparison purposes, households were classified by their CVD status (ie, whether a member or members of the household reported having CVD. The data from both countries were analyzed independently, and the results were compared accordingly. Descriptive statistics were utilized to report categorical variables, and the mean and SD of continuous variables were presented. To measure the CHE among households, the standard procedures adopted in previous studies was used.25,26 All expenditure categories were annualized. To compare the difference in mean CHE by household CVD status and the predictors of CHE, Student’s t-test and logistic regression were utilized. The logistic regression model is given as: π1−π=exp(β0+β1X1+…+βkXk), where π ∕ 1 − π is the probability for the event of CHE (ie, the outcome variable) and X1 represents the explanatory/predictor variables. Important risk factors for CVD were identified from literature reviews and were included in the logistic regression model as X1. All predictors were included and retained. A sensitivity analysis, using different thresholds of CHE, was then used to determine what variables are significant at different thresholds. All the data were analyzed using Stata version 15 (StataCorp, College Station, Texas).
## Predictor Variables
The covariates controlled for in the model include sex, age, and education of household head; tobacco use; alcohol use; CVD status; household access to health insurance; number of fruit servings; number of vegetable servings; daily vigorous physical activity; household per capita expenditure; household size; and presence of other non-CVD health conditions such as the presence of cervical and breast cancers (for women), arthritis, stroke, chronic lung disease, asthma, depression, cataracts, oral health, and injuries. A dummy variable was introduced in the regression model such that 1 (Yes) represented the presence of 1 or more of the non-CVD health conditions captured in the data; and 0 (No) for households who did not report any of the non-CVD conditions. The number of fruits and vegetable servings were captured per day and per year, while vigorous physical activities were captured per day and per week. Weekly and monthly data were annualized for data analysis. Covariates such as sociodemographic characteristics, tobacco use, alcohol use, number of fruit servings, number of vegetable servings, and daily vigorous physical activity were included in the model because they have been strongly associated with increasing or reducing the risks of CVDs and other chronic noncommunicable diseases apart from other nonmodifiable risk factors like age and family history.27,28 The intuition is that when an individual engages in a risky health behavior and falls ill as a result, there is a tendency that the individual could incur CHE due to increased demand for healthcare services. Although indirect, the path from risky health behavior to CHE affects the demand for healthcare services.
## RESULTS
Table 1 depicts the sociodemographic characteristics of households by CVD status in Ghana and South Africa. Data on 3938 individuals sampled in Ghana and 2400 persons sampled in South Africa were analyzed. In both countries, the mean age in households with CVD (Ghana, 49.28 ± 17.84 years; South Africa, 48.96 ± 15.85 years) was higher compared with households without CVD (Ghana, 40.66 ± 16.46 years; South Africa, 35.90 ± 13.25 years). There were more males relative to females who had CVD (147 [74.62] in Ghana and 187 [$67.03\%$] in South Africa). These figures also indicate that the prevalence of CVD is relatively higher in the latter country. Similarly, a higher proportion of respondents who were currently married had CVD in both countries, 128 ($64.97\%$) and 142 ($50.90\%$), respectively. The level of access to health insurance reported among households was generally low. Among households without CVD, the majority ($96.87\%$ and $88.87\%$ in both countries, respectively) reported not having access to health insurance. Likewise, in Ghana, $95.83\%$ of households with member(s) with CVD had no access to financial protection in the form of health insurance. Also, in South Africa, only $10.66\%$ of households with CVD reported having access to health insurance. Access to health insurance is slightly higher in South Africa than in Ghana.
**Table 1.**
| Variable | Ghana, n (%) | Ghana, n (%).1 | Ghana, n (%).2 | South Africa, n (%) | South Africa, n (%).1 | South Africa, n (%).2 |
| --- | --- | --- | --- | --- | --- | --- |
| Variable | No CVD | CVD | Total | No CVD | CVD | Total |
| Age (y) | 40.66 (45.21) | 49.28 (54.79) | 44.97a (100.00) | 35.90 (42.32) | 48.96 (57.70) | 42.43a (100.00) |
| Education (y) | 6.10 (54.61) | 5.07 (45.39) | 6.03a (100.00) | 8.95 (5.57) | 6.84 (6.01) | 8.65a (100.00) |
| Sex | Sex | Sex | Sex | Sex | Sex | Sex |
| Male | 1468 (53.25) | 147 (74.62) | 1615 (54.61) | 1001 (50.89) | 187 (67.03) | 1236 (52.57) |
| Female | 1289 (46.75) | 50 (25.38) | 1339 (45.39) | 966 (49.11) | 92 (32.97) | 1115 (47.43) |
| Marital status | Marital status | Marital status | Marital status | Marital status | Marital status | Marital status |
| Never married | 576 (20.92) | 20 (10.15) | 596 (20.31) | 972 (49.49) | 67 (24.01) | 1094 (46.59) |
| Currently married | 1618 (58.75) | 128 (64.97) | 1746 (59.07) | 723 (36.81) | 142 (50.90) | 899 (38.29) |
| Cohabiting | 74 (2.69) | 5 (2.54) | 79 (2.67) | 27 (1.37) | 2 (0.72) | 31 (1.32) |
| Separated/divorced | 196 (7.12) | 15 (7.61) | 211 (7.18) | 56 (2.85) | 14 (5.02) | 71 (3.02) |
| Widowed | 249 (9.03) | 29 (14.72) | 278 (9.36) | 93 (4.74) | 43 (15.41) | 144 (6.13) |
| Unknown | 41 (1.49) | — | 41 (1.40) | 93 (4.74) | 11 (3.94) | 109 (4.64) |
| Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance | Health insurance |
| No | 1947 (96.87) | 138 (95.83) | 2205 (96.50) | 1621 (88.87) | 243 (89.34) | 2192 (89.51) |
| Yes | 63 (3.13) | 6 (4.17) | 69 (3.50) | 203 (11.13) | 29 (10.66) | 257 (10.49) |
Tobacco use, alcohol misuse, low fruit and vegetable intake, and inadequate physical activity are among the major risk factors for CVD. Table 2 shows the summary of common risk factors for CVD among households in the 2 SSA countries. The prevalence of tobacco use in South Africa ($73.84\%$) is very high compared with Ghana ($7.48\%$). However, more households reported alcohol consumption in Ghana ($45.31\%$) than South Africa ($29.77\%$). Similarly, the percentage of households that consumed tobacco and reported having CVD in Ghana ($6.34\%$) is lower relative to that of South Africa ($14.28\%$). The mean fruit and vegetable servings among households with CVD (4.02 and 3.06) is higher than that among households without CVD (3.90 and 2.51). In South Africa, the average number of fruit servings among households with CVD (1.96) is also higher relative to households without CVD (1.88). In both countries, households without CVDs had greater rigorous physical activity on average.
**Table 2.**
| Risk Factor | Unnamed: 1 | Ghana | Unnamed: 3 | Unnamed: 4 | South Africa | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| | No CVD | CVD | Total | No CVD | CVD | Total |
| Tobacco use, n (%) | | | | | | |
| No | 2535 (92.52) | 184 (93.4) | 2752 (92.6) | 515 (26.3) | 73 (26.16) | 627 (26.9) |
| Yes | 205 (7.48) | 13 (6.6) | 220 (7.4) | 1443 (73.7) | 206 (73.84) | 1704 (73.1) |
| Alcohol use, n (%) | | | | | | |
| No | 1499 (54.69) | 106 (53.81) | 1626 (54.69) | 1373 (70.23) | 192 (68.82) | 1628 (69.96) |
| Yes | 1242 (45.31) | 91 (46.19) | 1347 (45.31) | 582 (29.77) | 87 (31.18) | 699 (30.04) |
| No. of fruit servings, mean (SD); range | 3.90 (4.01);0.00-50.00 | 4.02 (4.36);0.00-40.00 | 3.91 (4.08);0.00-50.00 | 1.88 (1.57)0.00-12.00 | 1.96 (1.98);0.0-21.00 | 1.87 (1.63);0.00, 21.00 |
| No. of vegetable servings, mean (SD); range | 2.51 (1.92);0.00-50.00 | 3.06 (3.91);0.00-50.00 | 2.55 (2.11);0.00-50.00 | 1.96 (1.53);0.00-12.00 | 1.95 (1.53);0.00-12.00 | 1.96 (1.61);0.00-21.00 |
| Vigorous physical activities, mean (SD); range | 2.510 (2.700);0-7 | 1.363 (2.310);0-7 | 2.431 (2.691);0-7 | 1.099 (1.940);0-7 | 0.743 (1.604);0-7 | 1.036 (1.891);0-7 |
The mean share of health expenditure in relation to household expenditure by CVD status is shown in Table 3. This share was calculated using total household expenditure and total household nonfood expenditure or capacity to pay, respectively. The share of medical expenditure in household expenditure and nonfood expenditure was higher among with CVD patients in Ghana. Likewise, in South Africa, the share of health spending in total expenditure was similar across households irrespective of whether the household had CVD. Nonetheless, the share of medical expenditure with respect to total household nonfood expenditure was higher among households with CVD in South Africa.
**Table 3.**
| Unnamed: 0 | Unnamed: 1 | Ghana | Unnamed: 3 | Unnamed: 4 | South Africa | Unnamed: 6 |
| --- | --- | --- | --- | --- | --- | --- |
| | No CVD (n = 2098) | CVD (n = 158) | Total (n = 2394) | No CVD (n = 1325) | CVD (n = 192) | Total (n = 1775) |
| Share of total household expenditure, mean (SD); range | 0.04 (0.06);0.00, 0.63 | 0.06 (0.09);0.00, 0.49 | 0.040.07;(0.00, 0.87) | 0.02 (0.06);0.00, 0.78 | 0.02 (0.07);0.00, 0.47 | 0.02 (0.06);0.00, 0.78 |
| Share of total household nonfood expenditure, mean (SD); range | 0.11 (0.15);0.00, 0.89 | 0.17 (0.22);0.00, 0.95 | 0.12 (0.16);0.00, 0.96 | 0.03 (0.10);0.00, 0.94 | 0.04 (0.12);0.00, 0.78 | 0.03 (0.10);0.00, 0.94 |
Table 4 shows the CHE head count by household CVD status in the 2 countries. The level of CHE among households in both countries was higher with the $5\%$ threshold compared with the $10\%$, $25\%$, and $40\%$ thresholds. In Ghana and at the $5\%$ threshold, the level of CHE was $27.78\%$ among households without CVD relative to $37.34\%$ among households with CVD. At the $10\%$ threshold, more households with CVDs ($24.05\%$) incurred CHE relative to those without CVD ($12.39\%$) in Ghana. This trend was consistent for other thresholds. Also in South Africa, households that reported having a member with CVD experienced higher CHE across all the thresholds examined compared with households without CVD.
**Table 4.**
| CHE Threshold Level | Ghana | Ghana.1 | Ghana.2 | South Africa | South Africa.1 | South Africa.2 |
| --- | --- | --- | --- | --- | --- | --- |
| CHE Threshold Level | No CVD | CVD | Total | No CVD | CVD | Total |
| 5%, n (%) | 5%, n (%) | 5%, n (%) | 5%, n (%) | 5%, n (%) | 5%, n (%) | 5%, n (%) |
| No | 1516 (72.22) | 99 (62.66) | 1615 (71.56) | 1202 (90.72) | 172 (89.58) | 1374 (90.57) |
| Yes | 583 (27.78) | 59 (37.34) | 642 (28.44) | 123 (9.28) | 20 (10.42) | 143 (9.43) |
| 10%, n (%) | 10%, n (%) | 10%, n (%) | 10%, n (%) | 10%, n (%) | 10%, n (%) | 10%, n (%) |
| No | 1839 (87.61) | 120 (75.95) | 1959 (86.80) | 1257 (94.87) | 180 (93.75) | 1437 (94.73) |
| Yes | 260 (12.39) | 38 (24.05) | 298 (13.20) | 68 (5.13) | 12 (6.25) | 80 (5.27) |
| 25%, n (%) | 25%, n (%) | 25%, n (%) | 25%, n (%) | 25%, n (%) | 25%, n (%) | 25%, n (%) |
| No | 2057 (98.00) | 150 (94.94) | 2207 (97.78) | 1304 (98.42) | 186 (96.88) | 1490 (98.22) |
| Yes | 42 (2.00) | 8 (5.06) | 50 (2.22) | 21 (1.58) | 6 (3.13) | 27 (1.78) |
| 40%, n (%) | 40%, n (%) | 40%, n (%) | 40%, n (%) | 40%, n (%) | 40%, n (%) | 40%, n (%) |
| No | 1933 (92.09) | 135 (85.44) | 2068 (91.63) | 1297 (97.89) | 186 (96.88) | 1483 (97.76) |
| Yes | 166 (7.91) | 23 (14.56) | 189 (8.37) | 28 (2.11) | 6 (3.13) | 34 (2.24) |
The difference in mean CHE is compared among households by CVD status as shown in Table 5. In Ghana, estimates show that households who reported having CVD significantly incurred CHE compared with households without CVD for all the thresholds. There is a similar pattern for households in South Africa, although it was significant only for the $25\%$ threshold. For the $5\%$, $10\%$, $25\%$, and $40\%$ thresholds, households with CVD incurred about $9\%$, $11\%$, $3\%$, and $6\%$ higher CHE, respectively, relative to the comparison group in Ghana. However, in South Africa, this difference is less evident, except for the $25\%$ threshold, where households with CVD incurred roughly $2\%$ higher CHE compared with households without CHE.
**Table 5.**
| CHE Threshold Level | Ghana | Ghana.1 | South Africa | South Africa.1 |
| --- | --- | --- | --- | --- |
| CHE Threshold Level | Estimated Difference (SE) | 95% CI | Estimated Difference (SE) | 95% CI |
| 5% | -0.096a (0.037) | -0.168, -0.023 | -0.011 (0.023) | -0.056, 0.033 |
| 10% | -0.1166a (0.028) | -0.171, -0.062 | -0.011 (0.017) | -0.045, 0.023 |
| 25% | -0.031a (0.012) | -0.054, -0.007 | -0.015b (0.010) | -0.035, 0.005 |
| 40% | -0.066a (0.022) | -0.111, -0.02 | -0.010 (0.011) | -0.033, 0.012 |
Table 6 depicts the factors associated with the experience of CHE among households with and without CVD. In Ghana, sex, age, CVD status, engagement in daily vigorous activity, household per capita expenditure, and household size were associated with CHE across all the thresholds. For instance, households with CVD were more likely to experience greater CHE across all the thresholds examined in Ghana. Households who reported having CVD were twice as likely to incur CHE at the $5\%$ threshold (OR, 1.946; CI, 0.965-1.095), 3 times more likely at $10\%$ threshold (OR, 2.710; CI, 1.401-5.239), and 4 times more likely to experience CHE at both $25\%$ and $40\%$ thresholds (OR, 3.696; CI, 0.956-14.286), and (OR, 4.107; CI, 1.908-8.841), respectively. In South Africa, only household CVD status, household health insurance status, and the presence of other disease conditions apart from CVD were associated with incurring CHE. Households who reported having CVD were 3 times more likely to incur CHE than households without CVD (OR, 3.002; CI, 1.013-8.902).
**Table 6.**
| Unnamed: 0 | CHE Threshold Level: Ghana | CHE Threshold Level: Ghana.1 | CHE Threshold Level: Ghana.2 | CHE Threshold Level: Ghana.3 | CHE Threshold Level: Ghana.4 | CHE Threshold Level: Ghana.5 | CHE Threshold Level: Ghana.6 | CHE Threshold Level: Ghana.7 | CHE Threshold Level: South Africa | CHE Threshold Level: South Africa.1 | CHE Threshold Level: South Africa.2 | CHE Threshold Level: South Africa.3 | CHE Threshold Level: South Africa.4 | CHE Threshold Level: South Africa.5 | CHE Threshold Level: South Africa.6 | CHE Threshold Level: South Africa.7 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | 5% | 5% | 10% | 10% | 25% | 25% | 40% | 40% | 5% | 5% | 10% | 10% | 25% | 25% | 40% | 40% |
| | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI | OR (SE) | 95% CI |
| Sex | 1.070 (0.168) | 0.786-1.457 | 1.399 (0.305) | 0.912-2.145 | 2.998b (1.610) | 1.046-8.589 | 2.274c (0.596) | 1.361-3.801 | 0.998 (0.211) | 0.660-1.509 | 0.985 (0.274) | 0.571-1.697 | 1.842 (0.849) | 0.747-4.544 | 1.397 (0.563) | 0.634-3.078 |
| Age (y) | 1.742c (0.352) | 1.172-2.590 | 1.321 (0.354) | 0.781-2.233 | 0.792 (0.449) | 0.261-2.406 | 0.895 (0.303) | 0.461-1.739 | 0.814 (0.219) | 0.480-1.381 | 1.185 (0.385) | 0.627-2.242 | 0.951 (0.509) | 0.333-2.715 | 1.339 (0.608) | 0.550-3.261 |
| Education (y) | 0.908 (0.136) | 0.678-1.217 | 1.187 (0.245) | 0.792-1.779 | 1.233 (0.656) | 0.434-3.500 | 1.017 (0.255) | 0.622-1.663 | 0.700a (0.130) | 0.486-1.008 | 0.677a (0.144) | 0.447-1.026 | 1.206 (0.449) | 0.581-2.504 | 1.156 (0.354) | 0.634-2.105 |
| Tobacco use | 1.072 (0.086) | 0.917-1.254 | 1.030 (0.108) | 0.838-1.266 | 1.197 (0.353) | 0.671-2.135 | 1.307 (0.253) | 0.894-1.910 | 0.895 (0.228) | 0.543-1.476 | 0.699 (0.233) | 0.364-1.344 | 0.377 (0.267) | 0.094-1.511 | 0.543 (0.283) | 0.195-1.510 |
| Alcohol use | 1.018 (0.038) | 0.946-1.095 | 0.984 (0.051) | 0.889-1.090 | 0.945 (0.113) | 0.956-14.286 | 1.025 (0.066) | 0.904-1.162 | 1.054 (0.068) | 0.930-1.195 | 1.033 (0.086) | 0.878-1.215 | 1.178 (0.194) | 0.853-1.626 | 1.133 (0.145) | 0.882-1.456 |
| CVD status | 1.946b (0.548) | 1.121-3.380 | 2.710c (0.912) | 1.401-5.239 | 3.696a (2.550) | 0.956-14.286 | 4.107c (1.607) | 1.908-8.841 | 1.354 (0.412) | 0.746-2.460 | 1.446 (0.524) | 0.710-2.944 | 3.002b (1.665) | 1.013-8.902 | 1.889 (0.993) | 0.674-5.294 |
| Insurance | 0.506 (0.348) | 0.131-1.949 | 0.891 (0.711) | 0.187-4.254 | | | 0.709 (0.776) | 0.083-6.056 | 1.736b (0.447) | 1.048-2.877 | 1.699a (0.545) | 0.906-3.186 | 2.864b (1.385) | 1.110-7.391 | 2.906b (1.208) | 1.287-6.565 |
| No. of fruit servings | 0.996 (0.015) | 0.966-1.026 | 1.017 (0.019) | 0.980-1.054 | 1.033 (0.039) | 0.960-1.112 | 1.019 (0.023) | 0.976-1.065 | 1.036 (0.065) | 0.917-1.171 | 1.068 (0.085) | 0.914-1.249 | 1.006 (0.169) | 0.723-1.399 | 0.987 (0.134) | 0.755-1.289 |
| No. of vegetable servings | 1.009 (0.042) | 0.930-1.095 | 0.966 (0.052) | 0.868-1.074 | 0.912 (0.108) | 0.723-1.150 | 0.928 (0.062) | 0.814-1.057 | 0.975 (0.064) | 0.856-1.109 | 0.996 (0.080) | 0.850-1.167 | 0.771 (0.136) | 0.545-1.090 | 0.933 (0.120) | 0.725-1.201 |
| Daily vigorous physical activity | 1.056a (0.030) | 1.000-1.116 | 1.031 (0.038) | 0.959-1.108 | 0.967 (0.075) | 0.831-1.125 | 0.949 (0.042) | 0.870-1.034 | 0.968 (0.053) | 0.869-1.077 | 0.944 (0.074) | 0.810-1.100 | 1.135 (0.129) | 0.908-1.419 | 1.108 (0.108) | 0.915-1.341 |
| Household per capita expenditure | 1.000c (0.000) | 1.000-1.000 | 1.000a (0.000) | 1.000-1.000 | 1.000 (0.000) | 1.000-1.000 | 1.000b (0.000) | 1.000-1.000 | 1.000 (0.000) | 1.000-1.000 | 1.000 (0.000) | 1.000-1.000 | 1.000 (0.000) | 0.999-1.000 | 1.000 (0.000) | 0.999-1.000 |
| Household size | 0.936b (0.028) | 0.883-0.991 | 0.946 (0.036) | 0.877-1.020 | 0.926 (0.068) | 0.802-1.068 | 0.921a (0.043) | 0.841-1.010 | 0.986 (0.048) | 0.896-1.086 | 1.015 (0.067) | 0.892-1.155 | 1.022 (0.108) | 0.831-1.257 | 1.070 (0.087) | 0.913-1.255 |
| Other health conditions | 0.999 (0.218) | 0.652-1.531 | 0.862 (0.264) | 0.473-1.570 | 1.736 (1.048) | 0.532-5.669 | 0.629 (0.284) | 0.260-1.523 | 0.381 (0.467) | 0.035-4.206 | 0.059a (0.093) | 0.003-1.296 | 0.004b (0.011) | 0.000-0.825 | 0.002c (0.004) | 0.000-0.163 |
| Constant | 0.061c (0.058) | 0.009-0.393 | 0.029c (0.038) | 0.002-0.391 | 0.003a (0.010) | 0.000-1.064 | 0.020a (0.040) | 0.000-1.058 | 0.998 (0.211) | 0.660-1.509 | 0.985 (0.274) | 0.571-1.697 | 1.842 (0.849) | 0.747-4.544 | 1.397 (0.563) | 0.634-3.078 |
## DISCUSSION
Many chronic health conditions like CVD predispose individuals and families to CHE, especially in SSA, where the majority of the countries are still grappling with how to achieve UHC. This study assessed the spillover effects of having CVD on the risks of incurring higher CHE among households in 2 SSA countries, Ghana and South Africa. As such, this study is one of the few in SSA that examined the economic burden of CVD in this way.
As depicted in the sociodemographic background to this study, the average age of the head of households with CVD was higher than that of households without CVD. This finding is supported by previous studies where the incidence of CVD, like other chronic diseases, is reported to increase with age.29 Furthermore, evidence in this study revealed that the number of households covered by health insurance is low in both countries but slightly higher in South Africa than in Ghana. The implication is that there is an overreliance on OOP payments for medical care in the 2 countries, which may result in enormous financial hardship for individuals and households. A study reviewing the challenges of mobilizing substantial financial resources to achieve UHC in SSA showed that OOP spending remains the largest source of finance for healthcare services in the region, accounting for approximately $36\%$ of current medical expenditure, which is very high relative to the global average of $22\%$.30 Likewise, the low access to health insurance reported among households in this study is reflected in the share of household medical spending in total expenditure. In Ghana, households on average spent as much as $12\%$ of total nonfood expenditure on healthcare services. However, in South Africa, the share of medical expenditure in total nonfood spending was only $3\%$. More importantly, the share of medical expenditure in total household expenditure was revealed to be higher among households with CVD in both countries and across all the considered thresholds, relative to households without CVD. This finding is intuitive since chronic diseases like CVD require regular demand for healthcare services; consequently, this will lead to a larger portion of household resources devoted to accessing medical services. Earlier studies have also published findings that corroborate this evidence. In 2007, a study applied rigorous econometric modeling to investigate the economic burden of CVD in the United States. The study found that almost $30\%$ of all medical spending among major health insurers was due to claims related to CVD treatment.31 Another study in the same country examined the disproportional health spending associated with various health conditions among the elderly. The study reported that the average medical outlay of older persons with chronic health conditions was 5 times greater than those without chronic disease.32 In particular, the study showed that expenditure for treating CVD was the highest among all chronic disease conditions. In SSA, a study on the pattern of chronic disease conditions and the self-reported financial situation of the elderly in South Africa revealed that having chronic disease was associated with household economic conditions.33 Moreover, the risk of incurring CHE increases with a higher economic burden of health expenditure due to increase in the demand for medical services, which is often triggered by the presence of chronic disease conditions in families. This can be seen in the level of CHE experienced among households with regard to their CVD status in this study. Evidence in Ghana and South Africa showed that the extent of CHE incurred in households with CVD was higher compared with that incurred in households without this health condition. Comparing results in both countries, CHE head count was disproportionately higher in Ghana relative to South Africa and this can be as a result of the higher access to health insurance in South Africa as revealed earlier. This suggests that Ghana and South Africa as well as other similar SSA countries need to do more to financially protect their populations to forestall the catastrophic impacts of health expenditure in the event of unanticipated health disruptions. In addition, the result in this study revealed that the difference in CHE incurred among households with a member(s) with CVD and households without persons suffering from CVD, was significantly different as shown in the t-test result. In the model fitted for CHE, the presence of CVD was associated with whether a household experienced CVD in Ghana across the 4 thresholds considered, whereas the effect of CVD was found significant only at the $25\%$ threshold in South Africa. Similarly, this finding is consistent with the evidence reported in previous studies. For instance, a research conducted in China to evaluate the inequality in the experience of CHE induced by having a household member(s) with hypertension revealed that such households incurred a disconcerting level of CHE compared with families with no chronic conditions.34 In 2019, Haakenstad et al35 implemented a cross-country analysis to examine the disease-specific differentials in the incidence of CHE in many countries of the world. Their results showed that heart-related health conditions were associated with about a $1.9\%$ increase in CHE and that this link was stronger compared with other diseases assessed. Overall, much more needs to be done to protect households from the impoverishing effects of excessive health expenditure in both countries and in other similar countries in SSA.
## Limitations
Because the majority of SSA countries are data poor, this study was limited by data availability. First, the survey utilized for this study was conducted just over 10 years ago, although a preliminary literature search showed that progress has been slow, as evidenced in the persistently low coverage of health insurance in many countries in SSA. However, the data set remains the most comprehensive and reliable source of data for this type of analysis to date. The data used for this study allowed the study to provide evidence-based information that will be useful in the design of healthcare financing policies among the countries in SSA. Nonetheless, future studies will benefit from regular surveillance of the disease-specific burden of OOP health expenditure among households in SSA. These limitations should be considered when interpreting the findings presented in this study.
## CONCLUSION
Research evidence abounds on the increase in the prevalence of CVDs in SSA, imposing substantial economic burdens on individuals and households in the region. One way to evaluate the economic burden of diseases at a microeconomic level is to assess the risk of CHE associated with the disease condition. This study found evidence that suggests that CVD predisposes households to the risk of higher CHE in SSA. Equity in health financing presupposes that access to health insurance should be predicated on individual health needs. Therefore, findings in this study emphasize the need to target and prioritize the health needs of individuals with regard to healthcare financing interventions. Furthermore, governments in Ghana and South Africa, including other countries in SSA, should intensify efforts to fully adopt and effectively implement the WHO’s Global Action Plan for the Prevention and Control of Non-Communicable Diseases. This will certainly facilitate the prevention and control of CHE related to CVDs in the region. Some of the “best buys” policy interventions recommended for curtailing chronic noncommunicable diseases include the control of tobacco use, excessive/unhealthy alcohol intake, reduction in the consumption of sugar-sweetened beverages, and health education to prevent sedentary lifestyle, among other recommendations.36–38 In terms of the consumption of tobacco, excessive alcohol intake and the consumption of sugar-sweetened beverages, governments can adopt highly effective tools such as regular increase in the excise taxes levied on those commodities in addition to the use of other economic and legislative tools.36
## Author Contributions
F.I.P.A. and T.A.O. participated in the conception of the study. F.I.P.A. wrote the first draft of the manuscript, and it was reviewed and corrected by T.A.O. Both authors approved the final version of the manuscript.
## Disclosures
The authors report no potential conflicts of interest.
## Ethics Approval and Consent
This paper used data from the WHO Study on Global AGEing and Adult Health (SAGE) implemented by the World Health Organization ICF in conjunction with the participating countries. Ethical approval for the study was obtained from the World Health Organization’s Ethical Review Board and from each site’s respective ethical board.
## Data Availability
The data set used for this study is available based on request through the WHO Multi-Country Studies Data Archive (https://apps.who.int/healthinfo/systems/surveydata/index.php/catalog).
## Acknowledgments
We thank the World Health Organization for granting us the license to use the data, without which this study would not have been possible.
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|
---
title: Is dietary quality associated with depression? An analysis of the Australian
Longitudinal Study on Women’s Health data
authors:
- Megan Lee
- Joanne Bradbury
- Jacqui Yoxall
- Sally Sargeant
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10024974
doi: 10.1017/S0007114522002410
license: CC BY 4.0
---
# Is dietary quality associated with depression? An analysis of the Australian Longitudinal Study on Women’s Health data
## Body
Over the last decade, there has been an increase in interest in the relationship between nutrition and mental health in epidemiological studies(1–3). Poor diet and poor mental health are leading causes of global mortality and morbidity[4]. Mental health disorders currently represent one of the most substantial global burdens of disease, estimated as costing USA$2·5 trillion, including costs such as medication, psychotherapy, workplace absenteeism and income losses(4–7). In Australia between 2017 and 2018, over 2·5 million people experienced depression, with a prevalence of 10·4 %. Females aged 25 to 34 (11·8 %) reported higher rates than their male counterparts (10·2 %)[8]. The role of nutrition in chronic lifestyle diseases such as type 2 diabetes[9], CVD(10–12), some cancers[13,14], metabolic syndrome and obesity[15,16] is generally well documented. However, the role of nutrition in mental health is less well known[17,18] and has provoked growing interest in the association between dietary patterns, diet quality and the association with symptoms of depression(19–22).
Research proposes that a wide variety of biological mechanisms are involved in the heterogenous and complex relationship between nutrition and depression including decreased monoamine function, dysfunctional hypothalamic pituitary adrenal axis, neuro-progression/brain plasticity, mitochondrial disturbances[23,24], cytokine-mediated inflammatory processes, increased oxidative stress, immune responses[25], immuno-inflammation, gut dysbiosis and gut/brain axis relationships[24,26]. However research on the role of these biological mechanisms and nutrition in depression is relatively new and focuses on single food components(27–29) and nutritional supplementation rather than whole-of-diet sources(17,30–32).
Dietary patterns are defined as ‘the quantity, variety or combination of different foods and beverages in a diet and the frequency with which they are habitually consumed’ (Sanchez-Villegas et al., 2018, p. 4). Diet quality is defined as ‘The nutritional adequacy of an individual’s dietary pattern and how closely this aligns with national dietary guidelines’[33] and is commonly used as a measure of healthy and unhealthy dietary patterns assessing high and low diet quality[22,34,35]. Healthy dietary patterns are generally rich in fresh vegetables and fruits, nuts, seeds, whole grains, fermented foods, legumes and water[36]. Most of the research on dietary patterns and depression involves observational epidemiological studies that indicate an association between healthy dietary patterns and decreased depressive symptoms[37,38], while unhealthy dietary patterns high in ultra-processed, refined and sugary foods are associated with higher symptoms of depression[39,40]. Currently, there are four randomised control trials that have assessed the effect of changing from an unhealthy to a healthy dietary pattern(41–44). All four Australian randomised control trials found a significant improvement in depression scores between the intervention and social control groups. However, evidence arising from meta-analyses and systematic reviews shows inconsistent or inconclusive findings when the research is viewed as a whole(39,40,45–49). These findings could be clarified through further prospective longitudinal studies on dietary intake and depression[50].
One study that examines both diet quality and depressive symptoms prospectively is the Australian Longitudinal Study of Women’s Health (ALSWH[51]). Previous research using ALSWH data has examined the role of diet quality and depressive symptoms using longitudinal analysis(27,52–55). Not all have found an association. For instance, Lai et al. [ 53] utilised data from the ALWSH, which focused on Australian women born between 1946 and 1951 and found a significant inverse association between diet quality and depressive symptoms (β = − 0·24, $$P \leq .001$$). However, they also demonstrated that these associations were no longer significant after adjusting for covarying factors such as BMI, smoking and alcohol status and physical activity (β = − 0·04, $$P \leq .100$$). This result suggested that a relationship between diet and depression may be explained by covarying lifestyle factors(56–59). A follow-up analysis using the ALSWH data by the same authors[52] resulted in lower odds of depressive symptoms in high (OR = 0·86; 95 % CI (0·77, 0·96)) and moderate (OR = 0·94; 95 % CI (0·80, 0·99)) diet quality tertiles compared with low diet quality. This suggests that maintaining diet quality over the long term could reduce the odds of depressive symptoms. The authors recommended more longitudinal research using a younger cohort from the ALSWH.
One cross-sectional survey measured diet quality and depressive symptoms in 3963 Japanese middle-aged women ($M = 47$·9, SD = 4·2 years)[60]. After adjusting for covariates, they found that high diet quality was associated with lower depressive symptoms compared with participants with low diet quality (OR = 0·65, 95 % CI (0·45, 0·78)). Apart from this study, there is a paucity of research that focuses on the relationship between diet quality and depressive symptoms in young women. To fill this gap in the literature, we conducted a secondary analysis of the ALSWH data examining whether there is a longitudinal association between dietary quality and depressive symptoms in a cohort of young Australian women. The sample for the current study was sourced from the 1973–1978 cohort, including data from two time points in 2003 (Mean age = 27·6, sd = 1·5) and 2009 (Mean age = 33·7, sd = 1·5) where diet quality, depressive symptoms and all covarying factors were measured. These data have not been analysed in previous research, and it is our aim to replicate the previous studies analysis with fresh data.
## Abstract
Depression is a chronic and complex condition experienced by over 300 million people worldwide. While research on the impact of nutrition on chronic physical illness is well documented, there is growing interest in the role of dietary patterns for those experiencing symptoms of depression. This study aims to examine the association of diet quality (Dietary Questionnaire for Epidemiological Studies version 2) and depressive symptoms (Centre for Epidemiological Studies for Depression short form) of young Australian women over 6 years at two time points, 2003 (n 9081, Mean age = 27·6) and 2009 (n 8199, Mean age = 33·7) using secondary data from the Australian Longitudinal Study on Women’s Health. A linear mixed-effects model found a small and significant inverse association of diet quality on depressive symptoms (β = −0·03, 95 % CI (−0·04, −0·02)) after adjusting for covarying factors such as BMI, social functioning, alcohol and smoking status. These findings suggest that the continuation of a healthy dietary pattern may be protective of depressive symptoms. Caution should be applied in interpreting these findings due to the small effect sizes. More longitudinal studies are needed to assess temporal relationships between dietary quality and depression.
## Participants
The ALSWH[51] is a continuing longitudinal cohort study of more than 50 000 women in Australia. It is divided into four age cohorts of women born between 1921 to 1926, 1946 to 1951, 1973 to 1978 and 1989 to 1995[53,61]. On commencement in 1996, 40 392 women were recruited into the first three cohorts, followed by 17 069 into the 2012 fourth cohort[62]. Participants were randomly selected from the Australian health insurance database, Medicare, including all Australian permanent residents. Response rates for each cohort were estimated as 37 % to 40 % (1921–1926), 53 % to 56 % (1946–1951), 41 % to 42 % (1973–1978) and 70 % (1989–1995). Women completed a survey containing questions relating to their health outcomes every 3 to 4 years from 1996 to 2018. The study protocol followed the Declaration of Helsinki guidelines[63], and formal ethical approval was given by the Human Research Ethics Committees of the University of Queensland and the University of Newcastle in Australia. Participants supplied informed consent before being included[51].
The analysis for this particular paper is targeted to the cohort of women who were born between 1973 and 1978. Participants completed a baseline questionnaire in 1996 (n 14 247) and every 3 years thereafter; 2000 (n 9688), 2003 (n 9081), 2006 (n 9145), 2009 (n 8199), 2012 (n 8009), 2015 (n 7186) and 2018 (n 7121). The sample for the current study includes data from two time points in 2003 (n 9081, Mean age = 27·6) and 2009 (n 8199, Mean age = 33·7) where diet quality, depressive symptoms and all covarying factors were measured.
## Depressive symptoms ()
The Centre for Epidemiological Studies Depression short form (CESD-10) was used in the ALSWH to ‘assess depressive symptoms during the past week at each survey’[64]. The CESD-10 includes ten of twenty items from the original CESD[65]. Response format is a four-point Likert scale, ranging from 0 (none of the time) to 3 (all of the time). Total scores are obtained by summing across items ranging from 0 to 30, with higher scores indicating greater depressive symptom severity. The CESD-10 was designed to measure depressive symptoms experienced in the general population rather than provide a clinical diagnosis. A score greater than ten is the standard cut-off to classify people experiencing depressive symptoms[65].
The CESD-10 has high internal consistency (Cronbach α = 0·88), 92 % specificity identifying those without depressive symptoms, 91 % sensitivity identifying those with depressive symptoms and 92 % positive predictive values reflecting the presence of depressive symptoms[66].
## Food frequency questionnaire
The Dietary Questionnaire for Epidemiological Studies version 2 (DQES v2) was administered to the selected cohort in 2003 and again in 2009 in the ALSWH. The DQES v2 is a self-report FFQ developed by the Cancer Council Victoria that measures dietary intake in epidemiological studies[67]. In the DQES v2, participants report dietary consumption of seventy-two foods over the previous 12 months. Additional questions are asked on the frequency of consuming fruit, vegetables, meat, meat alternatives, milk, bread, butter, spreads, cheese, sugar and eggs[67]. A study of 237 Australian participants indicated test-retest reliability of the DQESv2 with weighted κ of 0·58 over 12 months[68]. This test-retest reliability is similar to the widely used Commonwealth Scientific and Industrial Research Organisation FFG[69]. The DQES v2 has been used in previous research relating to dietary patterns and depression in women[52,53] and for most food types was comparable to other FFQ (Hodge et al., 2000). As a measure of diet quality, the Australian Recommended Food Score (ARFS) was applied to data collected using the DQES v2[70].
## Diet quality (Australian Recommended Food Score)
The ARFS uses scoring in line with the Australian Dietary Guidelines (ADG) and the Australian Guide to Eating[71]. The ARFS is calculated by summing points within eight subscales: vegetable intake (twenty-one items), fruit (twelve items), protein foods (seven items), plant-based protein (six items), bread and cereals (thirteen items), dairy products (eleven items), water (one item) and fats (two items). Foods are given one point for a frequency of more than once/week. Scores range from 0 to 73, with higher values corresponding to healthier dietary quality. The ARFS has been validated using the Australian Eating Survey[33] and used in previous studies using ALSWH cohorts[52,53].
## Covariates
Covarying factors commonly associated with depressive symptoms in the literature were included BMI – measured by calculating self-report weight in kilograms divided by height in metres squared; social functioning – measured by averaging two items of the thirty-six-Item Short Form Survey (SF-36[72]); with α reliability levels of 0·85[73]; anxiety – measured using self-report of clinical diagnosis (yes/no); alcohol status – measured using three self-report items (independent of the ARFS diet quality score) on how often and how much alcohol was consumed each week and engagement in binge drinking (no risk; binge less than once a month; binge once a month or more; more than two drinks/d on average) in line with classifications from the National Health and Medical Research Council[74]; smoking status – measured using three self-report items on how often and how many cigarettes smoked each week and; education level – no qualification, school certificate, higher school certificate, trade certificate, diploma, undergraduate degree and postgraduate degree.
## Data analysis strategy
We calculated descriptive statistics for both time points (2003 and 2009) using mean, median, standard deviation, histograms and boxplots for continuous variables (CESD-10, ARFS, SF36 and age) and frequencies, percentages and bar charts for categorical variables (BMI, clinically diagnosed depression and anxiety, education, marital, smoking and alcohol status). Assumptions for linear mixed-effects model, including linearity and equal variance, were assessed using histograms and scatterplots of residuals[75]. Normality of distributed errors was observed using probability-probability (pp) and quantile-quantile (qq) plots. The Akaike Information Criterion (AIC) and log-ratio tests were used to assess the model fit. AIC is used to determine the information lost by adding a variable to the model, with lower AIC indicating a better fit[76]. Criteria for retaining or excluding variables in the final models were a substantial reduction of the AIC and a significant log-ratio test. Only data that were complete for both time points 2003 and 2009 for each participant were included in the model (n 8199).
A linear mixed-effects model was used to predict depression total scores (continuous CESD-10 score) as a function of diet quality (continuous ARFS total score) and year (2003 and 2009), with participant as a random effect. The model was formulated as follows with i indicating individual and j indicating time: Continuous covariates (BMI and social function) and categorical covariates (anxiety, alcohol status, smoking status, physical activity, geographical location, marital status, socio-economic status and education level) were added one at a time in a stepwise fashion. Each step in the model reduced the AIC and was associated with a significant log-ratio test, apart from physical activity, geographical location, socio-economic status and marital status. Therefore, these four variables were removed. The final model was specified with the CESD-10 as the outcome, ARFS total score as the predictor, with covariates as BMI, social function, anxiety, alcohol status, smoking status and education level added stepwise.
## Participant characteristics
Participant characteristics of women at 2003 and 2009 are summarised in Table 1. At baseline, in 2003, 9081 participants were included (mean age = 27·6, sd = 1·5). At the final time point in 2009, 8199 (90 %) participants remained (mean age = 33·7, sd = 1·5). At this time point, 77 % of women were partnered, 56 % had completed a university degree, 41 % had smoked cigarettes in their lifetime, 88 % currently consumed alcohol and 10 % were clinically diagnosed with anxiety. According to WHO[77] BMI categorisation, 45 % of women were classified as overweight or obese compared with 37 % in 2003. In 2003, sample mean diet quality was 29·2 (sd = 9·3) compared with 33·2 (sd = 9·3) in 2009. In relation to depression, 13 % [2003] and 18 % [2009] of the cohorts were clinically diagnosed with depression, while CESD-10 scores across the cohort fell below the cut-off for experiencing depressive symptoms 7·0 (sd = 5·3) in 2003 and 6·4 (sd = 5·2) in 2009.
Table 1.Participant characteristics over time(Numbers and percentages)2003 (n 9081)2009 (n 8199) n % n %Education level No qualification891541 High school220825143818 Trade/certificate215824204026 University degree437250456556 Total88278097Marital status Partnered554961632077 Unpartnered349639184923 Total90458169Smoking status Never smoked517157488260 Ex-smoker167419210526 Smokes <10 d1102125487 Smokes 10–19 d71384065 Smokes > 20 d38842403 Total90488181Alcohol status Non-drinker731899012 Low-risk drinker552261483859 Rarely drinks246227197724 Risky drinker28032874 High-risk drinker491741 Total90448166BMI Underweight36342093 Normal weight473258417852 Overweight179222205325 Obese121715161420 Total81048054Diagnosed depression Yes112513133918 No781087622982 Total89357568Diagnosed anxiety Yes545675310 No839094681590 Total89357568Mean sd Mean sd Age (years)27·61·533·71·5CESD-107·05·36·45·2SF36 SF80·222·582·722·2ARFS29·29·333·29·3CESD-10, centre for epidemiological studies depression score; SF36 SF, medical outcomes short-form – social function score; ARFS, Australian recommended food score.
## Linear mixed-effects model
In the unadjusted model, there was a small, significant inverse association of ARFS on CESD-10 (β = -0·06, $P \leq .001$), indicating that for every point increase in diet quality, as measured by the ARFS total score, there was a 0·06-point reduction in depressive symptoms, as measured by the CESD-10 total score (online supplementary material). Each step in the model reduced the AIC and was associated with a significant log-ratio test indicating all variables in the table contributed to the model. There was no significant interaction between ARFS total score and year. Therefore, only the main effects were included in the adjusted model. After adjusting for all covariates in the model, there remained a small but significant inverse association of ARFS on CESD-10 (β = -0·03, 95 % CI (-0·04, -0·02)), indicating that for each point increase in diet quality there is a.03 point reduction in depressive symptoms (Table 2).
Table 2.Adjusted model of ARFS total score on CESD-10 between 2003 (n 9081) and 2009 (n 8199)(Standardised and unstandardised β coefficients) β β se Z P 95 % CIARFS total score-0·03-0·060·004-8·29< 0·001-0·04, −0·02Years 2003 2009–0·21–0·020·064–3·350·001–0·34, −0·09Anxiety Yes No1·810·090·12814·15< 0·0011·56, 2·06BMI0·080·090·00612·20< 0·0010·07, 0·09Social function–0·13–0·540·002–81·16< 0·001–0·13, −0·12Alcohol status Non-drinker Low-risk drinker–0·04–0·010·122–0·290·772–0·27, 0·20 Rarely drinks0·240·020·1301·820·069–0·02, 0·49 Risky drinker0·670·020·2173·070·0020·24, 1·09 High-risk drinker1·330·020·4293·090·0020·49, 2·17Smoking status Never smoked Ex-smoker0·260·020·0912·840·0050·08, 0·43 Smokes <10 d0·310·020·1222·550·0110·07, 0·55 Smokes 10–19 d0·800·040·1495·36<0·0010·51, 1·09 Smokes >= 20 d0·980·030·1945·04<0·0010·60, 1·36Education level No qualification Year 10–0·49–0·020·376–1·310·189–1·23, 0·24 Year 12–0·57–0·040·366–1·570·116–1·29, 0·14 Trade certificate–0·72–0·020·411–1·760·078–1·53, 0·08 Diploma–0·65–0·050·363–1·800·073–1·36, 0·060 Undergraduate–0·95–0·090·362–2·610·009–1·66, −0·24 Postgraduate–0·89–0·060·370–2·400·016–1·61, −1·63CESD-10, centre for epidemiological studies depression score; ARFS, Australian recommended food score; β, unstandardised β coefficient; β, standardised β coefficient.
## Discussion
This analysis of the ALSWH longitudinal cohort study measured the size and significance of the association between Australian womens’ diet quality and depressive symptoms over 6 years between 2003 and 2009. In a linear mixed-effects model, ARFS scoring was applied to the DQESv2 FFQ to measure diet quality and depressive symptoms between at both time points. After adjusting for covariates, diet quality was inversely associated with depressive symptoms at both time points in this large cohort.
This longitudinal data analysis suggests that a continuation of healthy diet quality predicts lower depressive symptoms for women who already have a healthy diet. The findings are statistically significant after adjusting for various cofactors but had small effect sizes. Caution must be applied when interpreting these results. Although statistically significant, the small effect sizes may not suggest clinical significance. Therefore, it is unclear how much change from an unhealthy to a healthy diet would be needed to infer a result in depressive symptoms in clinical application. A reason for the small effect sizes may be that although 13 % to 18 % of the cohort were clinically diagnosed with depression, overall, when measuring depressive symptoms using the CESD-10 scores, the cohort, on average reported lower than the cut-off scores for depressive symptoms. These findings are comparable with other data analyses using the ALSWH to examine diet quality and depressive symptoms using the same diet quality score (ARFS) and depressive symptoms score (CESD-10) as our study. In their study using the 1946 to 1951 cohort (n 7877) of women in the ALSWH who were 67 years old in 2018, Lai et al. [ 2017] reported 6 % reduced odds of depressive symptoms in women who had moderate to high diet quality compared with those who had lower diet quality using the ARFS (moderate v. low: OR = 0·94, 95 % CI (0·80, 0·99)), high v. low: OR = 0·86, 95 % CI (0·77, 0·96)). Similarly, Rienks et al.[54] found that after adjusting for covariates in the 1946 to 1951 cohort, women who had a greater consumption of foods within a Mediterranean dietary pattern had 8 % lower odds of depressive symptoms in 2001 (OR = 0·82, 95 % CI (0·77, 0·88)) and lower odds of depressive symptoms in 2004 (OR = 0·83, 95 % CI (0·75, 0·91)).
Similarly, another longitudinal study measured the association between dietary patterns and depressive symptoms using reduced rank regression in 903 Japanese participants after a 3-year follow-up[78]. They found that high adherence compared with low adherence to a healthy Japanese dietary pattern – high in fish, soya products, green tea, vegetables, mushrooms and seaweed was associated with a reduced odds of depressive symptoms (OR = 0·57, 95 % CI (0·35, 0·93)). However, a longitudinal study in the UK assessing dietary patterns and depressive symptoms in young female parents aged 29 to 40 years (n 7698) over 4 years[79] found no significant association after adjusting for covariates.
The ARFS measurement of diet quality used in this study implies that the diversity of healthy foods may be an important factor for depressive symptoms. Participants who recorded limited intake from each food group received lower scores on the ARFS than those who recorded a diverse range of different foods, despite eating a large quantity of one type of healthful food[33]. This finding suggests that consumption of a broader range of fruits, vegetables, seafood, meats, nuts, seeds, legumes, whole grains and dairy products is as (if not more) important as eating the recommended amount from each food group[80]. The components of these foods, such as antioxidants[81], probiotics, prebiotics[82] and complex carbohydrates[83], are known to reduce oxidative stress, chronic inflammation and improve the health of the gut microbiome, which is already identified as contributing to a reduction in depressive symptoms[84]. A recent cohort study comparing microbiome samples from 10 000 citizen-scientists from Australia, the UK and the USA found that consuming more than thirty different plant types each week was beneficial to the gut and psychiatric health[85].
This current analysis also found several other predictors of depressive symptoms within the models in addition to diet quality. When assessing diet quality using the ARFS, higher scores in anxiety and BMI were associated with increased depressive symptoms, and women who had higher social functioning had lower odds of depressive symptoms.
This study’s strengths are that the data were collected from a large sample of women representing the Australian population over 6 years. A further strength of this study is the ability to adjust across various socio-demographic and health-related factors within the model enhance the strength of this particular analysis. Furthermore, the ability to assess the impact of diet and socio-demographic factors specific to a female cohort is appropriate as women have higher reported rates of depressive symptoms than men in Australia[86]. However, caution must be applied in suggesting a causal role between diet and depression and from the small effects found as the clinical significance of these findings could be uncertain. Clinical significance is distinctly different from statistical significance and indicates whether the association could make a demonstrated, clinically meaningful difference to an individual receiving treatment in the real world[87]. Additionally, some variables included in the model are along the causal pathway between depressive symptoms and diet quality (for example, BMI). It was beyond the scope of this cross-sectional study to explore the potential causal roles of variables. Future research could explore mediation and moderation impacts of the other significant variables in the model. A further limitation is that the ARFS measurement of diet quality gave scores for some food types, which were not representative of the definition the authors use of a healthy dietary pattern (high intake of fruits, vegetables, nuts, seeds, legumes, wholegrains, water and low intake of processed, sugary and refined foods) including ice cream, white bread and rice and processed meat products. The ARFS also disadvantaged participants who followed a plant-based dietary pattern as scores were given for meats, eggs and dairy, which are frequently excluded by those following vegetarian and vegan diets. This disadvantage could result in participants who followed a plant-based dietary pattern having reduced scores and potentially being categorised as consuming an unhealthy diet when the opposite may have occurred. Further, the 1-year recall of food consumed was the basis for the diet quality measurement. The reliability of an individual’s recall of foods eaten over this time is questionable and may influence the accuracy of the results[88].
In this report, a longitudinal analysis using linear mixed-effects models, diet quality measured by a FFQ in 2003 and 2009 had a small and statistically significant association. However, this association may not be clinically meaningful. Other predictors of depression were important, including anxiety, BMI and social functioning. This ALSWH longitudinal cohort study analysis has highlighted small inverse findings in the association between dietary patterns and depressive symptoms in Australian women. Further analysis of longitudinal and intervention studies is needed to assess temporal relationships and causality between dietary patterns and depression.
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---
title: 'The sweet tooth of infancy: Is sweetness exposure related to sweetness liking
in infants up to 12 months of age?'
authors:
- Carina Müller
- Claire Chabanet
- Gertrude G. Zeinstra
- Gerry Jager
- Camille Schwartz
- Sophie Nicklaus
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10024975
doi: 10.1017/S0007114522002628
license: CC BY 4.0
---
# The sweet tooth of infancy: Is sweetness exposure related to sweetness liking in infants up to 12 months of age?
## Body
Humans have an innate preference for sweet taste(1–3). In childhood, the sweet taste is generally more liked than in adulthood and is preferred over all other tastes in the first 20 months of life[4,5]. Despite the universal trait of liking sweetness, there are great inter-individual differences regarding which specific tastes and sweetness levels are liked the most[6]. A child’s preferred level of sweetness may ultimately affect his or her food intake and weight, as a greater preference for sweetness has been associated with consumption of high-energy foods and, moreover, with overweight and obesity in 6-to-9-year-old children[7,8]. However, it remains difficult to explain how differences in sweetness liking arise[9]. A body of research argues that food and taste preferences may be linked to sex[10] or genetic differences[11]. Previously, boys were found to like sweet foods more at 8 to 12 years than girls[10], and genotypes at the TAS2R38 taste gene locus were associated with sucrose preference and liking of sweet-tasting foods in 5-to-10-year-old children[11]. Further, research suggests that individuals vary in their hedonic liking response to sweetness due to their phenotype[12] and that children move along a continuum between sweet likers and sweet dislikers[6].
In addition to sex and genetic differences, there is an ongoing discussion about whether repeated exposure to sweet taste alters the degree of sweetness liking or not(13–17). Some research suggests that sweetness liking may be maintained or increased through dietary exposure to sweet-tasting foods(15–17). For example, babies who were regularly fed sweetened water during their first months of life liked sweetened water more at 6 months and at 2 years than children who were not regularly fed sweetened water in infancy[16,17]. However, this link was not found when sweetness liking was tested with a sweetened fruit-flavoured drink instead of sweetened water at 2 years of age[17]. This suggests that the relationship between early sweetness exposure and sweetness liking may depend on the medium used for both exposing to sweetness and measuring sweetness liking. In a recent systematic review, equivocal evidence for the presence and possible direction of a relationship between sweetness exposure and sweetness liking was found[18]. On the one hand, a higher intake of added sugars in the first year of life was related to a higher preference for sweetness at the age of 4 to 7 years[14]. Further, a higher intake of foods with added sugars during the first year of life was linked to a higher intake at the age of 3 to 7 years[13]. On the other hand, neither added sugar consumption nor the consumption frequency of sweet foods was linked to sweetness liking in 7-to-12-year-old and 6-to-9-year-old children, respectively[8,19]. Those findings demonstrate that the link between sweetness exposure and liking is more complex than initially indicated by earlier studies, and far from being systematically shown.
In addition to the different study results and conclusions, very limited research aims to shed light on the association between exposure to sweet taste and the degree of sweetness liking in the first 12 months of life[20]. During this time, infants go through a major dietary transition – from exclusive milk feeding over complementary feeding to eating what is consumed by the family[21,22]. In addition, what infants eat in those early months could influence their eating behaviour in the long term, as diet quality and food preferences in infancy are related to food intake and food preferences through adolescence and early adulthood(23–27). Moreover, research suggests that dietary patterns are already established by the age of 18 months, and those set at 2 years build the root for lifelong eating habits[21]. Due to the importance of the first few months for long-term eating behaviour, it is important to examine this period to assess the role of or potential relationship between repeated exposure to sweet-tasting foods and the degree of sweetness liking at this crucial stage of life.
When dietary intake is no longer limited to breast or formula milk, the level of sweetness exposure starts to differ more between children[28,29]. Hence, this study was conducted to define the role of dietary exposure to sweetness on sweetness liking during two crucial changes in early infant feeding: at 3 to 6 months (start of complementary feeding) and at 10 to 12 months (transition to the family table). We predicted that higher exposure to sweetness would be associated with higher sweetness liking in those periods. Further, we hypothesised that the link between sweetness exposure and sweetness liking would be stronger at 12 than 6 months, as infants’ sweetness exposure was found to be higher at 10 to 12 compared with 3 to 6 months[28]. Acknowledging that sweetness liking may already differ between infants at birth, we also investigated the associations between sweetness liking at 3 months and sweetness liking at 6 and 12 months. We hypothesised that repeated exposure would override innate sweetness preferences so that sweetness liking at 3 months would have a low impact on sweetness liking at 6 and 12 months.
## Abstract
Infants become increasingly exposed to sweet-tasting foods in their first year of life. However, it is still unclear whether repeated exposure to sweet taste is linked to infants’ sweetness liking during this period. Making use of data from the OPALINE cohort, this study aimed to examine the link between sweetness exposure and sweetness liking during two important periods in early infant feeding: at the start of complementary feeding (3–6 months) and the transition to the family table (10–12 months). Infants’ sweetness exposure was assessed using 7-d food records which were completed by mothers every month (n 312), reporting daily consumption rates of formula/breast milk or complementary food and the type of formula milk and/or complementary foods for each feeding occasion. Infants’ sweetness liking was studied in the laboratory at 3, 6 and 12 months of age by assessing their response to a lactose–water solution and the amount drunk of this solution compared with plain water. Linear regressions and structural equation model assessed associations between exposure to and liking for sweetness at 6 and 12 months. Neither at 6 (n 182) nor at 12 months (n 197) was sweetness exposure associated with sweetness liking. While sweetness liking at 3 months was unrelated to liking at 6 months, the latter predicted sweetness liking at 12 months. These findings demonstrate no association between sweetness exposure at 3 to 12 months and liking at 6 and 12 months despite a sharp increase in sweetness exposure in that period. However, sweetness liking at 6 and 12 months was positively associated.
## General design
We conducted secondary data analyses using the French birth cohort study ‘Observatoire des Préférences Alimentaires du Nourrisson et de l’Enfant’ (Observatory of Infant and Child Food Preferences), OPALINE in short. Between 2005 and 2009, 312 women were recruited before their last trimester of pregnancy via leaflets and posters at doctors’ and pediatricians’ consulting rooms in maternity hospitals and clinics, as well as pharmacies and day care centres. To be included in the study, parents had to be at least 18 years old and have children in good health after birth. Children’s degree of sweetness liking was assessed in the laboratory at ages 3, 6, 12 and 20 months (based on the expected delivery date, not the actual delivery date). In this paper, only the measurements at 3, 6 and 12 months are included in the analyses as the procedure at 20 months was slightly different[4]. During their last trimester of pregnancy, mothers participated in interviews on sociodemographic and health characteristics. After the child’s birth, mothers reported their children’s birth date, sex, gestational age, and birth weight and length. Further, between birth and 1 year, mothers filled out 12 monthly 7-d food records at home in which they indicated the number and timing of each feeding occasion with breast or formula milk. Furthermore, they described the complementary foods they might have given, the food type (home-made food, ready-prepared baby food, ready-prepared adult food, plus the brand if relevant), and texture (e.g. drained, lumpy or thick), and whether they had added any ingredient (e.g. sugar or salt) to the foods. They were not asked to report the amount consumed by the child. Mothers also recorded special occurrences in between two monthly food records, such as the introduction of new foods. Based on the records, foods were grouped into milk, home-made foods, ready-prepared baby foods and ready-prepared adult foods. Detailed explanations of the recording method and overall procedures used in the OPALINE study were explained previously[4,28,30,31]. This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the local ethical committee (Comité Consultatif de Protection des Personnes dans la Recherche Biomédicale de Bourgogne-ID RCB 2007-A00808–45). Written informed consent was obtained from both parents prior to the first experimental session. The protocol related to the present analysis was preregistered at https://osf.io/6tz4c. The OPALINE data can be accessed upon request.
## Sweetness exposure
Infants’ exposure to sweetness (SweetExposure) was measured through the diet (based on the food records) as the cumulative frequency of the taste intensities of all consumed foods during each period of recording[28,32,33]. For that, all foods were linked to their sweetness intensity through existing databases[32,33]. In short, the data sets (one for baby food and one for adult foods) included information for each food item based on the evaluation from trained panellists who tasted and rated the taste intensity on a continuous scale from 0 to 10, inspired by the Spectrum method[34]. Following this method, four sweet reference solutions were used and arranged on the scale with increasing intensity to give reference intensities to the panellists to aim for standardised ratings[28]. For the adult food database, the panellists conducted home-based measurements where the reference solutions were replaced by reference foods[34]. Mixed dishes were rated according to the mean of the ingredients’ tastes. An extensive explanation of the sweetness intensity measurements can be found elsewhere[28]. The exposure to sweetness was then calculated for each month and child by the sum of sweetness intensities of food items weighted by consumption frequencies and divided by the number of days for which the food diary products were completed. Based on that, the average daily mean of SweetExposure was calculated for the 3 to 6 and 10 to 12 months periods.
## Sweetness liking
Infants’ degree of sweetness liking (SweetLiking) was estimated at 3, 6 and 12 months by comparing the infant’s acceptance of a sweet solution (0·20 M lactose + water) to water. The solution was chosen because it is similar to the lactose concentration in breast milk and reflects moderate intensity[31]. The sweet solution was prepared every 3 d, kept refrigerated at +4° C and used at room temperature during the sweetness liking test. The bottles contained 30 ml of the solution or water at 3 and 6 months and 50 ml at 12 months. Further, mothers reported which nipple shape and material the child was used so that the experience during the laboratory assessment would be similar to infants’ home experience. In the data collection process, liking was also assessed for the other basic tastes (sour, salty, bitter and umami), and the researchers were blind to the order in which these tastes were tested. For this paper, only the degree of sweetness liking is reported.
## Procedure
Children were tested individually in a room designed for infant testing at the Centre Européen des Sciences du Goût (Dijon, France) or the FLAVIC (FLAvour VIsion Consumer Behaviour) joint research unit (Dijon, France). Children sat in a bouncer (3 and 6 months) or a high chair (12 months). Each of them was accompanied by one parent, while one of the four researchers included in the study conducted the test. To ensure that children were in a similar hunger state at the beginning of the test, parents were asked to refrain from giving the child water, milk or food 1 h before the test. On the test day, compliance with this instruction was checked by asking the parent to report the last time the child had consumed something on that day. In videotaped sessions, children were presented with four bottles in the following order: water, sweet solution, sweet solution and water. Each of the bottles was presented for 45 s by gently rubbing the nipple against the child’s mouth, followed by a 15-s break before presenting the next bottle. In case the child did not drink, he/she was offered a toy. When necessary, the parent sat closer to the child, or the child was put on the parent’s lap. The degree of sweetness liking was defined through three scores: the ingestion ratio (IR) and two liking ratios (liking rated by the experimenter and liking rated by the parent). Sweetness liking measures were only recorded if the child drank at least 1·0 g from the sweet solution. Otherwise, it was considered missing.
## Ingestion ratio
The first indicator for the degree of sweetness liking, the IR, was created by measuring the amount the child drank from the sweet solution bottles and the water bottles. For that, the bottles were weighed to the nearest 0·1 g (Sartorius U3600S; Sartorius AG) before and after each solution was presented to the child. Based on the weights, the IR was calculated by first summing the amount drunk from the two sweet solutions. This sum was then divided by the sum of amounts drunk from all four bottles so that each child could be assigned an IR between 0 and 1.
IR = (Grams sweet bottle 1 + Grams sweet bottle 2) / (Grams Sweet Bottle 1 + Grams Sweet Bottle 2 + Grams Water Bottle 1+ Grams Water Bottle 2)
## Liking ratio
For the second and third sweetness liking indicators, the experimenters and parents rated the child’s reaction to each of the four bottles on a five-point Likert scale (1 – strong rejection; 2 – slight rejection; 3 – neutral; 4 – slight acceptance and 5- strong acceptance). The child’s overall behaviour, including facial expressions, was taken into account. As a result, each of the four bottles was assigned a score between 1 and 5. Based on those scores, the experimenter liking ratio (LRE) and parent liking ratio (LRP) were calculated similarly to the IR by summing the scores of the two sweet solutions and dividing them by the sum of all four scores. Again, ratios between 0 and 1 were possible.
LRE/LRP = (Score sweet bottle 1 + Score sweet bottle 2) /(Score Sweet Bottle 1 + Score Sweet Bottle 2 + Score Water Bottle 1 + Score Water Bottle 2) For each of the three liking ratios, a ratio of 0·5 indicated indifference to the sweet solution relative to water. A ratio of > 0·5 indicated a preference for the sweet solution relative to water, and a ratio of < 0·5 indicated rejection of the sweet solution relative to water.
## Subjects
In this paper, data were included from 312 mothers, leading to a sample of 319 children including seven pairs of twins. Of those, information on the degree of sweetness liking was available for 153 infants at 3 months, 216 at 6 months and 215 at 12 months. Further, of the 319 infants, information on sweetness exposure was available for 251 infants for the 3 to 6 months period and 264 for the 10 to 12 months period.
For SweetLiking, missing data were considered as occurring at random because they were due to children being sick or drinking less than 1·0 g of the sweet solution on the assessment day. Similarly, missing data for SweetExposure were considered as occurring at random because they were due to incomplete food records. As many observations as possible were included for each analysis, resulting in different sample sizes for each analysis.
Characteristics of the included mother–infant pairs are displayed in Table 1. The infants were on average exclusively breastfed for about 3 months, and more than half of the infants were male (54 %). The majority of mothers (89 %) had a high or medium level of education.
Table 1.Mother and infant characteristics, OPALINE* (n 319)Infant characteristics n %SexMale17254Female14746Mean sd Weight (kg)At birth3·250·48At 3 months5·780·72At 6 months7·470·78At 12 months9·550·96Length (cm)At birth49·524·55At 3 months60·082·74At 6 months66·712·52At 12 months74·882·66Duration of exclusive breast-feeding (months)2·971·93Age at beginning of complementary feeding (months)5·460·84Maternal characteristicsMean sd Age at delivery (years)30·754·58Pre-pregnancy BMI (kg/m2)22·143·43 n %Level of education† Low3311Middle12539High15950*Observatoire des Préférences Alimentaires du Nourrisson et de l’Enfant (Observatory of Infant and Child Food Preferences).†Level of education: Low = high school diploma or less; middle = secondary education or undergraduate education; high = university or higher vocational training.
## Statistical analyses
Statistical analyses were performed using R, version 4.1.1 for Windows. The sweetness liking data were merged across the experimenters and sessions, as previously no difference was found in the judgement of the experimenters[31]. Results are expressed as mean values and standard deviations. Inferences about the association between the degree of sweetness liking and sweetness exposure were made based on P-values, the size of regression coefficients and CI.
To assess whether SweetLiking at 3 months differed from SweetLiking at 6 and 12 months and whether liking at 6 months differed from liking at 12 months, paired samples t tests were conducted. Again, as many observations as possible were used for each t test (n 137 (difference 3 and 6 months); n 164 (difference 6 and 12 months); n 122 (difference between 3 and 12 months)). In addition, another paired samples t test was conducted to assess whether exposure to sweetness differed at 3 to 6 months compared with 10 to 12 months, including all individuals with complete data on sweetness exposure at both investigated periods (n 247).
The relationship between sweetness exposure and liking was tested using linear regressions. To reduce the number of linear regression models, we only used one of the two liking ratios, because both (LRE and LRP) measured the child’s hedonic liking and correlated strongly with each other at each of the time points of assessment (3 months (r[151] = 0·43, $P \leq 0$·001); 6 months (r[214] = 0·48, $P \leq 0$·001); and 12 months (r[209] = 0·64, $P \leq 0$·001)). The LRE was chosen as it was based on the judgement of the same four experimenters, whereas the LRP was based on the judgement of each parent. All linear regression models were conducted twice, with the IR and LRE as the outcome variables. First, the link between the degree of sweetness liking (IR and LRE) at 6 months (12 months respectively) and exposure to sweetness at 3 to 6 months (10–12 months, respectively) was tested with a bivariate linear regression. For that, all individuals with complete data on IR and LRE at 6 months (12 months) and sweetness exposure at 3 to 6 months (10–12 months) were included in the analyses. Next, multiple linear regressions were conducted by adding confounders to the bivariate models. Based on the directed acyclic graph method, the duration of exclusive breast-feeding and the mother’s level of education were selected as confounders for inclusion in the regression analyses[4,5,28,35,36]. Next, an interaction term between the child’s sex and sweetness exposure, as well as the main effect of sex, were introduced but removed again from the final models due to non-significance. Because missing values were judged to be at random, the bivariate analyses were performed a second time for complete cases only to assess the robustness of the results. Only individuals with available data for all four variables (SweetLiking at 6 months, SweetLiking at 12 months, SweetExposure at 3 to 6 months and SweetExposure at 10 to 12 months) were included in the complete case models.
Finally, to get a global picture of the relationships between exposure and liking, all available data regarding SweetLiking at 3, 6 and 12 months and SweetExposure at 3 to 6 and 10 to 12 months were gathered in a structural equation model (supplementary material Fig. S1, n 319). Compared with the linear regressions, the structural equation model allows for consideration of all (in)dependent variables in the same model and therefore to test relationships simultaneously[37]. Structural models were estimated using the R package Lavaan 0.6–7, by full information maximum likelihood to address missing values[38]. All three SweetLiking variables (IR, LRE and LRP) were used for a more precise estimation of liking at each age. The first step was to check whether the construct measurement (confirmatory factor analysis, CFA) was satisfactory concerning the latent variables (SweetLiking at 3, 6 and 12 months) as measured by the three indicators (observed variables IR, LRE and LRP). More precisely, the CFA model was built, and fit criteria [CFI (0.985), TLI (0.978) and RMSEA (0.034)] were used to evaluate the quality of the measurement model and convergent validity. It was checked that the estimation showed high loadings and low correlations between constructs. Next, the structural model was built. SweetLiking at 3 months of age was included to account for individual differences in sweetness liking that may occur during the milk feeding period before differences in sweetness exposure begin to increase. It was assessed whether SweetLiking at 3 months predicts SweetLiking at 6 months, whether SweetLiking at 6 months predicts SweetLiking at 12 months and whether SweetExposure at 3 to 6 months (10–12 months) predicts SweetLiking at 6 months (12 months).
## Sweetness liking at 3, 6 and 12 months
The calculated average IR was 0·56 ± 0·12 at 3 months, 0·58 ± 0·13 at 6 months and 0·58 ± 0·18 at 12 months of age. The calculated average LRE was 0·53 ± 0·09 at 3 months, 0·54 ± 0·08 at 6 months and 0·52 ± 0·10 at 12 months of age (Fig. 1). Hence, on average, the sweet solution was preferred over water at all time points as all the IR and LRE values were above 0·5.
Fig. 1.Children’s degree of sweetness liking measured by the ingestion ratio and liking ratio (rated by the experimenter) at 3, 6 and 12 months.
Paired samples t tests confirmed that the IR did not change significantly between 3 and 6 months (t[136] = 1·14, $$P \leq 0$$·26, CI 95 % (–0·01, 0·05)), between 6 and 12 months (t[163] = 1·14, $$P \leq 0$$·26, CI 95 % (–0·01, 0·05)), and neither between 3 and 12 months (t[121] = 1·59, $$P \leq 0$$·11, CI 95 % (–0·01, 0·06)). The Levene paired test showed unequal variances with more variability regarding the IR in the twelfth month compared with the third month (t[121] = 2·88, $$P \leq 0$$·005, CI 95 % (0·01, 0·06)) or sixth month (t[163] = 3·80, $P \leq 0$·001, CI 95 % (0·02, 0·06)). However, variances in the IR did not differ between the third and sixth month (t[136] = 0·08, $$P \leq 0$$·94, CI 95 % (–0·02, 0·02)). Similarly, paired samples t tests confirmed that the LRE did not change significantly between 3 and 6 months (t[136] = −0·57, $$P \leq 0$$·57, CI 95 % (–0·02, 0·01)), between 6 and 12 months (t[163] = −0·82, $$P \leq 0$$·41, CI 95 % (–0·02, 0·01)), and between 3 and 12 months (t[121] = −0·73, $$P \leq 0$$·46, CI 95 % (–0·03, 0·01)). The Levene paired test indicated that the variances did not differ significantly regarding the LRE between the twelfth and third month (t[121] = –1·22, $$P \leq 0$$·23, CI 95 % (–0·03, 0·01)), between the twelfth and sixth month (t[163] = 0·98, $$P \leq 0$$·33, CI 95 % (–0·01, 0·02)), or between the third and sixth month (t[136] = 1·42, $$P \leq 0$$·16, CI 95 % (0·00, 0·02)).
## SweetExposure
The daily exposure to sweetness increased significantly from 3 to 6 months to 10 to 12 months as confirmed by the paired samples t test (t[246] = 28·64, $P \leq 0$·001, CI 95 % (7·43, 8·53)). Whereas SweetExposure was on average at 7·07 ± 2·53 during the 3 to 6 months period, it was double as high during the 10 to 12 months period with an average SweetExposure of 14·71 ± 3·87 (Fig. 2). The Levene paired test showed unequal variances with more variability in sweetness exposure in the 10 to 12 months period compared with the 3 to 6 months period (t[246] = 6·16, $P \leq 0$·001, CI 95 % (0·80, 1·55)).
Fig. 2.Infants’ exposure to sweetness during the 3 to 6 months and the 10 to 12 months periods.
## Effect of SweetExposure on SweetLiking (regression models)
Neither at 6 months nor at 12 months SweetLiking (indicated by IR and LRE) was associated with prior SweetExposure (Tables 2 and 3). No relationship was found in the bivariate analyses between SweetLiking at 6 months and SweetExposure at 3 to 6 months of age. This was true for both parameters measuring SweetLiking: IR (β (95 % CI): 0·006 (–0·002, 0·013)) and LRE (β (95 % CI): –0·002 (–0·007, 0·003)). This finding was supported when controlling for the duration of exclusive breast-feeding and the mothers’ educational level, as well as when only including individuals for whom we had complete data on all variables (SweetExposure 3 to 6 months, SweetExposure 10 to 12 months, SweetLiking 6 months and SweetLiking 12 months; Table 2). Moreover, the association between SweetExposure at 10–12 months and SweetLiking at 12 months was not significant: neither for the IR (β (95 % CI): –0·002 (–0·008, 0·005)) nor for the LRE (β (95 % CI): –0·002 (–0·006, 0·001)). Again, this finding was supported when controlling for the duration of exclusive breast-feeding and the mothers’ educational level, as well as when only including individuals for whom we had complete data on SweetExposure and SweetLiking at 3 to 6 and 10 to 12 months (Table 3).
Table 2.Regression models investigating the influence of sweetness exposure at 3 to 6 months on the degree of sweetness liking at 6 months for two sweetness liking outcome variables: ingestion ratio and liking ratio (rated by the experimenter)Ingestion ratioLiking ratio experimenter n β se * CI 95 %RSD† P β se * CI 95 %RSD† P Model 11820·0060·004–0·002, 0·0130·1280·14–0·0020·002–0·007, 0·0030·0830·40Model 21580·0030·004–0·005, 0·0110·1290·41–0·0040·003–0·009, 0·0020·0860·18Model 31500·0060·004–0·002, 0·0150·1290·13–0·0010·003–0·005, 0·0050·0790·96Model 1: Bivariate model (outcome variable: SweetLiking at 6 months (ingestion ratio or liking ratio experimenter); independent variable: SweetExposure at 3 to 6 months), n 182.Model 2: Multiple linear regression model (outcome variable: SweetLiking at 6 months (ingestion ratio or liking ratio experimenter); independent variables: SweetExposure at 3 to 6 months + duration of exclusive breast-feeding + maternal education level), n 158.Model 3: Bivariate model on complete cases (only individuals with complete information about exposure and liking at 3 to 6 and 10 to 12 months) (outcome variable: SweetLiking at 6 months (ingestion ratio or liking ratio experimenter); independent variable: SweetExposure at 3 to 6 months), n 150.*Standard error.†Residual standard error.
Table 3.Regression models investigating the influence of sweetness exposure at 10–12 months on the degree of sweetness liking at 12 months for two sweetness liking outcome variables: ingestion ratio and liking ratio (rated by the experimenter)Ingestion ratioLiking ratio experimenter n β se * CI 95 %RSD† P β se * CI 95 %RSD† P Model 1197–0·0020·003–0·008, 0·0050·1760·61–0·0020·002–0·006, 0·0010·1000·23Model 2167–0·0020·004–0·009, 0·0050·1780·57–0·0030·002–0·007, 0·0010·1020·10Model 3150–0·0030·004–0·010, 0·0040·1710·42–0·0020·002–0·006, 0·0010·0920·20Model 1: Bivariate model (outcome variable: SweetLiking at 12 months (ingestion ratio or liking ratio experimenter); independent variable: SweetExposure at 10–12 months), n 197.Model 2: Multiple linear regression model (outcome variable: SweetLiking at 12 months (ingestion ratio or liking ratio experimenter); independent variables: SweetExposure at 10–12 months + duration of exclusive breast-feeding + maternal education level), n 167.Model 3: Bivariate model on complete cases (only individuals with complete information about exposure and liking at 3 to 6 and 10 to 12 months) (outcome variable: SweetLiking at 12 months (ingestion ratio or liking ratio experimenter); independent variable: SweetExposure at 10–12 months), n 150.*Standard error.†Residual standard error.
## Structural equation model
The preliminary measurement model showed good fit (CFI = 0·99, TLI = 0·98, RMSEA = 0·03 (95 % CI (0·00, 0·06))), with high loadings (0·56 to 0·86, all $P \leq 0$·001) and low correlations between latent variables (0·07 to 0·34). The structural model also showed good fit (CFI = 0·97, TLI = 0·96, RMSEA = 0·04) and indicated that SweetLiking at 6 months predicted SweetLiking at 12 months (standardised estimate = 0·28, $$P \leq 0$$·01) (Fig. 3). However, SweetLiking at 3 months did not predict SweetLiking at 6 months (standardised estimate = 0·11, $$P \leq 0$$·41). In this model, no effect of SweetExposure on SweetLiking was found, as previously concluded from the regression analyses.
Fig. 3.SEM regression model and standardised parameters (n 319) showing the relationship between the degree of sweetness liking at 3, 6 and 12 months, as well as the associations between sweetness exposure at 3 to 6 months and the degree of sweetness liking at 6 months; and the relationship between sweetness exposure at 10 to 12 months and the degree of sweetness liking at 12 months. SEM, structural equation model; IR, ingestion ratio; LRE, liking ratio rated by the experimenter; LRP, liking ratio rated by the parent.
## Discussion
To the authors’ knowledge, this is the first study that investigated the relationship between exposure to sweet taste estimated over the whole diet and the degree of sweetness liking in the first year of life, during two major shifts in feeding mode (3 to 6 months – beginning of complementary feeding; 10 to 12 months – transition to the family table). In contrast to our hypothesis, no association between infant’s exposure to sweet taste in the diet and their degree of sweetness liking was found, neither at the beginning of complementary feeding at 6 months nor the transition to the family table at 12 months. Even though, as expected, the exposure to sweet tastes increased from 3 to 6 months to 10 to 12 months, the degree of sweetness liking did not change between these periods. In line with our hypothesis, sweetness liking at 3 months did not predict sweetness liking at 6 months. However, sweetness liking at 6 months did predict sweetness liking at 12 months.
## Sweet taste exposure in infancy
Most birth cohorts focused on nutrition tend to characterise dietary macro- and micro-nutrient intake instead of taste exposure[39,40]. Besides other papers investigating the OPALINE cohort, only one recent study focused on taste exposure[41]. However, the researchers used a different method than the one used in this study and investigated a slightly older cohort (1 to 2 years) than the age group we focused on. Hence, the level of sweetness exposure in the present study is difficult to compare with other studies for 3 to 12 months old infants. However, to give a reference, the level of sweetness exposure at 3 to 6 months in our study corresponds to a daily average intake of four bottles of infant formula, including one bottle with infant dry sweetened cereals. The level of sweetness exposure at 10–12 months corresponds to three bottles of infant formula, including one with dry sweetened cereals, a vegetable, meat, and starch purée with added fat, two fruit purées, and a fruit-flavoured yogurt[28]. In the OPALINE sample, fruits and vegetables were most often introduced first, followed by dairy products, cereal, meats, dessert, starchy foods, fish and biscuits[30]. This is comparable with what was found by the French national survey on food consumption of children up until the age of 3 years. Besides milk, the first consumed foods were fruits, vegetables and dairy products, followed by cereals, potatoes, meat, fish, rice and pasta[42].
## Relationship between sweet food consumption and sweetness liking
Previous studies draw contradictory conclusions regarding the link between exposure to sweetness, estimated by the consumption of sweet foods, and sweetness preference in childhood. On the one hand, when measuring sweetness preference using the same sweet food or drink with which sweetness exposure was estimated, the literature suggests a positive relationship between sweetness exposure and liking[16,17,43]. This was true for 6-month-old infants who showed a heightened preference for sweetened water after regularly being fed with it during their first 6 months of life compared with children without regular consumption of sweetened water[16]. This positive relationship was also found in 5-year-old children who liked a sweetened fruit-flavoured drink more after being exposed to it for 8 d[43]. Those findings contradict the present results where the association between sweetness exposure and the degree of sweetness liking over the first year of life could not be proven. This may be explained by the fact that in this study the relationship between sweetness exposure and liking was analysed by looking at the overall dietary exposure to sweet taste in the diet, whereas the papers above looked at the change in sweetness liking for the same sweet beverage the child had been exposed to. Previous studies demonstrated that the association between sweetness exposure and liking is not as straightforward when exposure and liking are tested with different mediums. For example, Beauchamp and Moran[17] could not replicate the positive relationship between sweetness exposure (through sweetened water) at 6 months and liking when testing sweetness liking with a fruit-flavoured drink instead of sweetened water at 2 years. However, when sweetness liking was assessed with sweetened water at this age, the association was still positive[17]. Furthermore, a systematic relationship between consuming sweet products and sweetness liking in 7-to-12-year-old children could not be proven, as sweetness liking was only related to candy and snack consumption but not to consumption of sweet drinks, dairy products, fruit, cereal or added sugar consumption[19]. This suggests that the association between exposure and liking is largely stimulus-specific and cannot be generalised to the whole diet. As children learn through repeated exposure which foods should taste sweet[5], children may get used to the taste of the specific sweetened beverages which resulted in an increased liking as previously shown[16,17,43]. However, this is not associated with a general increase in children’s preference for sweetness. This does not undermine the importance of food and flavour experiences, in particular during the first years of life, in the shaping of food preferences, but emphasises the fact that this learning is food-specific[22].
## No relationship between sweetness exposure and sweetness liking: possible interpretation
We hypothesised that higher exposure to sweet tastes during infants’ first year of life may result in a higher degree of sweetness liking. However, the results did not confirm this hypothesis. Apart from the possibility that this hypothesis is wrong, other aspects must be considered to interpret these findings. First, individual variations in the degree of sweetness liking at this early age may still be a result of genotypes, and not of environmental influences such as dietary exposures. This assumption is supported by a study by Mennella et al. [ 44] that found that in 5-to-10-year old children, the TAS2R38 taste gene locus was related to sucrose preferences and the liking of sweet-tasting foods and drinks, which was much less obvious in their mothers. Nonetheless, following this line of reasoning, sweetness liking at 3 months should have predicted sweetness liking at 6 months in our sample, yet it did not, even though sweetness was already preferred over water at 3 months. However, sweetness liking at 6 months did predict sweetness liking at 12 months. This result may be due to the fact that infants’ control over swallowing behaviour at 3 months of age was still too limited to reflect individual differences well enough, although it appeared adequate to reflect a preference for the sweetened water over plain water.
Second, sweetness exposure in this study sample may not have been varied enough to detect an effect on the degree of sweetness liking. This may be especially true because the parents in our study started to complementary feed their infants on average at the age of five and a half months, with little variability. Hence, infants may have been exposed to comparable sweetness intensity levels for most of the time in the first period due to exclusive milk feeding. In the second period when sweetness exposure varied more between infants, the duration of the exposure period (between 10 and 12 months) may have been too short, or the sweet taste intensities may not have been strong enough to detect an effect on sweetness liking. Also, Schwartz et al. [ 33] found that during the first 12 months, the taste intensities of the foods children were exposed to were relatively low, even though children mainly experienced sweet taste during the milk feeding period with an even increased sweetness exposure during the first year. Because in this study only exposure frequency to sweet tastes was assessed, but not how much of the sweet-tasting foods were consumed in each eating occasion, we cannot estimate the proportion of the overall energy intake coming from sweet-tasting foods. Hence, it must be considered that the overall sweetness intensity the infant was exposed to in the first 12 months of life was not strong enough to detect an effect on sweetness liking.
Third, the effect of sweetness exposure in infancy may affect the degree of sweetness liking only later in childhood, after a longer period of exposure to sweet-tasting foods. Liem and Mennella[14] found that children from parents who regularly added sugar to the child’s diet between 0 and 12 months were more likely to prefer apple juice with added sugar at 4 to 7 years. In line with this result, a diet rich in added sugars during the first 12 months was related to a higher degree of sweetness liking at 3 to 7 years[13]. In the present study, the IR during the sweetness liking test in the twelfth month varied more between children than in the third and sixth months. In combination with the increased exposure to sweetness in the second period (10–12 months), this may hint towards the possibility of a slight impact of sweetness exposure on the degree of sweetness liking, although this link was not detected in our analyses.
Lastly, growing evidence demonstrates that hedonic responses to sweet taste differ between individuals due to sweet-liking phenotypes[12]. It was found that children can be classified as either sweet likers or sweet dislikers[6]. Hence, children in our sample may also move along this continuum. The unequal variances regarding the IR may be a hint towards those differences in sweetness liking in our sample. However, we expected that sweetness exposure would lead to a similar effect across the continuum of children who rather like or dislike sweetness. Nevertheless, repeated exposure to sweetness could have led to a different effect on infants depending on their sweet-liking phenotype and may have influenced our outcome measure.
## Strengths, limitations and conclusion
Despite the care which was taken to account for uncontrolled factors, our study entails some limitations. First, in our study, only consumption frequencies were recorded, not portion sizes. Hence, infants with the same exposure frequencies of sweet-tasting foods but different portion sizes were assigned the same level of sweetness exposure. Thus, we cannot account for the contribution of portion size to sweetness exposure. Further, the methodological choice to measure sweetness liking may have undermined the liking value, because some infants may have not been used to the taste of sweetened water, since they were mostly fed milk during the first months of life. However, lactose was chosen to create the sweetened water solution as it is a taste infants were likely to have experienced, in particular in breast, or formula milk[31]. In addition to that, sweetness exposure was estimated based on the food records mothers filled out. We cannot assure that mothers have filled in the food records truthfully. Further, when a sweet food (e.g. applesauce) was consumed with sour food (e.g. yogurt), the sourness of the sour food may have reduced the perceived sweetness of the sweet food. Hence, we cannot judge the infants’ perceived sweetness exposure experience, but only sweetness exposure based on the rated sweetness intensity levels. Lastly, similar to other cohort studies, lower educated families were under-represented in our sample as almost 90 % of mothers had a high or medium level of education. A higher maternal education level is generally associated with better diet quality in mothers and children and was previously associated with higher exposure to neutral tastes in children aged 1 and 2 years[41,45,46]. Following that, conclusions from our study are less applicable to children of lower-educated mothers. It has to be mentioned that the subjective sweetness liking experience cannot be truly measured, but only relative sweetness liking. Following that, in our study, the degree of sweetness liking refers to a relative liking of sweetness compared with neutral stimuli (water). However, a strength of our study is that three proxies for relative liking were included in our analyses (IR, LRE and LRP) to get a broader picture of relative sweetness liking. Besides the limitations, our study contains multiple strengths. First, infants’ overall exposure to sweetness was taken into account instead of focusing on the macro-nutrient intake only. For that, the overall diet was included and not only exposure to particular sweet-tasting foods. Another strength is the longitudinal design which allowed examination of sweetness exposure and liking at multiple time points. Further, the fact that sweetness exposure was estimated based on twelve monthly 7-d food records gave an overall impression of infants’ exposure to sweetness over the first year instead of only delivering a snapshot of sweetness exposure at a single time point. As sweetness exposure was averaged based on the monthly food records for each of the periods, the impact of outliers (e.g. due to special occasions such as birthdays) regarding the intake of sweet-tasting foods was minimised.
In conclusion, this study did not show a relationship between sweetness exposure and the degree of sweetness liking in infants throughout observations from the third until the twelfth month of life. Although sweetness exposure increased from the first (3 to 6 months) to the second period (10 to 12 months), the degree of sweetness liking did not change. This was contrary to what was expected. Based on the findings, we hypothesise that, at such early age, inter-individual variations in sweetness liking may rather occur due to differences in genotypes, especially when variations in sweetness exposure between children are limited. The impact of high exposure to sweet-tasting foods in the first 12 months of life on the degree of sweetness liking later in life should be investigated longitudinally including time points in childhood up until adolescence to understand the impact of sweetness exposure on establishing (healthy) eating habits early on. Longitudinal research has shown that eating behaviour learned in infancy impacts eating behaviour in childhood[47]. Whether this is also true for early exposure to sweet taste remains unclear. Future studies should aim for more variability in the study sample with participants representing the whole sociodemographic continuum, to represent a variety of feeding habits, and should also consider the consumed quantities of sweet products in addition to the frequencies.
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|
---
title: Consumption of a light meal affects serum concentrations of one-carbon metabolites
and B-vitamins. A clinical intervention study
authors:
- Anita Helland
- Marianne Bratlie
- Ingrid V. Hagen
- Øivind Midttun
- Arve Ulvik
- Gunnar Mellgren
- Per M. Ueland
- Oddrun A. Gudbrandsen
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10024976
doi: 10.1017/S0007114522002446
license: CC BY 4.0
---
# Consumption of a light meal affects serum concentrations of one-carbon metabolites and B-vitamins. A clinical intervention study
## Body
The transfer of one-carbon units amid molecules within and between metabolic pathways is essential for maintaining cellular homeostasis, and this process efficiently control post-translational modifications and epigenetic, energetic and redox statuses. One-carbon units are transferred within the interlinked pathways methionine-homocysteine cycle, the folate cycle, the choline-oxidation pathway and the methylation of DNA, RNA, proteins and lipids[1]. Homocysteine is remethylated to methionine using methyl from either N5-methyl-tetrahydrofolate or betaine, thereby generating methionine as well as tetrahydrofolate or dimethylglycine, respectively. Methionine is converted to the methyl donor S-adenosylmethionine, which is transformed to S-adenosylhomocysteine after donating a methyl group. Alternatively, homocysteine may enter the transsulfuration pathway to produce cystathionine, which is further metabolised to cysteine and glutathione[1].
Betaine, choline (after oxidation to betaine), glucose and amino acids including methionine, serine and glycine are quantitatively important donors for one-carbon units obtained from the diet[1,2]. In addition, choline-derived methyl-glycine species (sarcosine and dimethylglycine), as well as serine and glycine from endogenous sources, are important contributors to the pool of tetrahydrofolate-bound one-carbon units[1]. Vitamins B2, B3, B6, B9 and B12, which are obtained from the diet, are important cofactors for several enzymes involved in the transfer of one-carbon units[2].
Disturbances in the pathways involved in the one-carbon metabolism have been associated with diseases. Among the well-known health issues related to disturbances in the one-carbon metabolism is the increased risk for neural tube defect in fetus of mothers with insufficient folate intake, and the increased risk for colorectal cancer with low folate intake[1]. Another well-described example is elevated concentration of homocysteine that has been associated with an increased risk of CVD[3,4]. The homocysteine concentration is higher in patients with vascular disease[5,6] and in patients with metabolic syndrome[7,8], and elevated homocysteine concentration has been shown to be causally related to increased risk of type 2 diabetes[9]. High homocysteine concentration is associated with low concentrations of folate and vitamin B12, both of which are required for the remethylation of homocysteine to methionine[1]. A high intake of folate reduces the risk for CVD[10], and a low serum folate concentration was associated with higher incidence of insulin resistance in non-diabetic USA adults[11]. Also the plasma concentration of the methyl donor dimethylglycine has been associated with several traditional risk factors for coronary artery disease in patients with stable angina pectoris[12], whereas a high choline concentration has been associated with the risk of long-term atrial fibrillation[13], an increased risk for acute myocardial infarction in non-smokers[14] and adverse cardiac events in patients with suspected acute coronary syndromes[15]. Methyl groups are also transferred to amino acids, and several methylated amino acids have been associated with increased risk for diseases. In patients with suspected stable angina pectoris, elevated circulating concentration of trimethyllysine is a predictor for type 2 diabetes[16] and for acute myocardial infarction[17]. Also, an elevated concentration of asymmetric dimethylarginine is an independent risk factor for CVD[18], and high circulating concentrations of both asymmetric[19] and symmetric[20] dimethylarginine are seen in patients with chronic kidney disease.
Epidemiological studies do not consistently use fasting or non-fasting blood samples, or may lack information about the prandial status of the study participants. Little is known about whether the circulating concentrations of metabolites and B-vitamins that participates in the one-carbon metabolism are affected by the prandial status. However, in vitro studies suggest that several enzymes involved in one-carbon metabolism are affected by increased concentrations of insulin and glucose, resulting in increased remethylation of homocysteine to methionine[21]. In addition, it has been shown that the concentration of methionine was higher while the concentration of free homocysteine was lower in plasma from healthy adults after consumption of a light breakfast[22]. In many trials, blood is typically sampled early in the day during working hours for practical reasons, i.e. with participants in a fasting state or after having consumed a light meal. Therefore, the main aim of the present study was to assess the effects of a light meal containing carbohydrates, proteins and fats on serum concentrations of a broad panel of one-carbon metabolites and B-vitamins involved in the one-carbon metabolism. Blood was collected from adults before, and 60 and 120 min after consumption of a standardised breakfast meal. Our hypothesis was that when the participants’ metabolic status changed from catabolic to anabolic after intake of a light meal, this would result in a lower homocysteine concentration but would also affect serum concentrations of other metabolites and B-vitamins involved in the one-carbon metabolism.
## Abstract
The transfer of one-carbon units between molecules in metabolic pathways is essential for maintaining cellular homeostasis, but little is known about whether the circulating concentrations of metabolites involved in the one-carbon metabolism are affected by the prandial status. Epidemiological studies do not always consistently use fasting or non-fasting blood samples or may lack information on the prandial status of the study participants. Therefore, the main aim of the present study was to investigate the effects of a light breakfast on serum concentrations of selected metabolites and B-vitamins related to the one-carbon metabolism; i.e. the methionine-homocysteine cycle, the folate cycle, the choline oxidation pathway and the transsulfuration pathway. Sixty-three healthy adults (thirty-six women) with BMI ≥ 27 kg/m2 were included in the study. Blood was collected in the fasting state and 60 and 120 min after intake of a standardised breakfast consisting of white bread, margarine, white cheese, strawberry jam and orange juice (2218 kJ). The meal contained low amounts of choline, betaine, serine and vitamins B2, B3, B6, B9 and B12. Serum concentrations of total homocysteine, total cysteine, flavin mononucleotide, nicotinamide and pyridoxal 5’-phosphate were significantly decreased, and concentrations of choline, betaine, dimethylglycine, sarcosine, cystathionine and folate were significantly increased following breakfast intake ($P \leq 0$·05). Our findings demonstrate that the intake of a light breakfast with low nutrient content affected serum concentrations of several metabolites and B-vitamins related to the one-carbon metabolism.
## Participants, study setting and ethics
The subjects in the present work were participants in a study designed to investigate the metabolic effects of high intake of fish for 8 weeks. In the present paper we present analyses of the samples collected at baseline. The study population consisted of adults of Norwegian ethnic origin (Caucasian) with overweight or obesity living in Bergen, Norway. Inclusion criteria were BMI ≥ 27 kg/m2, fasting blood glucose ≤ 7·0 mmol/l, and age 18–69 years. Exclusion criteria were pregnancy, incompatibility with fish consumption (allergies, intolerance and/or dislike), diagnosed diabetes mellitus, heart disease or gastrointestinal disease, use of medications affecting lipid metabolism or glucose homoeostasis, use of anti-inflammatory medications, use of supplements containing n-3 PUFA, intentional weight loss, and large fluctuation in body weight (>3 kg) during the preceding 2 months. The study design, as well as description of study participants, study setting and protocol for study visits have previously been described in detail[23]. Seventy-six participants were included[24], and sixty-eight participants completed the trial. Three participants were excluded (one had prediabetes and two did not comply with the protocol). For two participants, we did not have a sufficient amount of blood serum for analyses; hence, serum from sixty-three participants (thirty-six women) were included in the present study, with a geometric mean (5, 95 % CI) age of 42·8 (39·9, 45·8) years and geometric mean BMI (5, 95 % CI) of 32·9 (31·8, 34·0) kg/m2. All participants had serum creatinine concentration and urine albumin creatinine ratio within normal ranges[25]. Examinations were conducted at the Clinical Research Unit at the Haukeland University Hospital, Bergen, Norway.
The study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures were approved by the Regional Committee for Medical and Health Research Ethics of Western Norway (REC no.: $\frac{2011}{572}$). Written informed consent was obtained from all participants.
Health professionals performing blood sampling and personnel conducting the laboratory analyses were all blinded to participants’ identity, and all data were analysed anonymously. The trial is registered at clinicaltrials.gov as NCT02350595.
## Protocol for study visits
Examinations and samplings were conducted in the morning after an overnight fast; intake of food or drinks except water, or use of substances containing nicotine was not permitted after 10 pm the previous day. Physical exercise and alcohol were not allowed for 24 h before the visit. Blood was drawn from an antecubital vein by inserting a cannula connected to a three-way tap for repeated measures, and the system was flushed with sterile saline (0·9 %) before and after each blood sample. Blood was collected in BD Vacutainer SST II Advance gel tubes (Becton, Dickinson and Company) for isolation of serum. The staff complied with a strict protocol for pre-analytical sample handling to ensure high sample quality. Blood samples were centrifuged after 30 min at room temperature, and serum was immediately aliquoted and frozen at –80°C until analyses. Participants provided morning urine upon arrival to the hospital, and urine samples were immediately aliquoted and frozen (–80°C).
## Intervention
After the collection of fasting blood, the participants consumed a standardised breakfast consisting of one slice of white bread with 5 g margarine and 25 g strawberry jam, one slice of white bread with 5 g margarine and 20 g white cheese and 0·30 l orange juice. The estimated contents of macronutrient and energy in the standardised breakfast were 80 g carbohydrate, 14 g protein and 16 g fat, providing a total of 2218 kJ, as calculated using ‘Mat på Data 5·1’[26] and information provided by the manufacturers. The contents of vitamin B2 (riboflavin), vitamin B3 (total niacin), vitamin B6 (pyridoxine), vitamin B9 (total folate), vitamin B12 (cobalamin), betaine (total) and choline (total) (conducted by Eurofins Food & Feed Testing Norway AS, Moss, Norway) and contents of methionine, glycine and serine (conducted by Nofima BioLab, Bergen, Norway) in the breakfast are presented in Table 1. The breakfast was consumed within 15 min. Blood samples were collected in fasting state, as well as 60 and 120 min after the participants had consumed the standardised breakfast.
Table 1.Contents of vitamins B2, B3, B6, B9 and B12, total choline, total betaine, methionine, serine and glycine in the standardised breakfast and relevant RDA values for our study participantsAmino acidsPer breakfast servingRecommended daily allowance for adultsVitamin B2 (riboflavin)0·09 mg1·7 mg (men), 1·3 mg (women)* Vitamin B3 (total niacin)0·48 mg18 NE (men), 15 NE (women)* Vitamin B6 (pyridoxine)0·08 mg1·5 mg (men), 1·2 mg (women)* Vitamin B9 (total folate)79·6 µg300 µg (men), 400 µg (women)* Vitamin B12 (cyanocobalamin)0·58 µg2·0 µg (men and women)* Choline (total)53·8 mg400 mg (men and women)† Betaine (total)30·0 mgNot definedMethionine<LOD10·4 mg/kg bodyweight (men and women)‡ Serine0·66 gNot definedGlycine<LODNot definedNE: niacin equivalent.*Nordic nutrition recommendations.†European food safety authority.‡WHO/FAO/UNU joint report. Level of detection (LOD) for amino acids is 0.10 g/100 g sample, corresponding to 0.44 g/breakfast serving.
## Analyses in serum and urine
Serum and urine concentrations of total homocysteine, methionine, total cysteine, cystathionine, glycine and serine were measured using gas chromatography combined with tandem mass spectrometry[27]. Free choline, betaine (N,N,N-trimethylglycine), dimethylglycine, sarcosine (N-methylglycine), asymmetric dimethylarginine, symmetric dimethylarginine, trimethyllysine, 1-methylhistidine (π-methylhistidine), 3-methylhistidine (τ-methylhistidine) were measured in serum and urine, and creatinine was measured in urine, using liquid chromatography combined with tandem mass spectrometry[28]. 1-methylhistidine and 3-methylhistidine were measured by adding ion-pairs for the analytes and isotope-labelled internal standards to the existing assay[28]. Vitamin B2 (riboflavin and flavin mononucleotide), vitamin B3 (nicotinic acid, nicotinamide and N1-methylnicotinamide) and vitamin B6 (pyridoxal 5’-phosphate) were analysed in serum using liquid chromatography combined with tandem mass spectrometry[29]. Nicotinic acid, nicotinamide and N1-methylnicotinamide[30] with corresponding isotope labeled internal standards were added to the previously published assay[29]. Vitamin B12 [31] and folate[32] were measured in serum by microbiological assays. All biochemical analyses were performed by Bevital AS (Bergen, Norway, http://www.bevital.no).
All serum and urine samples for each analysis were analysed for each participant in random order on the same day, and samples were not thawed previously.
Reference values for serum folate and cobalamin concentrations were according to the Department of Medical Biochemistry and Pharmacology, Haukeland University Hospital; the action limit for treatment for folate deficiency is when serum folate concentration < 10 nmol/l, and the normal range for serum cobalamin concentration is defined as 175–700 pmol/l.
## Outcome measurements
The primary outcome of the present study was to compare concentrations of the selected metabolites and B-vitamins involved in the one-carbon metabolism; the methionine-homocysteine cycle, the folate cycle, the choline oxidation pathway, and the transsulfuration pathway, in fasting serum and serum collected 60 and 120 min after intake of a standardised breakfast. The secondary outcome was to compare men and women with regard to the fasting serum concentrations and the relative changes from fasting to postprandial serum concentrations of metabolites and B-vitamins, as well as urine concentrations of relevant metabolites.
## Sample size estimation
The present study exploits biological material collected at the baseline visit in an intervention study that was designed to investigate the effects of high intake of cod or salmon on post-prandial glucose regulation after a standardised breakfast in participants with overweight or obesity[23]. The sample size estimation for the original study showed that it was necessary to include seventy-six participants divided into three groups to ensure that twenty participants in each group completed the trial with satisfactory compliance, with a power of 80 % and α of 0·05[23]. Since the present study is, to the best of our knowledge, the first study to investigate the effects of a light breakfast on the one-carbon metabolism in healthy adults, data on effect size were not available for sample size calculation or minimally detectable effect sizes for the present study.
## Statistical analyses
Fasting serum concentrations and urine concentrations (relative to creatinine) are presented as geometric means (5, 95 % CI) for the serum one-carbon metabolites and B-vitamins. The changes in serum one-carbon metabolites and B-vitamins concentrations were calculated as ratios by dividing the concentration at 60 and 120 min by the fasting concentration. The t test was used to test if the ratios at each follow-up were different from 1, and paired t test was used to test if the ratios in the postprandial samples were different from each other. Genders were compared using independent samples t test. All results from T tests were Benjamini-Hochberg adjusted, and results with $P \leq 0$·05 were considered statistically significant. All statistical tests were performed using R version 4.0.3 (http://www.r-project.org).
## Description of the standardised breakfast
The contents of vitamins B2, B3, B6, B9 and B12, total choline, betaine, methionine, serine and glycine in the standardised breakfast are presented in Table 1. Table 1 also presents the RDA for our study participants based on Nordic Nutrition Recommendations[33], and recommendations from the European Food Safety Authority[34] and WHO/FAO/United Nations University[35] and shows that the standardised breakfast contributed with relatively low nutrient amounts compared with the RDAs.
## Fasting serum and urine concentrations of one-carbon metabolites, and serum B-vitamins
The concentrations of metabolites and relevant B-vitamins are presented for the total study population and with separate values for men and women in fasting serum (Table 2) and morning urine (shown relative to creatinine, Table 3). The serum concentration of nicotinic acid was below level of detection in all samples. For most analytes in serum and urine, the concentrations were similar between the genders; however, the serum concentrations of methionine, betaine, sarcosine, trimethyllysine and 3-methylhistidine were highest in men. In urine, the only differences between the genders were the higher concentrations of asymmetric and symmetric dimethyl arginine in women.
Table 2.*Fasting serum* concentrations of metabolites and B-vitamins related to the one-carbon metabolism(Mean values and 95 % confidence intervals)Total (n 63)Men (n 27)Women (n 36) P sex*Mean95 % CIMean95 % CIMean95 % CIMetabolites Total homocysteine (µmol/l)10·09·51, 10·610·59·69, 11·39·699·00, 10·40·31 Methionine (µmol/l)28·027·0, 29·130·028·4, 31·626·625·4, 27·86·0 × 10−3 Choline (µmol/l)9·929·42, 10·510·49·53, 11·39·598·97, 10·30·21 Betaine (µmol/l)30·027·9, 32·334·031·2, 37·027·324·6, 30·40·0060 Dimethylglycine (µmol/l)3·232·99, 3·493·393·00, 3·833·112·81, 3·450·52 Sarcosine (µmol/l)1·171·08, 1·261·291·15, 1·451·070·977, 1·180·046 Glycine (µmol/l)249236, 263234223, 246262240, 2860·14 Serine (µmol/l)133128, 138128121, 136137130, 1440·22 Total cysteine (µmol/l)299292, 306301293, 311296286, 3070·63 Cystathionine (µmol/l)0·2530·222, 0·2870·2660·212, 0·3330·2430·209, 0·2820·96Vitamins Flavin mononucleotide (vitamin B2, nmol/l)8·717·86, 9·669·217·85, 10·88·337·24, 9·580·52 Riboflavin (vitamin B2, nmol/l)15·613·7, 17·916·212·7, 20·615·212·9, 17·80·63 Nicotinamide (vitamin B3, nmol/l)220202, 241223192, 258219195, 2460·96 N1-methylnicotinamide (vitamin B3, nmol/l)120107, 134127107, 15211497·7, 1340·75 Pyridoxal 5’-phosphate (vitamin B6, nmol/l)39·234·8, 44·143·536·0, 52·636·030·9, 41·90·30 Folate (vitamin B9, nmol/l)18·816·6, 21·316·714·0, 19·920·617·2, 24·60·22 Cobalamin (vitamin B12, pmol/l)303281, 326318287, 352292261, 3250·61Methylated amino acids Asymmetric dimethylarginine (µmol/l)0·5860·563, 0·6100·5750·537, 0·6160·5940·565, 0·6250·80 Symmetric dimethylarginine (µmol/l)0·5740·547, 0·6020·5990·563, 0·6380·5550·518, 0·5940·16 Trimethyllysine (µmol/l)0·6670·594, 0·7490·8400·679, 1·040·5580·509, 0·6130·012 1-methylhistidine (µmol/l)4·453·50, 5·654·312·83, 6·574·563·40, 6·100·96 3-methylhistidine (µmol/l)4·384·11, 4·664·814·39, 5·264·073·75, 4·420·035*Men and women were compared using independent samples t test. $P \leq 0$·05 was considered significant.
Table 3.Urine concentrations (shown relative to creatinine concentration) of metabolites involved in the one-carbon metabolism(Mean values and 95 % confidence intervals)Concentrations (µmol/mmol creatinine)Total (n 63)Men (n 27)Women (n 36) P sex*Mean95 % CIMean95 % CIMean95 % CITotal homocysteine0·2270·193, 0·2680·2020·161, 0·2530·2510·198, 0·3190·31Methionine0·7090·632, 0·7950·7060·622, 0·8000·7110·589, 0·8591·00Choline1·751·57, 1·961·531·29, 1·821·961·70, 2·270·10Betaine4·803·72, 6·195·404·05, 7·214·352·89, 6·550·53Dimethylglycine2·532·10, 3·042·411·79, 3·232·632·05, 3·370·83Sarcosine0·1240·107, 0·1420·1210·096, 0·1530·1260·105, 0·1510·92Glycine74·565·1, 85·364·254·8, 75·384·368·5, 1040·12Serine21·619·8, 23·519·717·3, 22·423·320·9, 26·10·13Total cysteine8·617·48, 9·927·546·26, 9·099·627·82, 11·80·22Cystathionine2·191·80, 2·682·191·60, 3·002·191·67, 2·871·00Asymmetric dimethylarginine1·991·88, 2·101·721·60, 1·852·242·11, 2·387·5 × 10−6 Symmetric dimethylarginine1·821·74, 1·901·641·54, 1·741·981·89, 2·083·1 × 10−5 Trimethyllysine8·337·38, 9·409·237·45, 11·47·656·67, 8·760·271-methylhistidine38·430·2, 48·933·322·7, 48·843·331·5, 59·60·443-methylhistidine30·228·2, 32·231·929·1, 35·128·826·1, 31·60·27*Men and women were compared using independent samples t test. $P \leq 0$·05 was considered significant.
Most participants had a serum concentration of folate > 10 nmol/l and vitamin B12 > 175 pmol/l. Five participants (four men) had insufficient serum folate concentration, and for three of these (all men), the homocysteine concentration was above the measured median of 10·0 µmol/l. Two participants (both women) had vitamin B12 concentration below the reference range, and both had homocysteine concentration >10·0 µmol/l.
## Changes in serum concentrations of metabolites and B-vitamins following a light breakfast
The two measured metabolites in the methionine-homocysteine cycle were affected by intake of the breakfast; the serum concentration of total homocysteine was decreased by 6 and 7 % after 60 and 120 min, respectively, whereas the methionine concentration showed an initial $7\%$ increase followed by a decrease to a concentration similar to that in the fasting state (Fig. 1). The postprandial response in total homocysteine concentration was similar between the genders, whereas the ratio of methionine concentration at 120 min relative to fasting concentration was lower in men compared with women (P 0·027, data not presented).
Fig. 1.Relative changes from fasting to postprandial serum concentrations of total homocysteine (tHcy), methionine, choline, betaine, dimethylglycine (DMG), sarcosine, glycine, serine, total cysteine (tCys) and cystathionine (Cysta). Data are presented as ratios with 5, 95 % CI for 63 participants. Different letters indicate significant differences at time points (0, 60, 120 min); $P \leq 0$·05 was considered significant.
The serum concentrations of choline and the choline oxidation pathway metabolites, i.e. betaine, dimethylglycine and sarcosine, were increased after breakfast intake (Fig. 1). After 60 min, the increase was 6 % for choline, 14 % for betaine, 4 % for DMG and 13 % for sarcosine. The increase in betaine concentrations was more pronounced in women compared with men after both 60 and 120 min (P values 0·032 and 0·0059, respectively, data not presented). The increase in sarcosine concentration after 120 min was most prominent in women (P 0·027), with no differences between the genders for choline and dimethylglycine concentrations (data not presented). The glycine concentration was decreased by 2 % after 60 min (strongest decrease in men, P 0·032, data not presented) and was similar to fasting concentration after 120 min (Fig. 1). The serine concentration was first increased by 3 % followed by a decrease to 4 % below fasting concentration (Fig. 1), with no differences between the genders (data not presented).
The transsulfuration pathway interconverts homocysteine and cysteine via the intermediate cystathionine, and the decline in serum homocysteine concentration after breakfast intake was accompanied by lower total cysteine concentration (reduced by 3 and 5 % after 60 and 120 min, respectively); however, the concentration of the intermediate cystathionine was increased postprandially by 4 % after 60 min and further by 8 % after 120 min (Fig. 1). The changes in total cysteine and cystathionine concentrations were similar between the genders (data not presented).
The serum concentrations of flavin mononucleotide, nicotinamide, N1-methylnicotinamide and pyridoxal 5’-phosphate were reduced 120 min postprandially, with a decrease of 36, 27, 18 and 13 %, respectively, compared with fasting concentrations. The riboflavin concentration was increased by 6 % after 60 min followed by a reduction to a concentration below fasting concentration (Fig. 2). The concentration of folate was increased by 16 % after 60 min and by 8 % 120 min postprandially, while the cobalamin serum concentration was not affected by breakfast intake (Fig. 2). The decrease in N1-methylnicotinamide concentration 60 and 120 min after breakfast intake was more pronounced in men when compared with women (P values 0·0033 and 0·026), and the reduction in flavin mononucleotide concentration was more pronounced in women compared with men after 60 min (P 0·036) but was similar between genders after 120 min. The postprandial changes in concentrations of the other measured B-vitamins and their derivatives were similar between the genders (data not presented).
Fig. 2.Relative changes from fasting to postprandial serum concentrations of flavin mononucleotide (FMN), riboflavin, nicotinamide, N1-methylnicotinamide, pyridoxal 5’-phosphate (PLP), folate and cobalamin (B12). Data are presented as ratios with 5, 95 % CI for 63 participants. Different letters indicate significant differences at time points (0, 60, 120 min); $P \leq 0$·05 was considered significant.
## Changes in serum concentrations of methylated amino acids
Following breakfast intake, the serum concentrations of asymmetric dimethylarginine and symmetric dimethylarginine were increased by 6 and 11 %, respectively, after 60 min, and after 120 min the concentration of asymmetric dimethylarginine was reduced to fasting concentration, whereas concentration of symmetric dimethylarginine remained elevated (Fig. 3). Serum concentrations of trimethyllysine, 1-methylhistidine and 3-methylhistidine were reduced postprandially, and concentrations were 18, 16 and 8 % lower, respectively, after 120 min when compared with fasting concentrations. We observed no differences between the genders for fasting and postprandial concentrations of asymmetric dimethylarginine, symmetric dimethylarginine, trimethyllysine, 1-methylhistidine and 3-mehylhistidine (data not presented).
Fig. 3.Relative changes from fasting to postprandial serum concentrations of asymmetric dimethylarginine (ADMA), symmetric dimethylarginine (SDMA), trimethyllysine (TML), 1-methylhistidine (1-MeHistidine) and 3-methylhistidine (3-MeHistidine). Data are presented as ratios with 5, 95 % CI for 63 participants. Different letters indicate significant differences at time points (0, 60, 120 min); $P \leq 0$·05 was considered significant.
## Discussion
In this study, we present evidence that the serum concentrations of several metabolites and B-vitamins related to the one-carbon metabolism are affected by a light meal. The serum concentrations of total homocysteine, total cysteine, flavin mononucleotide, nicotinamide and pyridoxal 5’-phosphate were significantly decreased, and the concentrations of choline, betaine, dimethylglycine, sarcosine, cystathionine and folate were significantly increased following breakfast intake. The standardised breakfast contained relatively low amounts of vitamins B2, B3, B6, B9 and B12 and of choline, betaine and serine, with methionine and glycine below level of detection, suggesting that some of the observed changes may reflect altered metabolic control as the participants’ metabolic status changed from catabolic to anabolic, and not solely the availability of nutrients. We also present evidence that the one-carbon metabolism may be differently affected postprandially in men and women.
We observed a marked increase in serum concentrations of several metabolites in the choline oxidation pathway after breakfast intake. The serum concentrations of both choline and its oxidised product betaine (a methyl donor) and the further demethylated metabolites, dimethylglycine and sarcosine, were increased after breakfast. Choline is obtained from the diet, but the de novo synthesis from phosphatidylethanolamine (via phosphatidylcholine) in liver, using S-adenosylmethionine as a methyl donor, is also a significant source of choline[36]. The elevated concentrations of the upstream metabolites in the choline pathways, i.e. choline, betaine, dimethylglycine and sarcosine, observed postprandially might be a consequence of absorption of choline and/or betaine present in the meal. Although contents of choline and betaine in the breakfast were low, a simplified calculation reveals that a median bodyweight of 99·7 kg in our participants and using the factor of 70 ml blood/kg bodyweight (although even lower in adults with overweight or obesity/kg bodyweight[37]) gives a median blood volume of less than 7 litres. Thus, and without taking into account the absorption and distribution, the intake of the breakfast meal containing 53·8 mg choline and 30·0 mg betaine may be sufficient to bring about the observed increases in serum concentrations of choline and betaine of 6 and 14 %, respectively, seen 60 min after breakfast in the present study.
Increased availability of choline, and thereby of betaine, may promote betaine-dependent remethylation of homocysteine to methionine in the liver. In addition, an increased serum folate concentration, mainly in the form of 5-methyltetrahydrofolate, may favour increased 5-methyltetrahydrofolate-dependent remethylation of homocysteine to methionine. Increased flux through either pathway may explain the observation of lower homocysteine concentration after consumption of the breakfast meal. The increased methionine concentration after 60 min combined with the reduced homocysteine concentration after breakfast intake may be a result of increased remethylation of homocysteine. However, we cannot rule out the possibility that an intake of methionine from the breakfast may have been sufficient to significantly increase the serum methionine concentration after 60 min. Calculations using data from the USDA database[38] suggest that the methionine content in the breakfast is in the order of around 0·22 g/serving, which is considerably lower than the level of detection for our analyses corresponding to 0·44 g methionine per breakfast serving. Using the same formulas for calculation as for choline and betaine (above), and without taking into account the absorption and distribution, an intake of 0·22 g methionine may in theory be sufficient to induce the 5 % increase in serum methionine concentration seen after 60 min in our study, followed by a reduction after 120 min. This result, together with the knowledge that the methionine concentration peaks after 1 h during the methionine loading test (as demonstrated in several papers, including[39]), indicates that the methionine content, although low, in the breakfast may have contributed to the observed increase in serum methionine concentration postprandially. After the initial increase, the methionine concentration was reduced to a concentration comparable to the fasting concentration at 120 min, suggesting that methionine was recycled to homocysteine via S-adenosylmethionine and S-adenosylhomocysteine.
Studies in cultured liver cells present evidence that remethylation of homocysteine is stimulated by insulin and glucose[21]. Thus, the increase in insulin and glucose concentrations after breakfast intake, as we have previously published from this trial[23], may contribute to the lower homocysteine and higher methionine concentrations observed postprandially. The serum glucose concentration was significantly higher after 60 min when compared with 120 min and corresponds nicely with the highest methionine concentration after 60 min. Another possible explanation for the lower postprandial homocysteine concentration is an increased conversion of homocysteine to cystathionine through the transsulfuration pathway, which is supported by the increased cystathionine concentration postprandially.
The post-translational methylation of amino acids in proteins is mainly catalysed by S-adenosylmethionine-dependent methyltransferases. Thus, a higher rate of folate-dependent homocysteine remethylation to methionine after food intake may increase the availability of methyl groups delivered by S-adenosylmethionine. Intake of choline and betaine, although found in low amounts in the breakfast, may further contribute to the one-carbon pool. In the present study, we quantified only a few methylated forms of amino acids, but we did not observe a consistent increase in all measured methylated amino acids after breakfast intake. This is most likely due to the different sources of both methylated and non-methylated amino acids, which may originate from the diet or the endogenous body proteins, or both. The synthesis of asymmetric dimethylarginine and symmetric dimethylarginine from arginine residues in proteins is catalysed by S-adenosylmethionine-dependent methyltransferases. Assuming that proteolysis of proteins with methylated arginine residues is low in the anabolic state immediately after breakfast, the elevated concentrations of asymmetric dimethylarginine and symmetric dimethylarginine may reflect their reduced renal clearance or metabolism. The serum concentrations of N1-methylnicotinamide, trimethyllysine, 1-methylhistidine and 3-methylhistidine were, however, reduced after breakfast intake. N1-methylnicotinamide is produced from nicotinamide using S-adenosylmethionine as methyl donor. The observed postprandial reduction in N1-methylnicotinamide may suggest increased metabolism of nicotinamide, which is a precursor for the cofactor nicotinamide adenine dinucleotide, and is involved in choline oxidation pathway. For trimethyllysine and 3-methylhistidine, which are found in, e.g. myosin[40], the lower postprandial serum concentration may be due to lower muscle protein proteolysis in response to increased insulin concentration. A decreased proteolysis cannot explain the reduced 1-methylhistidine concentration since this modified amino acid is neither produced nor found in human muscles[41].
The active forms of vitamins B2, B3, B6 and B12 are important cofactors in the transfer of one-carbon units and in the transsulfuration pathway. The standardised breakfast contained very low amounts of these vitamins relative to the recommended daily allowances. In line with this, the serum concentrations of these vitamins were not increased after breakfast intake, with the exception of the 6 % increase in riboflavin after 60 min. Flavin mononucleotide, pyridoxal 5’-phosphate, nicotinamide and N1-methylnicotinamide showed lower serum concentration after breakfast intake, indicating an increased utilisation, with no change in vitamin B12 concentration postprandially. This underscores the importance of controlling for food intake in studies involving the one-carbon metabolism, even in settings where the consumed food has low contents of nutrients including B-vitamins.
In the present study, we observed higher fasting serum concentrations of methionine, sarcosine and betaine in men when compared with women, which is partly in line with observations in a larger study in cancer-free older adults[42]. The higher fasting serum concentrations of trimethyllysine and 3-methylhistidine observed in our male participants may be a consequence of the larger muscle mass in men, since both methylated amino acids are found in muscle proteins[40]. Although we observed no differences between the genders for asymmetric and symmetric dimethylarginine in fasting serum, the urine concentrations (relative to creatinine) of these dimethylarginines were markedly lower in men. The lower urine concentrations of asymmetric and symmetric dimethylarginine in men may indicate lower synthesis from arginine, possibly caused by an inhibiting effect of testosterone on the involved methyltransferases[43], since our participants had normal kidney function as evidenced by serum creatinine concentration and urine albumin creatinine ratio within normal ranges[25]. For the majority of metabolites and B-vitamins related to the one-carbon metabolism that were investigated in the present study, their relative changes in serum concentrations after breakfast were comparable between men and women. We did, however, observe some gender differences that should be mentioned. Some of the differences between the genders may be difficult to explain, such as the more prominent postprandial increases in serum concentrations of betaine and sarcosine seen in women, combined with no differences in choline and dimethylglycine between the genders. Another striking difference between the genders worth commenting was the markedly larger reduction in N1-methylnicotinamide concentration seen in men compared with women after breakfast. The breakdown of N1-methylnicotinamide by N1-methylnicotinamide oxidase is stimulated by testosterone, and the activity of the enzyme is several fold higher in male compared with female mice[44]. This may, at least in part, explain the distinct effect on N1-methylnicotinamide concentration in men after breakfast intake.
The present study has some strengths and limitations. The strengths of the study include that all participants consumed a well-characterised light meal following an over-night fast, and the sample size was relatively large and consisted of both men and women. A broad array of metabolites and B-vitamins related to one-carbon metabolic pathways were measured. The serum and urine samples were analysed using established methods with high precision. We strictly followed a defined protocol for pre-analytical handling of samples, and samples were thawed for the first time for these analyses and showed no signs of degradation. The limitations include the generalisability of the findings for other populations such as those with normal bodyweight, other age groups and patients with established metabolic disturbances or diseases and the use of meals with other ingredients and serving sizes.
## Conclusion
Our findings demonstrate that the consumption of a light breakfast high in carbohydrates but with low nutrient content was sufficient to induce changes in circulating concentrations of several metabolites and B-vitamins related to the one-carbon metabolism. The one-carbon metabolism may be differently affected postprandially in men and women, since we observed differences in changes from the fasting to the postprandial state for some of the measured metabolites between the genders. Our novel findings underline the importance of having information regarding the prandial state of the study participants or patients in epidemiological and intervention studies when exploring metabolites and B-vitamins related to the one-carbon metabolism.
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|
---
title: Associations of the serum n-6 PUFA with exercise cardiac power in men
authors:
- Haleh Esmaili
- Behnam Tajik
- Tomi-Pekka Tuomainen
- Sudhir Kurl
- Jukka T. Salonen
- Jyrki K. Virtanen
journal: The British Journal of Nutrition
year: 2023
pmcid: PMC10024979
doi: 10.1017/S0007114522002501
license: CC BY 4.0
---
# Associations of the serum n-6 PUFA with exercise cardiac power in men
## Body
CVD are the leading cause of mortality, morbidity and disability worldwide, with approximately one-third of all causes of death[1]. It has been shown that low cardiac capacity during exercise is an independent indicator of CHD, heart failure and CVD mortality(2–4); however, it does not consider the differences of the cardiovascular resistance and cardiac afterload[5]. Cardiovascular resistance refers to the reduction of peripheral vascular blood flow, while cardiovascular afterload refers to the high intraluminal pressure in arteries during ventricular contraction[6,7]. Exercise cardiac power (ECP) during an exercise test reflects the peak of cardiac capacity and cardiac output[4]. ECP is a beneficial prognostic method for the risk prediction of CVD by taking into account maximal oxygen uptake (VO2max) and maximal systolic blood pressure (SBP) during exercise[2]. ECP provides information of cardiorespiratory fitness, the differences in cardiovascular resistance and also SBP as the cardiac afterload.
Previously in the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD) cohort, lower ECP was associated with increased risk of sudden cardiac death and stroke in men[5,6]. Substantial evidence from epidemiological studies has found a strong inverse association of the ECP components, VO2max and elevated exercise-induced SBP, with CVD risk(8–10). In line with this finding, in the KIHD cohort, low VO2max and high SBP during exercise have been found to be associated with risk of sudden cardiac death in men[5]. Although data from clinical trials regarding effects of high n-6 PUFA intake (mainly replacing saturated fat in diet) in CVD prevention are controversial[11], epidemiological evidence suggests that higher intake of dietary n-6 PUFA play an important role in CVD prevention[12]. Linoleic acid (LA, 18:2n-6), as the predominant n-6 PUFA, is found primarily in vegetable oils, nuts and oily seeds[13]. LA can be endogenously converted to γ-linolenic acid (GLA, 18:3n-6), GLA to dihomo-γ-linolenic acid (DGLA; 20:3n-6) and DGLA to arachidonic acid (AA, 20:4n-6)[13]. AA can be also found in animal sources including meat, egg and fish[14]. The fatty acid desaturase and elongase enzymes responsible for the conversion are also involved in the conversion of the n-3 PUFA α-linolenic acid (ALA) to longer-chain n-3 PUFA. However, because LA is the major PUFA in the diet, it is the dominant substrate for the pathway[15]. High LA intake could theoretically be disadvantageous for cardiac health if the conversion of ALA to the longer-chain n-3 PUFA EPA and DHA is reduced; however, LA does not seem to modify the associations of the long-chain n-3 PUFA with CHD risk[16]. In fact, higher intake or tissue concentrations of LA have been reported to be associated with lower risk of total CVD and CVD mortality; however, the associations of the other n-6 PUFA, GLA, DGLA and AA with CVD risk are less investigated, and findings are more controversial compared with those with LA[17,18].
There is no prior research data on the associations of the n-6 PUFA with SBP during exercise, although the limited prior evidence indicates that the n-6 PUFA may have an impact on resting SBP[19,20]. Similarly, there is very limited prior data that suggest that some n-6 PUFA may associate with VO2max [21,22]. Therefore, our aim was to explore the associations of the serum n-6 PUFA with maximal SBP and VO2max during exercise and especially with ECP that take both of these factors into account, among middle-aged and older men from the KIHD cohort.
## Abstract
Low intake or tissue concentrations of the n-6 PUFA, especially to the major n-6 PUFA linoleic acid (LA), and low exercise cardiac power (ECP) are both associated with CVD risk. However, associations of the n-6 PUFA with ECP are unknown. The aim of the present study was to explore cross-sectional associations of the serum total n-6 PUFA, LA, arachidonic acid (AA), γ-linolenic acid (GLA) and dihomo-γ-linolenic acid (DGLA) concentrations with ECP and its components. In total, 1685 men aged 42–60 years from the Kuopio Ischaemic Heart Disease Risk Factor Study and free of CVD were included. ANCOVA was used to examine the mean values of ECP (maximal oxygen uptake (VO2max)/maximal systolic blood pressure (SBP)) and its components in quartiles of the serum total and individual n-6 PUFA concentrations. After multivariable adjustments, higher serum total n-6 PUFA concentration was associated with higher ECP and VO2max (for ECP, the extreme-quartile difference was 0·77 ml/mmHg (95 % CI 0·38, 1·16, P for trend across quartiles < 0·001) and for VO2max 157 ml/min (95 % CI 85, 230, P for trend < 0·001), but not with maximal SBP. Similar associations were observed with serum LA concentration. Higher serum AA concentration was associated with higher ECP but not with VO2max or maximal SBP. The minor serum n-6 PUFA GLA and DGLA were associated with higher maximal SBP during exercise test and DGLA also with higher VO2max but neither with ECP. In conclusion, especially LA concentration was associated with higher ECP. This may provide one mechanism for the cardioprotective properties of, especially, LA.
## Participants
The data used in the current study are from the KIHD cohort, collected at the baseline examinations of the KIHD in 1984 and 1989. The KIHD is a prospective population-based study to investigate risk factors for CVD, carotid atherosclerosis and related outcomes. The participants are an age-stratified sample of men from eastern Finland[23]. At the baseline, a total of 2682 men (82·9 % of the eligible) aged 42, 48, 54, or 60 years participated in the examinations. The KIHD study protocol was approved by the research ethics committee of the University of Kuopio. Each participant provided written informed consent. Study participants were not involved in the design, or conduct, or reporting, or dissemination plans of the current study. From the analyses, we excluded participants with missing data on ECP measurements (n 207), a history of CVD (n 677) and those with missing data on the serum n-6 PUFA (n 113). After the exclusions, 1685 men were included in the analysis.
## Ethical approval
This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects were approved by the Research Ethics Committee of the University of Kuopio (1·12·1983). Written informed consent was obtained from all participants.
## Measurements
Fasting venous blood samples were obtained between 08.00 and 10.00 at the baseline examinations of the KIHD in 1984–1989. The subjects were instructed to abstain from ingesting alcohol for 3 d and from smoking and eating for 12 h before giving the sample. Details of the medical history, current medications, smoking status, alcohol intake, serum lipids and lipoproteins, and resting blood pressure measurements have been published previously[23]. Physical activity was assessed according to the 12-month leisure-time physical activity questionnaire and expressed as kcal/d[24]. BMI was computed as the ratio of weight in kilograms to the square of height in metres. Self-administered questionnaires were used to evaluate the years of education and annual income of study participants. In this study, systolic/diastolic blood pressure > $\frac{140}{90}$ mmHg or use of antihypertensive medication was considered as hypertension[25]. To measure high-sensitivity serum C-reactive protein (CRP) concentrations, an immunometric assay (Immulite High Sensitivity CRP Assay; DPC) was used. Dietary intakes were assessed at the time of blood sampling in 1984–1989 with an instructed and interviewer-checked 4-d food recording by household measures[26]. The days were not necessarily consecutive for all participants, but for all participants one of the days was a weekend day. Participants used a picture book of common foods and dishes to help in estimation of portion sizes. A nutritionist checked the food records together with the participant at the study visit for possible errors or omissions.
## Serum fatty acid measurements
Serum esterified and non-stratified fatty acids were measured in one gas chromatographic run without preseparation in 1991 from samples that had been collected at baseline in 1981–1989 and had been stored at –80°C, as described in detail previously[27]. Serum fatty acids were extracted with chloroform-methanol. Chloroform phase was evaporated and treated with sodium methoxide, which methylated esterified fatty acids. Quantification was carried out with reference standards (Check Prep Inc., Elysian, MN). Each analyte had individual reference standard, and an internal standard was eicosan. Fatty acids were chromatographed in an NB-351 capillary column (HNU-Nordion, Helsinki, Finland) by a Hewlett-Packard 5890 Series II gas chromatograph (Hewlett-Packard Company, Avondale, PA, since 1999 Agilent Technologies Inc.) with a flame ionisation detector. Results were obtained and presented as a proportion of total serum fatty acids in μmol/l. For repeated serum fatty acid measurements, the CV was 8·7 % for LA (18:2n-6), 11·6 % for GLA (18:3n-6), 8·3 % for DGLA (20:3n-6) and 9·9 % for AA (20:4n-6). For the serum total n-6 PUFA concentration, we used the sum of LA, GLA, DGLA and AA.
## Assessment of exercise cardiac power
The maximal symptom-limited exercise tolerance test was performed at the KIHD baseline in 1984–1989 to assess oxygen consumption and SBP. A detailed description has been given previously[28]. The test was performed between 08.00 and 10.00 using an electrically braked bicycle ergometer (Medical Fitness Equipment 400 L bicycle Ergometer)[3] with a direct analysis of respiratory gases (Medical Graphics). The standard protocol included an increase in the workload of 20 W/min. The VO2max was defined as the highest value for or the plateau of oxygen uptake. Blood pressure was measured every 2 min both manually and automatically during exercise until the test was stopped and every 2 min after exercise. The highest SBP during the exercise test was considered as the maximal exercise SBP. ECP was defined as VO2max/maximal SBP during exercise[6]. For safety reasons, all tests were supervised by an experienced physician with the assistance of an experienced nurse. Electrocardiography was recorded with the Kone 620 electrocardiograph[29].
## Statistical analysis
Spearman’s correlation coefficients (r) were applied to estimate the correlations between the individual n-6 PUFA. The mean values of ECP, VO2max and maximal SBP during exercise in the exposure quartiles were analysed using ANCOVA. The extreme-quartile difference refers to the difference between the highest and the lowest quartile. Two different models were used to control for potential confounding factors, mainly based on our previous analysis between the long-chain n-3 PUFA and ECP[28] and on the associations with exposure in the current analyses. Model 1 was adjusted for age (years) and the year of examination. Model 2 included the variables in the model 1 plus BMI (kg/m2), smoking status (yes/no), leisure-time physical activity (kcal/d), antihypertensive medication use (yes/no), income (euros), years of education, serum long-chain n-3 PUFA (% of all serum fatty acids) and alcohol intake (g/week). Additional adjustments for other potential confounders, including energy intake, carbohydrate intake or bronchial asthma, did not appreciably change the associations (< 5 % change in estimates). Missing covariate values (< 0·5 %) were replaced by the cohort mean. For testing the linear trends across the n-6 PUFA quartiles, the median value of each fatty acid quartile was used as a continuous variable. All P-values were two-tailed (α = 0·05). SPSS software version 27 (IBM Corp) was used to analyse data.
## Baseline characteristics
The mean ± sd age of the participants was 52·8 ± 5·1 years. The mean ± sd serum concentrations, as a percentage of all serum fatty acids, were 33·15 ± 4·58 % for the serum total n-6 PUFA concentration, 26·70 ± 4·40 % for LA, 0·28 ± 0·11 % for GLA, 1·34 ± 0·27 % for DGLA and 4·82 ± 1·00 % for AA. The inter-correlations between the individual n-6 PUFA were weak, except for a moderate correlation between GLA and DGLA: (r = –0·20 for LA and GLA), (r = –0·09 for LA and DGLA), ($r = 0$·12 for LA and AA), ($r = 0$·55 for GLA and DGLA), ($r = 0$·19 for GLA and AA) and ($r = 0$·07 for DGLA and AA). Baseline characteristics of the participants according to quartiles of the total n-6 PUFA concentration are presented in the Table 1. Men with higher concentration were more likely to have a higher annual income and education, leisure-time physical activity, serum n-3 PUFA concentration, and HDL and LDL cholesterol concentrations, but lower serum TAG concentration and systolic and diastolic blood pressure. They were also younger and had lower BMI and alcohol intake, and they were less likely to have hypertension and diabetes, and less likely to smoke.
Table 1.Baseline characteristics according to the quartiles of total serum n-6 PUFA concentrations* Serum total n-6 PUFA quartile (%)Q1 (<30·14) (n 421)Q2 (30·14–33·29) (n 421)Q3 (33·30–36·17) (n 422)Q4 (> 36·17) (n 421)VariablesMean sd %Mean sd %Mean sd %Mean sd % P for trend § Age (years)53·44·853·14·952·75·152·15·6< 0·001Education (years)8·53·38·23·48·83·69·24·0< 0·001Income (euro)13 022810114 200925814 687941915 4009985< 0·001BMI (kg/m2)28.33·827·23·426·32·925·62·80·002Current smoker (%)31·128·930·427·5< 0·001Leisure-time physical activity (kcal/d)1281711301601391681621950·003Serum C-reactive protein (mg/l)2·293·862·103·012·263·331·923·060·23Serum TAG (mmol/l)1·951·181·260·551·060·440·910·38< 0·001Serum HDL-cholesterol (mmol/l)1·190·301·270·281·330·291·380·300·03Serum LDL-cholesterol (mmol/l)3·931·014·080·994·080·974·061·03< 0·001Systolic blood pressure (mmHg)13817135161331713217< 0·001Diastolic blood pressure (mmHg)9111901088108710< 0·001Alcohol intake (g/week)991397512262915481< 0·001Diabetes (%)8·45·34·72·7< 0·001Hypertension (%)72·262·454·450·40·01Asthma (%)2·42·92·42·90·58Medication use (%)† 19·412·412·411·70·006Energy intake (kcal/d)24406262468682248958525095990·10Carbohydrate intake (g/d)249332473224031236290·001Serum n-3 PUFA (%)‡ 4·731·954·860·964·960·925·080·970·004Serum LA (%)20·882·6325·081·4027·941·3532·172·59< 0·001Serum GLA (%)0·300·110·290·100·280·110·280·11< 0·001Serum DGLA (%)1·320·261·370·361·350·291·320·300·71Serum AA (%)4·280·994·860·964·960·925·080·97< 0·001Q, quartiles; LA, linoleic acid; GLA, γ-linolenic acid; DGLA, dihomo-γ-linolenic acid; AA, arachidonic acid.*Values are means (sd) or percentages. Data were analysed with linear regression for continuous variables and the χ2 test for categorical variables.†Antihypertensive and anti-hyperlipidemia medication.‡Proportion of all serum fatty acids.§ P for trend refers to the linear trend across the fatty acid quartiles.
## Serum n-6 PUFA concentrations and exercise cardiac power
The mean ± sd ECP was 12·45 ± 3·07 ml/mmHg. After adjustment for age and examination year (model 1), higher serum total n-6 PUFA concentration was associated with higher ECP ((the extreme-quartile difference in ECP in the serum total n-6 PUFA concentrations was 0·90 ml/mmHg (95 % CI 0·51, 1·29)) (Table 2). Further adjustments only slightly weakened the association (model 2, Table 2). Similar association with higher ECP was also observed with LA and AA (Table 2). Also, DGLA had a weak association with higher ECP, although the mean difference between the extreme quartiles did not reach statistical significance (Table 2). No significant associations were found between serum GLA and ECP.
Table 2.Exercise cardiac power in quartiles of serum n-6 PUFA concentrations * Exposure quartileSerum n-6 PUFA1 (n 421)95 % CI2 (n 421)95 % CI3 (n 422)95 % CI4 (n 421)95 % CI P for trend § Mean difference between the highest and the lowest quartile95 % CITotal n-6 PUFA (%)13·99–30·1330·14–33·2933·30–36·1736·18–47·25Model 1† 11·9811·71, 12·2612·3512·07, 12·6212·5912·32, 12·8612·8812·61, 13·16<0·0010·900·51, 1·29Model 2‡ 12·0511·78, 12·3212·3612·10, 12·6312·5812·31, 12·8412·8212·55, 13·09<0·0010·770·38, 1·16LA (%)10·30–23·8223·83–26·7026·71–29·6329·64–41·30Model 112·0411·77, 12·3212·3212·05, 12·5912·5812·30, 12·8512·8612·59, 13·13<0·0010·820·43, 1·20Model 212·0811·80, 12·3512·3112·04, 12·5712·6012·34, 12·8612·8212·55, 13·09<0·0010·740·34, 1·14GLA (%)0·06–0·200·21–0·270·28–0·350·36–0·87Model 112·6412·36, 12·9112·3512·08, 12·6312·5012·22, 12·7712·3112·04, 12·590·19–0·32–0·71, 0·07Model 212·6712·41, 12·9312·3212·06, 12·5812·4512·19, 12·7112·3612·09, 12·630·21–0·31–0·69, 0·07DGLA (%)0·57–1·151·16–1·331·34–1·501·51–3·02Model 112·3712·09, 12·6412·1511·88, 12·4212·6612·38, 12·9312·6312·35, 12·900·060·24–0·13, 0·65Model 212·2912·02, 12·5612·2311·97, 12·4912·6312·37, 12·9012·6512·38, 12·920·030·35–0·03, 0·75AA (%)1·68–4·144·15–4·764·77–5·445·45–9·21Model 112·1611·88, 12·4312·2611·99, 12·5312·6912·42, 12·9712·6912·41, 12·96<0·0010·530·14, 0·91Model 212·2711·99, 12·4512·3112·05, 12·5812·6612·40, 12·9212·7612·48, 12·940·010·500·29, 0·80LA, linoleic acid; GLA, γ-linolenic acid; DGLA, dihomo-γ-linolenic acid and AA, arachidonic acid.*Values are means (95 % CI). Data were analysed with ANCOVA.†Model 1: adjusted for age and examination years.‡Model 2: adjusted for model 1 plus BMI, smoking status, leisure-time physical activity, alcohol intake, use of antihypertensive medication, education, income and serum long-chain n-3 PUFA concentrations.§ P for trend refers to the linear trend across the fatty acid quartiles.
## Serum n-6 PUFA concentrations and VO2max
The mean ± sd VO2max was 2544 ± 578 ml/min. The serum total n-6 PUFA concentration was associated with higher VO2max after adjustment for age and year of examination (model 1) (the extreme-quartile difference 152 ml/min (95 % CI 79, 225), with little change in the multivariate-adjusted model (model 2). Also serum LA and DGLA concentrations were associated with higher VO2max ((the extreme-quartile difference 138 ml/min (95 % CI 64, 212) for LA and 143 ml/min (95 % CI 62, 223) for DGLA, respectively (model 2)). Serum AA was associated with higher VO2max in the model 1, but further adjustments attenuated the association, and it was not statistically significant anymore (Table 3). Serum GLA concentration was not associated with VO2max (Table 3).
Table 3.Maximal oxygen uptake (ml/min) in quartiles of serum n-6 PUFA concentrations* Exposure quartileSerum n-6 PUFA1 (n 421)95 % CI2 (n 421)95 % CI3 (n 422)95 % CI4 (n 421)95 % CI P for trend § Mean difference between the highest and the lowest quartile95 % CITotal n-6 PUFA (%)13·99–30·1330·14–33·2933·30–36·1736·18–47·25Model 1 † 24632412, 251525372485, 258825592508, 261126152563, 2667<0·00115279, 225Model 2‡ 24592409, 250925332485, 258225662517, 261426162566, 2666<0·00115785, 230LA (%)10·30–23·8223·83–26·7026·71–29·6329·64–41·30Model 124892437, 254025172465, 256825622511, 261426062555, 26590·00111845, 191Model 224762425, 252625102461, 255825752527, 262426132563, 2664<0·00113864, 212GLA (%)0·06–0·200·21–0·270·28–0·350·36–0·87Model 125722514, 2613)25422484, 259925992542, 265525582500, 26160·98–15–97, 67Model 224772522, 263225462493, 260025812527, 263425672513, 26220·97–10–88, 68DGLA (%)0·57–1·151·16–1·331·34–1·501·51–3·02Model 125142456, 257224982440, 255526082552, 266426502593, 2708<0·00113654, 218Model 225022447, 255825192465, 257226042551, 265626452590, 2700<0·00114363, 223AA (%)1·68, 4·144·15–4·764·77–5·445·45–9·21Model 124952434, 255625502493, 260726132556, 267126032548, 2658< 0·00110826, 190Model 225012450, 255325412492, 259025742526, 262225582506, 26100·1157–20, 134LA, linoleic acid; GLA, γ-linolenic acid; DGLA, dihomo-γ-linolenic acid and AA, arachidonic acid.*Values are means (95 % CI). Data were analysed with ANCOVA.†Model 1: adjusted for age and examination years.‡Model 2: adjusted for model 1 plus BMI, smoking status, leisure-time physical activity, alcohol intake, use of antihypertensive medication, education, income and serum long-chain n-3 PUFA concentrations.§ P for trend refers to the linear trend across the fatty acid quartiles.
## Serum n-6 PUFA concentrations and maximal systolic blood pressure during exercise
The mean ± sd maximal SBP during exercise was 206·7 ± 26·6 mmHg. The serum total n-6 PUFA, LA and AA concentrations were not associated with maximal SBP during exercise (Table 4). GLA and DGLA were associated with higher maximal exercise SBP ((the extreme-quartile difference in the multivariate-adjusted model was 4·1 mmHg (95 % CI 0·2, 8·0) for GLA and 4·9 mmHg (95 % CI 0·9, 8·9) for DGLA (model 2, Table 4)).
Table 4.Maximal systolic blood pressure during exercise (mmHg) in quartiles of serum n-6 PUFA concentrations* Exposure quartileSerum n-6 PUFA1 (n 421)95 % CI2 (n 421)95 % CI3 (n 422)95 % CI4 (n 421)95 % CI P for trend § Mean difference between the highest and the lowest quartile95 % CITotal n-6 PUFA (%)13·99–30·1330·14–33·2933·30–36·1736·18–47·25Model 1† 208·5205·9, 211·0207·3204·8, 209·9205·1202·6, 207·7205·6203·1, 208·20·07–2·9–6·5, 0·8Model 2‡ 206·9204·3, 209·4206·7204·2, 209·2205·9203·5, 208·9207·0204·5, 209·60·681·2–3·6, 3·9LA (%)10·30–23·8223·83–26·7026·71–29·6329·64–41·30Model 1209·7207·1, 212·8206·2203·6, 208·7205·6203·1, 208·2205·1202·6, 207·70·07–4·6–8·2, 0·1Model 2207·8205·2, 210·4205·7203·2, 208·2206·4203·9, 208·9206·6204·0, 209·20·63–1·2–5·1, 2·5GLA (%)0·06–0·200·21–0·270·28–0·350·36–0·87Model 1202·9200·1, 205·7207·7204·4, 210·0208·7206·0, 211·5208·2205·4, 211·00·015·31·4, 9·3Model 2203·3200·6, 206·1207·7205·0, 210·4208·5205·9, 211·2207·5204·7, 210·20·034·10·2, 8·0DGLA (%)0·57–1·151·16–1·331·33–1·501·51–3·02Model 1203·6200·8, 206·4207·3204·6, 210·1205·3202·6, 207·9211·1208·3, 213·80·0017·43·5, 11·4Model 2204·7201·9, 207·5207·9205·2, 210·6205·0202·3, 207·6209·7206·9, 212·40·0044·90·9, 8·9AA (%)1·68–4·144·15–4·764·77–5·445·45–9·21Model 1205·5202·6, 208·3207·7205·0, 210·5206·8204·1, 209·5207·0204·3, 209·60·621·5–2·5, 5·4Model 2205·9202·9, 208·9208·0205·4, 210·7206·9204·2, 209·6206·2203·4, 208·90·870·2–4·1, 4·4LA, linoleic acid; GLA, γ-linolenic acid; DGLA, dihomo-γ-linolenic acid and AA, arachidonic acid.*Values are means (95 % CI). Data were analysed with ANCOVA.†Model 1: adjusted for age and examination years.‡Model 2: model 1 plus BMI, smoking, leisure-time physical activity, alcohol intake, use of antihypertensive medication, education, income and serum long-chain n-3 PUFA concentrations.§ P for trend refers to the linear trend across the fatty acid quartiles.
## Sensitivity analysis
We investigated the associations of n-6 PUFA with ECP and its components after including only men with complete data in all variables (n 1631). However, the associations were not appreciably different compared with the main analysis. For example, the extreme-quartile difference in ECP in the serum total n-6 PUFA concentrations was 0·83 ml/mmHg (95 % CI 0·43, 1·23)).
## Discussion
Our results of this cross-sectional study showed that the serum concentrations of total n-6 PUFA and LA, the most abundant n-6 PUFA, were associated with higher ECP and VO2max but not with maximal SBP during the exercise test among middle-aged and older men in eastern Finland. AA was associated with higher ECP. The minor n-6 PUFA GLA and DGLA were associated with higher maximal SBP during exercise test and DGLA also with higher VO2max. To our knowledge, our study is the first study to explore the association of serum n-6 PUFA concentrations with ECP and may provide one potential mechanism how especially LA could exert its cardioprotective properties.
There is little prior evidence regarding the association between n-6 PUFA and VO2max. Inconsistent with our study, in the cross-sectional National Health and Nutrition Examination Survey (NHANES) among 449 healthy participants (20–50-year-old), lower VO2max was observed with higher plasma levels of AA, while no associations were observed with higher LA, GLA and DGLA concentrations[21]. There is no apparent explanation for the differences in our results compared with findings in NHANES. For example, measurement of the fatty acid concentrations in different blood compartments (total plasma/serum, TAG, phospholipids etc.) could explain the different findings, but both the NHANES study and our study used similar measurements (total plasma and total serum). Moreover, in a study among fifty patients with non-ischaemic heart failure in a univariate analysis, serum AA was associated with higher VO2max, but serum DGLA did not have an association[22]. The other n-6 PUFA were not investigated in that study.
Maximal SBP during exercise test shows the general condition of the cardiovascular system and is one of the predictors of CVD[30]. Although resting blood pressure is a strong indicator of future CVD and hypertension, it has been found that higher maximal SBP during exercise tests can be a valuable predictor of future CVD and CVD mortality[30]. There is no prior data published on the associations of the n-6 PUFA on maximal SBP during exercise, and the study findings regarding the associations of the n-6 PUFA with resting blood pressure are controversial. In a systematic review and meta-analysis, no association was reported between n-6 PUFA concentrations (including LA, GLA, DGLA, AA or any combination) and blood pressure among healthy adults or adults at high risk of CVD[31]. Some studies among healthy people have suggested that a higher serum concentration of LA is associated with lower resting blood pressure, while higher serum AA is associated with higher blood pressure[19,20,32]. In addition, higher dietary LA intake in healthy adults with hypercholesterolemia significantly reduced blood pressure and vascular resistance, both at rest and during acute stress[33]. Our findings of the direct association of GLA and DGLA with maximal exercise SBP are inconsistent with the previous review that highlighted the potential of GLA and DGLA to suppress inflammation and reduce blood pressure[34]. This inconsistency may be due to haemodynamic and vascular tone changes during an exercise, which is not taken into account for SBP at rest[35].
Potential mechanisms underlying the association of the serum LA with ECP may include the beneficial properties of this fatty acid to decrease inflammation and arterial stiffness, increase endothelium-vasodilation and improve pulse wave velocity and vascular resistance(33,36–39). Moreover, the vasoactive PG E1 and I2 that are derived from DGLA and AA, respectively, increase significantly during exercise[40] and have beneficial effects on nitric oxide synthesis, vascular resistance reduction, peripheral blood flow enhancement and cardiac function improvement[41,42]. In addition, LA and AA have been shown to associate with ion channel voltage in an animal model[43]. For example, in rats, AA can directly facilitate the activity of hyperpolarisation-activated cyclic nucleotide gated channels, which leads to increase in heart rate and cardiac output[44]. Heart rate can be related to blood pressure, particularly peripheral blood pressure[45].
The strengths of the current study include the population-based design with a large sample size, and the use of serum n-6 PUFA measurements instead of using dietary n-6 PUFA intakes, which reduces misclassification that would attenuate the associations towards the null. Use of serum fatty acid measurements also enabled us to investigate the minor, mainly endogenously produced n-6 PUFA GLA and DGLA. Although the LA concentration is mainly determined by diet, it is also affected by genetic factors in some extent[46]. In contrast, concentrations of GLA, DGLA and AA primarily depend on genetic and metabolic factors, including elongase and desaturase enzyme polymorphisms, availability of other nutrients and the LA:ALA ratio[47]. Other strengths include the extensive examinations of potential confounders and assessment of VO2max, which is considered as the ‘gold standard’ for cardiorespiratory fitness and cardiac output assessment[48]. However, this study had some limitations that should be considered. First, the study included only middle-aged and older men from eastern Finland, so our results may not be generalisable to other populations or to women. Second, because of the nature of the cross-sectional study, we were unable to assess causality. Third, since we had a large number of statistical analyses with several exposures and outcomes, some of the observed statistically significant associations may have occurred due to chance. Finally, despite the adjustment for a large set of potential confounders, residual confounding is still possible.
In conclusion, our results suggest that the serum concentrations of total n-6 PUFA and of the major n-6 PUFA, LA, were associated with higher ECP in middle-aged and older men from eastern Finland. The association with higher ECP was mainly due to the association with higher VO2max, as there were no associations with maximal SBP during exercise. The findings with the minor n-6 PUFA, GLA, DGLA and AA were less coherent. To enhance the knowledge of the mechanisms of the n-6 PUFA on cardiac function and to confirm our findings more studies in other populations with different ethnicities, ages and sexes are required.
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|
---
title: Skeletal muscle optoacoustics reveals patterns of circulatory function and
oxygen metabolism during exercise
authors:
- Angelos Karlas
- Nikolina-Alexia Fasoula
- Nikoletta Katsouli
- Michael Kallmayer
- Sabine Sieber
- Sebastian Schmidt
- Evangelos Liapis
- Martin Halle
- Hans-Henning Eckstein
- Vasilis Ntziachristos
journal: Photoacoustics
year: 2023
pmcid: PMC10025091
doi: 10.1016/j.pacs.2023.100468
license: CC BY 4.0
---
# Skeletal muscle optoacoustics reveals patterns of circulatory function and oxygen metabolism during exercise
## Abstract
Imaging skeletal muscle function and metabolism, as reported by local hemodynamics and oxygen kinetics, can elucidate muscle performance, severity of an underlying disease or outcome of a treatment. Herein, we used multispectral optoacoustic tomography (MSOT) to image hemodynamics and oxygen kinetics within muscle during exercise. Four healthy volunteers underwent three different hand-grip exercise challenges (60s isometric, 120s intermittent isometric and 60s isotonic). During isometric contraction, MSOT showed a decrease of HbO2, Hb and total blood volume (TBV), followed by a prominent increase after the end of contraction. Corresponding hemodynamic behaviors were recorded during the intermittent isometric and isotonic exercises. A more detailed analysis of MSOT readouts revealed insights into arteriovenous oxygen differences and muscle oxygen consumption during all exercise schemes. These results demonstrate an excellent capability of visualizing both circulatory function and oxygen metabolism within skeletal muscle under exercise, with great potential implications for muscle research, including relevant disease diagnostics.
## Introduction
Muscle contraction, the hallmark of exercise, reflects the conversion of metabolic energy into mechanical function. Accurate non-invasive monitoring of such processes is crucial for the objective assessment not only of the muscle function itself but also of general body performance. Relevant long pending questions may well be answered by measuring muscle metabolism, as reflected in its perfusion and oxygenation changes during exercise [1], questions such as: i) are the main determinants of the maximal oxygen transport and uptake—both major metrics of exercise capacity—the central adaptations to exercise or the local muscle phenomena [2], [3] or, ii) what is the contribution of skeletal muscle in the definition of basic body energy expenditure, as a barometer of obesity risk?
Muscle measurements are also useful in everyday practice. For example, in health-related exercise, measuring normal muscle function and metabolism is essential for the evaluation of an athlete’s performance and corresponding adjustment of the training program [4]. In disease conditions, such as peripheral arterial disease (PAD), neuromuscular disorders, and diabetes, accurate monitoring of muscle function and metabolism may enable the quantification of exercise tolerance and the optimization of therapeutic schemes [5], [6]. Thus, muscle measurements of circulatory function (hemodynamics) and oxygen kinetics could give not only insights in local muscle physiology and pathology but also serve as a window to the mechanisms regulating the coupling between the systemic response to exercise and local muscle metabolism.
Several techniques have been used for non-invasive monitoring of muscle function and metabolism for diagnostic purposes, therapy evaluation, or physiology studies. Most of the commonly used methods (e.g., contrast-enhanced ultrasound [CEUS], positron emission tomography [PET], single photon emission computed tomography [SPECT], contrast-enhanced magnetic resonance imaging [MRI]) visualize muscle function and metabolism using exogenous tracers that are taken up by the muscle. For example, the intramuscular circulation of injected microbubbles was imaged using CEUS on muscle in patients with PAD and diabetes, showing disturbed muscle perfusion after exercise [7], [8]. While CEUS can provide useful insights into muscle function, it provides no information about muscle metabolism, and does not allow for observations during prolonged functional challenges (e.g., exercise tests) since the agent is cleared from the vasculature by the liver, immune system, or by ultrasound-induced mechanical destruction, in only three to five minutes [9]. Moreover, the need for injection and venous cannulation increases discomfort for the patient. Similarly, nuclear medicine techniques such as PET or SPECT can also be used to characterize muscle perfusion and function indirectly through the clearance dynamics of injected radioactive tracers [10], [11]. However, both PET and SPECT suffer from low image resolution (≈ 4 mm) and expose patients to ionizing radiation [12].
Non-invasive muscle monitoring can also be achieved by imaging intramuscular levels of endogenous chemical metabolites, which is possible with magnetic resonance spectroscopy (MRS), muscle functional magnetic resonance imaging (mfMRI), and chemical exchange saturation transfer-magnetic resonance imaging (CEST-MRI) [13], [14], [15]. Several of these techniques have been employed for assessing muscle function and metabolism, most of which are non-ionizing and do not necessitate the use of injected contrast agents. However, such methods are based on indirect readouts of metabolites, without direct monitoring of perfusion and oxygenation/aerobic contrast. Furthermore, these techniques require elaborate infrastructure and expensive equipment, may lead to patient inconvenience (due to long examination times or claustrophobia [16]), and are generally low-throughput. Therefore, such magnetic resonance imaging modalities are not well-suited for large-scale functional studies and disseminated use.
Finally, muscle function and metabolism can also be imaged by measuring perfusion and oxygen kinetics, using techniques such near-infrared spectroscopy (NIRS) and diffuse optical tomography (DOT) [14], [17], [18]. These optical methods take advantage of the changes in the optical properties of hemoglobin according to its oxygenation status. While they have shown great potential for imaging muscle perfusion and oxygen metabolism based directly on hemoglobin contrast, the limited accuracy and resolution (5–10 mm) due to light scattering has prevented their wide-spread use [19], [20].
Multispectral optoacoustic tomography (MSOT) is able to overcome the above such limitations as it has the potential to provide imaging of muscle function and metabolism with high spatial (≈ 200–300 µm) and temporal (≈ 25–50 Hz) resolution—without the need for contrast agents—and high portability due to the newly available hand-held configurations. More importantly, hand-held MSOT can achieve detailed and real-time imaging of intramuscular oxygen kinetics and circulation mechanics based only on hemoglobin contrast (oxy- [HbO2] and deoxygenated [Hb]) and fluctuations over time, providing direct access to ‘aerobic’ processes. Contrary to purely optical techniques, MSOT employs fast light pulses to produce and sense ultrasound waves and is therefore unaffected by light scattering. MSOT, especially after recent image quality improvements [21], can be employed at depths of up to 3–4 cm and has already been used in the study of tumors and cardiovascular, metabolic, endocrine, neuromuscular and inflammatory diseases [20], [22], [23], [24], [25], [26], [27], [28]. Particularly relevant for the current study, MSOT has shown great potential for imaging skeletal muscle under disturbed blood flow conditions (arterial and venous occlusion) [29], [30], as well as before and after cycling exercise [31].
In this study, we explore the promising capability of hand-held MSOT for direct and label-free imaging HbO2 and Hb in real-time to investigate muscle function and metabolism during different types of exercise. We use MSOT to discriminate between HbO2 and Hb and resolve their dynamics over time and provide invaluable insights into the mechanics of circulation and the metabolism of oxygen within muscle during isometric, intermittent isometric, and isotonic exercise. We show that MSOT provides high-resolution imaging of ‘aerobic’ processes in a non-invasive and label-free, manner reaching depths of 3–4 cm in muscle tissue. Application of this approach on healthy volunteers demonstrates great potential for research and diagnostics of muscle exercise physiology in health and disease and could become a new method of choice for exercise performance evaluation.
## Study design and subject preparation
All participants signed an informed consent before included in the study. The study was approved by the ethics committee of the medical faculty of the Technical University of Munich (Protocol No: $\frac{324}{21}$S). All subjects ($$n = 4$$, 2 males, 2 females; mean age 35, range 34–36) were non-smokers, normotensive (blood pressure 110–$\frac{120}{75}$–80 mmHg) and of average weight and level of fitness. Subjects with any condition that may affect muscle function were not included. Also, the included participants were not taking any medication and were asked to avoid exercise for at least 8 h before the measurements to avoid any effect on the MSOT muscle measurements. Measurements took place in a quiet room with normal temperature (T ≈ 23 °C). Subjects were seated in a comfortable chair and the handgrip maximum voluntary contraction (MVC) of the dominant hand was measured using a hand-held digital dynamometer. MVC was measured three consecutive times (at 5 min intervals) and the average measured value was recorded as the subject’s handgrip MVC. Each subject was then provided with a handgrip workload in the dominant hand corresponding to 40 % of the MVC. The hand-held MSOT probe was placed over the dorsolateral region of the dominant forearm and the position was marked with permanent ink. The muscle was imaged over the same position during all exercise challenges.
## Imaging setup and data acquisition
A hybrid MSOT system with embedded ultrasound (US) imaging (Acuity©, iThera Medical GmbH, Munich, Germany) was employed for all measurements (Fig. 1a). The system is equipped with a customized hand-held probe for both tissue illumination and US sensing described in detail elsewhere [32]. We applied a periodic pattern of 28 different wavelengths (from 700 to 970 nm at steps of 10 nm) to illuminate tissue with short pulses of < 10 ns in duration at a rate of 25 Hz. For each single-wavelength light pulse, an optoacoustic frame was recorded (i.e., Single-Pulse-Per-Frame, SPPF operation mode). In parallel, co-registered ultrasound images were acquired at a rate of 8 Hz. This configuration yielded a MSOT: US frame ratio of 3:1. As shown in Fig. 1, the MSOT recordings lasted 210 s for the isometric and isotonic and 270 s for the intermittent isometric exercise test. Fig. 1MSOT principle of operation and study design. ( a) Hand-held hybrid MSOT/US system scanning the skeletal muscle of the forearm (wavelength range: 700–970 nm). During the scan, the MSOT probe stays in direct contact to the skin. For visualization purposes only, the MSOT probe is here depicted to be at a distance from the forearm. ( b) Acquired tomographic ultrasound image showing the skin interface, the subcutaneous fat layer (yellow), and the brachioradialis muscle (red) in the forearm. ( c-e) MSOT image of the same region corresponding to the distribution of (c) deoxygenated hemoglobin (Hb) (750 nm), (d) total hemoglobin (total blood volume, TBV) (800 nm), and (e) oxygenated hemoglobin (HbO2) (850 nm). Scale bars: 1 cm. ( f) Study design. Four ($$n = 4$$) healthy volunteers performed isometric, intermittent isometric, and isotonic exercise in their forearm during MSOT/US imaging. ( For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Fig. 1
## Image processing and data representation
Recorded US signals for each light pulse were reconstructed into planar MSOT images using a model-based approach with a non-negativity constraint [33], [34]. The forearm muscle region was semi-automatically segmented for all images of each exercise recording. To this end, manual segmentation of the muscle image was performed only in the first reconstructed image under ultrasound guidance (Fig. 1b) and by consensus from two experts. Subsequently, a customized semi-automated algorithm based on active contours performed the muscle image segmentation for all images of each exercise recording [29].
To produce the subject-specific plots (e.g., Fig. 2a, Fig. 3a, etc.), we first calculated the mean intensity values within the segmented muscle areas for all MSOT images. Then, for each exercise recording, the calculated values in the 750 nm images were considered as the intramuscular Hb values (Fig. 1c), the values of the 800 nm images were considered as the intramuscular total blood volume (TBV) values (Fig. 1d), and the ones corresponding to the 850 nm images were taken as the intramuscular HbO2 values (Fig. 1e). The reason for this selection is that at 750 nm, the light absorption of *Hb is* prominently higher than that of HbO2; at 800 nm, the light absorptions of Hb and HbO2 are equal (isosbestic point); and at 850 nm, the light absorption of HbO2 is substantially higher than that of Hb [20]. All calculations took place in the ‘raw’ reconstructed optoacoustic frames, i.e. the recorded MSOT frames, before processing them for visualization purposes. Fig. 2MSOT imaging before, during and after 60 s of isometric exercise. ( a) Representative plot of the mean optoacoustic signal within the segmented brachioradialis muscle area, in the 750 nm MSOT frames for Hb, the 800 nm MSOT frames for TBV and the 850 nm MSOT frames for HbO2. TBV: total blood volume, rest: 30-second baseline period before isometric exercise, E: 60-second period of isometric exercise, R1: first 60-second resting period after exercise, R2: second 60-second resting period after exercise. The orange arrows show the start (down) and the end (up) of the contraction. ( b) Top row: Representative 750 nm MSOT frames depicting the Hb-distribution in the segmented brachioradialis muscle for each time period described above. Middle row: Representative 800 nm MSOT frames depicting the TBV-distribution in the segmented brachioradialis muscle for each time period described above. Bottom row: Representative 850 nm MSOT frames depicting the HbO2-distribution in the segmented brachioradialis muscle for each time period described above. All frames show only the segmented muscle region. The white-dashed-line boxes show the regions of maximum signal change before and after the release of contraction. Scale bars: 1 cm. ( c) Box plot of the mean change in Hb- (left), TBV- (middle) and HbO2- (right) optoacoustic signal within the muscle area for each period, with regard to the corresponding baseline value for all four ($$n = 4$$) subjects. The orange disks indicate the boxplots corresponding to a muscle contraction period. ( For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Fig. 2Fig. 3MSOT imaging before, during and after 120 s of intermittent isometric exercise. ( a) Representative plot of the mean optoacoustic signal within the segmented brachioradialis muscle area, in the 750 nm MSOT frames for Hb, the 800 nm MSOT frames for TBV and the 850 nm MSOT frames for HbO2. TBV: total blood volume, rest: 30-second baseline period before isometric exercise, E1-E2-E3: 20-second period of isometric exercise, R1-R2-R3: 10-second resting period after each exercise period, E4: final 30-second exercise period, R4: first 60-second resting period after exercise, R5: second 60-second resting period after exercise. The orange arrows show the starts (down) and the ends (up) of the contractions. ( b) Top row: Representative 750 nm MSOT frames depicting the Hb-distribution in the segmented brachioradialis muscle for each resting time period described above. Middle row: Representative 800 nm MSOT frames depicting the TBV-distribution in the segmented brachioradialis muscle for each resting period described above. Bottom row: Representative 850 nm MSOT frames depicting the HbO2-distribution in the segmented brachioradialis muscle for each time period described above. All frames show only the segmented muscle region. Scale bars: 1 cm. ( c) Box plot of the mean change in Hb- (left), TBV- (middle) and HbO2- (right) optoacoustic signal within the brachioradialis muscle for each time period defined above, with regard to the corresponding baseline value for all four ($$n = 4$$) participating subjects. The orange disks indicate the boxplots corresponding to a muscle contraction period. ( For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Fig. 3 The images presented were processed further (denoised and contrast-enhanced to the same extent) for visualization purposes. To depict the maximum effect of each exercising (E) or resting (R) time period on the intramuscular hemodynamic parameters (Hb, TBV, HbO2), we selected the last recorded MSOT-frame of the corresponding period. The statistical boxplots of the whole group indicate the median, with the top edges of the box corresponding to the 25th and 75th percentiles, respectively.
## Exercise protocol
To interrogate the capability of MSOT to assess muscle function and metabolism during exercise, we conducted three experiments with different types of exercise at intervals of 30 min (Fig. 1f). The first experiment examined muscle physiological responses to sustained isometric contraction with a stable workload (40 % of MVC). It was done by imaging the forearm for 30 s at resting state, 60 s during isometric hand-grip contraction, and 120 s of relaxation. The second experiment examined the response to intermittent isometric contraction and the physiologic effect of short relaxation periods between isometric contractions. The muscle was imaged for 30 s at resting state, 3 cycles of 20 s isometric contraction followed by 10 s relaxation, 30 s isometric contraction and finally 120 s of relaxation. Finally, the third experiment examined muscular functional and metabolic responses to isotonic exercise. The dorsolateral forearm was imaged 30 s before, 60 s during isotonic exercise at a rate of 1 Hz (1 contraction/s), and 120 s after the end of exercise.
## Results
To investigate the patterns of circulatory function and oxygen metabolism within skeletal muscle during exercise, we conducted MSOT measurements in the brachioradialis muscle area during sustained handgrip tests. Three common types of exercise of gradual complexity were studied: i) isometric exercise: prolonged muscle contraction with stable intensity; ii) intermittent isometric exercise: short-duration muscle contractions with stable intensity interrupted by short-duration (e.g., 10 s) muscle relaxations; and iii) isotonic exercise: multiple fast muscle contractions (e.g., at ≈ 1 contraction/s) for a prolonged period (e.g., 60 s) with stable intensity.
Fig. 2 summarizes the results during isometric exercise which simulates the ‘quantum’ of all skeletal movements. The term ‘quantum’ is used here to describe the role of one single muscle contraction as ‘the minimum amount’ of the resulting muscle activity/effect, which produces a complex movement, e.g., of a limb. Fig. 2a shows a typical time course of HbO2, Hb and TBV (subject 1) at rest, during a 60 s sustained isometric handgrip exercise period (E) at 40 % maximum voluntary contraction (MVC) and throughout two (R1 and R2) 60 s resting periods. The plotted solid lines are fits of the measured MSOT values and represent the general trends for each measured hemoglobin parameter over time. An abrupt drop in TBV (−19.1 %) and HbO2 (−23.7 %) signals were observed at the onset of brachioradialis muscle contraction (Fig. 2a), while Hb (−15 %) decreased to a lesser extent (Fig. 2a). The contraction-induced reduction in Hb levels can be seen clearly in the respective MSOT images (Fig. 2b, top row). The drops in intramuscular TBV and HbO2 are also evident in the corresponding MSOT images (Fig. 2b, middle and bottom row) and are more prominent compared to the Hb images. After the initial drop, all hemoglobin parameters remained relatively stable for the entire 60 s period of isometric contraction (Fig. 2a). Upon muscle relaxation ($t = 90$ s), a rapid increase of all parameters was seen (≈ +$3.8\%$/s for Hb, ≈ +$3.6\%$/s for TBV, and ≈ +$4.2\%$/s for HbO2). This phenomenon is also apparent in the corresponding MSOT Hb-, TBV-, and HbO2- images (Fig. 2b) showing a clear increase in signal amplitude over the muscle area. The recorded post-relaxation increase can be characterized as ‘reactive hyperemia’ since the hemoglobin maxima were clearly higher than the resting state. The reactive hyperemia lasted for ≈ 60–70 s and included a slow recovery period (≈ -$0.4\%$/s for Hb, ≈ -$0.4\%$/s for TBV and ≈ $0.2\%$/s for HbO2, between R1 and R2) for all hemoglobin variables. Fig. 2c illustrates the statistical changes of Hb, HbO2, and TBV over the exercise test for all subjects, as shown by the boxplot of the median and the 25th and 75th percentiles. Upon transition from rest to work (exercise), the Hb group median dropped by 7.3 % (Fig. 2c, left), the TBV median by 13.9 % (Fig. 2c, middle), and the HbO2 median by 16.5 % (Fig. 2c, right). Upon muscle relaxation (R1), a clear increase in the TBV group median, HbO2 and, to a smaller extent, Hb was observed. Table 1 provides a detailed overview of the fluctuations of the intramuscular MSOT-extracted hemodynamic parameters per exercise or resting period for all types of exercise for the whole group. All values express the percentage change of each period compared to the corresponding baseline value. Table 1Fluctuations of MSOT-extracted muscle parameters compared to the baseline for all volunteers. NHb (normalized Hb): Hb/TBV, NHbO2 (normalized HbO2): HbO2/TBV.Table 1Isometric ExercisePhaseTime (s)HbNHbTBVHbO2NHbO2E60-$7.3\%$+$7.2\%$-$13.9\%$-$16.5\%$-$2.8\%$R160+$4.3\%$+$0.9\%$+$5.4\%$+$6.5\%$+$0.4\%$R260+$0.1\%$-$2.3\%$+$3.3\%$+$3.4\%$+$1.3\%$Intermittent Isometric ExercisePhaseTime (s)HbNHbTBVHbO2NHbO2E120+$0.2\%$+$3.1\%$-$1.4\%$-$1.8\%$+$1.0\%$R110+$2.0\%$+$3.2\%$+$1.4\%$+$0.9\%$-$1.2\%$E220+$2.9\%$+$4.9\%$+$0.1\%$-$1.3\%$-$1.3\%$R210+$3.2\%$+$4.1\%$+$3.4\%$+$0.9\%$-$1.2\%$E320-$2.5\%$+$3.5\%$-$5.7\%$-$7.3\%$-$1.3\%$R310+$7.1\%$+$4.4\%$+$9.3\%$+$9.5\%$-$1.0\%$E420-$1.3\%$+$4.1\%$-$4.2\%$-$5.4\%$-$1.0\%$R460+$3.8\%$-$2.2\%$+$7.2\%$+$11.1\%$+$1.1\%$R560+$5.8\%$-$3.4\%$+$10.1\%$+$16.7\%$+$0.8\%$Isotonic ExercisePhaseTime (s)HbNHbTBVHbO2NHbO2E120+$3.6\%$+$4.4\%$+$1.4\%$-$1.9\%$-$2.7\%$E220+$15.9\%$+$7.7\%$+$10.9\%$+$8.5\%$-$4.5\%$E320+$28.9\%$+$6.5\%$+$20.9\%$+$17.9\%$-$0.1\%$R160+$22.1\%$+$0.1\%$+$22.4\%$+$21.8\%$-$0.8\%$R260+$18.5\%$-$2.8\%$+$19.9\%$+$21.5\%$+$0.1\%$ *In* general, all MSOT-extracted hemodynamic parameters displayed similar kinetics over the testing period: a steep decline in signal amplitude at the onset of exercise, a relative steady state during the contractile phase, an exponential ‘hyperemic’ increase immediately after the release of contraction, and a gradual decrease during the recovery periods to near-baseline/resting levels.
As a next step, we investigated muscle function and metabolism during a more complex exercise pattern, intermittent isometric exercise, which includes periods of isometric contractions disrupted by periods of rest. Such an exercise pattern provides the opportunity to explore possible accumulative effects of repeated isometric contractions on muscle hemodynamics and oxygenation parameters. Fig. 3 summarizes the results for the intermittent isometric exercise challenge that consisted of four sequential forearm contraction periods (E1-E3 = 20 s and E4 = 30 s), three intervening rest periods (R1-R3 = 10 s) and two closing rest periods (R4-R5 = 60 s). Fig. 3a shows the MSOT-extracted Hb, TBV, and HbO2 changes as reported by the mean intensity pixels within the muscle area of a study participant (subject 3) throughout the testing period. For all extracted parameters, we observed a ‘cyclic’ pattern of decreases and increases corresponding to the contraction/blood washout and relaxation/hyperemia periods, respectively. Due to the four contraction/relaxation or blood washout/hyperemia cycles, the muscle seemed to gradually gain HbO2, TBV, and Hb in the course of the whole exercise challenge. All MSOT-extracted parameters increased in total, with their final levels (corresponding to the R5 resting period) clearly higher compared to baseline (e.g., for subject 3: ≈ +48.9 % for Hb, ≈ +39.1 % for TBV and ≈ +43.4 % for HbO2). During the intermittent isometric exercise of subject 3, the intramuscular Hb, TBV, and HbO2 increased at a rate of ≈ +$0.4\%$/s, ≈ +$0.3\%$/s and ≈ $0.4\%$/s respectively, yet at a much slower rate compared to the rate following the end of the isometric exercise. The recorded outcome represents the accumulative effect of consecutive isometric contraction periods which are characterized by a transient decrease of all parameters, as described above. Our results are supported by the corresponding single-wavelength MSOT images for Hb (750 nm), TBV (800 nm), and HbO2 (850 nm) (Fig. 3b). An overview of the dynamics of the MSOT-extracted parameters during the intermittent isometric exercise challenge for the whole cohort is provided in Table 1 (Fig. 3c).
As a final experiment, we performed MSOT measurements during isotonic contractions (Fig. 4), which are the type of contractions in aerobic (e.g., running, swimming) and resistance training (e.g., pushups, squats) exercise. These kinds of exercise are the most common in both health-related exercise and rehabilitation schemes. Readouts of subject 1 show a very short period (≈ 8 s, Fig. 4a) of decrease of all intramuscular parameters, followed by a relatively monotonic and fast increase of Hb, TBV, and HbO2 (≈ +$0.9\%$/s for Hb, ≈ +$1\%$/s for TBV and ≈ +$1.1\%$/s for HbO2) during the exercise phase, a phenomenon reflecting the cumulative effect of periodic muscle contraction and relaxation at a rate of ≈ 1 Hz. Furthermore, during the first minute after the end of exercise, all parameters remained roughly stable, indicating a clearly longer hyperemic plateau with a much longer hyperemic period (> 120 s) compared to the isotonic exercise readout of the same subject (≈ 60–70 s). Corresponding MSOT image sequences (Hb, TBV, and HbO2) for the baseline resting phase, three 20 s exercise (E1-E3) and two 60 s (R1, R2) post-exercise resting periods are given in Fig. 4b. A detailed record of the statistics of the hemodynamic fluctuations (compared to baseline) presented in Fig. 4c is provided in Table 1.Fig. 4MSOT imaging before, during and after 60 s of isotonic exercise. ( a) Representative plot of the mean optoacoustic signal within the segmented brachioradialis muscle area, in the 750 nm MSOT frames for Hb, the 800 nm MSOT frames for TBV and the 850 nm MSOT frames for HbO2. TBV: total blood volume, rest: 30-second baseline period before isometric exercise, E1-E2-E3: 20-second subperiods of the total 60-second period of isotonic exercise, R1: first 60-second resting period after exercise, R2: second 60-second resting period after exercise. The orange arrows show roughly the starts (down) and the ends (up) of the contractions. ( b) Top row: Representative 750 nm MSOT frames depicting the Hb-distribution in the segmented brachioradialis muscle for each time period described above. Middle row: Representative 800 nm MSOT frames depicting the TBV-distribution in the segmented brachioradialis muscle for each time period described above. Bottom row: Representative 850 nm MSOT frames depicting the HbO2-distribution in the segmented brachioradialis muscle for each time period described above. All frames show only the segmented muscle region. Scale bars: 1 cm. ( c) Box plot of the mean change in Hb- (left), TBV- (middle) and HbO2- (right) optoacoustic signal within the brachioradialis muscle for each period, with regard to the corresponding baseline value for all four ($$n = 4$$) participating subjects. The orange disks indicate the boxplots corresponding to a muscle contraction period. ( For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Fig. 4 To further investigate the intramuscular patterns of arterial and venous circulation in exercise and their association with local oxygen kinetics, we conducted an additional analysis of the MSOT readouts. Fig. 5 depicts the dynamics of Hb and HbO2 after their normalization to TBV, for all types of exercise. These normalized parameters (NHb and NHbO2) are indicators of the relative changes between the deoxygenated (Hb) and total (TBV) hemoglobin or the oxygenated (HbO2) and total hemoglobin, respectively. In particular, by monitoring the NHb and NHbO2 we could further highlight the exercise-induced circulatory function in the muscle or the relative changes in muscle venous and arterial blood, considered as fractions of the TBV. Compared to the fluctuations of the non-normalized Hb and HbO2 presented in Fig. 2, Fig. 3, Fig. 4, the NHb (Hb/TBV) and NHbO2 (HbO2/TBV) follow different patterns. For example, during isometric exercise (Fig. 5a), NHb is characterized by an increase while NHbO2 by a decrease. A complete record of the percentage change for each normalized parameter with reference to the corresponding baseline is provided in Table 1. The effect of diverging curves or behaviors observed in Fig. 5a is further highlighted in Fig. 5b, where the absolute difference between NHbO2 and NHb (|NHbO2-NHb|) is depicted. |NHbO2-NHb| is an indicator of the difference between the venous and the arterial fractions of muscle TBV or the arterio-venous difference in hemoglobin oxygenation / oxygen content. Such a parameter may reflect the muscle oxygen utilization during, or in response to, contraction. As observed, |NHbO2-NHb| for subject 3 (Fig. 5b) increased during isometric contraction and decreased after muscle relaxation, reaching the resting levels within the first minute of rest. Fig. 5b (right) provides the corresponding statistics for all subjects: |NHbO2-NHb| showed an increase of +$55.2\%$ compared to its baseline level during exercise (E), remained increased (+30.1 %) during the first post-exercise resting minute (R1) and clearly decreased (−15.2 %) compared to baseline two minutes after the start of muscle relaxation (R2). The corresponding normalized data for the intermittent isometric and isotonic exercises are presented in Fig. 5c and d and are also detailed in Table 1.Fig. 5MSOT-estimation of muscle oxygen kinetics during exercise. ( a) Left: Exemplary plot of normalized HbO2 (NHbO2 = HbO2/TBV) and normalized Hb (NHb = Hb/TBV) in muscle during 60 s of isometric contraction, box plot of the mean change in NHbO2 (middle) and NHb (right) in the measured muscle for each time period defined above, with regard to the corresponding baseline value for all four participating subjects. The orange arrows show the start (down) and the end (up) of the contraction. The orange disks indicate the boxplots corresponding to a muscle contraction period. ( b) Left: Representative plot of the absolute difference between the normalized HbO2 (NHbO2 = HbO2/TBV) and the normalized Hb (NHb = Hb/TBV) within the muscle, right: Box plot of the mean change in MSOT-estimated AV oxygen difference within the forearm muscle for each period, with regard to the corresponding baseline value for all four participating subjects. ( c) Left: Exemplary plot of NHbO2 and NHb in muscle during intermittent isometric exercise (3 sets of 20-second contraction and 10-second rest and 1 set of 30-second contraction), box plot of the mean change in NHbO2 (middle) and NHb (right) in the measured muscle for each time period defined above, with regard to the corresponding baseline value for all subjects. The orange arrows are roughly indicative of the exercise pattern due to space limitations. ( d) Left: Exemplary plot of NHbO2 and NHb in muscle during 60 s of isotonic exercise, box plot of the mean change in NHbO2 (middle) and NHb (right) in the measured muscle for each time period defined above, with regard to the corresponding baseline value for all ($$n = 4$$) subjects. The orange arrows are roughly indicative of the exercise pattern due to space limitations. ( E: exercise, R: rest). ( For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)Fig. 5
## Discussion
Muscle contractions/exercise are regulated by interrelated mechanical (e.g., increase of intramuscular pressure and stresses) and metabolic (e.g., oxygen exchange and nutrient uptake) events. In this work, we demonstrate that label-free muscle optoacoustic imaging can provide a direct view into muscle function and metabolism. In particular, we showcase the capability of hand-held MSOT to directly image skeletal muscle hemodynamics and oxygenation during different types of exercise by spatially and temporally monitoring Hb and HbO2 levels and offering detailed maps of muscle functional and metabolic parameters. These capabilities allow for the quantification of muscle performance and optimization in a training scheme. In addition, it opens possibilities for the direct examination of the target organ in a wide range of diseases which may affect the peripheral vasculature (e.g., PAD), general metabolism (diabetes mellitus), cardiovascular system (e.g., heart failure), or musculature (e.g., muscular dystrophies, metabolic myopathies).
Muscle blood perfusion is the most important parameter influencing muscle function and is directly associated with TBV changes [35]. Muscle perfusion consists of arterial and venous components and is thus directly related to intramuscular HbO2 and Hb levels. MSOT can be used to detect TBV, HbO2, and Hb levels, and thus monitor all main parameters of intramuscular circulation in a single test. In our study, the onset of forearm muscle contraction clearly provoked an abrupt drop of Hb, HbO2, and TBV in all three experimental protocols tested, although the intensity of the effect was influenced by the duration of the contraction. Accordingly, the rapid decline in all parameters in response to contraction was most pronounced in the first experimental setting (Fig. 2) which involved a single continuous 60 s isometric contraction. This hemodynamic response is typical of sustained isometric muscle contractions [36] and is thought to result from increases in intramuscular pressure [37] during contraction, causing obstruction of arterial blood inflow and increased venous blood outflow induced by the muscle pump (also known as “blood spurts”) [38]. Indeed, it has been previously reported that isometric MVC provokes a complete obstruction of muscle blood flow/perfusion in the vastus lateralis muscle [39]. In our experiments with the single 60 s MVC, the continuous obstruction in arterial and venous circulation is also demonstrated by the fact that the localized muscle Hb, HbO2, and TBV levels remained stable during the entire duration of the contraction after an immediate initial drop. During recovery, the muscle demonstrated a rapid hyperemia phase, due to post-contraction vasodilation [40], followed by a slower recovery to near baseline levels.
Our results reflect the function of the blood circulation during muscle contraction: a series of hydraulic/mechanical phenomena driven by the strong intramuscular pressure due to contraction. During MVC, the pressure within the muscle may increase by up to 6000 % compared to the resting state (< 15 mmHg), reaching values of up to 250–1000 mmHg depending on the muscle size [41], [42]. Thus, pressures of even 40 % of the MVC (100–400 mmHg) are around or above the systolic blood pressure of a healthy or hypertonic subject. Therefore, both the arterial and venous flow significantly decrease under the effect of the pressure applied on the vascular wall during contraction. This effect leads to i) a significant decrease in arterial blood inflow, ii) a significant decrease in venous blood outflow, and iii) washout of the blood filling the vascular bed of the muscle just before the contraction. These previously described physiologic phenomena have been well captured and corroborated by the MSOT-extracted parameters.
Muscles not only perform sustained contractions, but also repetitive contractions interspersed with brief resting periods, which we observed during intermittent isometric (Fig. 3) and isotonic exercise (Fig. 4). However, TBV, Hb, and HbO2 tended to progressively increase with each contraction-relaxation cycle and even throughout recovery, suggesting a more sustained/frequently triggered hyperemic response compared with the single contraction experiment (Fig. 1). A likely explanation is that the mechanical hindrance to blood flow that occurred during contraction was too low to cause complete suppression of arterial blood inflow, which in combination with the post-contraction hyperemia, resulted in accumulation of blood in the muscle vascular bed with each successive exercise.
The effect of the different exercise schemes/types on the muscle hemodynamic response is also explored and visually described in the provided figures. In Fig. 2 (isometric exercise) the muscle has the time to completely relax so that all hemodynamic and oxygenation parameters come back to the previous situation. On the contrary, in Fig. 3 (intermittent isometric exercise), the time given to the muscle to relax between the exercise periods (20 s each) is short (10 s each). Thus, at the start of each relaxing period the muscle is slightly more perfused/hyperemic compared to the ‘global’ resting baseline, so that finally (after all exercise cycles) the muscle is much more hyperemic compared to the end of a simple isometric exercise period, for example, and the hyperemia phase much more prolonged. If we had continued the recording, the values would have started reaching the resting state soon. This ‘incremental’ increase of the hemodynamic and oxygenation parameters is also observed in the Fig. 4 (isotonic exercise), albeit at a faster rate, where the muscle gains blood between each contraction (the muscle contracts at rates of almost 1 Hz) and, hence, needs more time to return to the baseline values, compared to the simple isometric exercise of Fig. 2.
Because exercise induces vasodilation and increases blood perfusion to meet the increased metabolic/oxygen demand, muscle contraction extends beyond a simple mechanic/hydraulic event. Of great importance are: i) the relative changes of HbO2 and Hb levels and ii) the absolute arteriovenous oxygen difference, which are both strong indicators of oxygen exchange kinetics between the muscle and the vessels or the muscle oxygen uptake during exercise.
The relative changes in Hb and HbO2, as expressed by the normalized parameters NHb and NHbO2, reveal a different aspect of muscle function during exercise. These data indicate relative changes in intramuscular Hb and HbO2 levels independent of TBV changes and give insights into the course of arteriovenous oxygen difference and, thus, oxygen exchange/kinetics over the course of sustained contractions. We observed that during sustained isometric contraction, NHb increased while NHbO2 decreased, reflecting a slight deoxygenation trend during contraction within the measured muscle. We also explored the absolute difference between NHbO2 and NHb and observed that this difference increased dramatically and then plateaued throughout the sustained contraction. Our observation may well reflect a metric of the muscle extracting oxygen from the blood. Following muscle relaxation, the difference in Hb and HbO2 signals rapidly returned to baseline resting levels. Likewise, the cyclical pattern in oxygenation-deoxygenation observed during the respective contraction-relaxation periods suggests that increased oxygen extraction was primarily related to the contractile phase of the cycle.
In the current study, we explored the circulatory phenomena and oxygen metabolism taking place during exercise of the brachioradialis muscle: a fast twitch muscle. Based on previous experience using MSOT to image hemoglobin gradients under disturbed blood flow within the gastrocnemius muscle, which contains 50 % slow twitch fibers [43], future MSOT studies could provide further insights into the possible differences in oxygen metabolism between fast and slow twitch muscles [44].
It is important to acknowledge some of the limitations of our study, such as the small sample size and the lack of data validation using independent methods that assess muscle activity. Future MSOT studies combined with MRI/MRS, CEUS, electromyography, NIRS, and other relevant techniques could provide valuable insights into muscle energetics and further assess the sensitivity of MSOT in capturing hemodynamic and oxygenation changes in muscles during exercise or disease. Furthermore, MSOT measurements might be characterized by variations, as for example seen in Fig. 2, Fig. 4, during the baseline as well as exercise and recovery phases. We believe that this could be the effect of possible laser light energy variations. It is indeed true that the laser output energy may slightly vary among different wavelengths/pulses or over time. Despite, the slight variations, our data show a generally stable behavior of the laser even for long recordings. However, the manufacturer has taken into account of this effect and a compensation mechanism has been implemented. Thus, the recorded images are weighted with the laser energy output so that the effect of different illumination energy on the image intensities is considered to be negligible.
## Conclusion
This is the first investigation using MSOT to monitor and quantify changes in hemodynamic and oxygen levels in skeletal muscle during isometric/isotonic resistance training. Our results demonstrate that MSOT can provide both sensitive and muscle-specific perfusion and oxygenation values, offering unique insights into muscle function and metabolism by direct imaging of ‘aerobic’ processes. Our data are in good agreement with prior investigations of muscle hemodynamics and oxygenation responses to resistance training while offering several benefits over other techniques, suggesting that MSOT could become an invaluable tool in the evaluation of exercise performance. By offering non-invasive, label-free, continuous monitoring of tissue perfusion and oxygenation of exercising muscle, MSOT could increase our understanding of the complex training variables on performance and aid in establishing training recommendations in health and disease. We anticipate that MSOT imaging of muscle activity will play an important role in sports science by improving the monitoring of athletic performance, rehabilitation and overall healthcare of patients with relevant diseases.
## Funding
This project received funding from the $\frac{10.13039}{501100000781}$European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 694968 (PREMSOT), the DZHK ($\frac{10.13039}{100010447}$German Centre for Cardiovascular Research; FKZ 81Z0600104) and the $\frac{10.13039}{501100013295}$Helmholtz Zentrum München funding program “Physician Scientists for Groundbreaking Projects”.
## CRediT authorship contribution statement
Angelos Karlas: Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Funding acquisition, Data curation, Supervision, Writing - original draft, Writing - review & editing. Nikolina-Alexia Fasoula: Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing. Nikoletta Katsouli: Methodology, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Michael Kallmayer: Investigation, Methodology, Writing - review & editing. Sabine Sieber: Writing - review & editing. Sebastian Schmidt: Writing - review & editing. Evangelos Liapis: Formal analysis, Validation, Writing - original draft, Writing - review & editing. Martin Halle: Supervision, Writing - review & editing. Hans-Henning Eckstein: Supervision, Resources, Writing - review & editing. Vasilis Ntziachristos: Supervision, Funding acquisition, Conceptualization, Methodology, Resources, Validation, Writing - review & editing.
## Declaration of Competing Interest
The authors declare the following financial interests/personal relationships, which may be considered as potential competing interests: Vasilis Ntziachristos reports a relationship with sThesis GmbH that includes: equity or stocks. Vasilis Ntziachristos reports a relationship with iThera Medical GmbH that includes: equity or stocks. Vasilis Ntziachristos reports a relationship with Spear UG that includes: equity or stocks. Vasilis Ntziachristos reports a relationship with i3 Inc. that includes: equity or stocks. All other authors declare that they have no competing interests.
## Data availability
Data will be made available on request.
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|
---
title: Innate immune cell-intrinsic ketogenesis is dispensable for organismal metabolism
and age-related inflammation
authors:
- Emily L. Goldberg
- Anudari Letian
- Tamara Dlugos
- Claire Leveau
- Vishwa Deep Dixit
journal: The Journal of Biological Chemistry
year: 2023
pmcid: PMC10025153
doi: 10.1016/j.jbc.2023.103005
license: CC BY 4.0
---
# Innate immune cell-intrinsic ketogenesis is dispensable for organismal metabolism and age-related inflammation
## Body
Mammals have evolved to prioritize glucose for energy. A complex, carefully regulated system has developed to control glucose availability and utilization for every cell in the body. However, during periods of starvation or limited glucose availability, mammals break down fat, leading to the production of ketone bodies, to supply energetic demand [1]. Ketone bodies are short chain fatty acids that fuel cellular ATP production through their ability to enter the TCA cycle. Thus, ketone bodies are often considered alternative metabolic fuels. Notably, many metabolic interventions that induce ketogenesis also extend lifespan in model organisms [2, 3, 4, 5]. Moreover, ketogenic diets (KD) improve markers of healthspan in old mice [6, 7]. Collectively, these studies underscore an important role for ketone bodies in aging and healthspan.
The metabolism of ketone bodies has been expertly reviewed previously [8]. Free fatty acids liberated from adipose tissue through lipolysis are broken down through β-oxidation in the liver, leading to the production of acetyl-CoA. As the concentration of acetyl-CoA increases in hepatocyte mitochondria, it is converted to ketone bodies through a series of enzymatic reactions. The final step in ketogenesis is catalyzed by the enzyme 3-Hydroxy-3-methylglutaryl-CoA lyase (HMGCL, encoded by the gene Hmgcl) to form the ketone body acetoacetate, which can then be converted to the other ketone bodies β-hydroxybutyrate (BHB) and acetone. Hepatocytes do not express the enzyme required for ketolysis Succinyl-CoA:3-Ketoacid-CoA Transferase (encoded by the gene Oxct1), and this preserves ketone bodies for extrahepatic tissues like the brain, heart, and skeletal muscle [9].
In addition to this classical regulation of ketogenesis, recent evidence shows nonhepatic sources of ketone bodies impact a variety of organ systems. In adipose tissue, beige adipocytes secrete BHB that is oxidized by adipocyte precursors to preserve adipogenic differentiation and limit fibrotic lineage skewing [10, 11]. BHB is also produced by small intestine stem cells and this is important for maintaining their stemness within crypts [12, 13]. Local ketone production has been reported in CD8 T cells and implicated to regulate their memory response [14]. Renal epithelial cells produce BHB to mediate protective effects of nicotinamide [15]. The failing heart also increases ketone body consumption [16, 17, 18]. Finally, macrophages can oxidize acetoacetate depending on their inflammatory state, and this is important for protecting against liver fibrosis [19, 20]. Collectively, these studies emphasize that ketone bodies may have autocrine/paracrine functions and have broad physiological importance.
Ketone bodies are also pleiotropic signaling molecules. BHB acts as a histone deacetylase inhibitor to control gene expression [21]. Similar to other short chain fatty acids, BHB can covalently bind lysine residues on histones and other proteins, although the importance of this posttranslational modification is not well understood [14, 22, 23, 24]. In addition, we previously showed that BHB inhibits NLRP3 inflammasome activation in macrophages [25] and neutrophils [26]. Persistent low-grade inflammation is believed to underlie many diseases of aging [27] and we have previously shown that the NLRP3 inflammasome is a key driver of age-related and obesity-driven inflammation [28, 29]. Based on the broad actions of BHB, we hypothesized that ketone bodies might be an important regulatory checkpoint for chronic inflammation in aging.
Ketone bodies impact a wide range of immune functions [14, 19, 25, 26, 30, 31, 32, 33, 34, 35, 36]. While several studies have investigated the fate of extracellular ketone bodies in immune function, less is known about immune cell-intrinsic ketogenesis. To test the importance of ketone body production in macrophages and neutrophils, we developed a novel mouse model by targeting Hmgcl (Hmgclfl/fl) to conditionally ablate ketone body synthesis in specific cell types. This strategy allowed us to focus exclusively on ketone body production, in contrast to preexisting models targeting the upstream rate-limiting enzyme HMGCS2 [13] or the downstream 3-hydroxybutyrate dehydrogenase 1 (BDH1) that interconverts acetoacetate to BHB [16]. By crossing these mice to liver-specific Albumin-Cre (HmgclAlb-Cre), we show that despite the presence of nonhepatic ketogenesis, the liver is the only organ that can produce enough ketone bodies to support survival under ketogenic conditions. We also find that neutrophil (using S100a8-Cre, HmgclS100a8-Cre)-intrinsic ketogenesis does not regulate age-related metabolic health. In addition, using LysM-Cre to ablate ketogenesis in all myeloid cells (HmgclLysM-Cre), we find only modest impacts on age- and obesity-induced metabolic dysregulation. These data suggest that innate immune inflammation is controlled by extracellular ketone bodies and that innate immune-intrinsic ketogenesis does not regulate age-related inflammation and metabolic health defects in aging.
## Abstract
Aging is accompanied by chronic low-grade inflammation, but the mechanisms that allow this to persist are not well understood. Ketone bodies are alternative fuels produced when glucose is limited and improve indicators of healthspan in aging mouse models. Moreover, the most abundant ketone body, β-hydroxybutyrate, inhibits the NLRP3 inflammasome in myeloid cells, a key potentiator of age-related inflammation. Given that myeloid cells express ketogenic machinery, we hypothesized this pathway may serve as a metabolic checkpoint of inflammation. To test this hypothesis, we conditionally ablated ketogenesis by disrupting expression of the terminal enzyme required for ketogenesis, 3-Hydroxy-3-Methylglutaryl-CoA Lyase (HMGCL). By deleting HMGCL in the liver, we validated the functional targeting and establish that the liver is the only organ that can produce the life-sustaining quantities of ketone bodies required for survival during fasting or ketogenic diet feeding. Conditional ablation of HMGCL in neutrophils and macrophages had modest effects on body weight and glucose tolerance in aging but worsened glucose homeostasis in myeloid cell-specific Hmgcl-deficient mice fed a high-fat diet. Our results suggest that during aging, liver-derived circulating ketone bodies might be more important for deactivating the NLRP3 inflammasome and controlling organismal metabolism.
## Results
To test the role of ketogenesis within innate immune cells, we first generated a mouse model containing a loxP-flanked region of exon 2 within the *Hmgcl* gene and verified homozygosity in genomic DNA (Fig. 1, A and B). To validate functional gene targeting, we first crossed these Hmgclfl/fl mice with the liver-specific Albumin-Cre (HmgclAlb-Cre) and confirmed protein deletion (Fig. 1C). In contrast to whole-body HMGCL deficiency that is embryonic lethal [37], liver-specific Hmgcl-deficient mice fed a normal chow diet were viable. When fed a KD, HmgclAlb-Cre mice failed to increase circulating blood BHB concentration (Fig. 1D) and Cre+ mice had lower blood glucose (Fig. 1E). These data agree with a prior study in an independent Hmgclfl/fl model that was published while we were developing our mice [38]. Interestingly, when fed KD, the mice also fail to induce lysine β-hydroxybutyrlation (referred to as Kbhb) (Fig. 1C), a newly described posttranslational modification by BHB [22]. Concomitant with their inability to induce hepatic ketogenesis, HmgclAlb-Cre mice failed to maintain body weight during KD feeding (Fig. 1F), primarily due to increased adipose tissue lipolysis (Fig. 1G). Notably, the weight-loss phenotype could be rescued if we cycled mice off KD every 24 h in exchange for standard chow (Fig. 1H), demonstrating the specificity of HMGCL-mediated hepatic ketogenesis in maintaining body weight. In contrast to liver-specific BDH1 KO mice that still increase ketone body concentrations in response to fasting [39] and liver-specific PPARa KO mice that have lower but inducible ketone bodies in response during sepsis [40], HmgclAlb-Cre mice also fail to induce ketogenesis in response to fasting (Fig. 1I) and they have lower blood glucose levels under fasting conditions (Fig. 1J). All together, our data functionally validate HMGCL ablation in this new mouse model and demonstrate the liver is the only organ that can supply enough ketone body production under ketogenic conditions and that no other tissues can compensate for hepatic ketogenesis to meet whole-body energy requirements during KD feeding or fasting. Figure 1Development and validation of a novel mouse model to conditionally ablate ketogenesis. A mouse was generated to study cell-specific ketogenesis by targeting expression of Hmgcl. A, targeting vector design to introduce loxP sites to allow Cre-mediated excision of exon 2 within the *Hmgcl* gene. B, representative DNA genotyping gel. C, liver-specific Hmgcl ablated mice were fed a ketogenic diet for 1 week. Protein expression of HMGCL, Actin, and pan-Kbhb were assessed in livers of Cre+ and Cre- HmgclAlb-Cre mice by Western blot. D, blood BHB, (E) blood glucose, and (F) body weights were measured in HmgclAlb-Cre mice–fed KD each morning. G, glycerol release from Efat and Sfat was measured after 48 h of KD feeding. For (D–G), each symbol represents an individual mouse, and data are presented as mean ± SD, and all statistical differences were calculated by 2-way ANOVA to compare genotypes at each time point. H, body weights were measured each morning during 1 week of cycling KD feeding. Statistical differences between Cre+ and Cre- HmgclAlb-Cre littermates were calculated by 2-way ANOVA. Data are represented as mean ± SD. I, blood BHB and (J) blood glucose levels were measured after 24 h fasting in HmgclAlb-Cre mice. For I and J, each symbol represents an individual mouse and statistical differences were calculated by student’s t test. Data are represented as mean ± SD. BHB, β-hydroxybutyrate; HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase; Kbhb, lysine β-hydroxybutyrlation; KD, ketogenic diet.
Based on our prior findings that neutrophil NLRP3 inflammasome activation can be regulated by BHB and the unexpected expression of ketogenic enzymes in these short-lived glycolytic immune cells (Fig. 2), we tested the importance of neutrophil-intrinsic ketogenesis by crossing the Hmgclfl/fl mice to the neutrophil-specific S100a8-Cre (HmgclS100a8-Cre). Cre specificity was assessed by crossing to the mT/mG reporter mouse, mT/mGS100a8-Cre (Fig. 3, A and B), that indelibly marks Cre-recombined cells with membrane GFP (mG). We induced NLRP3 inflammasome activation in isolated bone marrow neutrophils and found that HMGCL does not regulate NLRP3-dependent IL-1β secretion (Fig. 3C). The immune phenotyping of adult male and female mice revealed that HMGCL ablation in neutrophils does not impact peripheral neutrophil abundance nor does it have indirect effects on other lymphoid populations (Fig. 3, D–F). However, when we gave HmgclS100a8-Cre mice intraperitoneal (ip) injections of monosodium urate crystals, the causative agent of gout and potent neutrophil stimulus, HMGCL-deficient neutrophils had similar migration into the peritoneal cavity, but there was an overall lower inflammatory response based on Il1b and *Tnfa* gene expression (Figs. 3, G–J and S1). These data confirm efficient deletion of HMGCL using S100a8-Cre that does not impact overall neutrophil abundance in the periphery, but HMGCL-deficient neutrophils have moderately lower inflammatory responses to certain stimuli. Figure 2Comparison of HMGCL expression between liver and myeloid cells. HMGCL protein expression was compared in neutrophils, bone marrow-derived macrophages (BMDMs), and whole liver tissue by Western blot. Short and long exposures are provided as indicated. Each lane is an individual mouse. HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase. Figure 3Validation and baseline characterization of HMGCL ablation in neutrophils. For (A and B), S100a8-Cre+ mice were crossed to mTmG reporter mice to verify neutrophil-specificity. A, the total abundance of Cre+ (mGFP+) cells in each tissue were measured by flow cytometry. Each symbol represents an individual animal except for Efat and Sfat, which are pooled $$n = 2$$ mice/symbol. B, within Cre+ cells in each tissue, the cell lineage composition was determined by flow cytometry. For both graphs, data are expressed as mean ± SD. C, representative western blots from stimulated neutrophils isolated from bone marrow of HmgclS100a8-Cre mice after NLRP3 inflammasome activation with LPS + ATP. Top panel show IL-1β secretion in culture supernatants. Lower blots show HMGCL and Actin expression in cell lysates. For (D–J), HmgclS100a8-Cre+/− mice were compared to Hmgclfl/fl littermates. Baseline B220+ B cells, CD3+ T cells, and neutrophils were assessed in (D) bone marrow, (E) blood, and (F) spleen. Inflammatory response was assessed in the total peritoneal cell exudate by measuring (G) macrophage and neutrophil infiltration and gene expression of (H) Hmgcl, (I) Il1b, and (J) Tnfa by RT-PCR. Male (circles) and female (squares) Cre+ (black) and Cre- (blue) littermates were combined for analysis and each symbol represents an individual mouse. Data are expressed as mean ± SD. For (G), statistical differences were calculated by 2-way ANOVA to test for differences between genotypes for macrophage or neutrophil differences. For (H–J), statistical differences were calculated by t test. HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase; LPS, lipopolysaccharide.
Upon aging, male HmgclS100a8-Cre mice showed modest differences in body weights (Fig. 4A). Likewise, neutrophil-specific deletion of Hmgcl also modestly protected 15 to 20 month-old male mice from age-related glucose intolerance compared to male Cre-negative littermate controls (Fig. 4B). To test if HMGCL ablation altered acute inflammatory responses during aging, we analyzed physiological responses to intraperitoneal injection with gram-negative bacterial cell wall component lipopolysaccharide (LPS) that activates TLR4 signaling. While we measured expected changes in blood glucose and body temperature in LPS-challenged male and female mice, Cre+ mice did not have significantly lower body temperature after LPS injection, possibly due to using a low dose (Figs. 4, C–E and S2). Collectively, these data suggest that neutrophil-intrinsic HMGCL expression, and hence neutrophil-intrinsic ketogenesis, has modest impacts on age-related health indicators but is not a major regulator of inflammation during aging. Figure 4Neutrophil-specific Hmgcl ablation does not impact age-related inflammation. HmgclS100a8-Cre male mice were aged to at least 18 months old. A, body weights of independent cohorts at varying ages. Statistical differences were calculated by 2-way ANOVA. B, glucose tolerance test of males aged 15 to 18 months old. Area under the curve was quantified for each animal (right side panel). Statistical differences were calculated by paired 2-way ANOVA (left) or t test (right). For (C–E), 18 to 20 month-old males (circles) and 13 to 17 month-old females (squares) were injected with LPS or PBS control and changes in (C) blood glucose, (D) blood BHB, and (E) body temperature were measured 4 h later to assess the physiological response to acute inflammation. Statistical differences in (C–E) were calculated by 1-way ANOVA with Sidak’s correction for multiple comparisons within each genotype (ns: not significant). For all graphs, each symbol represents an individual mouse and all data are expressed as mean ± SD. BHB, β-hydroxybutyrate; HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase; LPS, lipopolysaccharide.
Because macrophage function is also regulated by ketone bodies [19, 25], we broadened our scope by assessing the role of HMGCL in all myeloid-lineage cells. For these experiments, we crossed Hmgclfl/fl mice with mice expressing the generic myeloid LysM-Cre driver (HmgclLysM-Cre) to ablate HMGCL in all myeloid cells in vivo. After 1 week of KD feeding, Cre-positive and Cre-negative adult littermates had similar blood BHB levels (Fig. 5A), confirming that myeloid cells do not contribute to whole-body circulating BHB. In contrast to the modest phenotype in aged HmgclS100a8-Cre mice, HmgclLysM-Cre mice had no differences in body weight, glucose tolerance, or fasting-induced weight loss in 18 month-old male Cre-positive and Cre-negative littermate controls (Fig. 5, B–D).Figure 5Myeloid-specific Hmgcl expression does not regulate metabolic health during aging. A, HmgclLysM-Cre were fed KD for 1 week to measure blood BHB levels. Statistical differences were calculated by 1-way ANOVA with Tukey’s correction for multiple comparisons. Each symbol represents an individual mouse and data are represented as mean ± SD. For (B–D), HmgclLysM-Cre male mice were aged to 18 months old and assessed for basic metabolic health parameters compared to their Cre-negative Hmgclfl/fl littermates. B, body weights, (C) glucose tolerance, and (D) 16-h fasting-induced weight loss were measured. For all graphs, each symbol represents an individual mouse. For B and D, statistical differences were calculated by unpaired student’s t test between Cre- and Cre+ groups. For C, each mouse was individually tracked, so statistical differences were calculated by paired 2-way ANOVA. Data are expressed as mean ± SD. BHB, β-hydroxybutyrate; HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase; KD, ketogenic diet.
Next, we tested if myeloid cell-intrinsic ketogenesis regulates obesity-induced inflammation. Male HmgclLysM-Cre mice were fed a high-fat diet for 12 weeks to induce obesity. Despite no differences in body weight or fasting blood glucose, Cre-positive mice had lower glucose tolerance than Cre-negative littermates (Fig. 6, A–C). However, both genotypes had similar prevalence of adipose tissue CD11b+F$\frac{4}{80}$+ macrophages in their epididymal adipose tissue (Fig. 6D). Likewise, we measured no difference in M1/M2 polarization in Hmgcl-sufficient and Hmgcl-deficient bone marrow-derived macrophages (BMDMs) in vitro (Fig. 6, E–H). These data suggest the importance of ketogenesis in innate immune cells may depend on both cell type and physiological state. Figure 6Myeloid-specific Hmgcl expression modestly impacts metabolic phenotypes in high-fat diet-induced obese mice. Male HmgclLysM-Cre mice were fed a high-fat diet for 12 weeks to induce obesity. A, body weights were measured weekly throughout the experiment. Each symbol represents an individual mouse that was tracked over time and statistical differences were calculated by paired 2-way ANOVA. Line represents mean. B, 16-h fasting blood glucose was measured prior to glucose injection for glucose tolerance test. Each symbol represents an individual mouse and data are represented as mean ± SD. Statistical difference was calculated by t test. C, glucose tolerance was measured by ip glucose tolerance test (GTT, left) and total area under the curve was quantified (right). GTT Statistical differences were calculated by paired 2-way ANOVA and AUC differences were calculated by student’s t test. Data are represented as mean ± SD and each symbol in AUC panel represents an individual mouse. D, abundance of adipose tissue macrophages was determined by flow cytometry, defined as live CD45+CD11b+F$\frac{4}{80}$+. Each symbol represents a sample pooled from $$n = 2$$ mice. For (E–H), bone marrow–derived macrophages from HmgclLysM-Cre mice were untreated (M0) or stimulated for 24 h with LPS+IFNγ (M1) or IL-4 (M2) to assess the role of HMGCL in macrophage polarization. Gene expression of (E) Hmgcl, (F) Arg1, (G) Tnfa, and (H) Nos2 were measured by RT-PCR. Each symbol represents a BMDM sample that was generated by pooling bone marrow from $$n = 2$$ mice prior to differentiation. All data are expressed as mean ± SD and statistical differences were calculated by 2-way ANOVA to compare gene expression changes between genotypes within each polarization condition. BMDM, bone marrow–derived macrophage; HMGCL, 3-Hydroxy-3-Methylglutaryl-CoA Lyase; LPS, lipopolysaccharide.
## Discussion
Prior studies have conditionally ablated ketogenesis in extrahepatic tissues by targeting the pathway rate-limiting enzyme HMGCS2 [13, 41]. However, this enzyme catalyzes the production of β-hydroxy-β-methylglutaryl CoA (HMG-CoA) from acetoacetyl CoA. HMG-CoA is then converted to acetoacetate by HMGCL. Therefore, possible confounding effects of loss of HMG-CoA cannot be ruled out. Notably, HMG-CoA can also be produced from leucine catabolism, so our approach blocks ketogenesis from this pathway as well. To focus exclusively on ketone body synthesis, we developed the Hmgclfl/fl mouse to ablate the terminal enzyme leading to production of all three ketone bodies, acetoacetate, BHB, and acetone. We validated the functional targeting of Hmgcl disruption in the HmgclAlb-Cre mice by showing these mice fail to upregulate ketogenesis in response to KD and fasting. Our results in the HmgclAlb-Cre mice also formally demonstrate that liver is the only organ that supports life-sustaining ketone body production during KD feeding and fasting and that all other combined sources of ketogenesis cannot compensate for the loss of hepatic ketogenesis.
The importance of ketone body production in nonhepatocytes is not completely understood. We and others have shown that immune cells are highly sensitive to ketone bodies through metabolic and nonmetabolic mechanisms [14, 19, 25, 26, 30, 31, 32, 33, 34, 35, 36]. Moreover, the source of ketone bodies in immune modulation has not been defined and is likely different for each cell type and physiological condition. It is especially perplexing that short-lived neutrophils, with no obvious metabolic reliance on ketone bodies, would express ketogenic or ketolytic pathways. Given that innate immune cells express ketogenic enzymes and that their functions are impacted by both BHB and acetoacetate [19, 25, 26], we designed this study to define the role of neutrophil- and macrophage-intrinsic ketogenesis in regulating inflammation and metabolic health during aging. Because both BHB and acetoacetate have anti-inflammatory roles in macrophages, we expected the deletion of HMGCL in these cells to cause elevated inflammation that would exaggerate metabolic dysfunction in old mice. Surprisingly, we found only modest effects of conditional HMGCL ablation on physiological indicators of metabolic health in aged mice. In aged male neutrophil-specific HmgclS100a8-Cre mice, we observed reproducible but small differences in body weights during aging, and this translated to corresponding slight improvement in glucose tolerance. We cannot rule out the possibility that Cre+ T and B cells, or Cre+ cells of unknown lineage in adipose tissue, albeit low in number, may be contributing to this phenotype (Fig. 3B). However, while adult HmgclS100a8-Cre mice had lower inflammatory gene expression in response to monosodium urate crystal, aged HmgclS100a8-Cre mice showed no change to acute inflammatory challenge with LPS. In contrast, mice with broader disruption of ketogenesis in all myeloid-lineage cells (HmgclLysM-Cre) did not replicate the aging phenotypes seen in HmgclS100a8-Cre mice. These data raise the possibility that ketogenesis in different innate immune cell types may have different competing effects on whole-body physiology. Our data also suggest that exogenous ketone bodies from other organs are important for controlling age-related neutrophil and macrophage inflammation. Unfortunately, due to the >$20\%$ weight loss, that necessitates euthanasia of HmgclAlb-Cre mice under ketogenic conditions, we were unable to directly test this possibility.
The results from our study still do not explain why innate immune cells express ketogenic enzymes. This is particularly intriguing in neutrophils, which are short-lived and highly glycolytic, and therefore have no obvious metabolic requirement for ketone bodies. Moreover, the lack of Bdh1 expression in macrophages and neutrophils limits their potential metabolic ketone body utilization to acetoacetate, although this does not preclude a nonmetabolic role for exogenous BHB. Of note, prior literature examining ketone body utilization in innate immune cells has focused on BMDM, which do not reflect the diversity of macrophages in vivo. We suspect that the role of ketone pathways in innate immune cells may be cell type– and disease-specific. We studied this in the context of aging and obesity due to our prior work linking NLRP3 inflammasome activation to metabolic inflammation in these conditions [28, 29] and our subsequent discovery that BHB inhibits the NLRP3 inflammasome [25, 26]. However, our in vitro data show that cell-intrinsic ketogenesis does not impact neutrophil NLRP3 activation or macrophage polarization. These data are in agreement with our prior work that acetoacetate does not inhibit NLRP3 inflammasome activation and that these cells likely do not produce their own BHB [25]. These data probably also explain, at least in part, the modest phenotypes we observed in vivo. However, a limitation of the current study is that we only tested the role of *Hmgcl* gene deletion in a single mouse strain, C57BL6/J, and other strains may yield different results. Future studies using in vivo substrate tracing testing alternative sources of ketone bodies, and using additional disease models, should be carefully considered for testing the role of ketone pathways in immune cells.
## Animals
All mice were housed in specific pathogen-free conditions under normal 12 h light/dark cycles. Hmgclfl/fl mice were generated by the Pennington Biomedical Research Center Transgenics Core. Genetic targeting to insert loxP sights into exon 2 of the Hmgcl allele was achieved by recombination of a PL253-loxP-frt-neo cassette in albino C57BL/6 embryonic stem cells. Upon confirmation of desired vector design and neomycin selection for initial enrichment of the targeted clone, we used these embryonic stem cells for injection into blastocysts for the generation of heterozygous Hmgcl-loxP–floxed mice. Using PCR to genotype these neo-founder mice, we then removed the neomycin-resistant drug marker by crossing to a recombinase FLP-derived mouse which recognizes the flippase recognition target for mediated cleavage and generation of our founder mice. These mice were crossed with C57BL6/J mice (Jax #000664) and then intercrossed to maintain a Hmgclfl/fl colony. Hmgclfl/fl mice were crossed to lineage-specific Cre-drivers (all on the C57BL6/J background) to generate conditional KO strains in liver (Albumin-Cre Jax #003574; [42]), neutrophils (S100a8-Cre Jax #021614; [43]), and myeloid cells (LysM-Cre Jax #004781; [44]). The majority of experiments were performed at Yale, and Hmgclfl/fl and HmgclS100a8-Cre were sent to UCSF to breed mice for a portion of the neutrophil-specific experiments. For all comparisons, Cre-positive and Cre-negative Hmgclfl/fl littermates were used and genotypes were co-housed throughout lifespan until experimental endpoint to minimize effects of potential microbiome differences. All experimental procedures were performed with approvals from the Yale and UCSF Institutional Animal Care and Use Committee.
## Physiological measurements
Mice were housed in specific pathogen-free facilities and maintained on 12 h light/dark cycles. Standard and high-fat diets were irradiated for sterilization and all mice were provided ad libitum access to sterile drinking water in hydropacs. All tissue collections and physiological measurements were measured in the morning, within the first 5 h of the lights-on cycle, unless specifically described as otherwise. For KD experiments, mice were fed ad libitum KD for up to 1 week, as indicated in each figure ($89.5\%$ of calories from fat, $10.4\%$ of calories from protein, $0.1\%$ of calories from carbohydrates; Research Diets D19042606). For obesity experiments, male mice were fed a high-fat diet ($60\%$ of calories from fat; Research Diets D12492) for 12 weeks. For fasting experiments, mice were fasted for 24 h, beginning in the morning before tissue collection. For endotoxemia experiments, mice were challenged with LPS (O55:B5; 1 mg LPS/kg body weight) and euthanized 4 h later for analysis. Handheld meters were used to measure blood glucose (Contour Next) and BHB (Precision Xtra) levels in whole blood. For glucose tolerance tests, mice were fasted for 16 h prior to intraperitoneal injection of glucose (0.6 g glucose/kg body weight for old mice, 0.4 g glucose/kg body weight). Lipolysis was assessed by measuring glycerol release from adipose tissue explants (Sigma #MAK117). Body temperature was measured with a rectal temperature probe (BAT-12 microprobe thermometer, Physitemp).
## Neutrophil isolation
Primary neutrophils were isolated from the bone marrow of femurs of mice using the StemCell Technologies magnetic negative selection kit (Catalog # 19762) according to the manufacturer’s protocol. Femurs from at least $$n = 2$$ mice were pooled for neutrophil isolations, as we found the higher cell input improved enrichment purity. Purity was confirmed to be at least $95\%$ [26]. For NLRP3 activation, cells were treated with LPS (1 μg/ml, Sigma #L4391-1MG, strain 0111:B4) for 4 h, followed by 45 min ATP (5 mM, Sigma # A7699-1G).
## Bone marrow–derived macrophages
Mouse femurs were flushed, red blood cells were lysed with ACK lysis buffer, and remaining bulk bone marrow cells were cultured in RPMI ($10\%$ FBS + $1\%$ antibiotic/antimycotic) in the presence of MCSF (20 ng/ml, Peprotech #315-02) for 7 days as described previously [25]. For BMDM polarization, cells were counted and replated at 106/ml in a 24-well plate. The following day, media was replaced with media (M0) or media containing LPS (1 μg/ml, O11:B4) + IFNγ (20 ng/ml, eBioscience #14-8311-63) for M1 polarization or IL-4 (10 ng/ml, eBioscience #14-8041-62) for M2 polarization. BMDM were cultured for 24 h in polarization media.
## Adipose tissue digestion
Gonadal white adipose tissue was minced and digested in 10 ml of digestion buffer for 45 min in a shaking (225 rpm) water bath. Digestion buffer: 1× HBSS supplemented with BSA ($3\%$ w/v, Sigma #A9647), Collagenase II (0.8 mg/ml, Worthington Biochemical #LS004174), calcium chloride (Sigma #21115, 1.2 mM), magnesium chloride (Sigma #M1028, 1 mM), and zinc chloride (Sigma #39059, 0.8 mM). After digestion, cells were centrifuged at 300g for 10 min and floating adipocyte fraction was discarded. The pellet containing the remaining stromal vascular fraction was washed at least twice with RPMI ($5\%$ FBS) over a 70 μm nylon filter, and red blood cells were lysed with ACK lysis buffer.
## Western blot
For all western blots, cell lysates were prepared in RIPA buffer containing protease and phosphatase inhibitors. Total protein concentrations were measured by BCA assay (Bio-Rad) and equal amounts of total protein were analyzed. Antibodies used were IL-1β (GeneTex # GTX74034), HMGCL (Proteintech # 16898-1-AP), Kbhb (PTM Biosciences #PTM-1201RM), and β-actin (Cell Signaling # 4967S).
## Flow cytometry
Cells were made into single-cell suspension by filtering over 70 μm filter. Red blood cells were lysed with ACK lysing buffer. Cells were incubated with Fc block and then stained with antibodies for standard lineage markers (CD3, B220, CD11b, Ly6G) for 30 min on ice, followed by three washes with FACS buffer, and then immediately acquired on a BD LSR II equipped with violet, red, green, and blue lasers, or an Attune NxT Flow Cytometer equipped with a blue, violet, green, and red laser. Data was analyzed with FlowJo. Antibodies were purchased from Biolegend.
## Gene expression
mRNA was isolated from cells in QIAzol using the Qiagen RNeasy kit. cDNA was transcribed using the iScript cDNA synthesis kit (Bio-Rad). Gene expression was measured by RT-PCR by ΔΔCt method and expressed relative to 18s (Table S1).
## Statistical analyses
All graphs and statistical analyses were done in Prism (v9, GraphPad). For comparisons of two groups, two-sided student’s t-tests were used to calculate statistical differences. Comparisons of more than two groups were analyzed by 1-way ANOVA. To compare groups that were tracked over time, mice were individually tracked and statistical differences were calculated by paired 2-way ANOVA. p-values are provided in each figure, $p \leq 0.05$ was considered not significant (ns).
## Data availability
All data are contained within the article.
## Supporting information
This article contains supporting information.
Supporting Table S1 and Figures S1, S2
## Conflict of interest
The authors declare they have no competing interests.
## Author contributions
E. L. G. and V. D. D. conceptualization; E. L. G. and V. D. D. methodology; E. L. G., A. L., T. D., and C. L. investigation; E. L. G. and V. D. D. writing–original draft; E. L. G. and A. L. visualization; E. L. G. and V. D. D. funding acquisition; A. L., T. D., and C. L. writing–review and editing; E. L. G. and A. L. formal analysis.
## Funding and additional information
The Goldberg lab is funded in part by 5R00AG058801, pilot awards from the $\frac{10.13039}{100008069}$UCSF NORC P30DK098722 and $\frac{10.13039}{100011074}$UCSF Liver Center P30DK026743, and the Chan Zuckerberg Biohub. The Dixit lab is funded in part by R01AR070811, P01AG051459, R01AG076782, R01AG068863, R01AG073969, and U54AG079759. The LCA is funded by 5P30CA082103-23. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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|
---
title: Determination of in vitro hepatotoxic potencies of a series of perfluoroalkyl
substances (PFASs) based on gene expression changes in HepaRG liver cells
authors:
- Jochem Louisse
- Styliani Fragki
- Deborah Rijkers
- Aafke Janssen
- Bas van Dijk
- Liz Leenders
- Martijn Staats
- Bas Bokkers
- Marco Zeilmaker
- Aldert Piersma
- Mirjam Luijten
- Ron Hoogenboom
- Ad Peijnenburg
journal: Archives of Toxicology
year: 2023
pmcid: PMC10025204
doi: 10.1007/s00204-023-03450-2
license: CC BY 4.0
---
# Determination of in vitro hepatotoxic potencies of a series of perfluoroalkyl substances (PFASs) based on gene expression changes in HepaRG liver cells
## Abstract
Per- and polyfluoroalkyl substances (PFASs) are omnipresent and have been shown to induce a wide range of adverse health effects, including hepatotoxicity, developmental toxicity, and immunotoxicity. The aim of the present work was to assess whether human HepaRG liver cells can be used to obtain insight into differences in hepatotoxic potencies of a series of PFASs. Therefore, the effects of 18 PFASs on cellular triglyceride accumulation (AdipoRed assay) and gene expression (DNA microarray for PFOS and RT-qPCR for all 18 PFASs) were studied in HepaRG cells. BMDExpress analysis of the PFOS microarray data indicated that various cellular processes were affected at the gene expression level. From these data, ten genes were selected to assess the concentration–effect relationship of all 18 PFASs using RT-qPCR analysis. The AdipoRed data and the RT-qPCR data were used for the derivation of in vitro relative potencies using PROAST analysis. In vitro relative potency factors (RPFs) could be obtained for 8 PFASs (including index chemical PFOA) based on the AdipoRed data, whereas for the selected genes, in vitro RPFs could be obtained for 11–18 PFASs (including index chemical PFOA). For the readout OAT5 expression, in vitro RPFs were obtained for all PFASs. In vitro RPFs were found to correlate in general well with each other (Spearman correlation) except for the PPAR target genes ANGPTL4 and PDK4. Comparison of in vitro RPFs with RPFs obtained from in vivo studies in rats indicate that best correlations (Spearman correlation) were obtained for in vitro RPFs based on OAT5 and CXCL10 expression changes and external in vivo RPFs. HFPO-TA was found to be the most potent PFAS tested, being around tenfold more potent than PFOA. Altogether, it may be concluded that the HepaRG model may provide relevant data to provide insight into which PFASs are relevant regarding their hepatotoxic effects and that it can be applied as a screening tool to prioritize other PFASs for further hazard and risk assessment.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s00204-023-03450-2.
## Introduction
Per- and polyfluoroalkyl substances (PFASs) are very persistent chemicals and omnipresent in the environment (Wang et al. 2017). PFASs are defined as “fluorinated substances that contain at least one fully fluorinated methyl or methylene carbon atom (without any H/Cl/Br/I atom attached to it)” (OECD 2021). They are widely used in various industrial and consumer applications, such as firefighting foams, electronics, textiles, food contact materials, and cosmetics. The production and use of the most studied PFASs, perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS) have been restricted given the concerns of adverse effects to human health and the environment (EU 2019, 2020; UNEP 2009).
In experimental animals, PFASs have been shown to induce a wide range of adverse effects, including hepatotoxicity, developmental toxicity, immunotoxicity, and a decrease in thyroid hormone levels (ATSDR 2021; EFSA CONTAM Panel 2018, 2020). The most consistent endpoint is increased liver weight, characterized by a combined hyperplasia and hypertrophy, which has been observed for many PFASs with clear differences in potencies. Disturbances in lipid metabolism, including hepatocellular steatosis and other hepatotoxic effects, have also been reported (EFSA CONTAM Panel 2020). Also in humans, rather low serum levels of PFOS and PFOA have been associated with disturbed lipid homeostasis, in which the liver may play a role. However, the causality of this relationship has been debated (see for a recent review Fragki et al. [ 2021]). Furthermore, epidemiological evidence has correlated serum levels of both PFOS and PFOA to a small elevation in serum levels of the hepatic enzyme ALT (alanine transferase), a biomarker for liver damage (Gallo et al. 2012). However, whether that limited increase in ALT reflects serious liver damage is questionable.
Bil et al. ( 2021, 2022a) used data on hepatotoxicity in individual studies with male rats to derive external relative potency factors (RPFs) for 16 PFASs (using PFOA as index chemical). External RPFs of 7 other PFASs were estimated based on read across. In addition, Bil et al. ( 2022b) reported eight internal RPFs, which are based on the same toxicological information as the external RPFs reported by Bil et al. ( 2021, 2022a), but estimated by translating external doses to internal blood concentrations using kinetic models. For assessment of risks upon combined exposure to PFASs, such RPFs may be of use to take potency differences in PFASs into account. In that regard, external RPFs may be of use when considering external exposure and internal RPFs when considering internal exposure.
The number of existing PFASs is estimated to be around a few thousands, and for many of these, toxicity data are lacking. Performing in vivo animal studies to obtain toxicity data for all these PFASs is not considered feasible, given the high costs and demand of resources, and also not desirable, because of ethical issues and the uncertainty related to possible species differences between laboratory animals and humans. Instead, novel approach methodologies (NAMs), such as in vitro toxicity assays, may be used, in the first place to prioritize those PFASs for which a more extensive hazard and risk assessment would be considered most relevant, and within a next-generation risk assessment paradigm, to provide in vitro effect concentrations that can be translated to in vivo oral equivalent dose levels (Punt et al. 2021), providing data that may be used for the risk assessment.
Recently, we demonstrated that treatment of HepaRG human liver cells with PFOA, PFOS, and PFNA resulted in an increase in triglyceride levels (Louisse et al. 2020), which is considered to be a potential relevant readout for PFAS-induced liver toxicity (Fragki et al. 2021). Furthermore, microarray analysis indicated that these three PFASs, at a concentration of 100 μM, downregulated genes involved in cholesterol biosynthesis. The data also pointed to, among others, changes in cellular processes, such as PERK/ATF4 signaling, tRNA aminoacylation and expression of amino acid transporters by PFOA, PFOS and PFNA. It is of interest to assess whether such in vitro effects may be of use for obtaining insight into potency differences of different PFASs. Therefore, the present study aimed to assess the concentration-dependent effects of 18 PFASs (Fig. 1) on triglyceride levels (applying the AdipoRed assay) and expression of genes (as measured with RT-qPCR) in HepaRG cells. This study includes 11 perfluoroalkyl carboxylic acids (PFCAs), 5·perfluoroalkyl sulfonic acids (PFSAs) and 2 perfluoroalkyl ether carboxylic acids (PFECAs, including GenX (HFPO-DA)). To identify genes for RT-qPCR analysis, concentration-dependent PFOS transcriptomic data were analyzed with BMDExpress software, providing insight into PFOS-induced effects on gene expression and their concentration-dependency in HepaRG cells. Based on these data, genes were selected to assess the concentration-dependent changes in expression upon exposure to the 18 PFASs (Fig. 1). Concentration–response data on the increase in triglyceride levels and effects on gene expression of the selected genes were analyzed with PROAST software to obtain insight into in vitro potency differences for the 18 PFASs. The obtained in vitro RPFs were compared with reported external and internal RPFs obtained from animal studies to provide insights into differences and similarities in the outcomes of using in vitro human cell-based and in vivo animal-based approaches. Fig. 1Chemical structures of the PFASs tested in the present study. Full names of abbreviations are provided in the Materials and methods section under ‘Chemicals’
## Chemicals
The following PFASs were tested in the present study: perfluoropentanoic acid (PFPeA; C5), perfluorohexanoic acid (PFHxA; C6), perfluoroheptanoic acid (PFHpA; C7), perfluorooctanoic acid (PFOA; C8), perfluorononanoic acid (PFNA; C9), perfluorodecanoic acid (PFDA; C10), perfluoroundecanoic acid (PFUnDA; C11), perfluorododecanoic acid (PFDoDA; C12), perfluorotetradecanoic acid (PFTeDA; C14), perfluorohexadecanoic acid (PFHxDA; C16), perfluorooctadecanoic acid (PFODA; C18), perfluorobutane sulfonate (PFBS; C4), perfluorohexane sulfonate (PFHxS; C6), perfluoroheptane sulfonate (PFHpS; C7), perfluorooctane sulfonate (PFOS; C8), perfluorodecane sulfonate (PFDS; C10), hexafluoropropylene oxide dimer acid (HFPO-DA, also known as GenX; C6) and hexafluoropropylene oxide trimer acid (HFPO-TA; C9) (Fig. 1). All stocks were prepared in $100\%$ dimethyl sulfoxide (DMSO HybriMax, Sigma-Aldrich), which were stored at – 20 °C. More information about suppliers, purity, catalog numbers, CAS numbers and maximum concentrations tested in the present study is presented in Supplementary Table 1. The highest concentration tested was determined by the degree of solubility of each PFAS.
## HepaRG cell culture
The human hepatic cell line HepaRG was obtained from Biopredic International (Rennes, France) and cultured in growth medium consisting of William’s Medium E + GlutaMAX™ (ThemoFisher Scientific, Landsmeer, The Netherlands) supplemented with $10\%$ fetal bovine serum (FBS; Corning (35-079-CV), United States of America), $1\%$ PS (100 U/mL penicillin, 100 µg/mL streptomycin; Capricorn Scientific, Ebsdorfergrund, Germany), 50 µM hydrocortisone hemisuccinate (sodium salt) (Sigma-Aldrich), and 5 µg/mL human insulin (PAN™ Biotech). Seeding, trypsinization (using $0.05\%$ Trypsin–EDTA (ThermoFisher Scientific)) and maintenance of the cells was performed according to the HepaRG instruction manual from Biopredic International. For cell viability and triglyceride accumulation studies, cells were seeded in black-coated 96-well plates (Greiner Bio-One, Frickenhausen, Germany; 9000 cells per well in 100 µL). *For* gene expression studies, cells were seeded in 24-well plates (Corning, Corning, NY; 55,000 cells per well in 500 µL). After two weeks on growth medium, cells were cultured for two days in growth medium supplemented with $0.85\%$ DMSO to induce differentiation. Subsequently, cells were cultured for 12 days in growth medium supplemented with $1.7\%$ DMSO (differentiation medium) for final differentiation. At this stage, cells were ready to be used for toxicity studies. Cells that were not immediately used were kept on differentiation medium for a maximum of three additional weeks. Cell cultures were maintained in an incubator (humidified atmosphere with $5\%$ CO2 at 37 °C) and the medium was refreshed every 2–3 days during culturing. Prior to toxicity studies, differentiated cells were incubated for 24 h in assay medium (growth medium containing $2\%$ FBS) supplemented with $0.5\%$ DMSO.
## Cell exposure
Test chemicals were diluted from 200-fold concentrated stock solutions in assay medium, providing a final DMSO concentration of $0.5\%$. In each experiment a solvent control ($0.5\%$ DMSO) was included. PFASs were tested in concentrations up to 400 µM (if solubility allowed). After exposure, effects of the PFASs on cell viability and gene expression were assessed. Highest tested concentrations that could be tested for each PFAS are presented in Supplementary Table 1.
## Stability studies HFPO-DA and HFPO-TA
To assess whether HFPO-DA and HFPO-TA are stable under the culture conditions applied in this study, we incubated 50 µM HFPO-DA or HFPO-TA in culture medium ($0.5\%$ DMSO) for 24 h in an incubator (humidified atmosphere with $5\%$ CO2 at 37 °C) and took samples at $t = 0$ h, 6 h and 24 h for quantification using LC–MS analysis. We also assessed stability of stock solutions in DMSO kept at – 20 °C. To 50 uL culture medium, 850 uL methanol (Actuall Chemicals, Oss, The Netherlands) containing internal standard (13C3-GenX (Wellington Laboratories, Canada)) was added. These dilutions were vortexed well before centrifugation at maximum speed for 10 min at 4 °C. Samples were further another 1200 times diluted with methanol and internal standard, and HFPO-DA and HFPO-TA concentrations were determined using LC–MS/MS analysis. LC–MS/MS analysis was based on a Sciex UHPLC system containing: 2 pumps (ExionLC AD); column oven (ExionLC AC); controller (ExionLC); degasser (ExionLC); and sample tray holder (ExionLC AD) (Sciex, Framingham, MA, USA). Luna Omega PS C18 analytical column (100Å, 100 × 2.1 mm i.d., 1.6 μm, Phenomenex, Torrance, CA, USA) was used to separate the PFASs at a column temperature of 40 °C. Additionally, a Gemini C18 analytical column (110Å, 50 × 3 mm i.d., 3 µm, Phenomenex, Torrance, CA, USA) was used as an isolator column, placed between the pump and the injector valve to isolate and delay interferences out of the LC system. The mobile phase consisted of 20 mM ammonium acetate (Merck Millipore, Darmstadt, Germany) in water Ultra LC/MS grade (Actu-All Chemicals, Oss, The Netherlands) (mobile phase A) and Acetonitrile ULC/MS grade (Biosolve, Dieuze, France) (mobile phase B). The injection volume used was 20 μL. The chromatographic gradient was operated at a flow rate of 0.8 mL min−1 starting from $15\%$ mobile phase B in the first 1.0 min, a linear increase to $98\%$ B in 6 min with a final hold of 0.5 min. The gradient was returned to $15\%$ B within 0.1 min for 0.7 min to equilibrate before the next injection, resulting in a total run of 8.3 min.
Detection was carried out by MS/MS using a Sciex QTRAP 7500 system (Sciex, Framingham, MA, USA) in negative electrospray ionization (ESI-) mode, with the following conditions: ion spray voltage (IS) of – 1500 V; curtain gas (CUR) of 45 psi; source temperature (TEM) of 400 °C; gas 1 (GS1) of 40 psi; gas 2 (GS2) of 80 psi; and collision gas (CAD) 9. The PFASs were fragmented using collision induced dissociation (CID) using argon as target gas. The analyses were performed in multiple reaction monitoring (MRM) mode, using two mass transitions per component selected based on the abundance of the signal and the selectivity of the transition. In Supplementary Table 2, information on the MRM transitions, entrance potential (EP), collision energy (CE) and cell exit potential (CXP) is presented. Data were acquired using SciexOS and processed using MultiQuantTM software (Sciex, Framingham, MA, USA).
Since a recent study has shown that certain PFECAs, including HFPO-DA and HFPO-TA degrade when present in acetonitrile, acetone or DMSO (Zhang et al. 2022), we assessed whether HFPO-DA and HFPO-TA are stable under the culture conditions applied in this study. To that end, HFPO-DA and HFPO-TA were added to cell culture medium ($0.5\%$ DMSO) at a concentration of 50 µM and incubated in an incubator (humidified atmosphere with $5\%$ CO2 at 37 °C). Samples were taken at $t = 0$ h, 6 h and 24 h, and were measured using LC–MS analysis. Results show that under these conditions, HFPO-DA and HFPO-TA are stable (Supplementary Fig. 1), indicating that these culture conditions are adequate to determine the effects of these PFASs on HepaRG cells.
## Cell viability studies
The effects of the 18 PFASs on the viability of HepaRG cells cultured in 96-well plates were determined using the WST-1 assay. This assay determines the conversion of the tetrazolium salt WST-1 (4-[3-(4-iodophenyl)-2-(4-nitrophenyl)-2H-5-tetrazolio]-1,3-benzene disulfonate) to formazan by metabolically active cells. For PFOA, PFNA, PFHxS and PFOS, the effects on cell viability were studied upon a 24-h and a 72-h exposure, given that both exposure times were studied for optimization of the exposure time for assessing effects of these PFASs on triglyceride accumulation. All other PFASs were only tested upon a 24-h exposure. After exposure, the medium was removed and the cells were washed with Dulbecco’s Phosphate Buffered Saline (DPBS; ThermoFisher Scientific). Next, WST-1 solution (Sigma-Aldrich) was added to the cell culture medium (1:10 dilution), and 100 µL was added to each well. After 1 h incubation in an incubator (humidified atmosphere with $5\%$ CO2 at 37 °C), the plate was shaken at 1000 rpm for 1 min, and absorbance at 450 nm was measured (background absorbance at 630 nm was subtracted) using a Synergy HT Microplate Reader (BioTek, Winooski, VT). Three independent studies, with in each study three technical replicates per condition, were performed. Cell viability upon PFAS treatments was expressed as percentage of the cell viability of the solvent control.
The effect of a 24-h (all PFASs) and 72-h (PFOA, PFNA, PFHxS, and PFOS) exposure of HepaRG cells to the PFASs on cell viability was determined using the WST-1 assay. Concentrations up to 400 µM were used, except for PFDoDA/ PFTeDA (up to 100 µM) and PFHxDA/PFODA (up to 25 µM), due to limited solubility of these PFASs (Supplementary Table 1). The results of the 72-h exposure studies indicate that PFOA is clearly cytotoxic at 400 µM, PFNA at 200 and 400 µM and that no effects were found for PFHxS and PFOS (Supplementary Fig. 2). The results of the 24-h exposure studies indicate that four of the 18 tested PFASs decrease cell viability in a concentration-dependent manner, being PFNA, PFDA, PFHpS and HPFO-TA (Supplementary Fig. 3), with HFPO-TA being the most potent PFAS, followed by PFDA and PFNA. The other PFASs did not show cytotoxicity in the WST-1 assay for the concentration range tested (Supplementary Fig. 3).
Maximum concentrations for the further studies were selected as the highest concentrations causing less than $25\%$ decrease in cell-based WST-1 conversion, amounting to 50 µM HPFO-TA, 100 µM PFNA, 100 µM PFDA and 200 µM PFHpS.
## Triglyceride accumulation studies
The effect of the 18 PFASs on triglyceride levels was determined using the AdipoRed assay essentially according to the instructions of the supplier (Lonza, Basel, Switzerland). We used the approach as applied in the study of Luckert et al. [ 2018], in which HepaRG cells were exposed to the steatotic compound cyproconazole. In that study, 72 h was shown to be the optimal time point to assess the effects of cyproconazole on triglyceride accumulation as determined with the AdipoRed assay. We first assessed whether this time point was also the optimal time point for assessing effects of PFASs on triglyceride accumulation, by studying the effects of a 24-h or a 72-h exposure to PFOA, PFNA, PFHxS and PFOS in the AdipoRed assay, also including cyproconazole as positive control. After exposure for 24 or 72 h, the medium was removed and the cells were washed with 200 μL DPBS and subsequently incubated for 10 min at room temperature with 200 μL AdipoRed-DPBS solution. The latter solution was prepared by adding 25 μL AdipoRed to 1 mL DPBS. Subsequently, fluorescence was measured using a $\frac{485}{20}$ nm excitation and $\frac{590}{35}$ emission filter set on the Synergy HT Microplate Reader. The results from that study indicate that a 24-h exposure was considered better than a 72-h exposure to study effects of PFASs (see Results section). Therefore, all other PFASs were tested upon a 24-h exposure. For each PFAS, three independent biological replicates, with three technical replicates per condition were obtained. Data were used for dose–response analysis using PROAST software (see below).
We first assessed whether a 24-h or a 72-h exposure was considered optimal to assess effects of PFASs on triglyceride accumulation, as measured with the AdipoRed assay, by determining the effects for PFOA, PFNA, PFHxS and PFOS, also including cyproconazole, for which earlier studies indicated that most effects were found upon a 72-h exposure (Luckert et al. 2018). The results show that for the four PFASs, in contrast to cyproconazole, more pronounced effects were observed upon exposure for 24 h compared to a 72-h exposure (Supplementary Fig. 4). Therefore, for all other PFASs, the effect of a 24-h exposure to the PFASs on triglyceride accumulation in HepaRG cells was determined. *In* general, PFAS-induced changes in AdipoRed signal were relatively limited, at maximum amounting to a 1.4-fold increase at 50 µM HPFO-TA versus the solvent control (Supplementary Fig. 5), compared to a measured maximum 1.6-fold increase for the positive control cyproconazole (Supplementary Fig. 4). Dose–response analysis using parallel curve fitting was applied on the AdipoRed data to determine in vitro RPF values, which could be obtained for PFNA, PFDoDA, PFHxDA, PFHxS, PFOS, and PFDS (Fig. 2). PFNA, PFDoDA, PFHxDA, and HFPO-TA were more potent than PFOA, but it must be noted that confidence intervals of PFHxDA’s RPF are large (Fig. 2). PFOS showed a similar potency as PFOA, and PFHxS and PFDS were slightly less potent than PFOA (Fig. 2).Fig. 2In vitro RPFs based on PROAST dose–response analysis of AdipoRed data obtained from HepaRG cells exposed to various PFASs. RPFs are presented as vertical lines, with the $5\%$ lower bound and $95\%$ upper bound of the confidence interval as whiskers. PFOA was used as index chemical, i.e., has an RPF of 1 (dotted line). NA not applicable, RPF could not be determined
## Whole genome gene expression: microarray hybridizations and BMDExpress analysis
To obtain insight into the PFOS concentration-dependent induced gene expression changes, differentiated cells were exposed for 24 h to 6.25, 12.5, 25, 50, 100, 200, or 400 µM PFOS. An exposure duration of 24 h was selected based on our previous study (Louisse et al. 2020). After exposure, total RNA was isolated and purified using the RNeasy Minikit (Qiagen). RNA quality and integrity was assessed using the RNA 6000 Nano chips on the Agilent 2100 Bioanalyzer (Agilent Technologies, Amsterdam, The Netherlands). Purified RNA (100 ng) was labeled with the Ambion WT expression kit (Invitrogen) and hybridized to Affymetrix Human Gene 2.1 ST arrays (Affymetrix, Santa Clara, CA). Hybridization, washing, and scanning were carried out on an Affymetrix GeneTitan platform according to the instruction by the manufacturer. Obtained data (CEL-files) were further processed using Bioconductor in R, performing quality control and normalization. For array normalization, the Robust Multiarray Average method (Bolstad et al. 2003; Irizarry et al. 2003) was applied. Probe sets were defined according to Dai et al. [ 2005]. In this method, probes are assigned to Entrez IDs as a unique gene identifier. CEL file normalization was performed with the Robust Multichip Average method using the Bioconductor oligo package (version 3.8) and the human Entrez-Gene custom CDF annotation from Brain Array version 23.0.0 containing 965,365 probes and 29,635 probesets (http://brainarray.mbni.med.umich.edu/Brainarray/Database/CustomCDF/CDF_download.asp).
BMDExpress is a software tool for BMD analysis of transcriptomic data (Yang et al. 2007; Phillips et al. 2019). BMDExpress-2 (Version 2.20.0180) was applied following the workflow (loading expression data, filtering, BMD analysis, and *Pathway analysis* (functional analysis)) as described on https://github.com/auerbachs/BMDExpress-2/wiki. Expression data were organized in a tab-delimited plain text file and are provided as Supplementary Material. Each column in the data matrix corresponds to an individual expression experiment. The first row contains information in the sample label, the second row on the PFOS concentration and all further rows the data for one probe ID. Regarding loading of the expression data, ‘Generic’ was selected for the platform, and ‘BASE2’ for the Log Transformation. Regarding the filtering, ANOVA was used, using a p value Cutoff of 0.05, applying the Benjamin & Hochberg correction for multiple testing, filtering out control genes, and without applying a Fold Change Filter (i.e., Fold Change Value of 1.0 was selected). Regarding BMD analysis, the continuous models Exp2, Exp3, Exp4, Exp5, Linear, Poly2, Poly3, Hill and Power were selected. A BMR factor of 1.021 was selected, as also applied by Chang et al. [ 2020]. Applying such a low response as BMR allows inclusion of genes that may show limited changes in expression. Application of a higher BMR may provide more robust BMC estimations, but may exclude genes (and as a possible consequence-related gene sets) that show limited gene expression changes that could be relevant from a biological perspective. Regarding the functional analysis, we performed a defined category analysis using gene sets from the Reactome Pathway Database (https://reactome.org/; Wu and Haw 2017), applying the following data source options: ‘Remove Promiscuous Probes’, ‘Remove BMD > Highest Dose from Category Descriptice Statistics’, ‘Remove BMD with p Value < Cutoff: 0.1’, ‘*Remove* genes with BMD/BMDL >: 20’, ‘*Remove* genes with BMDU/BMDL >: 40’, ‘Remove Genes With Max Fold Change <: 1.2’, and ‘Identify conflicting probe sets: 0.5’. The applied probe file and the category file used for the analysis are provided in the Supplementary Materials. For further analysis, we applied the following filters: Fisher’s Exact Two Tail ≤ 0.1, ‘genes that passed all filters’ of a gene set were set at 5, and the percentage of genes regulated of the gene was set at ≥ $20\%$. For the gene sets remaining upon application of these filters, information was collected and organized in an Excel file, which is available as Supplementary Material.
## RT-qPCR
For selected genes, concentration-dependent expression levels were determined in PFAS-exposed HepaRG cells. To that end, cells were exposed to increasing concentrations of the 18 PFASs for 24 h and total RNA was extracted from the cells using the RNeasy Mini Kit (Qiagen, Venlo, The Netherlands). Subsequently, 500 ng RNA was used to synthesize cDNA using the iScript cDNA synthesis kit (Bio-Rad Laboratories, Veenendaal, The Netherlands). Changes in gene expression were determined by RT-qPCR on a CFX384 real-time PCR detection system (Bio-Rad Laboratories) using SensiMix (Bioline; GC Biotech, Alphen aan den Rijn, The Netherlands). The PCR conditions consisted of an initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing extension at 60 °C for 15 s. *Relative* gene expression was quantified with the standard curve method, using a standard curve generated from a serial dilution of pooled sample cDNA, and subsequently normalized to RPL27 gene expression. Primer sequences were taken from the Harvard PrimerBank and ordered from Eurogentec (Liège, Belgium). Sequences of the used primers are listed in Table 1. The concentration–response data were subjected to dose–response analysis using PROAST software as described below. Table 1Primer sequences used for RT-qPCRNamePrimer sequenceForwardReverseANGPTL4CACAGCCTGCAGACACAACTCGGAGGCCAAACTGGCTTTGCATF4CCCTTCACCTTCTTACAACCTCTGCCCAGCTCTAAACTAAAGGACXCL10GAACTGTACGCTGTACCTGCATTGATGGCCTTCGATTCTGGAHMGCRTGATTGACCTTTCCAGAGCAAGCTAAAATTGCCATTCCACGAGCLSSGCACTGGACGGGTGATTATGGTCTCTTCTCTGTATCCGGCTGOAT5TGGTGTTTGCTCCAGCTTGGCCTTATCCACTCAGTAATGGGCPDK4TGGAGCATTTCTCGCGCTACACAGGCAATTCTTGTCGCAAARPL27ATCGCCAAGAGATCAAAGATAATCTGAAGACATCCTTATTGACGSLC7A11GGTCCATTACCAGCTTTTGTACGAATGTAGCGTCCAAATGCCAGTHRSPCAGGTGCTAACCAAGCGTTACCAGAAGGCTGGGGATCATCAYARS1TGGTCACACAGCACGATTCCCGGGGTATAAGAGGCCACTC
## Dose–response analysis of AdipoRed and RT-qPCR data with PROAST
AdipoRed data and RT-qPCR data were used for concentration–response modeling with dose–response analysis software PROAST version 70.2 and 70.7tmp (National Institute for Public Health and the Environment 2018) in R (version 4.2.0). Data were available from three independent experiments ($$n = 3$$). First, it was determined whether differences between the independent experiments (for individual PFASs) exist. For this, PROAST version 70.2 was used. This analysis was performed using the data of OAT5 gene expression. It appeared that the background (parameter a) differed for some PFASs between different experiments, based on which it was decided to not use summary data for the further dose–response analysis to determine RPFs, but to run the PROAST analyses (in version 70.7tmp) with the following covariates: substance (parameter b and var) and substance experiment (parameter a). Data of all PFASs were analyzed simultaneously to ensure the parallel curves required to derive RPFs (Bosgra et al. 2009; Bil et al. 2021, 2022a, b; van der Ven et al. 2022; van den Brand et al. 2022). Tab-delimited text files containing data on concentration, effect, and experiment number were made and analyzed as continuous data. Non-normalized gene expression and AdipoRed data were used for dose–response analysis since possible differences in background are accounted for by the covariate on background parameter a. Then, the exponential model,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$y=a*{c}^{1-\mathrm{exp}\left(-{\left(x/b\right)}^{d}\right)}$$\end{document}y=a∗c1-exp-x/bd with parameters a, b, c, and d describing the response at dose 0 (background value), the potency of the PFAS, maximum fold change in response compared with background response (upper or lower plateau), and steepness of the curve (on a log-dose scale), respectively, was fitted with and without fixing parameter c to a large value to determine if a maximum fold change could be established. The model (with or without fixed parameter c) with the lowest Akaike information criterion (AIC) was chosen to determine the RPFs and the corresponding $90\%$ confidence intervals (Bil et al. 2022a, b; van den Brand et al. 2022). PFOA was used as the index chemical. For some PFASs, it was not possible to determine an RPF and for some compounds, determination of the lower bound RPF (RPFL) was not possible, because the data did not show a clear trend.
## Comparison of obtained in vitro RPFs and reported in vivo RPFs
We compared the RPFs obtained from the different in vitro readouts (AdipoRed and selected genes) to assess whether different conclusions would be drawn based on the readout selection. Subsequently we compared the in vitro RPFs with RPFs reported in the literature obtained from in vivo rat studies, for which RPFs are available for external (Bil et al. 2021, 2022a) and internal exposure (Bil et al. 2022b).
## Transcriptomics studies PFOS-exposed HepaRG cells and BMDExpress analysis
HepaRG cells were exposed for 24 h to 0 (solvent control), 6.25, 12.5, 25, 50, 100, 200, or 400 µM PFOS and subjected to DNA microarray analysis. Data were analyzed using BMDExpress as described in the Materials and Methods section. With the applied criteria for the identification of regulated gene sets (Fisher’s Exact Two Tail ≤ 0.1, number of genes that passed all filters of a gene set ≤ 5, and the percentage of genes of the gene set regulated ≥ 20, see Materials and Methods section), 18 *Reactome* gene sets were upregulated (≥ $60\%$ of the regulated genes upregulated) and 90 downregulated (≥ $60\%$ of regulated genes downregulated). Figure 3 shows for each of the 108 regulated gene sets the percentage of genes that is affected by PFOS plotted against the median BMC value of the regulated genes. One can conclude that, in general, high micromolar concentrations of PFOS are required to cause effects and that differences in effect concentrations between gene sets are considered minor, based on the comparison of median BMC values. Gene sets related to cellular processes that were previously identified to be affected by PFOA, PFNA, and PFOS (Louisse et al. 2020) are indicated in Fig. 3. For the selection of genes to assess differences in potencies between different PFASs, genes related to these gene sets may be of particular interest, as these have been shown before to be regulated by at least three PFASs in HepaRG cells (Louisse et al. 2020). The expression data for the regulated genes for these selected gene sets are presented in Fig. 4. It must be noted that another 11 *Reactome* gene sets were identified to be regulated using the applied selection criteria (Fisher’s Exact Two Tail ≤ 0.1, number of genes that passed all filters of a gene set ≤ 5, and the percentage of genes regulated of the gene set ≥ 20, see Materials and Methods section) that were not clearly up- or downregulated, i.e., 40–$60\%$ of the regulated genes were upregulated and the other 40–$60\%$ of the regulated genes were downregulated. More detailed information on the results of the BMDExpress analysis for all these 119 regulated gene sets (90 downregulated, 18 upregulated, 11 not clearly up- or downregulated) is provided in the Supplementary Materials. Fig. 3Overview of upregulated (gray squares) and downregulated (white circles) *Reactome* gene sets based on microarray data of PFOS-exposed HepaRG cells as analyzed with BMDExpress. *Each* gene set is positioned based on the percentage of affected genes of the gene set and the median BMC value of the gene set. Gene sets related to cellular processes that were previously found to be affected by PFOA, PFOS and PFNA in HepaRG cells (Louisse et al. 2020) are indicated. More information on the regulated genes of these gene sets is presented in Fig. 4. More information on all affected gene sets is presented in the Supplementary Materials (color figure online)Fig. 4Concentration-dependent modulation of genes belonging to a selection of *Reactome* gene sets that are regulated in HepaRG cells upon PFOS exposure. *Regulated* gene sets presented here are A ‘cholesterol biosynthesis’ (R-HSA-191273), B ’ATF4 activates genes in response to endoplasmic reticulum stress’ (R-HSA-380994), C ‘cytosolic tRNA aminoacylation’ (R-HSA-379716), and D ‘amino acid transport across the plasma membrane’ (R-HSA-352230). For each PFOS exposure (concentration given in µM above the plots), data from three samples (three independent studies) are shown. Expression is normalized against average expression of the solvent control [0], showing the Log2 ratio of expression upon PFOS treatment versus expression in the control *As a* next step, the concentration–response data were analyzed to identify genes that were relatively sensitive to PFOS treatment. Besides those selected from gene sets as indicated in Figs. 3 and 4, such genes may be good candidates to assess relative potency differences between PFASs, as also PFASs with a relatively low potency may induce a response. For this, genes were selected for which a BMC value was obtained and that showed at 100 µM at least a twofold change compared to the solvent control. Microarray expression data of these genes are presented in Fig. 5. It is of interest to note that some of these are part of the selected gene sets presented in Fig. 4, whereas many are not. Fig. 5Concentration-dependent modulation of selected sensitive genes in HepaRG cells upon PFOS exposure. Data for genes are presented for which a BMC was obtained and that showed an average fold change at 100 µM of at least 2 compared to the solvent control. For each PFOS exposure (concentration given in μM above the plots), data from three samples (three independent studies) are shown. Expression is normalized against average expression of the solvent control [0], showing the Log2 ratio of expression upon PFOS treatment versus expression in the control In addition, the microarray data for PPARα response genes were examined, given that PPARα is a cellular target often mentioned in relation with PFAS-induced (liver) toxicity. Figure 6 shows the microarray data for PFOS-exposed cells for genes that were previously shown to be regulated by both the PPARα agonist GW7646 (Wigger et al. 2019) and by PFOS (Louisse et al. 2020) in HepaRG cells. It is of interest to note that some of these genes showed a non-typical concentration–response (PLIN1, SLC27A2, CPT2), i.e., showing a concentration-dependent increase in expression up to and including 100 µM, and a decrease at 200 and 400 µM (Fig. 6).Fig. 6Concentration-dependent modulation of PPARα-regulated genes by PFOS in HepaRG cells. Data for genes are presented that were previously shown to be induced by the PPARα agonist GW7646 (Wigger et al. 2019) and by PFOS (Louisse et al. 2020) in HepaRG cells. For each PFOS exposure (concentration given in μM above the plots), data from three samples (three independent studies) are shown. Expression is normalized against average expression of the solvent control [0], showing the Log2 ratio of expression upon PFOS treatment versus expression in the control
## Selection of genes for RT-qPCR analysis to assess potency differences of 18 PFASs
Subsequently, the concentration–response microarray data presented in Figs. 4, 5, 6 were analyzed in more detail to select genes suitable for analyzing the concentration-dependent effects of 18 PFASs (Fig. 1) and to provide insights into potency differences. To that end, genes were selected that showed clear concentration–response curves for PFOS and covering diverse biological processes, as well as genes with relatively low BMC values. The ten genes selected include five genes that were upregulated and five that were downregulated upon PFOS treatment (see concentration–response data for microarray data in Supplementary Fig. 6), and are shortly described below. ATF4: Activating transcription factor 4 (ATF4) is a transcription factor activated upon endoplasmic reticulum stress and/or amino acid starvation (Harding et al. 2000), upregulating genes that play a role in cell recovery, adaptation to stress conditions, and restoration of cell homeostasis (Rozpedek et al. 2016). Member of the upregulated gene set ‘ATF4 activates genes in response to endoplasmic reticulum stress’ (Fig. 4B).SLC7A11: The SLC7A11 gene codes for an amino acid transporter importing cysteine and exporting glutamate. It is one of the amino acid transporters that is upregulated by ATF4 upon amino acid starvation (Adams 2007; Krokowski et al. 2013; Han et al. 2013; Shan et al. 2016). Member of the upregulated gene set ‘Amino acid transport across the plasma membrane’ (Fig. 4D). Highly upregulated even at relatively low PFOS concentrations (Fig. 5).YARS1: Tyrosyl-tRNA synthetase (YARS) is an aminoacyl-tRNA synthetase (ARS) catalyzing the aminoacylation of transfer RNA (tRNA) by its cognate amino acid tyrosine. It is one of the ARS genes that is upregulated by ATF4 upon amino acid starvation (Adams 2007; Krokowski et al. 2013; Han et al. 2013; Shan et al. 2016). Member of the upregulated gene set ‘Cytosolic tRNA aminoacylation’ (Fig. 4C).PDK4: pyruvate dehydrogenase (PDH) kinase 4 (PDK4) (Kwon and Harris 2004) diminishes PDH activity, thereby reducing the conversion of pyruvate to acetyl-CoA. PDK4 expression has been reported to be upregulated upon fasting and/or a switching from glucose to fatty acids as an energy source (Zhang et al. 2014; Pettersen et al. 2019). PDK4 expression has been reported to be regulated by retinoic acid receptors (Kwon and Harris 2004) and by PPARα (e.g., Wigger et al. 2019). Thus, considered to be a PPARα response gene (Fig. 6). Highly upregulated even at relatively low PFOS concentrations (Fig. 5).ANGPTL4: angiopoietin-like protein 4 (ANGPTL4) is a member of the angiopoietin-related family, and has been reported to play a crucial role in regulating angiogenesis and glucolipid metabolism (Hato et al. 2008). Regulation of ANGPTL4 gene expression has been reported to be mediated via PPARs and HIF-1α (La Paglia et al. 2017). Thus, considered to be a PPARα response gene (Fig. 6).LSS: The protein encoded by the LSS gene catalyzes the conversion of (S)-2,3 oxidosqualene to lanosterol in the cholesterol biosynthesis pathway (Wada et al. 2020). Member of the downregulated gene set ‘Cholesterol biosynthesis’ (Fig. 4A).HMGCR: *The* gene codes for HMG-CoA reductase, the rate-limiting enzyme in the cholesterol biosynthetic pathway, which catalyzes the conversion of HMG-CoA to mevalonic acid (Luskey and Stevens 1985). Member of the downregulated gene set ‘Cholesterol biosynthesis’ (Fig. 4A). Highly downregulated even at relatively low PFOS concentrations (Fig. 5).OAT5: Organic anion transporter 5 (OAT5) is an anion exchanger. Expression in the liver has been reported to be regulated via hepatocyte nuclear factor-1α (HNF-1α) (Klein et al. 2010). Highly downregulated even at relatively low PFOS concentrations (Fig. 5).THRSP: Thyroid hormone responsive (THRSP) is primarily a nuclear protein that plays a role in the regulation of lipid metabolism. Expression has been reported to be downregulated upon fasting (Kuemmerle and Kinlaw 2011). Highly downregulated even at relatively low PFOS concentrations (Fig. 5).CXCL10: C-X-C motif chemokine ligand 10 (CXCL10) is a chemokine capable of stimulation of monocytes, natural killer cell and T cell migration, regulation of T cell and bone marrow progenitor maturation, modulation of adhesion molecule expression, and inhibition of angiogenesis (Neville et al. 1997). Highly downregulated even at relatively low PFOS concentrations (Fig. 5).
## Effects of 18 PFASs on expression of selected genes
To determine the relative potencies of the 18 PFASs, the HepaRG cell line was exposed for 24 h to increasing concentrations of the 18 PFASs shown in Fig. 1. After exposure, RNA was collected and used for RT-qPCR analysis of the ten selected genes. Supplementary Fig. 7 shows concentration–response data of these genes for PFOS, PFOA and HPFO-TA, the latter being the PFAS that was found to be most potent in the present study based on cell viability and triglyceride accumulation as well as for gene expression modulation. Concentration–response data for the 18 PFASs for all genes are presented in the Supplementary Materials. These data were then used to perform PROAST dose–response analysis using parallel curve fitting to obtain in vitro RPFs related to PFAS-induced gene expression changes. For the selected genes, only for OAT5 RPFs could be obtained for all tested PFASs (18 including PFOA). For CXCL10 and THRSP, RPFs were obtained for 14 PFASs, for LSS, HMGCR and ANGPTL4 for 13 PFASs, for ATF4 and PDK4 for 12 PFASs, and for SLC7A11 and YARS1 for 11 PFASs. Figure 7 presents the RPFs based on gene expression data for PDK4, HMGCR, OAT5, and THRSP. RPFs for all genes are presented in Supplementary Fig. 8.Fig. 7In vitro RPFs based on PROAST dose–response analysis of PDK4, HMGCR, OAT5, and THRSP gene expression data obtained from HepaRG cells exposed to various PFASs. RPFs are presented as vertical lines, with the $5\%$ lower bound and $95\%$ upper bound of the confidence interval as whiskers. PFOA was used as index chemical, i.e., has an RPF of 1 (dotted line). NA not applicable, RPF could not be determined Gene-specific differences in RPF values were observed, although some general patterns could be identified. *In* general, RPFs obtained for the PPAR response genes PDK4 and ANGPTL4 were similar, but differed for many PFASs from RPFs obtained from the other genes (Supplementary Figs. 8 and 9). For PDK4 and ANGPTL4, all studied PFASs, except HFPO-TA, were less potent than PFOA. For the majority of the other genes (ATF4, SLC7A11, YARS1, LSS, HMGCR, OAT5, and THRSP), PFNA, PFDA, PFUnDA, PFDoDA, PFHpS, PFOS, and HFPO-TA were consistently more potent than PFOA, and PFHpA, PFHxS, and PFDS less potent than PFOA.
For PFNA, PFHxS, PFOS, and HFPO-TA in vitro RPFs were obtained for all readouts (AdipoRed data and gene expression data) (Fig. 8), whereas for other PFASs, this was not the case (Supplementary Fig. 9). Of these 4 PFASs, RPFs related to all in vitro readouts were smaller than 1 for PFHxS. RPF patterns of PFPeA, PFHxA, PFHpA, PFBS, PFDS, and HFPO-DA were similar as for PFHxS, i.e., having in general RPFs lower than 1 (Supplementary Fig. 9). HFPO-TA was the only PFAS tested for which all in vitro RPFs were found to be larger than 1. For PFNA, RPFs related to expression of PPAR response genes (PDK4 and ANGPTL4) were smaller than 1, whereas these were larger than 1 for the other readouts. For PFOS, potencies for the two PPAR response genes and CXCL10 were lower than those of PFOA, whereas for other genes, these were similar or slightly higher. PFHpS showed a similar RPF pattern as that of PFOS, as well as PFDoDA, although for the latter PFAS no RPFs could be determined for the PPAR response genes (Supplementary Fig. 8). For the longer-chain PFASs PFTeDA, PFHxDA, and PFODA, RPFs were only obtained for 2, 4, and 5 readouts, respectively (Supplementary Fig. 9).Fig. 8In vitro RPFs based on PROAST dose–response analysis of gene expression and AdipoRed data obtained from HepaRG cells exposed to various PFASs. RPFs are presented as vertical lines, with the $5\%$ lower bound and $95\%$ upper bound of the confidence interval as whiskers. PFOA was used as index chemical, i.e., has an RPF of 1 (dotted line) We then performed a Spearman correlation analysis using GraphPad Prism 9 to assess whether potency rankings obtained with different in vitro readouts are correlated and to assess whether certain in vitro-based potency rankings correlate with in vivo potency rankings based on reported external in vivo RPFs (Bil et al. 2021, 2022a) or internal in vivo RPFs (Bil et al. 2022b). The results of the correlation analysis point to a reasonable correlation between most of the in vitro RPFs, except for ANGPTL4 and PDK4, both PPAR target genes (Supplementary Fig. 10). A reasonable correlation was found between the in vitro RPFs based on CXCL or OAT5 expression and external in vivo RPFs (Supplementary Fig. 10). Regarding internal RPFs, the best correlation was found for HMGCR expression, but it must be noted that this was only based on data for 4 PFASs. Figure 9 presents the external and internal in vivo RPFs in comparison with the in vitro RPFs for OAT5 and CXCL10 expression, and OAT5 and HMGCR expression, respectively. Although in vitro RPFs based on changes in OAT5 expression correlate well with external in vivo RPFs (Supplementary Fig. 10), PFHxDA and PFODA are major outliers, showing in vitro RPFs > 1 and in vivo external RPFs < 0.1 (Fig. 9A). The slightly better correlation between in vitro RPFs based on CXCL10 expression and external in vivo RPFs (Supplementary Fig. 10), may relate to the fact that for PDHxDA and PFODA, no in vitro RPFs could be obtained (Supplementary Figs. 8 and 9), being therefore excluded from the correlation analysis. The in vitro RPFs correlate to a lesser extent to internal RPFs than to external RPFs (Supplementary Fig. 10), showing the best correlation for RPFs based on HMGCR expression (Fig. 9B). As indicated above, this was only based on data for 4 PFASs. When comparing in vitro RPFs based on OAT5 expression with internal in vivo RPFs, it becomes clear that PFHxA and HFPO-DA are the main outliers, showing in vitro RPFs < 0.1 and in vivo RPFs > 1 (Fig. 9B). All in vitro and in vivo RPFs used for these analyses are presented in an Excel file that can be found in the Supplementary Materials. Fig. 9Comparison of A in vitro RPFs based on OAT5 or CXCL10 gene expression data with reported external RPFs for PFAS-induced liver toxicity in rats and B in vitro RPFs based on OAT5 or HMGCR gene expression data with reported internal RPFs for PFAS-induced liver toxicity in rats
## Discussion
The present study evaluated the in vitro toxicity of 18 PFASs in human HepaRG liver cells, by studying the effects on cellular triglyceride accumulation and gene expression changes, and assessed whether these in vitro data can be used to obtain insight into potency differences regarding hepatotoxicity of PFASs. In vitro RPFs could be obtained for 8 PFASs (including index chemical PFOA) based on the triglyceride accumulation data, whereas for the selected genes in vitro RPFs could be obtained for 11–18 PFASs (including index chemical PFOA). Only for PFNA, PFHxS, PFOS, and HFPO-TA in vitro RPFs were obtained for all readouts. For the readout OAT5 expression, in vitro RPFs were obtained for all PFASs. In vitro RPFs were found to correlate in general well with each other (Spearman correlation) except for the PPAR target genes ANGPTL4 and PDK4. Comparison of in vitro RPFs with reported in vivo RPFs in rats indicate that best correlations (Spearman correlation) were obtained for in vitro RPFs based on OAT5 and CXCL10 expression changes and external in vivo RPFs. HFPO-TA was found to be the most potent PFAS tested, being around tenfold more potent than PFOA.
To assess effects of PFASs on triglyceride accumulation, we applied the AdipoRed assay. Interestingly, we found for the PFASs more pronounced effects (and better concentration-dependent effects) upon a 24-h exposure than upon a 72-h exposure, in contrast to the fungicide cyproconazole, for which a 72-h exposure was found to show most effects, and which was used as a model steatotic compound in an in vitro study on adverse outcome pathway (AOP)-driven analysis of liver steatosis (Luckert et al. 2018). This may relate to different modes of action underlying chemical-induced steatotic effects, as indicated by the available AOPs on this endpoint (Vinken 2013, 2015; Mellor et al. 2016). It is of interest to note that upon a 72-h exposure, the AdipoRed signal returned in various exposure conditions for PFHxS and PFOS to the same levels as in the solvent control (Supplementary Fig. 4). The toxicological meaning of that finding is not clear, but it may point to a possible cellular response to increased cellular triglyceride levels at earlier time points. Various studies have shown a PFAS-induced increase of hepatic triglyceride levels in experimental animals. PFOA, PFNA, PFHxS and PFOS have been shown to increase hepatic triglycerides in male mice (Bijland et al. 2011; Das et al. 2017; Huck et al. 2018; Hui et al. 2017; Wan et al. 2012). As indicated in recent Opinions of the EFSA CONTAM Panel, thorough knowledge of the mode of action underlying the development of hepatocellular steatosis in PFAS-treated rodents is missing (EFSA CONTAM Panel 2018, 2020).
Although we identified some genes that can be considered relevant readouts to screen PFASs for possible liver toxicity, one would like to mechanistically relate the gene expression change(s) to adverse effects to the liver. Ideally such gene expression changes would be a key event (KE) of an AOP related to liver toxicity. The AOP-wiki was consulted to assess whether in vitro effects measured in the present study are part of (putative) AOPs related to liver toxicity (https://aopwiki.org/; latest access: 28-12-2022). Of the in vitro readouts of the present study, triglyceride accumulation was found in the AOP-wiki as proposed key event related to liver steatosis. In light of the possible endoplasmic reticulum stress induced by the PFASs tested (indicated by activation of ATF4 signaling), it is of interest to note that the updated AOP on liver steatosis (from Mellor et al. [ 2016] based on earlier work of Vinken (2013; 2015)) includes an induction of endoplasmic reticulum stress as a key event following increase of triglyceride accumulation. The selected genes are not present as key events in the AOPs present in the AOP-wiki, but it may still be possible that changes in expression of the genes can be related to certain KEs of relevance for liver toxicity, which would require a more extensive assessment. In the Supplementary Materials, some more information on the possible link of gene expression changes of the selected genes assessed in the present study in relation to (liver) toxicity is provided.
For comparison of the potencies of the various PFASs on the basis of transcriptomics data, different approaches can be followed. For example, benchmark concentration gene accumulation plots may be used for potency ranking (Ramaiahgari et al. 2019; Reardon et al. 2021). Furthermore, transcriptomics (TempO-Seq) data obtained upon exposure of human primary liver cell spheroids to a large number of different PFASs have been analyzed applying BMDExpress and potencies were derived and compared using the median benchmark concentration of all filtered genes that adhere to best-fit models (Rowan-Caroll et al. 2021; Reardon et al. 2021). Although these are meaningful approaches, we have chosen to determine transcriptional benchmark concentrations for individual differentially expressed genes using PROAST since this BMD modeling software allows for parallel curve fitting and has recently been used for the calculation of in vivo RPFs of various PFASs (Bil et al. 2021, 2022a, b). When comparing RPFs obtained for the different readouts, it was shown that for most readouts good correlations were found. However, correlations were rather poor for the PPAR response genes PDK4 or ANGPTL4 and the other readouts. It must be noted that for the 8 genes for which the RPFs correlate well, still considerable differences in RPFs exist. It is difficult to select one gene that would provide the best data on relative potencies, and it can be expected that the study set-up, including the choice of exposure time (24 h in the present study) will affect the RPFs obtained. The data should therefore rather be used to obtain a general indication of whether a certain PFAS is expected to be a relatively potent hepato-toxicant or whether it will be of less concern related to its hepatotoxic effects. As we obtained RPFs for all PFASs based on changes in OAT5 expression, the comparison of OAT5-based RPFs with available external and internal RPFs reported in the literature is of specific interest (Fig. 9). From that comparison, in vitro RPFs were in general good in line with RPFs based on in vivo studies, with most striking exceptions for PFHxDA and PFODA for external RPFs and PFHxA and HFPO-DA for internal RPFs. The discrepancy for PFHxDA and PFODA regarding external RPFs (high in vitro RPFs vs low external in vivo RPFs) may relate to a relatively low systemic uptake of these large molecules upon oral exposure. Relative differences in systemic exposure are accounted for using internal RPFs, for which kinetic models were applied to estimate internal exposure (Bil et al. 2022b). Internal RPFs are, however, not available for PFHxDA and PFODA. The discrepancy for PFHxA and HFPO-DA regarding internal RPFs (low in vitro RPFs vs high internal in vivo RPFs) is more difficult to explain. It is of interest to further investigate these in vitro–in vivo differences in future studies. They may, among others, relate to possible species differences in PFAS-induced effects on the liver (Fragki et al. 2021). Of course, also differences in exposure duration or other differences between the in vitro and in vivo situation may play a role. Studies that assess possible species differences in human and rat liver cells in vitro may shed more light on this. It shall be noted here that the evaluation of the predictive capability of in vitro assays should not necessarily be based on a comparison to animal in vivo data (van der Zalm et al. 2022). Ideally, one would like to compare the in vitro HepaRG data with effect data in humans. Epidemiological evidence has correlated PFOS and PFOA exposure to a small elevation in serum levels of the hepatic enzyme ALT (alanine transferase), a biomarker for liver damage (Gallo et al. 2012). As indicated before, it is questionable whether that limited increase in ALT is causally related to PFOS and PFOA exposure, and whether it reflects serious liver damage. Also, data on other PFASs are scarce or lacking, making these in vitro human vs in vivo human comparisons cumbersome. To obtain in vivo relative potencies based on in vitro toxicity data, information on toxicokinetics should be included in the assessment. In that regard, we have been working on the quantitative in vitro to in vivo extrapolation (QIVIVE) of the toxicity data of PFOA, PFNA, PFHxS, and PFOS, translating cell-associated PFAS levels to oral equivalent doses using physiologically based kinetic (PBK) modeling, providing information that will be of use in the assessment of relative potencies of PFASs in humans (Fragki et al. 2023).
Although the main aim of this study was to select in vitro readouts related to liver toxicity that can be used to determine in vitro potency differences for PFASs, the obtained concentration–response microarray data may be of use to increase our insights into mechanisms related to the liver toxicity of PFASs in humans. The BMDExpress analysis indicated 18 gene sets to be upregulated and 90 gene sets to be downregulated. Many of the regulated gene sets are related to cholesterol biosynthesis and lipid metabolism as also indicated by Rowan-Carroll et al. [ 2021], who assessed the concentration- and time-dependent effects of PFOA, PFBS, PFOS and PFDS on gene expression in human primary hepatocyte spheroids. In a later study, this work was extended to include more PFASs and to estimate relative potencies (Reardon et al. 2021), testing carboxylates (PCFAs), sulfonates (PFSAs) and fluorotelomers and sulfonamides. *In* general, PFCAs and PFSAs caused gene expression changes with increased potency with increasing carbon chain-length (Reardon et al. 2021), being in line with findings for some of the genes in the present study. *In* general, effective concentrations in the present study are for most genes in the high micromolar range, which are not expected to be reached in vivo in relevant exposure scenarios. Rowan-Carroll et al. [ 2021] and Reardon et al. [ 2021] found effects at low micromolar concentrations, which may relate to the difference in test system used (2D culture HepaRG cells in present study vs. 3D primary hepatocyte model) as well as difference in exposure duration (24 h in the present study vs. up to 14 days in the study of Rowan-Carroll et al. [ 2021] and up to 10 days in the study of Reardon et al. [ 2021]). We recently showed that HepaRG cells cultured in an organ-on-a-chip device can be cultured for at least 8 weeks, allowing chronic exposure studies (Duivenvoorde et al. 2021). Such long-term studies may provide more insights into effects at more relevant human effect concentrations, but given the low throughput, such models are less suitable for screening a large number of PFASs.
Of the PFASs tested in the present study, HFPO-TA was shown to be the most potent. Sheng et al. [ 2018] assessed the effects of HPFO-TA in mice and concluded it to be a potent hepatotoxicant, causing hepatomegaly, necrosis, and increase in serum ALT, as well as a dose-dependent decrease in total cholesterol and triglycerides in the liver, and they concluded it to be more potent than PFOA, which was tested in an earlier study from the same group (Yan et al. 2014). In 2017, Pan and coworkers were the first to report on the environmental occurrence (Xiaoqing River in China), bioaccumulation (in carp) and presence in human serum of HFPO-TA, concluding that the emerging usage of HFPO-TA in the fluoropolymer manufacturing industry raises concerns about its toxicity and potential health risks to aquatic organisms and humans (Pan et al. 2017). In a more recent study, HFPO-TA was measured in the serum of residents living near a fluorochemical plant in Shandong, China, showing median serum concentrations of ~ 2 ng/mL (low pM range), almost 100 times lower than the median PFOA serum concentrations of these individuals (Yao et al. 2020). Based on our in vitro studies, which seems to be in line with the limited in vivo evidence (Sheng et al. 2018), HFPO-TA is a rather toxic PFAS, suggesting that its production and/or application should be discouraged and that human exposure should be prevented.
Altogether, the present study shows an approach to select in vitro gene expression readouts in HepaRG cells that can be used to obtain information on relative potencies of PFASs related to liver toxicity in vitro. It may be concluded that the HepaRG model may provide relevant data to get insight into which PFASs are relevant regarding their hepatotoxic effects and that it can be applied as a screening tool to prioritize other PFASs for further hazard and risk assessment.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (XLSX 209 KB)Supplementary file2 (TXT 5272 KB)Supplementary file3 (XLSX 95 KB)Supplementary file4 (CSV 8579 KB)Supplementary file5 (TXT 308 KB)Supplementary file6 (XLSX 18 KB)Supplementary file7 (DOCX 971 KB)
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|
---
title: 'The Associations Between Illness Perceptions and Expectations About Return
to Work of Workers With Chronic Diseases and Their Significant Others: A Dyadic
Analysis'
authors:
- N. C. Snippen
- H. J. de Vries
- C. A. M. Roelen
- S. Brouwer
- M. Hagedoorn
journal: Journal of Occupational Rehabilitation
year: 2022
pmcid: PMC10025207
doi: 10.1007/s10926-022-10062-7
license: CC BY 4.0
---
# The Associations Between Illness Perceptions and Expectations About Return to Work of Workers With Chronic Diseases and Their Significant Others: A Dyadic Analysis
## Abstract
Purpose To examine the associations between illness perceptions and expectations about full return to work (RTW) of workers with chronic diseases and their significant others. Methods This study used cross-sectional data of 94 dyads consisting of workers with chronic diseases and their significant others. We performed dyadic analyses based on the Actor-Partner Interdependence Model (APIM), estimating associations of illness perceptions of the two members of the dyad with their own expectations about the worker’s full RTW within six months (actor effect) as well as with the other dyad member’s expectations about the worker’s RTW (partner effect). Results Illness perceptions of one dyad member were significantly associated with his or her own RTW expectations (actor effect composite illness perceptions score; B = −0.05, $p \leq .001$; rd =.37) and with the other dyad member’s RTW expectations (partner effect composite illness perceptions score; B = −0.04, $p \leq .001$; rd =.35). That is, more negative illness perceptions of one member of the dyad were associated with more negative RTW expectations in both dyad members. For most illness perception domains, we found small to moderate actor and partner effects on RTW expectations (rd range:.23–.44). Conclusions This study suggests that illness perceptions and RTW expectations should be considered at a dyadic level as workers and their significant others influence each other’s beliefs. When trying to facilitate adaptive illness perceptions and RTW expectations, involving significant others may be more effective than an individualistic approach targeted at the worker only.
### Supplementary Information
The online version contains supplementary material available at 10.1007/s10926-022-10062-7.
## Introduction
Workers with chronic diseases are at higher risk of involuntary early labor market exit because of work disability and unemployment as compared to workers without a chronic disease [1, 2]. In recent years, increasing attention has been paid to the role of illness perceptions in the context of work participation of workers with chronic diseases [3–7]. Previous research has shown that negative perceptions of workers concerning the duration, consequences, emotional impact, treatment efficacy, personal control and understanding of the illness are associated with increased risks of involuntary early labor market exit across various chronic health conditions [3, 5–7]. In addition, illness perceptions of workers have been shown to be strongly related to expectations about return to work (RTW) [4], which is one of the strongest prognostic factors of work-related outcomes like RTW, duration of sick leave and disability benefit receipt [7–14].
There is increasing evidence that significant others like partners, family members or friends affect an individual’s illness perceptions, adaptation to chronic illness and work participation through their interactions with the person with the disease [15–18]. Rather than illness perceptions being developed in isolation, the perceptions of individuals with chronic diseases and their significant others are connected [15]. It has therefore been proposed that coping and adaptation to chronic disease should be viewed from a dyadic perspective, in which the significant other’s appraisals, responses and interactions with the person with the chronic disease are also taken into account [17–21]. There is already some evidence that illness perceptions and RTW expectations of both workers and their significant others might play an important role in work participation outcomes of workers with persistent back pain [22, 23]. For instance, one study suggests that pessimistic beliefs about the likelihood of RTW of disability benefit claimants and their significant others may act as obstacles to work participation [22]. Another study found that couples in which the worker had become incapacitated for work had more negative perceptions about the consequences of the worker’s persistent back pain than couples in which the worker had remained in work despite persistent back pain [23]. However, the current level of evidence is low as the existing evidence is based on qualitative studies with relatively small study samples and quantitative knowledge on this topic is lacking. Moreover, to date the associations between illness perceptions and RTW expectations among workers with chronic diseases and their significant others has not been examined dyadically.
Gaining insight into effects of illness perceptions of workers and significant others on their RTW expectations could provide evidence-based recommendations regarding intrapersonal and interpersonal factors that can be targeted to modify RTW expectations in order to facilitate RTW [7–14]. Therefore, the aim of this study was to examine the associations between illness perceptions and RTW expectations of workers with chronic diseases and their significant others at a dyadic level. More specifically, we examined the associations of both the worker’s and his/her significant other’s illness perceptions with (i) a person’s own RTW expectations, and (ii) the other dyad member’s RTW expectations.
## Study Design
This study used cross-sectional data of dyads consisting of workers and their significant others, which was collected for the purpose of this study and subjected to dyadic analysis [24].
## Participants and Inclusion Criteria
We included dyads consisting of workers who had been on sick leave due to a chronic health condition for at least two weeks, and one of their significant others (i.e., partner, family member or friend). To be eligible for participation, workers had to be between 18 and 65 years of age, be or recently have been on sick leave due to chronic health problems, and have a significant other who was willing to participate in the study (self-chosen by the worker). In addition, participants had to be proficient in written Dutch. Furthermore, the source population consisted of employees only, with self-employed workers falling beyond this population. The inclusion period lasted from June 2019 until September 2020.
## Procedure
We recruited participants through Arbo Unie, a large Dutch occupational health service (OHS). In the Netherlands, the OHS advises sick-listed workers and their employers about RTW. For this purpose, sick-listed workers are invited for a consultation with an occupational health physician within six weeks after the first registered day of sick leave. During the 15-month inclusion period, an extra paragraph was added to the invitation for this consultation, informing workers and their significant others about this study. In the added paragraph, a link was included to a dedicated webpage with more detailed study information and the online questionnaires for both the worker and significant other.
At the start of the questionnaire, participants were screened for eligibility and asked to give informed consent. Participants who did not meet the inclusion criteria or did not give informed consent were excluded and automatically directed to the end of the questionnaire. To minimize attrition due to missing values, automatic response requests were used to alert participants about any unanswered questions when moving to another page of the questionnaire.
The Central Ethics Review Board of the University Medical Center Groningen approved the study protocol (CTc UMCG 201,700,925). Participants received written information regarding the confidentiality and anonymity of the study results and were given an opportunity to ask questions. Informed consent was obtained from all participants.
## Measures
Workers and significant others individually completed a questionnaire that measured expectations about the worker’s RTW, illness perceptions and sociodemographic characteristics.
## Primary Outcome
The primary outcome measure was expectations about the worker’s full RTW within six months, based on the ‘self-predicted certainty question’ of Heymans et al. [ 8]: “How certain are you that you will be fully back at work in six months?”. Workers answered the question on a 5-point scale: [1]“completely uncertain”, [2]“a little uncertain”, [3]“somewhat certain”, [4] “certain”, [5] “completely certain”. Full RTW was defined as working the contracted working hours [8]. Significant others answered the question “How certain are you that the worker will be fully back at work in six months?” on the same 5-point scale.
## Illness Perceptions
We measured illness perceptions of workers and significant others with respectively the Dutch version of the Brief Illness Perception Questionnaire (IPQ-B) [25, 26] and a significant other version of the IPQ-B, which was adapted from the spouse version of the IPQ-R [27]. In this study, we used the first eight items of the IPQ-B which were measured on a 11-point scale (ranging from zero to ten). The eight items assessed the worker’s and significant other’s illness perceptions about: [1] the influence of the illness on the worker’s daily life (consequences), [2] the duration of the illness (illness duration), [3] the worker’s control over the illness (personal control), [4] the extent to which treatment can help with controlling the illness (treatment control), [5] the severity of the symptoms experienced by the worker (illness identity), [6] the worker’s concern about the illness (concern), [7] the worker’s emotional response to the illness (emotional response), and [8] the worker’s degree of understanding of the illness (illness coherence).
Higher scores on consequences, illness duration, illness identity, concern, and emotional response reflected more negative perceptions, while higher scores on personal control, treatment control, and illness coherence reflected more positive perceptions. A composite illness perceptions score was computed by summing up the scores of the eight items, with a reverse scoring of the items on personal control, treatment control and illness coherence. For this composite score, we person-mean imputed data for participants with missing data on no more than three items. A higher composite score reflected more negative perceptions. The Cronbach's alpha of the IPQ-B composite score in this study was 0.71 for workers and 0.74 for significant others, which is similar to what was found in previous research [28–31].
## Covariates
Sociodemographic measures and data about workers’ and significant others’ perceived relationship quality was collected to describe the sample and potentially include as covariates. With regard to sociodemographic measures, we collected data about the workers’ age, gender, educational level (low, medium, or high), type of chronic disease (somatic, mental, mixed), and employment status (fulltime vs. parttime). In addition, data was collected about the significant others’ age, gender, educational level, chronic disease (yes/no), and their relationship with the worker (i.e., partner, parent, adult child, sibling, friend). Finally, we collected data from both workers and significant others about their perceived relationship quality with the other dyad member, using a relationship quality rating scale from 0 through 10, with zero representing the worst possible and ten the best possible relationship [32].
## Preliminary Analyses
Preliminary analyses were performed to ensure that there was no violation of the assumptions of normality and homogeneity of variance. In addition, we conducted a series of preliminary analyses to examine associations between demographic characteristics and the outcome variables (i.e., RTW expectations) to assess the need to include covariates in the analyses. Significant others’ age was significantly associated with their own expectations of the worker’s RTW (r = −0.329, $$p \leq 0.001$$). Gender, educational level, type of chronic disease, employment status, type of relationship with the other dyad member, and perceived relationship quality were not associated with dyad members’ RTW expectations.
## Dyadic Analyses
In preparation for the analyses, data was formatted in a pairwise structure in SPSS version 26 using the individual-to-pairwise macro from Kenny [33] and the predictor variables were grand-mean centered in accordance with the recommendations from Kenny et al. [ 24].
We performed dyadic analyses using the Actor-Partner Interdependence Model (APIM) [24] to determine dyadic associations between illness perceptions of workers and significant others (i.e., independent variable) and their expectations about the worker’s full RTW within six months (i.e., outcome variable). Interdependence means that the responses from the two individuals within a dyad are linked (i.e., non-independent). APIM analysis allows researchers to model the non-independence in the two dyad members’ responses by measuring the associations between their scores, as well as their intrapersonal (i.e., actor) effects and interpersonal (i.e., partner) effects [34]. Thus, in this study, worker and significant other expectations about the worker’s full RTW within six months were regressed on their own illness perceptions (i.e., actor effect) as well as on their counterpart’s illness perceptions (i.e., partner effect). Figure 1 displays the APIM framework applied to this study. We ran separate analyses for the composite illness perceptions score and each of the eight illness perception domains. Fig. 1Actor-Partner Interdependence Model applied to this study. Conceptual Actor-Partner Interdependence Model depicting the examined actor and partner effects of illness perceptions on expectations about the worker's return to work within worker-significant other dyads The analyses consisted of four steps, in which the two-intercept method of Multilevel Modeling was applied [24, 35]. In the first step the full APIM was estimated. To increase statistical power and simplify the models, we tested for differences in coefficients between dyad members (step 2) and, when appropriate, tested more parsimonious models in which intercepts, actor effects and partner effects that did not differ between workers and significant others were constrained to be equal for dyad members (step 3). Finally, in the fourth step, correlation coefficient effect sizes (rd) were estimated for the statistically significant effects in the final models. Each of the four analyses steps is described in more detail below. Furthermore, an example syntax for the first three steps is provided in Online Resource 1.
## Step 1: Estimating the Full APIM
In the first step, the full APIM was estimated including an intercept, actor effect and partner effect for each dyad member (i.e., the estimated model included two intercepts, two actor effects and two partner effects). A total of nine APIMs were conducted to test whether illness perceptions of workers and significant others were significantly associated with a dyad member’s own expectations about the worker’s RTW (actor effect) and the other member’s RTW expectations (partner effect). To account for the interdependence between dyad members’ scores, the actor and partner effects were estimated simultaneously and the correlations of dyad members’ predictor and outcome variables, respectively, were also modeled. The models controlled for workers’ and significant others’ age.
## Step 2: Testing for Differences Between Dyad Members
In the second step, contrast analyses were used to examine whether there were statistically meaningful differences between dyad members in the estimated intercepts, actor effects and partner effects. More specifically, we tested whether the intercepts, actor effects and partner effects significantly differed between workers and significant others (i.e., to examine whether actor effects and partner effects were stronger for one of the dyad members) or whether they could be considered to be equal for both dyad members. The findings of this step were used to develop more parsimonious models in step 3.
## Step 3: Estimating Average Intercepts and Effects Across Dyad Members
Based on the results obtained in the second step, in the third step, we tested more parsimonious models in which, when appropriate, the intercepts, actor effects and partner effects were constrained to be equal for dyad members. In addition to developing more parsimonious models with fewer beta coefficients, an important advantage of constraining the coefficients to be equal for dyad members is an increase in statistical power as the scores of both dyad members are used to estimate average beta coefficients (i.e., the number of observations used for each beta coefficient is doubled). We therefore estimated average beta coefficients across dyad members for the intercepts, actor effects and partner effects that could be considered to be equal for workers and significant others, and tested whether the average actor effects and partner effects were significantly associated with RTW expectations. The intercepts, actor-effects and partner effects that were statistically different between workers and significant others (step two) remained as separate beta coefficients in the models. The final models could therefore include separate coefficients for workers and significant others, average coefficients or a combination of separate and average coefficients.
## Step 4: Estimating Correlation Coefficient Effect Sizes
Finally, in the fourth step, we estimated correlation coefficient effect sizes (rd) for the statistically significant actor and partner effects in the final models [24]. Following the recommendations of Kenny et al. [ 24], we adjusted the effect sizes for the independent variables of which the scores of workers and significant others were strongly correlated (i.e., > 0.5 or < −0.5) to take into account the non-independence within dyads, and otherwise used the unadjusted effect sizes. We refer to the book of Kenny et al. [ 24] for more detailed information about determining the effect sizes in APIM analyses. Following the guidelines of Cohen [36], effects sizes of rd = 0.1, rd = 0.3, and rd = 0.5 were considered to be small, medium and large in magnitudes, respectively.
## Results
A total of 166 workers completed the questionnaire. Workers for whom there was no data available from a significant other were excluded from the analyses ($$n = 72$$). The final study sample consisted of 94 dyads of workers ($56.6\%$) and their significant others. There were no statistically significant differences between included and excluded workers with regard to age, gender, educational level, type of disease, comorbid conditions, perceived relationship quality, illness perceptions and RTW expectations. The mean age of included workers was 53.7 years (SD = 9.9, range: 25–65 years). A small majority of workers was male ($55.3\%$) and had a low or medium level of education ($53.2\%$). Most workers ($80.9\%$) indicated to have a somatic disease, particularly musculoskeletal disorders ($47.9\%$), cardiovascular disease ($19.1\%$), neurological conditions ($17.0\%$), and respiratory disease ($14.9\%$). Furthermore, $36.2\%$ of the workers had a mental illness, and almost half of the workers ($44.7\%$) had comorbid conditions. The mean age of significant others was 52.6 years (SD = 13.4, range: 20–96 years), the majority was the partner or spouse of the worker ($88.3\%$) (Table 1).Table 1Participant characteristics ($$n = 94$$ dyads)CharacteristicWorkersSignificant othersAge in years (SD)53.7(9.9)52.6(13.4)Gender Male52($55.3\%$)39($41.5\%$) Female42($44.7\%$)55($58.5\%$)Educational level Low17($18.1\%$)19($20.2\%$) Medium33($35.1\%$)44($46.8\%$) High43($45.7\%$)30($31.9\%$) Missing1($1.1\%$)1($1.1\%$)Relation to worker Partner/spouse–83($88.3\%$) Parent–5($5.3\%$) Adult child–4($4.3\%$) Sibling–1($1.1\%$) Friend–1($1.1\%$)Relationship quality, mean (range)8.7(6–10)8.6(5–10)Type of chronic disease Somatic59($62.8\%$)37($39.4\%$) Mental17($18.1\%$)5($5.3\%$) Mixed17($18.1\%$)6($6.4\%$) None–45($47.9\%$) Missing1($1.1\%$)1($1.1\%$)Number of chronic diseases 0–45($47.9\%$) 151($54.3\%$)27($28.7\%$) > 142($44.7\%$)21($22.3\%$) Missing1($1.1\%$)1($1.1\%$)*Employment status* Fulltime (≥ 36 h per week)59($62.8\%$)26($27.7\%$) Part-time (12—35 h per week)35($37.2\%$)38($40.4\%$) Not employed (< 12 h per week)–29($30.9\%$) Missing–1($1.1\%$)Mean scores (SD) RTW expectations (scale 1–6)3.0(1.3)3.0(1.4) Composite illness perceptions score (scale 0–80)48.7(10.2)46.4(10.5) Consequences (scale 1–6)7.7(2.0)7.4(2.0) Timeline (scale 0–10)6.2(3.0)6.0(3.0) Personal control (scale 0–10)4.1(2.4)4.8(2.7) Treatment control (scale 0–10)6.8(2.1)7.3(2.4) Illness identity (scale 0–10)7.6(1.8)7.2(1.9) Concern (scale 0–10)6.5(2.5)7.0(2.2) Illness coherence (scale 0–10)7.3(2.4)8.0(2.0) Emotional response (scale 0–10)6.7(2.4)6.6(2.5)SD standard deviation
## Representativeness of the Study Sample
There was no data available on the number and characteristics of sick-listed workers who received the invitation but decided not to participate in this study. However, we were able to compare our sample with a large and representative cohort from Arbo Unie consisting of 3,729 workers with a chronic disease who were sick-listed between January 2020 and September 2021. The mean age of workers was considerably higher in our study (53.7 years, SD = 9.9) than in the larger cohort (40.4 years, SD = 15.9). Furthermore, compared to workers in that cohort, a higher percentage of workers in our study sample was male ($55.3\%$ vs. $33.6\%$), had a musculoskeletal disorder ($47.9\%$ vs. $34.5\%$) or a mental illness ($36.2\%$ vs. $24.4\%$).
## Correlations
The correlation coefficients of all variables are depicted in Table 2. We found strong correlations between workers’ and significant others’ composite illness perceptions scores ($r = 0.64$) and their expectations about the worker’s RTW ($r = 0.77$). While most of the correlations between their scores on the illness perception domains were moderate to strong (r ≥ 0.41), there were weak correlations between workers and significant others for the domains illness identity ($r = 0.28$) and illness coherence ($r = 0.21$). Workers’ and significant others’ composite illness perceptions scores and scores on the domains consequences, timeline, treatment control, and concern were significantly associated with both their own and the other dyad member’s certainty that the worker would be fully back at work in six months (r ≤ −0.27 or r ≥ 0.34).Table 2Intercorrelations of worker and significant other illness perceptions and RTW expectations (condensed table)Composite illness perceptions scoreConsequencesTimelinePersonal controlTreatment controlIllness identityConcernIllness coherenceEmotional responseRTW expectations of significant othersbRTW expectations of workersbComposite illness perceptions score0.64**0.78**0.55**−0.54**−0.39**0.59**0.72**−0.28**0.69**−0.48**−0.50**Consequences0.67**0.48**0.41**−0.33**−0.180.53**0.59**−0.090.47**−0.43**−0.30**Timeline0.63**0.27*0.73**−0.11−0.22*0.21*0.26*0.030.07−0.44**−0.46**Personal control−0.42**−0.31**−0.130.41**0.44**−0.33**-0.180.11−0.28**0.41**0.38**Treatment control−0.34**−0.05−0.22*0.25*0.62**−0.010.000.09−0.080.37**0.37**Illness identity0.58**0.67**0.26*−0.190.040.28**0.53**0.110.34**−0.21*−0.15Concern0.76**0.55**0.46**−0.180.000.43**0.47**−0.020.67**−0.27**−0.31**Illness coherence−0.34**0.04−0.040.170.27**0.15−0.1490.24*−0.25*−0.10−0.09Emotional response0.66**0.40**0.21*−0.060.010.42**0.51**−0.140.58**−0.14−0.21*RTW expectations of workersa−0.60**−0.44**−0.51**0.190.39**−0.32**−0.44**0.05−0.150.77**1RTW expectations of significant othersa−0.57**−0.46**−0.45**0.140.34**−0.40**−0.48**0.09−0.27**10.77**Correlations among workers are below the diagonal; correlations among significant others are above the diagonal; the diagonal depicts the correlations between workers and significant others. * $p \leq 0.05$; **$p \leq 0.01.$ aCorrelations with illness perceptions of workers; bCorrelations with illness perceptions of significant others; RTW = return to work
## Actor and Partner Effects
An overview of the two-intercept models and the final models including effect sizes (rd) for all statistically significant actor and partner effects is provided in Table 3.Table 3Associations between illness perceptions and RTW expectations among dyads of workers and their significant othersTwo-intercept modelaSigFinal modelbrdBSDtBSDtSigComposite illness perceptions score ($$n = 94$$) Intercept worker3.140.1227.16 < 0.001**Intercept3.100.1029.81 < 0.001** Intercept significant other3.080.1225.83 < 0.001**Actor effect−0.050.01−5.80 < 0.001**0.37c Actor effect worker−0.060.01−4.43 < 0.001**Partner effect−0.040.01−5.58 < 0.001**0.35c Actor effect significant other−0.030.01−1.740.084 Partner effect worker−0.030.01−2.030.046* Partner effect significant other−0.060.01−4.02 < 0.001**Consequences ($$n = 94$$) Intercept worker3.130.1324.94 < 0.001**Intercept3.080.1126.82 < 0.001** Intercept significant other3.050.1224.66 < 0.001**Actor effect−0.250.04−5.92 < 0.001**0.43 Actor effect worker−0.290.07−3.96 < 0.001**Partner effect−0.160.04−3.79 < 0.001**0.29 Actor effect significant other−0.180.07−2.630.010* Partner effect worker−0.110.07−1.540.127 Partner effect significant other−0.230.07−3.180.002*Illness duration ($$n = 92$$) Intercept worker3.080.1225.24 < 0.001**Intercept3.060.1126.78 < 0.001** Intercept significant other3.050.1324.08 < 0.001**Actor effect−0.130.03−4.26 < 0.001**0.29c Actor effect worker−0.160.06−2.660.010*Partner effect−0.110.03−3.74 < 0.001*0.26c Actor effect significant other−0.110.06−1.850.067 Partner effect worker−0.080.06−1.440.155 Partner effect significant other−0.120.06−1.850.067Personal control of the worker ($$n = 94$$) Intercept worker3.020.1322.42 < 0.001**Intercept2.980.1224.13 < 0.001** Intercept significant other2.950.1321.99 < 0.001**Actor effect worker0.030.060.420.673ns Actor effect worker0.020.060.300.767Actor effect significant other0.220.054.41 < 0.001**0.44 Actor effect significant other0.220.054.12 < 0.001**Partner effect worker0.190.053.610.001**0.37 Partner effect worker0.190.053.610.001*Partner effect significant other−0.020.06−0.330.746ns Partner effect significant other−0.020.06−0.350.728Treatment control ($$n = 94$$) Intercept worker3.090.1323.47 < 0.001**Intercept3.060.1224.97 < 0.001** Intercept significant other3.040.1322.56 < 0.001**Actor effect0.150.043.82 < 0.001**0.26c Actor effect worker0.160.082.130.036*Partner effect0.130.043.380.001**0.23c Actor effect significant other0.150.072.080.040* Partner effect worker0.120.071.760.082 Partner effect significant other0.110.081.430.157Illness identity ($$n = 94$$) Intercept worker3.130.1423.05 < 0.001**Intercept3.100.1224.85 < 0.001** Intercept significant other3.080.1323.44 < 0.001**Actor effect−0.170.05−3.600.005**0.30 Actor effect worker−0.250.08−3.180.002*Partner effect worker−0.040.08−0.510.613ns Actor effect significant other−0.080.07−1.060.294Partner effect significant other−0.270.07−3.69 < 0.001**0.37 Partner effect worker−0.040.07−0.540.589 Partner effect significant other−0.290.08−3.77 < 0.001**Concern of the worker ($$n = 93$$) Intercept worker3.060.1323.61 < 0.001**Intercept3.010.1225.19 < 0.001** Intercept significant other2.980.1323.22 < 0.001**Actor effect−0.140.04−3.84 < 0.001**0.30 Actor effect worker−0.220.06−3.75 < 0.001**Partner effect−0.150.04−4.02 < 0.001**0.32 Actor effect significant other−0.040.07−0.570.567 Partner effect worker−0.080.07−1.180.242 Partner effect significant other−0.250.06−4.38 < 0.001**Illness coherence ($$n = 93$$) Intercept worker3.120.1521.03 < 0.001**Intercept3.100.1422.21 < 0.001** Intercept significant other3.090.1520.97 < 0.001**Actor effect−0.020.04−0.430.672ns Actor effect worker0.050.060.820.417Partner effect0.000.040.090.930ns Actor effect significant other−0.090.07−1.180.242 Partner effect worker−0.050.08−0.670.505 Partner effect significant other0.070.061.160.250Emotional response of the worker ($$n = 93$$) Intercept worker3.080.1422.07 < 0.001**Intercept3.050.1323.40 < 0.001** Intercept significant other3.040.1421.96 < 0.001**Actor effect−0.030.04−0.710.481ns Actor effect worker−0.050.07−0.710.482Partner effect−0.120.04−3.240.001**0.23c Actor effect significant other0.010.070.220.826 Partner effect worker−0.090.07−1.310.195 Partner effect significant other−0.160.07−2.310.023*aunadjusted beta coefficients; bbeta coefficients adjusted for age; cadjusted effect size in accordance with recommendations from Kenny, Kashy and Cook [22]; *$p \leq 0.05$; **$p \leq 0.01$; ns = non-significant; N = number of dyads included
## Composite Illness Perceptions Score
Both actor and partner effects of illness perceptions on expectations about the worker’s RTW were identified in the two-intercept model. Contrast analysis showed that there were no statistically significant differences between workers and significant others with regard to the intercepts, actor effects and partner effects. The average actor effect (B = -0.05, SD = 0.01, t[168] = −5.80, $p \leq 0.001$) and average partner effect (B = −0.04, SD = 0.01, t[171] = −5.58, $p \leq 0.001$) were both significantly associated with RTW expectations of workers and significant others. In other words, the illness perceptions of workers and significant others were significantly associated with a dyad member’s own RTW expectations, as well as the expectations of the other dyad member. In this context, more negative illness perceptions were related to more negative expectations about the worker’s RTW. The effect sizes for the actor and partner effects were 0.37 and 0.35 respectively, reflecting medium sized effects. The final model is shown in Fig. 2.Fig. 2Final Actor-Partner Interdependence model with beta coefficients for the association between the illness perceptions score and expectations about the worker’s full RTW. * $p \leq .05.$ As there were no statistically significant differences in effects between workers and significant others, the average actor and partner effects were estimated in the final model. Actor-Partner Interdependence Model depicting the average actor and partner effects of dyad members’ composite illness perception scores on their expectations about the worker's full return to work
## Domains of Illness Perceptions
For most illness perception domains, we found small to moderate actor effects and partner effects on RTW expectations (rd range: 0.23–0.44). For the domain personal control of the worker, only perceptions of significant others were significantly associated with expectations of workers ($B = 0.19$, SD = 0.05, t[87] = 3.61, $$p \leq 0.001$$) and significant others ($B = 0.22$, SD = 0.05, t[91] = 4.41, $p \leq 0.001$) about the worker’s RTW. For the domain emotional response, only a partner effect was found (B = −0.12, SD = 0.04, t[153] = −3.24, $$p \leq 0.001$$). There were no significant effects of dyad members’ perceptions about the worker’s illness coherence on expectations about RTW of the worker.
## Discussion
The results of this study show that most illness perceptions and RTW expectations are moderately to strongly correlated between workers with chronic diseases and their significant others, indicating that dyad members’ illness perceptions and RTW expectations are interdependent. Moreover, we found evidence that illness perceptions of workers and their significant others are associated with both their own and the other dyad member’s expectations (i.e., intrapersonal and interpersonal effects) about full RTW of the worker with the chronic disease. More specifically, within dyads of workers and significant others, more negative illness perceptions were related to more negative expectations on whether the sick-listed worker would be fully back at work in six months.
Our results are in line with prior studies reporting that illness perceptions of patients and their spouses are often similar and strongly correlated [18, 21, 37, 38]. For instance, Richardson et al. found positive correlations between cancer patients and caregivers for most illness perception domains [38]. Similar to our findings, other studies among patients and their spouses have found evidence of intrapersonal and interpersonal associations between illness perceptions and quality of life [38], perceptions of spouse undermining (i.e., negative reactions of the spouse towards the patient, such as criticism or anger) [37], and patients’ well-being [18]. Moreover, our results support previous qualitative studies that have suggested that not only the worker’s own perceptions and appraisals, but also the perceptions and appraisals of their significant others are important in the context of work participation and RTW [22, 23].
Our findings highlight the importance of interpersonal and dyadic processes in the development of illness perceptions and expectations about RTW and add to the empirical evidence regarding the role of significant others in this context. While this study does not provide insight into how and why illness perceptions and RTW expectations of workers and significant others are interrelated, as mentioned before, interactions between the worker and the significant other have been shown to play an important role in the development of illness perceptions and in how the worker and significant other adapt to the chronic disease [15–18]. Regarding this study, workers and significant others sharing information and discussing issues related to the worker’s illness and return to work could explain the strong interdependence between their illness perceptions and expectations about the worker’s RTW. Similarly, the interpersonal associations between illness perceptions and RTW expectations within dyads might be driven by responses and interactions elicited by the worker’s and significant other’s illness perceptions. For example, triggered by negative perceptions about the disease, a significant other might respond solicitously toward the worker (e.g., encourage resting, discouraging RTW), which could in turn negatively affect the worker’s RTW expectations. Similarly, a worker’s negative illness perceptions could lead to maladaptive or unhelpful illness behaviors such as catastrophizing or withdrawing from activities [39, 40], which can lead to negative RTW expectations of the significant other.
## Strengths and Limitations
The strength of this study is reflected in its dyadic design, which enabled us to extend previous literature on the intrapersonal associations between illness perceptions and RTW expectations to the interpersonal level. Applying the APIM framework allowed us to study both intrapersonal and interpersonal associations while taking the dyad members’ interdependence into account. A limitation of this study is that no causal effects between illness perceptions and RTW expectations could be tested, as we used an observational cross-sectional design. Another limitation is that some selection bias seems to have occurred, possibly limiting the generalizability of our study findings. More specifically, compared to a representative cohort of workers with a chronic disease from Arbo Unie, the mean age in our sample was considerably higher, and a relatively high percentage of workers in our study was male and had a musculoskeletal disorder or a mental illness. In addition, most participants in our study rated the quality of their relationship with the other dyad member with an eight or above, which might indicate that workers and significant others who were less satisfied with their relationship were less inclined to participate in this study. This selection bias may have influenced our results if dyadic processes differ depending on the type of disease, relationship satisfaction, age or gender. For instance, as relationship satisfaction has been shown to be positively associated with similarity of illness representations of patients with chronic diseases and their partners [41], it is possible that the illness perceptions and RTW expectations were more similar in our study than among workers and significant others who are less satisfied with their relationship.
## Practical Implications
The findings of this study add to our understanding of the dyads’ role in RTW by indicating that illness perceptions and RTW expectations are probably the result of a dyadic process between workers and their significant others. Our findings confirm the importance of addressing illness perceptions and RTW expectations of the sick-listed worker and suggest that occupational health professionals should also assess illness perceptions and RTW expectations of significant others. An assessment of RTW expectations of both workers and their significant others could help occupational health professionals to identify workers at risk of long-term sickness absence [42]. In addition, exploring whether illness perceptions of workers and their significant others play a role can provide insight into inadequate or maladaptive perceptions and coping strategies that may be modified to achieve more realistic RTW expectations and facilitate sustainable RTW. This might be especially useful in situations in which the RTW expectations are unrealistically positive or negative and markedly different from the expectations of the occupational health professional. In this context, occupational health professionals could use the revised or brief version of the IPQ to explore and discuss illness perceptions of workers and significant others [43, 44]. Furthermore, occupational health professionals could consult with the worker and the significant other to assess their illness perceptions and RTW expectations and modify inadequate or maladaptive perceptions by providing information about the worker’s disease and RTW process [43–47]. If appropriate, occupational health professionals could refer the worker and significant other to other health care providers such as a psychologist, social worker, or medical specialist to intervene on inaccurate and maladaptive illness perceptions [43–47].
## Recommendations for Future Research
While prior research has shown that a worker’s expectations about RTW is an important prognostic factor of RTW, more research is needed to investigate the intrapersonal and interpersonal associations of illness perceptions and RTW expectations of workers and their significant others with actual RTW. In addition, more research is needed to explore the pathways through which illness perceptions are related to RTW expectations and actual RTW. For instance, future research might investigate the relationship between illness perceptions within dyads and duration of sick leave, and whether this relationship is mediated by RTW expectations of workers and their significant others. Furthermore, additional research is needed to determine whether the interpersonal associations of illness perceptions with RTW expectations differ depending on the disease and the type of relationship between the worker and his or her significant other. For example, prior research suggests that living together with a partner and the way patients and their partners interact with each other in their shared daily life play an important role in the functioning of patients with chronic diseases [48]. It is therefore likely that the interpersonal associations between illness perceptions and RTW expectations are stronger for dyads in which the significant other is the worker’s partner rather than a family member or friend not living with the worker. In addition, more research is needed to obtain additional information on how and why illness perceptions and RTW expectations of workers and significant others are interrelated as this could provide valuable insight into how significant others could be involved in the RTW process of sick-listed workers. Such research might use a dyadic diary approach to gain insight into how verbal and non-verbal communication between workers and significant others relate to their illness perceptions and RTW expectations. Finally, future research should focus on the development and evaluation of interventions aimed at promoting adaptive illness perceptions and RTW expectations in dyads of workers with chronic diseases and their significant others.
## Conclusion
This study adds to our understanding of the dyads’ role in the RTW process by indicating that illness perceptions and RTW expectations are likely to be the result of a dyadic process between workers and their significant others. When trying to facilitate adaptive illness perceptions and RTW expectations to support sustainable RTW, involving significant others may be more effective than an individualistic approach targeted at the worker only.
## Supplementary Information
Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 21 KB)
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